eda.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/sumedhakoranga/wikihow_most_helpful_article_predictor/blob/main/eda.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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      "source": [
        "##Initialization"
      ],
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        "id": "VGw8jlp8bNRJ"
      }
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    {
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      "execution_count": null,
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        "id": "b_4UklRcjmKo"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"wikihow.csv\")\n",
        "df.head()"
      ],
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          "height": 256
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        "outputId": "99fdfd24-b52c-41a1-faa2-be961d1a64d8"
      },
      "execution_count": null,
      "outputs": [
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              "   character_count  word_count  method_count  mean_method_size  \\\n",
              "0             7107        1508             2       3357.500000   \n",
              "1             5180        1176             2       2419.500000   \n",
              "2             3822         902             3       1114.333333   \n",
              "3            11614        2429             3       3604.333333   \n",
              "4             9633        2004             4       2310.500000   \n",
              "\n",
              "   mean_paragraph_size  size_largest_method  size_shortest_method  \\\n",
              "0           379.823529                 4321                  2394   \n",
              "1           317.000000                 3171                  1668   \n",
              "2           263.333333                 1521                   881   \n",
              "3           393.111111                 4148                  2832   \n",
              "4           535.411765                 2753                  1922   \n",
              "\n",
              "   std_method_size  step_count  mean_steps_per_method  ...   Kincaid  \\\n",
              "0       963.500000          16               8.000000  ...  7.523760   \n",
              "1       751.500000          14               7.000000  ...  4.951055   \n",
              "2       288.598144          11               3.666667  ...  6.020412   \n",
              "3       561.058721          26               8.666667  ...  7.798307   \n",
              "4       329.196370          16               4.000000  ...  8.716429   \n",
              "\n",
              "        ARI  Coleman_Liau  FleschReadingEase  GunningFogIndex        LIX  \\\n",
              "0  8.159750      9.908877          67.453457        10.820887  36.866542   \n",
              "1  5.505902      7.708411          81.614246         8.897157  33.807519   \n",
              "2  6.727450      7.309495          81.109399         9.797251  31.810200   \n",
              "3  8.728719     10.376311          66.362137        11.577962  38.743177   \n",
              "4  9.423549     10.661749          61.827048        13.316999  42.773535   \n",
              "\n",
              "   SMOGIndex       RIX  DaleChallIndex  percent_helpful  \n",
              "0  10.376433  3.264706       10.086871               78  \n",
              "1   9.052403  2.652632        9.111023               99  \n",
              "2   9.310165  2.527273        8.038576               88  \n",
              "3  10.914639  3.584906        9.965255               82  \n",
              "4  12.116729  4.327869        9.123631              100  \n",
              "\n",
              "[5 rows x 45 columns]"
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          "metadata": {},
          "execution_count": 4
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    {
      "cell_type": "code",
      "source": [
        "df.info()"
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        "id": "cTt-v1imkIb4",
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      "execution_count": null,
      "outputs": [
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 19917 entries, 0 to 19916\n",
            "Data columns (total 45 columns):\n",
            " #   Column                            Non-Null Count  Dtype  \n",
            "---  ------                            --------------  -----  \n",
            " 0   character_count                   19917 non-null  int64  \n",
            " 1   word_count                        19917 non-null  int64  \n",
            " 2   method_count                      19917 non-null  int64  \n",
            " 3   mean_method_size                  19917 non-null  float64\n",
            " 4   mean_paragraph_size               19917 non-null  float64\n",
            " 5   size_largest_method               19917 non-null  int64  \n",
            " 6   size_shortest_method              19917 non-null  int64  \n",
            " 7   std_method_size                   19917 non-null  float64\n",
            " 8   step_count                        19917 non-null  int64  \n",
            " 9   mean_steps_per_method             19917 non-null  float64\n",
            " 10  introduction_size                 19917 non-null  int64  \n",
            " 11  summary_size                      19917 non-null  int64  \n",
            " 12  references_count                  18991 non-null  float64\n",
            " 13  references_count_per_text_length  18991 non-null  float64\n",
            " 14  references_count_per_method       18991 non-null  float64\n",
            " 15  image_count                       19917 non-null  int64  \n",
            " 16  image_count_per_method            19917 non-null  float64\n",
            " 17  num_votes                         19917 non-null  int64  \n",
            " 18  is_expert                         19917 non-null  bool   \n",
            " 19  views                             19821 non-null  float64\n",
            " 20  co_authors                        19821 non-null  float64\n",
            " 21  warnings                          19917 non-null  int64  \n",
            " 22  tips                              19917 non-null  int64  \n",
            " 23  to_be_verb                        19917 non-null  int64  \n",
            " 24  aux_verb                          19917 non-null  int64  \n",
            " 25  conjunction                       19917 non-null  int64  \n",
            " 26  pronoun                           19917 non-null  int64  \n",
            " 27  preposition                       19917 non-null  int64  \n",
            " 28  nominalization                    19917 non-null  int64  \n",
            " 29  sentence_beginning_pronoun        19917 non-null  int64  \n",
            " 30  sentence_beginning_interrogative  19917 non-null  int64  \n",
            " 31  sentence_beginning_article        19917 non-null  int64  \n",
            " 32  sentence_beginning_subordination  19917 non-null  int64  \n",
            " 33  sentence_beginning_conjunction    19917 non-null  int64  \n",
            " 34  sentence_beginning_preposition    19917 non-null  int64  \n",
            " 35  Kincaid                           19917 non-null  float64\n",
            " 36  ARI                               19917 non-null  float64\n",
            " 37  Coleman_Liau                      19917 non-null  float64\n",
            " 38  FleschReadingEase                 19917 non-null  float64\n",
            " 39  GunningFogIndex                   19917 non-null  float64\n",
            " 40  LIX                               19917 non-null  float64\n",
            " 41  SMOGIndex                         19917 non-null  float64\n",
            " 42  RIX                               19917 non-null  float64\n",
            " 43  DaleChallIndex                    19917 non-null  float64\n",
            " 44  percent_helpful                   19917 non-null  int64  \n",
            "dtypes: bool(1), float64(19), int64(25)\n",
            "memory usage: 6.7 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####All our given features are numerical."
      ],
      "metadata": {
        "id": "IzGQcVinkTVf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "LVcBdKCbkOt_",
        "outputId": "54e8c032-7a67-4e21-e9bf-cccfe7dfab79"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       character_count    word_count  method_count  mean_method_size  \\\n",
              "count     19917.000000  19917.000000  19917.000000      19917.000000   \n",
              "mean       6464.873023   1469.306723      3.122508       2019.712830   \n",
              "std        2883.777988    635.526879      1.150353        870.291473   \n",
              "min         489.000000    110.000000      1.000000        113.636364   \n",
              "25%        4502.000000   1043.000000      3.000000       1446.666667   \n",
              "50%        5999.000000   1372.000000      3.000000       1905.666667   \n",
              "75%        7845.000000   1776.000000      3.000000       2462.625000   \n",
              "max       46657.000000  10348.000000     32.000000      10598.000000   \n",
              "\n",
              "       mean_paragraph_size  size_largest_method  size_shortest_method  \\\n",
              "count         19917.000000         19917.000000          19917.000000   \n",
              "mean            365.109300          2632.425365           1475.179696   \n",
              "std             141.042703          1229.746922            722.206039   \n",
              "min               5.285714           145.000000              5.000000   \n",
              "25%             274.000000          1817.000000            993.000000   \n",
              "50%             353.266667          2426.000000           1376.000000   \n",
              "75%             443.866667          3224.000000           1833.000000   \n",
              "max            2009.250000         22624.000000           8739.000000   \n",
              "\n",
              "       std_method_size    step_count  mean_steps_per_method  ...  \\\n",
              "count     19917.000000  19917.000000           19917.000000  ...   \n",
              "mean        495.042789     15.351459               5.078788  ...   \n",
              "std         399.215274      5.465800               1.515980  ...   \n",
              "min           0.000000      4.000000               0.916667  ...   \n",
              "25%         223.069994     12.000000               4.000000  ...   \n",
              "50%         401.184107     14.000000               5.000000  ...   \n",
              "75%         660.061782     18.000000               5.750000  ...   \n",
              "max        8086.895805    135.000000              37.000000  ...   \n",
              "\n",
              "            Kincaid           ARI  Coleman_Liau  FleschReadingEase  \\\n",
              "count  19917.000000  19917.000000  19917.000000       19917.000000   \n",
              "mean       5.865233      6.664585      7.940235          79.537715   \n",
              "std        1.785608      1.813711      1.681774          10.894442   \n",
              "min       -1.368682     -1.015958     -0.510905          30.674616   \n",
              "25%        4.592827      5.400488      6.741477          72.614510   \n",
              "50%        5.758338      6.547967      7.793182          80.357867   \n",
              "75%        7.030684      7.794361      8.996336          87.309031   \n",
              "max       14.140287     19.907310     15.892796         122.165513   \n",
              "\n",
              "       GunningFogIndex           LIX     SMOGIndex           RIX  \\\n",
              "count     19917.000000  19917.000000  19917.000000  19917.000000   \n",
              "mean          9.648025     32.817879      9.299388      2.685554   \n",
              "std           1.850669      5.629854      1.420950      0.893512   \n",
              "min           2.988070     11.393484      4.290994      0.315789   \n",
              "25%           8.316755     28.831439      8.273033      2.042857   \n",
              "50%           9.467206     32.378327      9.205926      2.567568   \n",
              "75%          10.795892     36.382463     10.231298      3.200000   \n",
              "max          19.733495     67.665113     15.595093     11.318681   \n",
              "\n",
              "       DaleChallIndex  percent_helpful  \n",
              "count    19917.000000      19917.00000  \n",
              "mean         8.797033         87.90596  \n",
              "std          0.906490          6.95029  \n",
              "min          5.934804         50.00000  \n",
              "25%          8.172361         83.00000  \n",
              "50%          8.720730         88.00000  \n",
              "75%          9.330471         93.00000  \n",
              "max         15.674366        100.00000  \n",
              "\n",
              "[8 rows x 44 columns]"
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>character_count</th>\n",
              "      <th>word_count</th>\n",
              "      <th>method_count</th>\n",
              "      <th>mean_method_size</th>\n",
              "      <th>mean_paragraph_size</th>\n",
              "      <th>size_largest_method</th>\n",
              "      <th>size_shortest_method</th>\n",
              "      <th>std_method_size</th>\n",
              "      <th>step_count</th>\n",
              "      <th>mean_steps_per_method</th>\n",
              "      <th>...</th>\n",
              "      <th>Kincaid</th>\n",
              "      <th>ARI</th>\n",
              "      <th>Coleman_Liau</th>\n",
              "      <th>FleschReadingEase</th>\n",
              "      <th>GunningFogIndex</th>\n",
              "      <th>LIX</th>\n",
              "      <th>SMOGIndex</th>\n",
              "      <th>RIX</th>\n",
              "      <th>DaleChallIndex</th>\n",
              "      <th>percent_helpful</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>19917.000000</td>\n",
              "      <td>19917.000000</td>\n",
              "      <td>19917.000000</td>\n",
              "      <td>19917.000000</td>\n",
              "      <td>19917.000000</td>\n",
              "      <td>19917.000000</td>\n",
              "      <td>19917.000000</td>\n",
              "      <td>19917.000000</td>\n",
              "      <td>19917.000000</td>\n",
              "      <td>19917.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>19917.000000</td>\n",
              "      <td>19917.000000</td>\n",
              "      <td>19917.000000</td>\n",
              "      <td>19917.000000</td>\n",
              "      <td>19917.000000</td>\n",
              "      <td>19917.000000</td>\n",
              "      <td>19917.000000</td>\n",
              "      <td>19917.000000</td>\n",
              "      <td>19917.000000</td>\n",
              "      <td>19917.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>6464.873023</td>\n",
              "      <td>1469.306723</td>\n",
              "      <td>3.122508</td>\n",
              "      <td>2019.712830</td>\n",
              "      <td>365.109300</td>\n",
              "      <td>2632.425365</td>\n",
              "      <td>1475.179696</td>\n",
              "      <td>495.042789</td>\n",
              "      <td>15.351459</td>\n",
              "      <td>5.078788</td>\n",
              "      <td>...</td>\n",
              "      <td>5.865233</td>\n",
              "      <td>6.664585</td>\n",
              "      <td>7.940235</td>\n",
              "      <td>79.537715</td>\n",
              "      <td>9.648025</td>\n",
              "      <td>32.817879</td>\n",
              "      <td>9.299388</td>\n",
              "      <td>2.685554</td>\n",
              "      <td>8.797033</td>\n",
              "      <td>87.90596</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2883.777988</td>\n",
              "      <td>635.526879</td>\n",
              "      <td>1.150353</td>\n",
              "      <td>870.291473</td>\n",
              "      <td>141.042703</td>\n",
              "      <td>1229.746922</td>\n",
              "      <td>722.206039</td>\n",
              "      <td>399.215274</td>\n",
              "      <td>5.465800</td>\n",
              "      <td>1.515980</td>\n",
              "      <td>...</td>\n",
              "      <td>1.785608</td>\n",
              "      <td>1.813711</td>\n",
              "      <td>1.681774</td>\n",
              "      <td>10.894442</td>\n",
              "      <td>1.850669</td>\n",
              "      <td>5.629854</td>\n",
              "      <td>1.420950</td>\n",
              "      <td>0.893512</td>\n",
              "      <td>0.906490</td>\n",
              "      <td>6.95029</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>489.000000</td>\n",
              "      <td>110.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>113.636364</td>\n",
              "      <td>5.285714</td>\n",
              "      <td>145.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.916667</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.368682</td>\n",
              "      <td>-1.015958</td>\n",
              "      <td>-0.510905</td>\n",
              "      <td>30.674616</td>\n",
              "      <td>2.988070</td>\n",
              "      <td>11.393484</td>\n",
              "      <td>4.290994</td>\n",
              "      <td>0.315789</td>\n",
              "      <td>5.934804</td>\n",
              "      <td>50.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>4502.000000</td>\n",
              "      <td>1043.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1446.666667</td>\n",
              "      <td>274.000000</td>\n",
              "      <td>1817.000000</td>\n",
              "      <td>993.000000</td>\n",
              "      <td>223.069994</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>4.592827</td>\n",
              "      <td>5.400488</td>\n",
              "      <td>6.741477</td>\n",
              "      <td>72.614510</td>\n",
              "      <td>8.316755</td>\n",
              "      <td>28.831439</td>\n",
              "      <td>8.273033</td>\n",
              "      <td>2.042857</td>\n",
              "      <td>8.172361</td>\n",
              "      <td>83.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5999.000000</td>\n",
              "      <td>1372.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1905.666667</td>\n",
              "      <td>353.266667</td>\n",
              "      <td>2426.000000</td>\n",
              "      <td>1376.000000</td>\n",
              "      <td>401.184107</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>5.758338</td>\n",
              "      <td>6.547967</td>\n",
              "      <td>7.793182</td>\n",
              "      <td>80.357867</td>\n",
              "      <td>9.467206</td>\n",
              "      <td>32.378327</td>\n",
              "      <td>9.205926</td>\n",
              "      <td>2.567568</td>\n",
              "      <td>8.720730</td>\n",
              "      <td>88.00000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7845.000000</td>\n",
              "      <td>1776.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2462.625000</td>\n",
              "      <td>443.866667</td>\n",
              "      <td>3224.000000</td>\n",
              "      <td>1833.000000</td>\n",
              "      <td>660.061782</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>5.750000</td>\n",
              "      <td>...</td>\n",
              "      <td>7.030684</td>\n",
              "      <td>7.794361</td>\n",
              "      <td>8.996336</td>\n",
              "      <td>87.309031</td>\n",
              "      <td>10.795892</td>\n",
              "      <td>36.382463</td>\n",
              "      <td>10.231298</td>\n",
              "      <td>3.200000</td>\n",
              "      <td>9.330471</td>\n",
              "      <td>93.00000</td>\n",
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              "      <th>max</th>\n",
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              "      <td>10348.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>10598.000000</td>\n",
              "      <td>2009.250000</td>\n",
              "      <td>22624.000000</td>\n",
              "      <td>8739.000000</td>\n",
              "      <td>8086.895805</td>\n",
              "      <td>135.000000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>14.140287</td>\n",
              "      <td>19.907310</td>\n",
              "      <td>15.892796</td>\n",
              "      <td>122.165513</td>\n",
              "      <td>19.733495</td>\n",
              "      <td>67.665113</td>\n",
              "      <td>15.595093</td>\n",
              "      <td>11.318681</td>\n",
              "      <td>15.674366</td>\n",
              "      <td>100.00000</td>\n",
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              "<p>8 rows × 44 columns</p>\n",
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              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c860159b-1e12-48ab-a019-71a5f1a9459b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Finding missing values"
      ],
      "metadata": {
        "id": "ozruuW2_lirH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hn7l5V6SkZUM",
        "outputId": "96e2cd68-0bfc-4495-88dc-afcd19630998"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "character_count                       0\n",
              "word_count                            0\n",
              "method_count                          0\n",
              "mean_method_size                      0\n",
              "mean_paragraph_size                   0\n",
              "size_largest_method                   0\n",
              "size_shortest_method                  0\n",
              "std_method_size                       0\n",
              "step_count                            0\n",
              "mean_steps_per_method                 0\n",
              "introduction_size                     0\n",
              "summary_size                          0\n",
              "references_count                    926\n",
              "references_count_per_text_length    926\n",
              "references_count_per_method         926\n",
              "image_count                           0\n",
              "image_count_per_method                0\n",
              "num_votes                             0\n",
              "is_expert                             0\n",
              "views                                96\n",
              "co_authors                           96\n",
              "warnings                              0\n",
              "tips                                  0\n",
              "to_be_verb                            0\n",
              "aux_verb                              0\n",
              "conjunction                           0\n",
              "pronoun                               0\n",
              "preposition                           0\n",
              "nominalization                        0\n",
              "sentence_beginning_pronoun            0\n",
              "sentence_beginning_interrogative      0\n",
              "sentence_beginning_article            0\n",
              "sentence_beginning_subordination      0\n",
              "sentence_beginning_conjunction        0\n",
              "sentence_beginning_preposition        0\n",
              "Kincaid                               0\n",
              "ARI                                   0\n",
              "Coleman_Liau                          0\n",
              "FleschReadingEase                     0\n",
              "GunningFogIndex                       0\n",
              "LIX                                   0\n",
              "SMOGIndex                             0\n",
              "RIX                                   0\n",
              "DaleChallIndex                        0\n",
              "percent_helpful                       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#To fill the missing values\n",
        "The methodology use is:\n",
        "\n",
        "1) Fill with mean if the feature has Gaussian distribution\n",
        "\n",
        "2) Fill with meadian otherwise"
      ],
      "metadata": {
        "id": "07Fd6v5HlrqQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To find if the feature is Gaussian or not we will plot histograms of target varibale"
      ],
      "metadata": {
        "id": "k_C5qZlLmF5K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(df.percent_helpful, bins = 30, color='skyblue')\n",
        "plt.xlabel('Rating Percentage')\n",
        "plt.ylabel('Frequency')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "9gMWbj1Alob2",
        "outputId": "f5b5f9e6-abb5-4668-826f-4f05784713b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Histogram for all the features having missing values"
      ],
      "metadata": {
        "id": "EN8iwqS9mp0k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axis = plt.subplots(1, 3, figsize=(15,5))\n",
        "for i, c in enumerate(['references_count', 'references_count_per_text_length', 'references_count_per_method']):\n",
        "    axis[i].hist(df[c], bins=30, color='skyblue')\n",
        "    axis[i].set_xlabel(c)\n",
        "\n",
        "fig, axis = plt.subplots(1, 2, figsize=(19.88,5))\n",
        "for i, c in enumerate(['views', 'co_authors']):\n",
        "    axis[i].hist(df[c], bins=30, color='skyblue')\n",
        "    axis[i].set_xlabel(c)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        },
        "id": "JxA6Yp2qmO0j",
        "outputId": "1b69c6df-fb8c-4c9f-c5b2-27fa9c3ae28b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 3 Axes>"
            ],
            "image/png": 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JRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1988x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Since none of our plots is creatinf a gaussian figure so we are going to fill the missing values with Median"
      ],
      "metadata": {
        "id": "eY66qtw2m8aM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for attribute in ['references_count', 'references_count_per_text_length', 'references_count_per_method', 'views', 'co_authors']:\n",
        "  df[attribute] = df[attribute].fillna(df[attribute].median())"
      ],
      "metadata": {
        "id": "_fuZzbvPm13N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mr5At95knJQ-",
        "outputId": "ee72fe02-9a35-4ef6-a458-74b2b835a52b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "character_count                     0\n",
              "word_count                          0\n",
              "method_count                        0\n",
              "mean_method_size                    0\n",
              "mean_paragraph_size                 0\n",
              "size_largest_method                 0\n",
              "size_shortest_method                0\n",
              "std_method_size                     0\n",
              "step_count                          0\n",
              "mean_steps_per_method               0\n",
              "introduction_size                   0\n",
              "summary_size                        0\n",
              "references_count                    0\n",
              "references_count_per_text_length    0\n",
              "references_count_per_method         0\n",
              "image_count                         0\n",
              "image_count_per_method              0\n",
              "num_votes                           0\n",
              "is_expert                           0\n",
              "views                               0\n",
              "co_authors                          0\n",
              "warnings                            0\n",
              "tips                                0\n",
              "to_be_verb                          0\n",
              "aux_verb                            0\n",
              "conjunction                         0\n",
              "pronoun                             0\n",
              "preposition                         0\n",
              "nominalization                      0\n",
              "sentence_beginning_pronoun          0\n",
              "sentence_beginning_interrogative    0\n",
              "sentence_beginning_article          0\n",
              "sentence_beginning_subordination    0\n",
              "sentence_beginning_conjunction      0\n",
              "sentence_beginning_preposition      0\n",
              "Kincaid                             0\n",
              "ARI                                 0\n",
              "Coleman_Liau                        0\n",
              "FleschReadingEase                   0\n",
              "GunningFogIndex                     0\n",
              "LIX                                 0\n",
              "SMOGIndex                           0\n",
              "RIX                                 0\n",
              "DaleChallIndex                      0\n",
              "percent_helpful                     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Feature Redundance"
      ],
      "metadata": {
        "id": "oA_HyxNsnSKp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Feature-Feature Correlation"
      ],
      "metadata": {
        "id": "cMmvrliUt298"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, we will look at correlation between features to find if there are any redundant features.\n",
        "\n",
        "We will try to find linear correlation between features using Pearson's correlation coefficient and non-linear correlation using Spearman's correlation."
      ],
      "metadata": {
        "id": "CusWFGqWnYw7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Linear Correlation"
      ],
      "metadata": {
        "id": "hoBf4lznoLzN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pearson_corr = df.drop('percent_helpful', axis=1).corr(method='pearson')\n",
        "pearson_corr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "SJhK5IVxnM6O",
        "outputId": "95b2bfcc-ce7a-426b-a109-c00491d68fa6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                  character_count  word_count  method_count  \\\n",
              "character_count                          1.000000    0.988764      0.282684   \n",
              "word_count                               0.988764    1.000000      0.288452   \n",
              "method_count                             0.282684    0.288452      1.000000   \n",
              "mean_method_size                         0.794190    0.782827     -0.214187   \n",
              "mean_paragraph_size                      0.698996    0.667653     -0.035805   \n",
              "size_largest_method                      0.828598    0.815389     -0.091352   \n",
              "size_shortest_method                     0.589214    0.581864     -0.296081   \n",
              "std_method_size                          0.579474    0.567710      0.018580   \n",
              "step_count                               0.541815    0.563880      0.451643   \n",
              "mean_steps_per_method                    0.254914    0.277330     -0.290797   \n",
              "introduction_size                        0.281792    0.247741      0.003095   \n",
              "summary_size                             0.075104    0.084986     -0.029205   \n",
              "references_count                         0.416294    0.396383      0.153304   \n",
              "references_count_per_text_length        -0.255498   -0.263016     -0.042883   \n",
              "references_count_per_method              0.235101    0.214671     -0.198536   \n",
              "image_count                              0.532721    0.538602      0.403775   \n",
              "image_count_per_method                   0.290967    0.293950     -0.206668   \n",
              "num_votes                                0.017189    0.020143     -0.014327   \n",
              "is_expert                                0.077717    0.070622     -0.021958   \n",
              "views                                    0.086152    0.096446      0.032963   \n",
              "co_authors                               0.179683    0.197734      0.082645   \n",
              "warnings                                 0.081820    0.098085      0.031487   \n",
              "tips                                     0.105365    0.128416      0.046916   \n",
              "to_be_verb                               0.813183    0.800279      0.193723   \n",
              "aux_verb                                 0.797055    0.784656      0.217405   \n",
              "conjunction                              0.871492    0.864371      0.245315   \n",
              "pronoun                                  0.806472    0.829806      0.220193   \n",
              "preposition                              0.927144    0.952229      0.271935   \n",
              "nominalization                           0.614982    0.541162      0.136963   \n",
              "sentence_beginning_pronoun               0.671873    0.679327      0.207967   \n",
              "sentence_beginning_interrogative         0.393957    0.402282      0.145001   \n",
              "sentence_beginning_article               0.464326    0.450030      0.134503   \n",
              "sentence_beginning_subordination         0.615300    0.641460      0.172875   \n",
              "sentence_beginning_conjunction           0.275254    0.290859      0.075655   \n",
              "sentence_beginning_preposition           0.538326    0.526581      0.133057   \n",
              "Kincaid                                  0.425547    0.324147     -0.023578   \n",
              "ARI                                      0.436732    0.341326     -0.032113   \n",
              "Coleman_Liau                             0.333617    0.203557     -0.000779   \n",
              "FleschReadingEase                       -0.355762   -0.235662      0.003184   \n",
              "GunningFogIndex                          0.441046    0.352021     -0.025194   \n",
              "LIX                                      0.414626    0.308273     -0.015076   \n",
              "SMOGIndex                                0.418059    0.317717     -0.010819   \n",
              "RIX                                      0.421090    0.317806     -0.017049   \n",
              "DaleChallIndex                           0.201903    0.115529     -0.003942   \n",
              "\n",
              "                                  mean_method_size  mean_paragraph_size  \\\n",
              "character_count                           0.794190             0.698996   \n",
              "word_count                                0.782827             0.667653   \n",
              "method_count                             -0.214187            -0.035805   \n",
              "mean_method_size                          1.000000             0.762631   \n",
              "mean_paragraph_size                       0.762631             1.000000   \n",
              "size_largest_method                       0.921439             0.718716   \n",
              "size_shortest_method                      0.870765             0.649067   \n",
              "std_method_size                           0.563360             0.448437   \n",
              "step_count                                0.222027            -0.139618   \n",
              "mean_steps_per_method                     0.461983            -0.148155   \n",
              "introduction_size                         0.247392             0.342254   \n",
              "summary_size                              0.087432             0.073128   \n",
              "references_count                          0.303293             0.225948   \n",
              "references_count_per_text_length         -0.236050            -0.244698   \n",
              "references_count_per_method               0.387063             0.222424   \n",
              "image_count                               0.245821            -0.031715   \n",
              "image_count_per_method                    0.440528            -0.016118   \n",
              "num_votes                                 0.033455             0.030718   \n",
              "is_expert                                 0.075377             0.139051   \n",
              "views                                     0.079204             0.052975   \n",
              "co_authors                                0.140589             0.105955   \n",
              "warnings                                  0.060696             0.009484   \n",
              "tips                                      0.087388             0.029488   \n",
              "to_be_verb                                0.664765             0.626336   \n",
              "aux_verb                                  0.627761             0.568272   \n",
              "conjunction                               0.690873             0.598651   \n",
              "pronoun                                   0.658784             0.582147   \n",
              "preposition                               0.734468             0.602592   \n",
              "nominalization                            0.505356             0.522715   \n",
              "sentence_beginning_pronoun                0.516131             0.439135   \n",
              "sentence_beginning_interrogative          0.306584             0.274458   \n",
              "sentence_beginning_article                0.357160             0.323938   \n",
              "sentence_beginning_subordination          0.494449             0.423242   \n",
              "sentence_beginning_conjunction            0.230252             0.220592   \n",
              "sentence_beginning_preposition            0.434816             0.414813   \n",
              "Kincaid                                   0.452434             0.603722   \n",
              "ARI                                       0.472045             0.613435   \n",
              "Coleman_Liau                              0.328627             0.458236   \n",
              "FleschReadingEase                        -0.358113            -0.502171   \n",
              "GunningFogIndex                           0.473116             0.612574   \n",
              "LIX                                       0.434336             0.578425   \n",
              "SMOGIndex                                 0.435781             0.575702   \n",
              "RIX                                       0.445227             0.591085   \n",
              "DaleChallIndex                            0.211722             0.312241   \n",
              "\n",
              "                                  size_largest_method  size_shortest_method  \\\n",
              "character_count                              0.828598              0.589214   \n",
              "word_count                                   0.815389              0.581864   \n",
              "method_count                                -0.091352             -0.296081   \n",
              "mean_method_size                             0.921439              0.870765   \n",
              "mean_paragraph_size                          0.718716              0.649067   \n",
              "size_largest_method                          1.000000              0.648070   \n",
              "size_shortest_method                         0.648070              1.000000   \n",
              "std_method_size                              0.820605              0.114329   \n",
              "step_count                                   0.310053              0.082690   \n",
              "mean_steps_per_method                        0.406030              0.421472   \n",
              "introduction_size                            0.234823              0.213564   \n",
              "summary_size                                 0.084145              0.068010   \n",
              "references_count                             0.320948              0.224098   \n",
              "references_count_per_text_length            -0.239022             -0.179225   \n",
              "references_count_per_method                  0.334918              0.366319   \n",
              "image_count                                  0.322158              0.112696   \n",
              "image_count_per_method                       0.397446              0.389861   \n",
              "num_votes                                    0.042469              0.009837   \n",
              "is_expert                                    0.033913              0.116833   \n",
              "views                                        0.089730              0.043360   \n",
              "co_authors                                   0.153572              0.088041   \n",
              "warnings                                     0.080501              0.016965   \n",
              "tips                                         0.101524              0.043111   \n",
              "to_be_verb                                   0.693978              0.492608   \n",
              "aux_verb                                     0.649000              0.471271   \n",
              "conjunction                                  0.719982              0.516702   \n",
              "pronoun                                      0.661228              0.518232   \n",
              "preposition                                  0.752554              0.558307   \n",
              "nominalization                               0.524842              0.378381   \n",
              "sentence_beginning_pronoun                   0.542150              0.380369   \n",
              "sentence_beginning_interrogative             0.311969              0.229160   \n",
              "sentence_beginning_article                   0.391303              0.243127   \n",
              "sentence_beginning_subordination             0.486337              0.398991   \n",
              "sentence_beginning_conjunction               0.249645              0.164624   \n",
              "sentence_beginning_preposition               0.450961              0.325385   \n",
              "Kincaid                                      0.430648              0.382537   \n",
              "ARI                                          0.454832              0.392329   \n",
              "Coleman_Liau                                 0.324648              0.264266   \n",
              "FleschReadingEase                           -0.344239             -0.299253   \n",
              "GunningFogIndex                              0.452494              0.396675   \n",
              "LIX                                          0.421165              0.358844   \n",
              "SMOGIndex                                    0.420025              0.361542   \n",
              "RIX                                          0.432525              0.367194   \n",
              "DaleChallIndex                               0.228211              0.150100   \n",
              "\n",
              "                                  std_method_size  step_count  \\\n",
              "character_count                          0.579474    0.541815   \n",
              "word_count                               0.567710    0.563880   \n",
              "method_count                             0.018580    0.451643   \n",
              "mean_method_size                         0.563360    0.222027   \n",
              "mean_paragraph_size                      0.448437   -0.139618   \n",
              "size_largest_method                      0.820605    0.310053   \n",
              "size_shortest_method                     0.114329    0.082690   \n",
              "std_method_size                          1.000000    0.269782   \n",
              "step_count                               0.269782    1.000000   \n",
              "mean_steps_per_method                    0.235888    0.572694   \n",
              "introduction_size                        0.140404   -0.004560   \n",
              "summary_size                             0.058321    0.034516   \n",
              "references_count                         0.223483    0.265722   \n",
              "references_count_per_text_length        -0.166572   -0.112820   \n",
              "references_count_per_method              0.181060    0.046714   \n",
              "image_count                              0.270379    0.843777   \n",
              "image_count_per_method                   0.240028    0.462359   \n",
              "num_votes                                0.053079   -0.000917   \n",
              "is_expert                               -0.045562   -0.079469   \n",
              "views                                    0.083516    0.075134   \n",
              "co_authors                               0.124511    0.137507   \n",
              "warnings                                 0.086754    0.105027   \n",
              "tips                                     0.093717    0.128728   \n",
              "to_be_verb                               0.488978    0.370561   \n",
              "aux_verb                                 0.444614    0.410972   \n",
              "conjunction                              0.499323    0.475517   \n",
              "pronoun                                  0.428806    0.398123   \n",
              "preposition                              0.505746    0.552801   \n",
              "nominalization                           0.367902    0.216729   \n",
              "sentence_beginning_pronoun               0.378157    0.406745   \n",
              "sentence_beginning_interrogative         0.210361    0.199733   \n",
              "sentence_beginning_article               0.296566    0.262429   \n",
              "sentence_beginning_subordination         0.299672    0.331014   \n",
              "sentence_beginning_conjunction           0.185393    0.096446   \n",
              "sentence_beginning_preposition           0.312355    0.240554   \n",
              "Kincaid                                  0.272876   -0.106171   \n",
              "ARI                                      0.299178   -0.097085   \n",
              "Coleman_Liau                             0.219948   -0.061228   \n",
              "FleschReadingEase                       -0.219374    0.084547   \n",
              "GunningFogIndex                          0.291607   -0.089789   \n",
              "LIX                                      0.278003   -0.088890   \n",
              "SMOGIndex                                0.273505   -0.076968   \n",
              "RIX                                      0.287399   -0.087396   \n",
              "DaleChallIndex                           0.187036   -0.066147   \n",
              "\n",
              "                                  mean_steps_per_method  ...  \\\n",
              "character_count                                0.254914  ...   \n",
              "word_count                                     0.277330  ...   \n",
              "method_count                                  -0.290797  ...   \n",
              "mean_method_size                               0.461983  ...   \n",
              "mean_paragraph_size                           -0.148155  ...   \n",
              "size_largest_method                            0.406030  ...   \n",
              "size_shortest_method                           0.421472  ...   \n",
              "std_method_size                                0.235888  ...   \n",
              "step_count                                     0.572694  ...   \n",
              "mean_steps_per_method                          1.000000  ...   \n",
              "introduction_size                             -0.033716  ...   \n",
              "summary_size                                   0.039503  ...   \n",
              "references_count                               0.133831  ...   \n",
              "references_count_per_text_length              -0.054838  ...   \n",
              "references_count_per_method                    0.259458  ...   \n",
              "image_count                                    0.453533  ...   \n",
              "image_count_per_method                         0.766793  ...   \n",
              "num_votes                                      0.009861  ...   \n",
              "is_expert                                     -0.078333  ...   \n",
              "views                                          0.045786  ...   \n",
              "co_authors                                     0.068599  ...   \n",
              "warnings                                       0.074007  ...   \n",
              "tips                                           0.097866  ...   \n",
              "to_be_verb                                     0.153228  ...   \n",
              "aux_verb                                       0.176050  ...   \n",
              "conjunction                                    0.227442  ...   \n",
              "pronoun                                        0.186698  ...   \n",
              "preposition                                    0.287933  ...   \n",
              "nominalization                                 0.064965  ...   \n",
              "sentence_beginning_pronoun                     0.191675  ...   \n",
              "sentence_beginning_interrogative               0.080778  ...   \n",
              "sentence_beginning_article                     0.111436  ...   \n",
              "sentence_beginning_subordination               0.159766  ...   \n",
              "sentence_beginning_conjunction                 0.037747  ...   \n",
              "sentence_beginning_preposition                 0.101601  ...   \n",
              "Kincaid                                       -0.117732  ...   \n",
              "ARI                                           -0.098487  ...   \n",
              "Coleman_Liau                                  -0.105237  ...   \n",
              "FleschReadingEase                              0.120817  ...   \n",
              "GunningFogIndex                               -0.092361  ...   \n",
              "LIX                                           -0.107605  ...   \n",
              "SMOGIndex                                     -0.096557  ...   \n",
              "RIX                                           -0.102282  ...   \n",
              "DaleChallIndex                                -0.071508  ...   \n",
              "\n",
              "                                  sentence_beginning_preposition   Kincaid  \\\n",
              "character_count                                         0.538326  0.425547   \n",
              "word_count                                              0.526581  0.324147   \n",
              "method_count                                            0.133057 -0.023578   \n",
              "mean_method_size                                        0.434816  0.452434   \n",
              "mean_paragraph_size                                     0.414813  0.603722   \n",
              "size_largest_method                                     0.450961  0.430648   \n",
              "size_shortest_method                                    0.325385  0.382537   \n",
              "std_method_size                                         0.312355  0.272876   \n",
              "step_count                                              0.240554 -0.106171   \n",
              "mean_steps_per_method                                   0.101601 -0.117732   \n",
              "introduction_size                                       0.197065  0.393521   \n",
              "summary_size                                            0.033456 -0.013044   \n",
              "references_count                                        0.249028  0.207337   \n",
              "references_count_per_text_length                       -0.115760 -0.098177   \n",
              "references_count_per_method                             0.149940  0.208088   \n",
              "image_count                                             0.252472  0.024937   \n",
              "image_count_per_method                                  0.138748  0.042844   \n",
              "num_votes                                              -0.005693  0.010344   \n",
              "is_expert                                               0.066776  0.106368   \n",
              "views                                                   0.019938 -0.016454   \n",
              "co_authors                                              0.050444 -0.020206   \n",
              "warnings                                                0.007407 -0.042209   \n",
              "tips                                                    0.005859 -0.090698   \n",
              "to_be_verb                                              0.449756  0.387747   \n",
              "aux_verb                                                0.476643  0.361565   \n",
              "conjunction                                             0.394054  0.381798   \n",
              "pronoun                                                 0.411488  0.230532   \n",
              "preposition                                             0.520413  0.243811   \n",
              "nominalization                                          0.434878  0.574271   \n",
              "sentence_beginning_pronoun                              0.343029  0.123839   \n",
              "sentence_beginning_interrogative                        0.268743  0.037405   \n",
              "sentence_beginning_article                              0.347938  0.167393   \n",
              "sentence_beginning_subordination                        0.387878  0.168915   \n",
              "sentence_beginning_conjunction                          0.191997  0.029926   \n",
              "sentence_beginning_preposition                          1.000000  0.232544   \n",
              "Kincaid                                                 0.232544  1.000000   \n",
              "ARI                                                     0.239630  0.954067   \n",
              "Coleman_Liau                                            0.209246  0.889458   \n",
              "FleschReadingEase                                      -0.208208 -0.951495   \n",
              "GunningFogIndex                                         0.254445  0.942061   \n",
              "LIX                                                     0.242565  0.936427   \n",
              "SMOGIndex                                               0.252133  0.932505   \n",
              "RIX                                                     0.241598  0.935506   \n",
              "DaleChallIndex                                          0.142517  0.628226   \n",
              "\n",
              "                                       ARI  Coleman_Liau  FleschReadingEase  \\\n",
              "character_count                   0.436732      0.333617          -0.355762   \n",
              "word_count                        0.341326      0.203557          -0.235662   \n",
              "method_count                     -0.032113     -0.000779           0.003184   \n",
              "mean_method_size                  0.472045      0.328627          -0.358113   \n",
              "mean_paragraph_size               0.613435      0.458236          -0.502171   \n",
              "size_largest_method               0.454832      0.324648          -0.344239   \n",
              "size_shortest_method              0.392329      0.264266          -0.299253   \n",
              "std_method_size                   0.299178      0.219948          -0.219374   \n",
              "step_count                       -0.097085     -0.061228           0.084547   \n",
              "mean_steps_per_method            -0.098487     -0.105237           0.120817   \n",
              "introduction_size                 0.390166      0.343789          -0.357171   \n",
              "summary_size                     -0.017101     -0.023017           0.017358   \n",
              "references_count                  0.189052      0.202486          -0.211727   \n",
              "references_count_per_text_length -0.119560     -0.048344           0.053160   \n",
              "references_count_per_method       0.193523      0.187990          -0.201550   \n",
              "image_count                       0.018467      0.059125          -0.049787   \n",
              "image_count_per_method            0.042028      0.050007          -0.048764   \n",
              "num_votes                         0.007959     -0.002250          -0.006607   \n",
              "is_expert                         0.083718      0.083905          -0.102244   \n",
              "views                            -0.019147     -0.034452           0.024440   \n",
              "co_authors                       -0.037482     -0.056298           0.027077   \n",
              "warnings                         -0.045616     -0.069730           0.054216   \n",
              "tips                             -0.092170     -0.112542           0.099049   \n",
              "to_be_verb                        0.360509      0.296489          -0.347605   \n",
              "aux_verb                          0.330225      0.280184          -0.329077   \n",
              "conjunction                       0.364044      0.278341          -0.326768   \n",
              "pronoun                           0.202036      0.092434          -0.167115   \n",
              "preposition                       0.268271      0.112262          -0.143638   \n",
              "nominalization                    0.550640      0.580387          -0.583158   \n",
              "sentence_beginning_pronoun        0.077738      0.097727          -0.139021   \n",
              "sentence_beginning_interrogative  0.027360      0.030213          -0.039034   \n",
              "sentence_beginning_article        0.192110      0.189145          -0.156220   \n",
              "sentence_beginning_subordination  0.149648      0.041981          -0.105515   \n",
              "sentence_beginning_conjunction    0.005742     -0.018492          -0.020252   \n",
              "sentence_beginning_preposition    0.239630      0.209246          -0.208208   \n",
              "Kincaid                           0.954067      0.889458          -0.951495   \n",
              "ARI                               1.000000      0.872847          -0.854736   \n",
              "Coleman_Liau                      0.872847      1.000000          -0.944137   \n",
              "FleschReadingEase                -0.854736     -0.944137           1.000000   \n",
              "GunningFogIndex                   0.928521      0.811422          -0.859749   \n",
              "LIX                               0.942837      0.907651          -0.896746   \n",
              "SMOGIndex                         0.887794      0.852970          -0.902270   \n",
              "RIX                               0.951268      0.881300          -0.875541   \n",
              "DaleChallIndex                    0.656494      0.674122          -0.624464   \n",
              "\n",
              "                                  GunningFogIndex       LIX  SMOGIndex  \\\n",
              "character_count                          0.441046  0.414626   0.418059   \n",
              "word_count                               0.352021  0.308273   0.317717   \n",
              "method_count                            -0.025194 -0.015076  -0.010819   \n",
              "mean_method_size                         0.473116  0.434336   0.435781   \n",
              "mean_paragraph_size                      0.612574  0.578425   0.575702   \n",
              "size_largest_method                      0.452494  0.421165   0.420025   \n",
              "size_shortest_method                     0.396675  0.358844   0.361542   \n",
              "std_method_size                          0.291607  0.278003   0.273505   \n",
              "step_count                              -0.089789 -0.088890  -0.076968   \n",
              "mean_steps_per_method                   -0.092361 -0.107605  -0.096557   \n",
              "introduction_size                        0.396289  0.382078   0.384562   \n",
              "summary_size                            -0.008400 -0.023550  -0.005463   \n",
              "references_count                         0.194250  0.194957   0.199902   \n",
              "references_count_per_text_length        -0.120799 -0.104804  -0.103597   \n",
              "references_count_per_method              0.196460  0.190054   0.194766   \n",
              "image_count                              0.028834  0.039503   0.050423   \n",
              "image_count_per_method                   0.050327  0.051748   0.060329   \n",
              "num_votes                                0.011810  0.011406   0.012801   \n",
              "is_expert                                0.093176  0.086145   0.090629   \n",
              "views                                   -0.009566 -0.020455  -0.008863   \n",
              "co_authors                              -0.027962 -0.033870  -0.025910   \n",
              "warnings                                -0.042948 -0.057081  -0.043716   \n",
              "tips                                    -0.090058 -0.098912  -0.090296   \n",
              "to_be_verb                               0.400614  0.364273   0.398644   \n",
              "aux_verb                                 0.363537  0.327323   0.360666   \n",
              "conjunction                              0.359684  0.335656   0.344302   \n",
              "pronoun                                  0.232046  0.192371   0.211270   \n",
              "preposition                              0.270312  0.221339   0.224604   \n",
              "nominalization                           0.600903  0.602880   0.617533   \n",
              "sentence_beginning_pronoun               0.125098  0.111100   0.142095   \n",
              "sentence_beginning_interrogative         0.052984  0.051774   0.061279   \n",
              "sentence_beginning_article               0.226206  0.204024   0.228736   \n",
              "sentence_beginning_subordination         0.198029  0.139795   0.177718   \n",
              "sentence_beginning_conjunction           0.033752  0.019382   0.036774   \n",
              "sentence_beginning_preposition           0.254445  0.242565   0.252133   \n",
              "Kincaid                                  0.942061  0.936427   0.932505   \n",
              "ARI                                      0.928521  0.942837   0.887794   \n",
              "Coleman_Liau                             0.811422  0.907651   0.852970   \n",
              "FleschReadingEase                       -0.859749 -0.896746  -0.902270   \n",
              "GunningFogIndex                          1.000000  0.919102   0.979224   \n",
              "LIX                                      0.919102  1.000000   0.914295   \n",
              "SMOGIndex                                0.979224  0.914295   1.000000   \n",
              "RIX                                      0.926048  0.989468   0.914108   \n",
              "DaleChallIndex                           0.639924  0.694945   0.649724   \n",
              "\n",
              "                                       RIX  DaleChallIndex  \n",
              "character_count                   0.421090        0.201903  \n",
              "word_count                        0.317806        0.115529  \n",
              "method_count                     -0.017049       -0.003942  \n",
              "mean_method_size                  0.445227        0.211722  \n",
              "mean_paragraph_size               0.591085        0.312241  \n",
              "size_largest_method               0.432525        0.228211  \n",
              "size_shortest_method              0.367194        0.150100  \n",
              "std_method_size                   0.287399        0.187036  \n",
              "step_count                       -0.087396       -0.066147  \n",
              "mean_steps_per_method            -0.102282       -0.071508  \n",
              "introduction_size                 0.381184        0.271649  \n",
              "summary_size                     -0.027448       -0.038339  \n",
              "references_count                  0.190177        0.173445  \n",
              "references_count_per_text_length -0.111965        0.035669  \n",
              "references_count_per_method       0.187933        0.176943  \n",
              "image_count                       0.035432        0.018787  \n",
              "image_count_per_method            0.049970        0.033429  \n",
              "num_votes                         0.011712        0.009145  \n",
              "is_expert                         0.077580        0.030294  \n",
              "views                            -0.021038       -0.020848  \n",
              "co_authors                       -0.034596       -0.095475  \n",
              "warnings                         -0.054041       -0.063836  \n",
              "tips                             -0.092479       -0.115773  \n",
              "to_be_verb                        0.364765        0.103200  \n",
              "aux_verb                          0.323478        0.041185  \n",
              "conjunction                       0.340526        0.127811  \n",
              "pronoun                           0.192395       -0.134501  \n",
              "preposition                       0.232587       -0.005137  \n",
              "nominalization                    0.599352        0.368054  \n",
              "sentence_beginning_pronoun        0.098368       -0.054155  \n",
              "sentence_beginning_interrogative  0.043534       -0.078580  \n",
              "sentence_beginning_article        0.203081        0.199111  \n",
              "sentence_beginning_subordination  0.141717       -0.062786  \n",
              "sentence_beginning_conjunction    0.017675       -0.065365  \n",
              "sentence_beginning_preposition    0.241598        0.142517  \n",
              "Kincaid                           0.935506        0.628226  \n",
              "ARI                               0.951268        0.656494  \n",
              "Coleman_Liau                      0.881300        0.674122  \n",
              "FleschReadingEase                -0.875541       -0.624464  \n",
              "GunningFogIndex                   0.926048        0.639924  \n",
              "LIX                               0.989468        0.694945  \n",
              "SMOGIndex                         0.914108        0.649724  \n",
              "RIX                               1.000000        0.684733  \n",
              "DaleChallIndex                    0.684733        1.000000  \n",
              "\n",
              "[44 rows x 44 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>character_count</th>\n",
              "      <th>word_count</th>\n",
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              "      <th>Kincaid</th>\n",
              "      <th>ARI</th>\n",
              "      <th>Coleman_Liau</th>\n",
              "      <th>FleschReadingEase</th>\n",
              "      <th>GunningFogIndex</th>\n",
              "      <th>LIX</th>\n",
              "      <th>SMOGIndex</th>\n",
              "      <th>RIX</th>\n",
              "      <th>DaleChallIndex</th>\n",
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              "      <th>character_count</th>\n",
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              "      <th>word_count</th>\n",
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              "      <td>0.115529</td>\n",
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              "    <tr>\n",
              "      <th>method_count</th>\n",
              "      <td>0.282684</td>\n",
              "      <td>0.288452</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.214187</td>\n",
              "      <td>-0.035805</td>\n",
              "      <td>-0.091352</td>\n",
              "      <td>-0.296081</td>\n",
              "      <td>0.018580</td>\n",
              "      <td>0.451643</td>\n",
              "      <td>-0.290797</td>\n",
              "      <td>...</td>\n",
              "      <td>0.133057</td>\n",
              "      <td>-0.023578</td>\n",
              "      <td>-0.032113</td>\n",
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              "      <td>-0.025194</td>\n",
              "      <td>-0.015076</td>\n",
              "      <td>-0.010819</td>\n",
              "      <td>-0.017049</td>\n",
              "      <td>-0.003942</td>\n",
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              "    <tr>\n",
              "      <th>mean_method_size</th>\n",
              "      <td>0.794190</td>\n",
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              "      <td>1.000000</td>\n",
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              "      <td>0.563360</td>\n",
              "      <td>0.222027</td>\n",
              "      <td>0.461983</td>\n",
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              "      <td>0.434336</td>\n",
              "      <td>0.435781</td>\n",
              "      <td>0.445227</td>\n",
              "      <td>0.211722</td>\n",
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              "    <tr>\n",
              "      <th>mean_paragraph_size</th>\n",
              "      <td>0.698996</td>\n",
              "      <td>0.667653</td>\n",
              "      <td>-0.035805</td>\n",
              "      <td>0.762631</td>\n",
              "      <td>1.000000</td>\n",
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              "      <td>-0.148155</td>\n",
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              "      <td>0.414813</td>\n",
              "      <td>0.603722</td>\n",
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              "      <td>0.458236</td>\n",
              "      <td>-0.502171</td>\n",
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              "      <td>0.575702</td>\n",
              "      <td>0.591085</td>\n",
              "      <td>0.312241</td>\n",
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              "    <tr>\n",
              "      <th>size_largest_method</th>\n",
              "      <td>0.828598</td>\n",
              "      <td>0.815389</td>\n",
              "      <td>-0.091352</td>\n",
              "      <td>0.921439</td>\n",
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              "      <td>0.406030</td>\n",
              "      <td>...</td>\n",
              "      <td>0.450961</td>\n",
              "      <td>0.430648</td>\n",
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              "      <td>-0.344239</td>\n",
              "      <td>0.452494</td>\n",
              "      <td>0.421165</td>\n",
              "      <td>0.420025</td>\n",
              "      <td>0.432525</td>\n",
              "      <td>0.228211</td>\n",
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              "    <tr>\n",
              "      <th>size_shortest_method</th>\n",
              "      <td>0.589214</td>\n",
              "      <td>0.581864</td>\n",
              "      <td>-0.296081</td>\n",
              "      <td>0.870765</td>\n",
              "      <td>0.649067</td>\n",
              "      <td>0.648070</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.114329</td>\n",
              "      <td>0.082690</td>\n",
              "      <td>0.421472</td>\n",
              "      <td>...</td>\n",
              "      <td>0.325385</td>\n",
              "      <td>0.382537</td>\n",
              "      <td>0.392329</td>\n",
              "      <td>0.264266</td>\n",
              "      <td>-0.299253</td>\n",
              "      <td>0.396675</td>\n",
              "      <td>0.358844</td>\n",
              "      <td>0.361542</td>\n",
              "      <td>0.367194</td>\n",
              "      <td>0.150100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std_method_size</th>\n",
              "      <td>0.579474</td>\n",
              "      <td>0.567710</td>\n",
              "      <td>0.018580</td>\n",
              "      <td>0.563360</td>\n",
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              "      <td>0.269782</td>\n",
              "      <td>0.235888</td>\n",
              "      <td>...</td>\n",
              "      <td>0.312355</td>\n",
              "      <td>0.272876</td>\n",
              "      <td>0.299178</td>\n",
              "      <td>0.219948</td>\n",
              "      <td>-0.219374</td>\n",
              "      <td>0.291607</td>\n",
              "      <td>0.278003</td>\n",
              "      <td>0.273505</td>\n",
              "      <td>0.287399</td>\n",
              "      <td>0.187036</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>step_count</th>\n",
              "      <td>0.541815</td>\n",
              "      <td>0.563880</td>\n",
              "      <td>0.451643</td>\n",
              "      <td>0.222027</td>\n",
              "      <td>-0.139618</td>\n",
              "      <td>0.310053</td>\n",
              "      <td>0.082690</td>\n",
              "      <td>0.269782</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.572694</td>\n",
              "      <td>...</td>\n",
              "      <td>0.240554</td>\n",
              "      <td>-0.106171</td>\n",
              "      <td>-0.097085</td>\n",
              "      <td>-0.061228</td>\n",
              "      <td>0.084547</td>\n",
              "      <td>-0.089789</td>\n",
              "      <td>-0.088890</td>\n",
              "      <td>-0.076968</td>\n",
              "      <td>-0.087396</td>\n",
              "      <td>-0.066147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean_steps_per_method</th>\n",
              "      <td>0.254914</td>\n",
              "      <td>0.277330</td>\n",
              "      <td>-0.290797</td>\n",
              "      <td>0.461983</td>\n",
              "      <td>-0.148155</td>\n",
              "      <td>0.406030</td>\n",
              "      <td>0.421472</td>\n",
              "      <td>0.235888</td>\n",
              "      <td>0.572694</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.101601</td>\n",
              "      <td>-0.117732</td>\n",
              "      <td>-0.098487</td>\n",
              "      <td>-0.105237</td>\n",
              "      <td>0.120817</td>\n",
              "      <td>-0.092361</td>\n",
              "      <td>-0.107605</td>\n",
              "      <td>-0.096557</td>\n",
              "      <td>-0.102282</td>\n",
              "      <td>-0.071508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>introduction_size</th>\n",
              "      <td>0.281792</td>\n",
              "      <td>0.247741</td>\n",
              "      <td>0.003095</td>\n",
              "      <td>0.247392</td>\n",
              "      <td>0.342254</td>\n",
              "      <td>0.234823</td>\n",
              "      <td>0.213564</td>\n",
              "      <td>0.140404</td>\n",
              "      <td>-0.004560</td>\n",
              "      <td>-0.033716</td>\n",
              "      <td>...</td>\n",
              "      <td>0.197065</td>\n",
              "      <td>0.393521</td>\n",
              "      <td>0.390166</td>\n",
              "      <td>0.343789</td>\n",
              "      <td>-0.357171</td>\n",
              "      <td>0.396289</td>\n",
              "      <td>0.382078</td>\n",
              "      <td>0.384562</td>\n",
              "      <td>0.381184</td>\n",
              "      <td>0.271649</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>summary_size</th>\n",
              "      <td>0.075104</td>\n",
              "      <td>0.084986</td>\n",
              "      <td>-0.029205</td>\n",
              "      <td>0.087432</td>\n",
              "      <td>0.073128</td>\n",
              "      <td>0.084145</td>\n",
              "      <td>0.068010</td>\n",
              "      <td>0.058321</td>\n",
              "      <td>0.034516</td>\n",
              "      <td>0.039503</td>\n",
              "      <td>...</td>\n",
              "      <td>0.033456</td>\n",
              "      <td>-0.013044</td>\n",
              "      <td>-0.017101</td>\n",
              "      <td>-0.023017</td>\n",
              "      <td>0.017358</td>\n",
              "      <td>-0.008400</td>\n",
              "      <td>-0.023550</td>\n",
              "      <td>-0.005463</td>\n",
              "      <td>-0.027448</td>\n",
              "      <td>-0.038339</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>references_count</th>\n",
              "      <td>0.416294</td>\n",
              "      <td>0.396383</td>\n",
              "      <td>0.153304</td>\n",
              "      <td>0.303293</td>\n",
              "      <td>0.225948</td>\n",
              "      <td>0.320948</td>\n",
              "      <td>0.224098</td>\n",
              "      <td>0.223483</td>\n",
              "      <td>0.265722</td>\n",
              "      <td>0.133831</td>\n",
              "      <td>...</td>\n",
              "      <td>0.249028</td>\n",
              "      <td>0.207337</td>\n",
              "      <td>0.189052</td>\n",
              "      <td>0.202486</td>\n",
              "      <td>-0.211727</td>\n",
              "      <td>0.194250</td>\n",
              "      <td>0.194957</td>\n",
              "      <td>0.199902</td>\n",
              "      <td>0.190177</td>\n",
              "      <td>0.173445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>references_count_per_text_length</th>\n",
              "      <td>-0.255498</td>\n",
              "      <td>-0.263016</td>\n",
              "      <td>-0.042883</td>\n",
              "      <td>-0.236050</td>\n",
              "      <td>-0.244698</td>\n",
              "      <td>-0.239022</td>\n",
              "      <td>-0.179225</td>\n",
              "      <td>-0.166572</td>\n",
              "      <td>-0.112820</td>\n",
              "      <td>-0.054838</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.115760</td>\n",
              "      <td>-0.098177</td>\n",
              "      <td>-0.119560</td>\n",
              "      <td>-0.048344</td>\n",
              "      <td>0.053160</td>\n",
              "      <td>-0.120799</td>\n",
              "      <td>-0.104804</td>\n",
              "      <td>-0.103597</td>\n",
              "      <td>-0.111965</td>\n",
              "      <td>0.035669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>references_count_per_method</th>\n",
              "      <td>0.235101</td>\n",
              "      <td>0.214671</td>\n",
              "      <td>-0.198536</td>\n",
              "      <td>0.387063</td>\n",
              "      <td>0.222424</td>\n",
              "      <td>0.334918</td>\n",
              "      <td>0.366319</td>\n",
              "      <td>0.181060</td>\n",
              "      <td>0.046714</td>\n",
              "      <td>0.259458</td>\n",
              "      <td>...</td>\n",
              "      <td>0.149940</td>\n",
              "      <td>0.208088</td>\n",
              "      <td>0.193523</td>\n",
              "      <td>0.187990</td>\n",
              "      <td>-0.201550</td>\n",
              "      <td>0.196460</td>\n",
              "      <td>0.190054</td>\n",
              "      <td>0.194766</td>\n",
              "      <td>0.187933</td>\n",
              "      <td>0.176943</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>image_count</th>\n",
              "      <td>0.532721</td>\n",
              "      <td>0.538602</td>\n",
              "      <td>0.403775</td>\n",
              "      <td>0.245821</td>\n",
              "      <td>-0.031715</td>\n",
              "      <td>0.322158</td>\n",
              "      <td>0.112696</td>\n",
              "      <td>0.270379</td>\n",
              "      <td>0.843777</td>\n",
              "      <td>0.453533</td>\n",
              "      <td>...</td>\n",
              "      <td>0.252472</td>\n",
              "      <td>0.024937</td>\n",
              "      <td>0.018467</td>\n",
              "      <td>0.059125</td>\n",
              "      <td>-0.049787</td>\n",
              "      <td>0.028834</td>\n",
              "      <td>0.039503</td>\n",
              "      <td>0.050423</td>\n",
              "      <td>0.035432</td>\n",
              "      <td>0.018787</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>image_count_per_method</th>\n",
              "      <td>0.290967</td>\n",
              "      <td>0.293950</td>\n",
              "      <td>-0.206668</td>\n",
              "      <td>0.440528</td>\n",
              "      <td>-0.016118</td>\n",
              "      <td>0.397446</td>\n",
              "      <td>0.389861</td>\n",
              "      <td>0.240028</td>\n",
              "      <td>0.462359</td>\n",
              "      <td>0.766793</td>\n",
              "      <td>...</td>\n",
              "      <td>0.138748</td>\n",
              "      <td>0.042844</td>\n",
              "      <td>0.042028</td>\n",
              "      <td>0.050007</td>\n",
              "      <td>-0.048764</td>\n",
              "      <td>0.050327</td>\n",
              "      <td>0.051748</td>\n",
              "      <td>0.060329</td>\n",
              "      <td>0.049970</td>\n",
              "      <td>0.033429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num_votes</th>\n",
              "      <td>0.017189</td>\n",
              "      <td>0.020143</td>\n",
              "      <td>-0.014327</td>\n",
              "      <td>0.033455</td>\n",
              "      <td>0.030718</td>\n",
              "      <td>0.042469</td>\n",
              "      <td>0.009837</td>\n",
              "      <td>0.053079</td>\n",
              "      <td>-0.000917</td>\n",
              "      <td>0.009861</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.005693</td>\n",
              "      <td>0.010344</td>\n",
              "      <td>0.007959</td>\n",
              "      <td>-0.002250</td>\n",
              "      <td>-0.006607</td>\n",
              "      <td>0.011810</td>\n",
              "      <td>0.011406</td>\n",
              "      <td>0.012801</td>\n",
              "      <td>0.011712</td>\n",
              "      <td>0.009145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>is_expert</th>\n",
              "      <td>0.077717</td>\n",
              "      <td>0.070622</td>\n",
              "      <td>-0.021958</td>\n",
              "      <td>0.075377</td>\n",
              "      <td>0.139051</td>\n",
              "      <td>0.033913</td>\n",
              "      <td>0.116833</td>\n",
              "      <td>-0.045562</td>\n",
              "      <td>-0.079469</td>\n",
              "      <td>-0.078333</td>\n",
              "      <td>...</td>\n",
              "      <td>0.066776</td>\n",
              "      <td>0.106368</td>\n",
              "      <td>0.083718</td>\n",
              "      <td>0.083905</td>\n",
              "      <td>-0.102244</td>\n",
              "      <td>0.093176</td>\n",
              "      <td>0.086145</td>\n",
              "      <td>0.090629</td>\n",
              "      <td>0.077580</td>\n",
              "      <td>0.030294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>views</th>\n",
              "      <td>0.086152</td>\n",
              "      <td>0.096446</td>\n",
              "      <td>0.032963</td>\n",
              "      <td>0.079204</td>\n",
              "      <td>0.052975</td>\n",
              "      <td>0.089730</td>\n",
              "      <td>0.043360</td>\n",
              "      <td>0.083516</td>\n",
              "      <td>0.075134</td>\n",
              "      <td>0.045786</td>\n",
              "      <td>...</td>\n",
              "      <td>0.019938</td>\n",
              "      <td>-0.016454</td>\n",
              "      <td>-0.019147</td>\n",
              "      <td>-0.034452</td>\n",
              "      <td>0.024440</td>\n",
              "      <td>-0.009566</td>\n",
              "      <td>-0.020455</td>\n",
              "      <td>-0.008863</td>\n",
              "      <td>-0.021038</td>\n",
              "      <td>-0.020848</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>co_authors</th>\n",
              "      <td>0.179683</td>\n",
              "      <td>0.197734</td>\n",
              "      <td>0.082645</td>\n",
              "      <td>0.140589</td>\n",
              "      <td>0.105955</td>\n",
              "      <td>0.153572</td>\n",
              "      <td>0.088041</td>\n",
              "      <td>0.124511</td>\n",
              "      <td>0.137507</td>\n",
              "      <td>0.068599</td>\n",
              "      <td>...</td>\n",
              "      <td>0.050444</td>\n",
              "      <td>-0.020206</td>\n",
              "      <td>-0.037482</td>\n",
              "      <td>-0.056298</td>\n",
              "      <td>0.027077</td>\n",
              "      <td>-0.027962</td>\n",
              "      <td>-0.033870</td>\n",
              "      <td>-0.025910</td>\n",
              "      <td>-0.034596</td>\n",
              "      <td>-0.095475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>warnings</th>\n",
              "      <td>0.081820</td>\n",
              "      <td>0.098085</td>\n",
              "      <td>0.031487</td>\n",
              "      <td>0.060696</td>\n",
              "      <td>0.009484</td>\n",
              "      <td>0.080501</td>\n",
              "      <td>0.016965</td>\n",
              "      <td>0.086754</td>\n",
              "      <td>0.105027</td>\n",
              "      <td>0.074007</td>\n",
              "      <td>...</td>\n",
              "      <td>0.007407</td>\n",
              "      <td>-0.042209</td>\n",
              "      <td>-0.045616</td>\n",
              "      <td>-0.069730</td>\n",
              "      <td>0.054216</td>\n",
              "      <td>-0.042948</td>\n",
              "      <td>-0.057081</td>\n",
              "      <td>-0.043716</td>\n",
              "      <td>-0.054041</td>\n",
              "      <td>-0.063836</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tips</th>\n",
              "      <td>0.105365</td>\n",
              "      <td>0.128416</td>\n",
              "      <td>0.046916</td>\n",
              "      <td>0.087388</td>\n",
              "      <td>0.029488</td>\n",
              "      <td>0.101524</td>\n",
              "      <td>0.043111</td>\n",
              "      <td>0.093717</td>\n",
              "      <td>0.128728</td>\n",
              "      <td>0.097866</td>\n",
              "      <td>...</td>\n",
              "      <td>0.005859</td>\n",
              "      <td>-0.090698</td>\n",
              "      <td>-0.092170</td>\n",
              "      <td>-0.112542</td>\n",
              "      <td>0.099049</td>\n",
              "      <td>-0.090058</td>\n",
              "      <td>-0.098912</td>\n",
              "      <td>-0.090296</td>\n",
              "      <td>-0.092479</td>\n",
              "      <td>-0.115773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>to_be_verb</th>\n",
              "      <td>0.813183</td>\n",
              "      <td>0.800279</td>\n",
              "      <td>0.193723</td>\n",
              "      <td>0.664765</td>\n",
              "      <td>0.626336</td>\n",
              "      <td>0.693978</td>\n",
              "      <td>0.492608</td>\n",
              "      <td>0.488978</td>\n",
              "      <td>0.370561</td>\n",
              "      <td>0.153228</td>\n",
              "      <td>...</td>\n",
              "      <td>0.449756</td>\n",
              "      <td>0.387747</td>\n",
              "      <td>0.360509</td>\n",
              "      <td>0.296489</td>\n",
              "      <td>-0.347605</td>\n",
              "      <td>0.400614</td>\n",
              "      <td>0.364273</td>\n",
              "      <td>0.398644</td>\n",
              "      <td>0.364765</td>\n",
              "      <td>0.103200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>aux_verb</th>\n",
              "      <td>0.797055</td>\n",
              "      <td>0.784656</td>\n",
              "      <td>0.217405</td>\n",
              "      <td>0.627761</td>\n",
              "      <td>0.568272</td>\n",
              "      <td>0.649000</td>\n",
              "      <td>0.471271</td>\n",
              "      <td>0.444614</td>\n",
              "      <td>0.410972</td>\n",
              "      <td>0.176050</td>\n",
              "      <td>...</td>\n",
              "      <td>0.476643</td>\n",
              "      <td>0.361565</td>\n",
              "      <td>0.330225</td>\n",
              "      <td>0.280184</td>\n",
              "      <td>-0.329077</td>\n",
              "      <td>0.363537</td>\n",
              "      <td>0.327323</td>\n",
              "      <td>0.360666</td>\n",
              "      <td>0.323478</td>\n",
              "      <td>0.041185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conjunction</th>\n",
              "      <td>0.871492</td>\n",
              "      <td>0.864371</td>\n",
              "      <td>0.245315</td>\n",
              "      <td>0.690873</td>\n",
              "      <td>0.598651</td>\n",
              "      <td>0.719982</td>\n",
              "      <td>0.516702</td>\n",
              "      <td>0.499323</td>\n",
              "      <td>0.475517</td>\n",
              "      <td>0.227442</td>\n",
              "      <td>...</td>\n",
              "      <td>0.394054</td>\n",
              "      <td>0.381798</td>\n",
              "      <td>0.364044</td>\n",
              "      <td>0.278341</td>\n",
              "      <td>-0.326768</td>\n",
              "      <td>0.359684</td>\n",
              "      <td>0.335656</td>\n",
              "      <td>0.344302</td>\n",
              "      <td>0.340526</td>\n",
              "      <td>0.127811</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pronoun</th>\n",
              "      <td>0.806472</td>\n",
              "      <td>0.829806</td>\n",
              "      <td>0.220193</td>\n",
              "      <td>0.658784</td>\n",
              "      <td>0.582147</td>\n",
              "      <td>0.661228</td>\n",
              "      <td>0.518232</td>\n",
              "      <td>0.428806</td>\n",
              "      <td>0.398123</td>\n",
              "      <td>0.186698</td>\n",
              "      <td>...</td>\n",
              "      <td>0.411488</td>\n",
              "      <td>0.230532</td>\n",
              "      <td>0.202036</td>\n",
              "      <td>0.092434</td>\n",
              "      <td>-0.167115</td>\n",
              "      <td>0.232046</td>\n",
              "      <td>0.192371</td>\n",
              "      <td>0.211270</td>\n",
              "      <td>0.192395</td>\n",
              "      <td>-0.134501</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>preposition</th>\n",
              "      <td>0.927144</td>\n",
              "      <td>0.952229</td>\n",
              "      <td>0.271935</td>\n",
              "      <td>0.734468</td>\n",
              "      <td>0.602592</td>\n",
              "      <td>0.752554</td>\n",
              "      <td>0.558307</td>\n",
              "      <td>0.505746</td>\n",
              "      <td>0.552801</td>\n",
              "      <td>0.287933</td>\n",
              "      <td>...</td>\n",
              "      <td>0.520413</td>\n",
              "      <td>0.243811</td>\n",
              "      <td>0.268271</td>\n",
              "      <td>0.112262</td>\n",
              "      <td>-0.143638</td>\n",
              "      <td>0.270312</td>\n",
              "      <td>0.221339</td>\n",
              "      <td>0.224604</td>\n",
              "      <td>0.232587</td>\n",
              "      <td>-0.005137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nominalization</th>\n",
              "      <td>0.614982</td>\n",
              "      <td>0.541162</td>\n",
              "      <td>0.136963</td>\n",
              "      <td>0.505356</td>\n",
              "      <td>0.522715</td>\n",
              "      <td>0.524842</td>\n",
              "      <td>0.378381</td>\n",
              "      <td>0.367902</td>\n",
              "      <td>0.216729</td>\n",
              "      <td>0.064965</td>\n",
              "      <td>...</td>\n",
              "      <td>0.434878</td>\n",
              "      <td>0.574271</td>\n",
              "      <td>0.550640</td>\n",
              "      <td>0.580387</td>\n",
              "      <td>-0.583158</td>\n",
              "      <td>0.600903</td>\n",
              "      <td>0.602880</td>\n",
              "      <td>0.617533</td>\n",
              "      <td>0.599352</td>\n",
              "      <td>0.368054</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sentence_beginning_pronoun</th>\n",
              "      <td>0.671873</td>\n",
              "      <td>0.679327</td>\n",
              "      <td>0.207967</td>\n",
              "      <td>0.516131</td>\n",
              "      <td>0.439135</td>\n",
              "      <td>0.542150</td>\n",
              "      <td>0.380369</td>\n",
              "      <td>0.378157</td>\n",
              "      <td>0.406745</td>\n",
              "      <td>0.191675</td>\n",
              "      <td>...</td>\n",
              "      <td>0.343029</td>\n",
              "      <td>0.123839</td>\n",
              "      <td>0.077738</td>\n",
              "      <td>0.097727</td>\n",
              "      <td>-0.139021</td>\n",
              "      <td>0.125098</td>\n",
              "      <td>0.111100</td>\n",
              "      <td>0.142095</td>\n",
              "      <td>0.098368</td>\n",
              "      <td>-0.054155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sentence_beginning_interrogative</th>\n",
              "      <td>0.393957</td>\n",
              "      <td>0.402282</td>\n",
              "      <td>0.145001</td>\n",
              "      <td>0.306584</td>\n",
              "      <td>0.274458</td>\n",
              "      <td>0.311969</td>\n",
              "      <td>0.229160</td>\n",
              "      <td>0.210361</td>\n",
              "      <td>0.199733</td>\n",
              "      <td>0.080778</td>\n",
              "      <td>...</td>\n",
              "      <td>0.268743</td>\n",
              "      <td>0.037405</td>\n",
              "      <td>0.027360</td>\n",
              "      <td>0.030213</td>\n",
              "      <td>-0.039034</td>\n",
              "      <td>0.052984</td>\n",
              "      <td>0.051774</td>\n",
              "      <td>0.061279</td>\n",
              "      <td>0.043534</td>\n",
              "      <td>-0.078580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sentence_beginning_article</th>\n",
              "      <td>0.464326</td>\n",
              "      <td>0.450030</td>\n",
              "      <td>0.134503</td>\n",
              "      <td>0.357160</td>\n",
              "      <td>0.323938</td>\n",
              "      <td>0.391303</td>\n",
              "      <td>0.243127</td>\n",
              "      <td>0.296566</td>\n",
              "      <td>0.262429</td>\n",
              "      <td>0.111436</td>\n",
              "      <td>...</td>\n",
              "      <td>0.347938</td>\n",
              "      <td>0.167393</td>\n",
              "      <td>0.192110</td>\n",
              "      <td>0.189145</td>\n",
              "      <td>-0.156220</td>\n",
              "      <td>0.226206</td>\n",
              "      <td>0.204024</td>\n",
              "      <td>0.228736</td>\n",
              "      <td>0.203081</td>\n",
              "      <td>0.199111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sentence_beginning_subordination</th>\n",
              "      <td>0.615300</td>\n",
              "      <td>0.641460</td>\n",
              "      <td>0.172875</td>\n",
              "      <td>0.494449</td>\n",
              "      <td>0.423242</td>\n",
              "      <td>0.486337</td>\n",
              "      <td>0.398991</td>\n",
              "      <td>0.299672</td>\n",
              "      <td>0.331014</td>\n",
              "      <td>0.159766</td>\n",
              "      <td>...</td>\n",
              "      <td>0.387878</td>\n",
              "      <td>0.168915</td>\n",
              "      <td>0.149648</td>\n",
              "      <td>0.041981</td>\n",
              "      <td>-0.105515</td>\n",
              "      <td>0.198029</td>\n",
              "      <td>0.139795</td>\n",
              "      <td>0.177718</td>\n",
              "      <td>0.141717</td>\n",
              "      <td>-0.062786</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sentence_beginning_conjunction</th>\n",
              "      <td>0.275254</td>\n",
              "      <td>0.290859</td>\n",
              "      <td>0.075655</td>\n",
              "      <td>0.230252</td>\n",
              "      <td>0.220592</td>\n",
              "      <td>0.249645</td>\n",
              "      <td>0.164624</td>\n",
              "      <td>0.185393</td>\n",
              "      <td>0.096446</td>\n",
              "      <td>0.037747</td>\n",
              "      <td>...</td>\n",
              "      <td>0.191997</td>\n",
              "      <td>0.029926</td>\n",
              "      <td>0.005742</td>\n",
              "      <td>-0.018492</td>\n",
              "      <td>-0.020252</td>\n",
              "      <td>0.033752</td>\n",
              "      <td>0.019382</td>\n",
              "      <td>0.036774</td>\n",
              "      <td>0.017675</td>\n",
              "      <td>-0.065365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sentence_beginning_preposition</th>\n",
              "      <td>0.538326</td>\n",
              "      <td>0.526581</td>\n",
              "      <td>0.133057</td>\n",
              "      <td>0.434816</td>\n",
              "      <td>0.414813</td>\n",
              "      <td>0.450961</td>\n",
              "      <td>0.325385</td>\n",
              "      <td>0.312355</td>\n",
              "      <td>0.240554</td>\n",
              "      <td>0.101601</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.232544</td>\n",
              "      <td>0.239630</td>\n",
              "      <td>0.209246</td>\n",
              "      <td>-0.208208</td>\n",
              "      <td>0.254445</td>\n",
              "      <td>0.242565</td>\n",
              "      <td>0.252133</td>\n",
              "      <td>0.241598</td>\n",
              "      <td>0.142517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kincaid</th>\n",
              "      <td>0.425547</td>\n",
              "      <td>0.324147</td>\n",
              "      <td>-0.023578</td>\n",
              "      <td>0.452434</td>\n",
              "      <td>0.603722</td>\n",
              "      <td>0.430648</td>\n",
              "      <td>0.382537</td>\n",
              "      <td>0.272876</td>\n",
              "      <td>-0.106171</td>\n",
              "      <td>-0.117732</td>\n",
              "      <td>...</td>\n",
              "      <td>0.232544</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.954067</td>\n",
              "      <td>0.889458</td>\n",
              "      <td>-0.951495</td>\n",
              "      <td>0.942061</td>\n",
              "      <td>0.936427</td>\n",
              "      <td>0.932505</td>\n",
              "      <td>0.935506</td>\n",
              "      <td>0.628226</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ARI</th>\n",
              "      <td>0.436732</td>\n",
              "      <td>0.341326</td>\n",
              "      <td>-0.032113</td>\n",
              "      <td>0.472045</td>\n",
              "      <td>0.613435</td>\n",
              "      <td>0.454832</td>\n",
              "      <td>0.392329</td>\n",
              "      <td>0.299178</td>\n",
              "      <td>-0.097085</td>\n",
              "      <td>-0.098487</td>\n",
              "      <td>...</td>\n",
              "      <td>0.239630</td>\n",
              "      <td>0.954067</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.872847</td>\n",
              "      <td>-0.854736</td>\n",
              "      <td>0.928521</td>\n",
              "      <td>0.942837</td>\n",
              "      <td>0.887794</td>\n",
              "      <td>0.951268</td>\n",
              "      <td>0.656494</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Coleman_Liau</th>\n",
              "      <td>0.333617</td>\n",
              "      <td>0.203557</td>\n",
              "      <td>-0.000779</td>\n",
              "      <td>0.328627</td>\n",
              "      <td>0.458236</td>\n",
              "      <td>0.324648</td>\n",
              "      <td>0.264266</td>\n",
              "      <td>0.219948</td>\n",
              "      <td>-0.061228</td>\n",
              "      <td>-0.105237</td>\n",
              "      <td>...</td>\n",
              "      <td>0.209246</td>\n",
              "      <td>0.889458</td>\n",
              "      <td>0.872847</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.944137</td>\n",
              "      <td>0.811422</td>\n",
              "      <td>0.907651</td>\n",
              "      <td>0.852970</td>\n",
              "      <td>0.881300</td>\n",
              "      <td>0.674122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FleschReadingEase</th>\n",
              "      <td>-0.355762</td>\n",
              "      <td>-0.235662</td>\n",
              "      <td>0.003184</td>\n",
              "      <td>-0.358113</td>\n",
              "      <td>-0.502171</td>\n",
              "      <td>-0.344239</td>\n",
              "      <td>-0.299253</td>\n",
              "      <td>-0.219374</td>\n",
              "      <td>0.084547</td>\n",
              "      <td>0.120817</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.208208</td>\n",
              "      <td>-0.951495</td>\n",
              "      <td>-0.854736</td>\n",
              "      <td>-0.944137</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.859749</td>\n",
              "      <td>-0.896746</td>\n",
              "      <td>-0.902270</td>\n",
              "      <td>-0.875541</td>\n",
              "      <td>-0.624464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GunningFogIndex</th>\n",
              "      <td>0.441046</td>\n",
              "      <td>0.352021</td>\n",
              "      <td>-0.025194</td>\n",
              "      <td>0.473116</td>\n",
              "      <td>0.612574</td>\n",
              "      <td>0.452494</td>\n",
              "      <td>0.396675</td>\n",
              "      <td>0.291607</td>\n",
              "      <td>-0.089789</td>\n",
              "      <td>-0.092361</td>\n",
              "      <td>...</td>\n",
              "      <td>0.254445</td>\n",
              "      <td>0.942061</td>\n",
              "      <td>0.928521</td>\n",
              "      <td>0.811422</td>\n",
              "      <td>-0.859749</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.919102</td>\n",
              "      <td>0.979224</td>\n",
              "      <td>0.926048</td>\n",
              "      <td>0.639924</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LIX</th>\n",
              "      <td>0.414626</td>\n",
              "      <td>0.308273</td>\n",
              "      <td>-0.015076</td>\n",
              "      <td>0.434336</td>\n",
              "      <td>0.578425</td>\n",
              "      <td>0.421165</td>\n",
              "      <td>0.358844</td>\n",
              "      <td>0.278003</td>\n",
              "      <td>-0.088890</td>\n",
              "      <td>-0.107605</td>\n",
              "      <td>...</td>\n",
              "      <td>0.242565</td>\n",
              "      <td>0.936427</td>\n",
              "      <td>0.942837</td>\n",
              "      <td>0.907651</td>\n",
              "      <td>-0.896746</td>\n",
              "      <td>0.919102</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.914295</td>\n",
              "      <td>0.989468</td>\n",
              "      <td>0.694945</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SMOGIndex</th>\n",
              "      <td>0.418059</td>\n",
              "      <td>0.317717</td>\n",
              "      <td>-0.010819</td>\n",
              "      <td>0.435781</td>\n",
              "      <td>0.575702</td>\n",
              "      <td>0.420025</td>\n",
              "      <td>0.361542</td>\n",
              "      <td>0.273505</td>\n",
              "      <td>-0.076968</td>\n",
              "      <td>-0.096557</td>\n",
              "      <td>...</td>\n",
              "      <td>0.252133</td>\n",
              "      <td>0.932505</td>\n",
              "      <td>0.887794</td>\n",
              "      <td>0.852970</td>\n",
              "      <td>-0.902270</td>\n",
              "      <td>0.979224</td>\n",
              "      <td>0.914295</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.914108</td>\n",
              "      <td>0.649724</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RIX</th>\n",
              "      <td>0.421090</td>\n",
              "      <td>0.317806</td>\n",
              "      <td>-0.017049</td>\n",
              "      <td>0.445227</td>\n",
              "      <td>0.591085</td>\n",
              "      <td>0.432525</td>\n",
              "      <td>0.367194</td>\n",
              "      <td>0.287399</td>\n",
              "      <td>-0.087396</td>\n",
              "      <td>-0.102282</td>\n",
              "      <td>...</td>\n",
              "      <td>0.241598</td>\n",
              "      <td>0.935506</td>\n",
              "      <td>0.951268</td>\n",
              "      <td>0.881300</td>\n",
              "      <td>-0.875541</td>\n",
              "      <td>0.926048</td>\n",
              "      <td>0.989468</td>\n",
              "      <td>0.914108</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.684733</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DaleChallIndex</th>\n",
              "      <td>0.201903</td>\n",
              "      <td>0.115529</td>\n",
              "      <td>-0.003942</td>\n",
              "      <td>0.211722</td>\n",
              "      <td>0.312241</td>\n",
              "      <td>0.228211</td>\n",
              "      <td>0.150100</td>\n",
              "      <td>0.187036</td>\n",
              "      <td>-0.066147</td>\n",
              "      <td>-0.071508</td>\n",
              "      <td>...</td>\n",
              "      <td>0.142517</td>\n",
              "      <td>0.628226</td>\n",
              "      <td>0.656494</td>\n",
              "      <td>0.674122</td>\n",
              "      <td>-0.624464</td>\n",
              "      <td>0.639924</td>\n",
              "      <td>0.694945</td>\n",
              "      <td>0.649724</td>\n",
              "      <td>0.684733</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>44 rows × 44 columns</p>\n",
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              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d6bbea0f-18b6-4209-a33d-f93bb6830c9c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(6, 6))\n",
        "\n",
        "plt.title(\"Correlation Plot\\nAbsolute value of Pearson's Correlation Coefficient\\n\\n\")\n",
        "sns.heatmap(pearson_corr,\n",
        "            cmap=sns.diverging_palette(230, 10, as_cmap=True),\n",
        "            square=True,\n",
        "            vmin=0,\n",
        "            vmax=1,\n",
        "            ax=ax)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 745
        },
        "id": "qygNs_kZnsdN",
        "outputId": "5b8bab65-a6fa-4ad1-f628-595359b6435d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Non Linear Correlation"
      ],
      "metadata": {
        "id": "r7dPZ8BboYQ7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "spearman_corr = df.drop('percent_helpful', axis=1).corr(method='spearman')\n",
        "spearman_corr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "WDKOOb-RnxGw",
        "outputId": "daed2556-810a-43b9-f09b-d2ae81bfc635"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                  character_count  word_count  method_count  \\\n",
              "character_count                          1.000000    0.987185      0.382224   \n",
              "word_count                               0.987185    1.000000      0.389709   \n",
              "method_count                             0.382224    0.389709      1.000000   \n",
              "mean_method_size                         0.809764    0.792921     -0.171152   \n",
              "mean_paragraph_size                      0.716238    0.675493     -0.000587   \n",
              "size_largest_method                      0.834141    0.817735     -0.040237   \n",
              "size_shortest_method                     0.611749    0.598459     -0.295216   \n",
              "std_method_size                          0.552871    0.542369      0.124530   \n",
              "step_count                               0.568180    0.595421      0.557108   \n",
              "mean_steps_per_method                    0.302967    0.327925     -0.247584   \n",
              "introduction_size                        0.292313    0.257404      0.032412   \n",
              "summary_size                             0.094198    0.095883      0.001417   \n",
              "references_count                         0.384823    0.373089      0.193179   \n",
              "references_count_per_text_length        -0.273089   -0.275521     -0.071759   \n",
              "references_count_per_method              0.223879    0.209800     -0.199810   \n",
              "image_count                              0.560916    0.570175      0.511036   \n",
              "image_count_per_method                   0.336932    0.342589     -0.179406   \n",
              "num_votes                               -0.023086   -0.026648     -0.024069   \n",
              "is_expert                                0.109111    0.105519     -0.001914   \n",
              "views                                    0.095042    0.108131      0.028187   \n",
              "co_authors                               0.215656    0.222958      0.093407   \n",
              "warnings                                 0.044575    0.056406      0.023138   \n",
              "tips                                     0.051980    0.069968      0.025661   \n",
              "to_be_verb                               0.778161    0.756602      0.260915   \n",
              "aux_verb                                 0.789276    0.776047      0.296747   \n",
              "conjunction                              0.852892    0.842742      0.336828   \n",
              "pronoun                                  0.805499    0.823078      0.303460   \n",
              "preposition                              0.918870    0.947993      0.371137   \n",
              "nominalization                           0.653818    0.580382      0.183980   \n",
              "sentence_beginning_pronoun               0.647870    0.653885      0.272335   \n",
              "sentence_beginning_interrogative         0.366704    0.377704      0.160374   \n",
              "sentence_beginning_article               0.440532    0.430477      0.167311   \n",
              "sentence_beginning_subordination         0.603594    0.623814      0.227928   \n",
              "sentence_beginning_conjunction           0.214157    0.220255      0.076081   \n",
              "sentence_beginning_preposition           0.517851    0.504511      0.174328   \n",
              "Kincaid                                  0.460554    0.352038      0.000738   \n",
              "ARI                                      0.462206    0.358192     -0.011675   \n",
              "Coleman_Liau                             0.369371    0.233605      0.036408   \n",
              "FleschReadingEase                       -0.398505   -0.272668     -0.029093   \n",
              "GunningFogIndex                          0.471542    0.377294     -0.007234   \n",
              "LIX                                      0.444953    0.331519      0.009015   \n",
              "SMOGIndex                                0.447958    0.341655      0.013469   \n",
              "RIX                                      0.455152    0.345419      0.006244   \n",
              "DaleChallIndex                           0.211985    0.116210     -0.001376   \n",
              "\n",
              "                                  mean_method_size  mean_paragraph_size  \\\n",
              "character_count                           0.809764             0.716238   \n",
              "word_count                                0.792921             0.675493   \n",
              "method_count                             -0.171152            -0.000587   \n",
              "mean_method_size                          1.000000             0.763606   \n",
              "mean_paragraph_size                       0.763606             1.000000   \n",
              "size_largest_method                       0.934045             0.721740   \n",
              "size_shortest_method                      0.856823             0.659438   \n",
              "std_method_size                           0.519212             0.406629   \n",
              "step_count                                0.276491            -0.094127   \n",
              "mean_steps_per_method                     0.484521            -0.113931   \n",
              "introduction_size                         0.255630             0.352897   \n",
              "summary_size                              0.097710             0.093407   \n",
              "references_count                          0.284730             0.205533   \n",
              "references_count_per_text_length         -0.255564            -0.276541   \n",
              "references_count_per_method               0.363008             0.197536   \n",
              "image_count                               0.292402             0.003666   \n",
              "image_count_per_method                    0.476424            -0.001990   \n",
              "num_votes                                -0.000834             0.003152   \n",
              "is_expert                                 0.101764             0.147187   \n",
              "views                                     0.089640             0.049008   \n",
              "co_authors                                0.179872             0.156238   \n",
              "warnings                                  0.038818            -0.000460   \n",
              "tips                                      0.050147            -0.004811   \n",
              "to_be_verb                                0.661982             0.664579   \n",
              "aux_verb                                  0.647562             0.613876   \n",
              "conjunction                               0.690273             0.610257   \n",
              "pronoun                                   0.663490             0.618479   \n",
              "preposition                               0.736368             0.605971   \n",
              "nominalization                            0.575944             0.625134   \n",
              "sentence_beginning_pronoun                0.519260             0.461393   \n",
              "sentence_beginning_interrogative          0.294946             0.279035   \n",
              "sentence_beginning_article                0.366580             0.325093   \n",
              "sentence_beginning_subordination          0.498594             0.436844   \n",
              "sentence_beginning_conjunction            0.185495             0.202888   \n",
              "sentence_beginning_preposition            0.440223             0.441233   \n",
              "Kincaid                                   0.480912             0.622858   \n",
              "ARI                                       0.490825             0.617050   \n",
              "Coleman_Liau                              0.363550             0.491036   \n",
              "FleschReadingEase                        -0.398072            -0.541363   \n",
              "GunningFogIndex                           0.498028             0.622616   \n",
              "LIX                                       0.460032             0.593999   \n",
              "SMOGIndex                                 0.460364             0.588919   \n",
              "RIX                                       0.472245             0.605884   \n",
              "DaleChallIndex                            0.220874             0.301680   \n",
              "\n",
              "                                  size_largest_method  size_shortest_method  \\\n",
              "character_count                              0.834141              0.611749   \n",
              "word_count                                   0.817735              0.598459   \n",
              "method_count                                -0.040237             -0.295216   \n",
              "mean_method_size                             0.934045              0.856823   \n",
              "mean_paragraph_size                          0.721740              0.659438   \n",
              "size_largest_method                          1.000000              0.666877   \n",
              "size_shortest_method                         0.666877              1.000000   \n",
              "std_method_size                              0.753108              0.101433   \n",
              "step_count                                   0.353426              0.119515   \n",
              "mean_steps_per_method                        0.452145              0.408204   \n",
              "introduction_size                            0.241851              0.236813   \n",
              "summary_size                                 0.101310              0.080603   \n",
              "references_count                             0.290488              0.225244   \n",
              "references_count_per_text_length            -0.263961             -0.185618   \n",
              "references_count_per_method                  0.317515              0.350759   \n",
              "image_count                                  0.362538              0.141318   \n",
              "image_count_per_method                       0.452723              0.393312   \n",
              "num_votes                                    0.007239             -0.018989   \n",
              "is_expert                                    0.067274              0.153488   \n",
              "views                                        0.106485              0.044283   \n",
              "co_authors                                   0.204423              0.105391   \n",
              "warnings                                     0.051225              0.009062   \n",
              "tips                                         0.057152              0.020321   \n",
              "to_be_verb                                   0.679973              0.503997   \n",
              "aux_verb                                     0.666082              0.498034   \n",
              "conjunction                                  0.709729              0.528575   \n",
              "pronoun                                      0.670577              0.527209   \n",
              "preposition                                  0.753671              0.566268   \n",
              "nominalization                               0.585165              0.448970   \n",
              "sentence_beginning_pronoun                   0.542095              0.389600   \n",
              "sentence_beginning_interrogative             0.303726              0.224559   \n",
              "sentence_beginning_article                   0.386444              0.265852   \n",
              "sentence_beginning_subordination             0.499106              0.408758   \n",
              "sentence_beginning_conjunction               0.193709              0.134992   \n",
              "sentence_beginning_preposition               0.450770              0.339786   \n",
              "Kincaid                                      0.465054              0.405056   \n",
              "ARI                                          0.474834              0.413057   \n",
              "Coleman_Liau                                 0.362493              0.290399   \n",
              "FleschReadingEase                           -0.389620             -0.328837   \n",
              "GunningFogIndex                              0.482204              0.418159   \n",
              "LIX                                          0.448693              0.382329   \n",
              "SMOGIndex                                    0.450394              0.379714   \n",
              "RIX                                          0.459525              0.394456   \n",
              "DaleChallIndex                               0.225167              0.168289   \n",
              "\n",
              "                                  std_method_size  step_count  \\\n",
              "character_count                          0.552871    0.568180   \n",
              "word_count                               0.542369    0.595421   \n",
              "method_count                             0.124530    0.557108   \n",
              "mean_method_size                         0.519212    0.276491   \n",
              "mean_paragraph_size                      0.406629   -0.094127   \n",
              "size_largest_method                      0.753108    0.353426   \n",
              "size_shortest_method                     0.101433    0.119515   \n",
              "std_method_size                          1.000000    0.323305   \n",
              "step_count                               0.323305    1.000000   \n",
              "mean_steps_per_method                    0.267492    0.614877   \n",
              "introduction_size                        0.137888    0.008033   \n",
              "summary_size                             0.072140    0.033925   \n",
              "references_count                         0.194210    0.295259   \n",
              "references_count_per_text_length        -0.177219   -0.105790   \n",
              "references_count_per_method              0.155672    0.077929   \n",
              "image_count                              0.322343    0.867587   \n",
              "image_count_per_method                   0.278499    0.532216   \n",
              "num_votes                                0.020500   -0.024617   \n",
              "is_expert                               -0.009655   -0.040773   \n",
              "views                                    0.096399    0.100273   \n",
              "co_authors                               0.170345    0.157974   \n",
              "warnings                                 0.053788    0.074928   \n",
              "tips                                     0.045327    0.095184   \n",
              "to_be_verb                               0.454905    0.344502   \n",
              "aux_verb                                 0.444273    0.414070   \n",
              "conjunction                              0.473649    0.498595   \n",
              "pronoun                                  0.429459    0.428764   \n",
              "preposition                              0.492323    0.582904   \n",
              "nominalization                           0.387524    0.217509   \n",
              "sentence_beginning_pronoun               0.372587    0.398590   \n",
              "sentence_beginning_interrogative         0.207513    0.209583   \n",
              "sentence_beginning_article               0.273167    0.247474   \n",
              "sentence_beginning_subordination         0.311460    0.342024   \n",
              "sentence_beginning_conjunction           0.135675    0.087523   \n",
              "sentence_beginning_preposition           0.300630    0.234037   \n",
              "Kincaid                                  0.278389   -0.066457   \n",
              "ARI                                      0.283946   -0.064476   \n",
              "Coleman_Liau                             0.234836   -0.039351   \n",
              "FleschReadingEase                       -0.241186    0.051266   \n",
              "GunningFogIndex                          0.288819   -0.052623   \n",
              "LIX                                      0.273283   -0.056741   \n",
              "SMOGIndex                                0.277927   -0.040669   \n",
              "RIX                                      0.278693   -0.056547   \n",
              "DaleChallIndex                           0.152996   -0.057019   \n",
              "\n",
              "                                  mean_steps_per_method  ...  \\\n",
              "character_count                                0.302967  ...   \n",
              "word_count                                     0.327925  ...   \n",
              "method_count                                  -0.247584  ...   \n",
              "mean_method_size                               0.484521  ...   \n",
              "mean_paragraph_size                           -0.113931  ...   \n",
              "size_largest_method                            0.452145  ...   \n",
              "size_shortest_method                           0.408204  ...   \n",
              "std_method_size                                0.267492  ...   \n",
              "step_count                                     0.614877  ...   \n",
              "mean_steps_per_method                          1.000000  ...   \n",
              "introduction_size                             -0.027799  ...   \n",
              "summary_size                                   0.038214  ...   \n",
              "references_count                               0.166310  ...   \n",
              "references_count_per_text_length              -0.051552  ...   \n",
              "references_count_per_method                    0.279013  ...   \n",
              "image_count                                    0.510876  ...   \n",
              "image_count_per_method                         0.831480  ...   \n",
              "num_votes                                     -0.004358  ...   \n",
              "is_expert                                     -0.049513  ...   \n",
              "views                                          0.095685  ...   \n",
              "co_authors                                     0.102091  ...   \n",
              "warnings                                       0.074117  ...   \n",
              "tips                                           0.092066  ...   \n",
              "to_be_verb                                     0.155957  ...   \n",
              "aux_verb                                       0.203667  ...   \n",
              "conjunction                                    0.270001  ...   \n",
              "pronoun                                        0.221409  ...   \n",
              "preposition                                    0.330700  ...   \n",
              "nominalization                                 0.080209  ...   \n",
              "sentence_beginning_pronoun                     0.212507  ...   \n",
              "sentence_beginning_interrogative               0.097258  ...   \n",
              "sentence_beginning_article                     0.130303  ...   \n",
              "sentence_beginning_subordination               0.192391  ...   \n",
              "sentence_beginning_conjunction                 0.037050  ...   \n",
              "sentence_beginning_preposition                 0.107265  ...   \n",
              "Kincaid                                       -0.082780  ...   \n",
              "ARI                                           -0.069218  ...   \n",
              "Coleman_Liau                                  -0.084701  ...   \n",
              "FleschReadingEase                              0.091902  ...   \n",
              "GunningFogIndex                               -0.059251  ...   \n",
              "LIX                                           -0.079327  ...   \n",
              "SMOGIndex                                     -0.064863  ...   \n",
              "RIX                                           -0.076768  ...   \n",
              "DaleChallIndex                                -0.070396  ...   \n",
              "\n",
              "                                  sentence_beginning_preposition   Kincaid  \\\n",
              "character_count                                         0.517851  0.460554   \n",
              "word_count                                              0.504511  0.352038   \n",
              "method_count                                            0.174328  0.000738   \n",
              "mean_method_size                                        0.440223  0.480912   \n",
              "mean_paragraph_size                                     0.441233  0.622858   \n",
              "size_largest_method                                     0.450770  0.465054   \n",
              "size_shortest_method                                    0.339786  0.405056   \n",
              "std_method_size                                         0.300630  0.278389   \n",
              "step_count                                              0.234037 -0.066457   \n",
              "mean_steps_per_method                                   0.107265 -0.082780   \n",
              "introduction_size                                       0.214906  0.389224   \n",
              "summary_size                                            0.052667  0.027438   \n",
              "references_count                                        0.223700  0.199755   \n",
              "references_count_per_text_length                       -0.118412 -0.108469   \n",
              "references_count_per_method                             0.147859  0.192993   \n",
              "image_count                                             0.250690  0.048745   \n",
              "image_count_per_method                                  0.147151  0.046841   \n",
              "num_votes                                              -0.014098  0.003334   \n",
              "is_expert                                               0.096967  0.104017   \n",
              "views                                                   0.020006 -0.047269   \n",
              "co_authors                                              0.059849  0.028987   \n",
              "warnings                                               -0.002986 -0.039776   \n",
              "tips                                                   -0.014118 -0.104963   \n",
              "to_be_verb                                              0.425630  0.455553   \n",
              "aux_verb                                                0.437171  0.408653   \n",
              "conjunction                                             0.385742  0.424518   \n",
              "pronoun                                                 0.416311  0.293552   \n",
              "preposition                                             0.496158  0.261338   \n",
              "nominalization                                          0.411470  0.653431   \n",
              "sentence_beginning_pronoun                              0.326565  0.167180   \n",
              "sentence_beginning_interrogative                        0.272818  0.060239   \n",
              "sentence_beginning_article                              0.301665  0.176010   \n",
              "sentence_beginning_subordination                        0.386923  0.192617   \n",
              "sentence_beginning_conjunction                          0.162046  0.068954   \n",
              "sentence_beginning_preposition                          1.000000  0.259970   \n",
              "Kincaid                                                 0.259970  1.000000   \n",
              "ARI                                                     0.268751  0.955026   \n",
              "Coleman_Liau                                            0.235175  0.883096   \n",
              "FleschReadingEase                                      -0.237823 -0.949888   \n",
              "GunningFogIndex                                         0.276105  0.939074   \n",
              "LIX                                                     0.266106  0.931811   \n",
              "SMOGIndex                                               0.269749  0.927111   \n",
              "RIX                                                     0.270403  0.936354   \n",
              "DaleChallIndex                                          0.161667  0.629652   \n",
              "\n",
              "                                       ARI  Coleman_Liau  FleschReadingEase  \\\n",
              "character_count                   0.462206      0.369371          -0.398505   \n",
              "word_count                        0.358192      0.233605          -0.272668   \n",
              "method_count                     -0.011675      0.036408          -0.029093   \n",
              "mean_method_size                  0.490825      0.363550          -0.398072   \n",
              "mean_paragraph_size               0.617050      0.491036          -0.541363   \n",
              "size_largest_method               0.474834      0.362493          -0.389620   \n",
              "size_shortest_method              0.413057      0.290399          -0.328837   \n",
              "std_method_size                   0.283946      0.234836          -0.241186   \n",
              "step_count                       -0.064476     -0.039351           0.051266   \n",
              "mean_steps_per_method            -0.069218     -0.084701           0.091902   \n",
              "introduction_size                 0.390969      0.337053          -0.353384   \n",
              "summary_size                      0.020019      0.014656          -0.024765   \n",
              "references_count                  0.184188      0.189941          -0.204740   \n",
              "references_count_per_text_length -0.123314     -0.064305           0.069098   \n",
              "references_count_per_method       0.184716      0.168947          -0.184855   \n",
              "image_count                       0.039653      0.069664          -0.067423   \n",
              "image_count_per_method            0.047274      0.043655          -0.045149   \n",
              "num_votes                         0.000063      0.009810          -0.006634   \n",
              "is_expert                         0.089190      0.083653          -0.104440   \n",
              "views                            -0.046107     -0.057216           0.054299   \n",
              "co_authors                        0.002276     -0.001588          -0.031653   \n",
              "warnings                         -0.041703     -0.065642           0.053905   \n",
              "tips                             -0.100776     -0.114199           0.112555   \n",
              "to_be_verb                        0.425473      0.366134          -0.422117   \n",
              "aux_verb                          0.374105      0.318838          -0.378152   \n",
              "conjunction                       0.403944      0.314488          -0.372402   \n",
              "pronoun                           0.261806      0.156610          -0.236912   \n",
              "preposition                       0.284635      0.130372          -0.166600   \n",
              "nominalization                    0.622247      0.638831          -0.660867   \n",
              "sentence_beginning_pronoun        0.116124      0.128197          -0.180102   \n",
              "sentence_beginning_interrogative  0.058969      0.039785          -0.049019   \n",
              "sentence_beginning_article        0.197459      0.191239          -0.164292   \n",
              "sentence_beginning_subordination  0.175341      0.076547          -0.137493   \n",
              "sentence_beginning_conjunction    0.042657      0.014364          -0.057662   \n",
              "sentence_beginning_preposition    0.268751      0.235175          -0.237823   \n",
              "Kincaid                           0.955026      0.883096          -0.949888   \n",
              "ARI                               1.000000      0.875046          -0.859583   \n",
              "Coleman_Liau                      0.875046      1.000000          -0.938742   \n",
              "FleschReadingEase                -0.859583     -0.938742           1.000000   \n",
              "GunningFogIndex                   0.922081      0.801821          -0.857217   \n",
              "LIX                               0.937217      0.904432          -0.895579   \n",
              "SMOGIndex                         0.881645      0.849988          -0.900873   \n",
              "RIX                               0.946545      0.889974          -0.884963   \n",
              "DaleChallIndex                    0.638286      0.689721          -0.643707   \n",
              "\n",
              "                                  GunningFogIndex       LIX  SMOGIndex  \\\n",
              "character_count                          0.471542  0.444953   0.447958   \n",
              "word_count                               0.377294  0.331519   0.341655   \n",
              "method_count                            -0.007234  0.009015   0.013469   \n",
              "mean_method_size                         0.498028  0.460032   0.460364   \n",
              "mean_paragraph_size                      0.622616  0.593999   0.588919   \n",
              "size_largest_method                      0.482204  0.448693   0.450394   \n",
              "size_shortest_method                     0.418159  0.382329   0.379714   \n",
              "std_method_size                          0.288819  0.273283   0.277927   \n",
              "step_count                              -0.052623 -0.056741  -0.040669   \n",
              "mean_steps_per_method                   -0.059251 -0.079327  -0.064863   \n",
              "introduction_size                        0.394831  0.381819   0.379357   \n",
              "summary_size                             0.031225  0.017415   0.032274   \n",
              "references_count                         0.187467  0.188861   0.190567   \n",
              "references_count_per_text_length        -0.127306 -0.112871  -0.111923   \n",
              "references_count_per_method              0.185683  0.179004   0.179687   \n",
              "image_count                              0.053604  0.057857   0.071113   \n",
              "image_count_per_method                   0.058185  0.051549   0.061511   \n",
              "num_votes                                0.003413  0.009625   0.010351   \n",
              "is_expert                                0.094332  0.089313   0.089246   \n",
              "views                                   -0.030242 -0.044317  -0.029002   \n",
              "co_authors                               0.021541  0.014627   0.028093   \n",
              "warnings                                -0.036499 -0.054792  -0.039734   \n",
              "tips                                    -0.102248 -0.108426  -0.102687   \n",
              "to_be_verb                               0.466433  0.432724   0.460627   \n",
              "aux_verb                                 0.403346  0.367912   0.393910   \n",
              "conjunction                              0.402427  0.372656   0.379792   \n",
              "pronoun                                  0.287580  0.249550   0.258816   \n",
              "preposition                              0.290482  0.238923   0.239356   \n",
              "nominalization                           0.660646  0.671677   0.680890   \n",
              "sentence_beginning_pronoun               0.163006  0.147819   0.175940   \n",
              "sentence_beginning_interrogative         0.072128  0.067887   0.069954   \n",
              "sentence_beginning_article               0.224624  0.203828   0.226421   \n",
              "sentence_beginning_subordination         0.222403  0.167824   0.197384   \n",
              "sentence_beginning_conjunction           0.071008  0.054187   0.069066   \n",
              "sentence_beginning_preposition           0.276105  0.266106   0.269749   \n",
              "Kincaid                                  0.939074  0.931811   0.927111   \n",
              "ARI                                      0.922081  0.937217   0.881645   \n",
              "Coleman_Liau                             0.801821  0.904432   0.849988   \n",
              "FleschReadingEase                       -0.857217 -0.895579  -0.900873   \n",
              "GunningFogIndex                          1.000000  0.910654   0.978758   \n",
              "LIX                                      0.910654  1.000000   0.909188   \n",
              "SMOGIndex                                0.978758  0.909188   1.000000   \n",
              "RIX                                      0.917994  0.995693   0.912010   \n",
              "DaleChallIndex                           0.623847  0.681521   0.648232   \n",
              "\n",
              "                                       RIX  DaleChallIndex  \n",
              "character_count                   0.455152        0.211985  \n",
              "word_count                        0.345419        0.116210  \n",
              "method_count                      0.006244       -0.001376  \n",
              "mean_method_size                  0.472245        0.220874  \n",
              "mean_paragraph_size               0.605884        0.301680  \n",
              "size_largest_method               0.459525        0.225167  \n",
              "size_shortest_method              0.394456        0.168289  \n",
              "std_method_size                   0.278693        0.152996  \n",
              "step_count                       -0.056547       -0.057019  \n",
              "mean_steps_per_method            -0.076768       -0.070396  \n",
              "introduction_size                 0.386465        0.281860  \n",
              "summary_size                      0.019565       -0.018722  \n",
              "references_count                  0.187896        0.187717  \n",
              "references_count_per_text_length -0.119976        0.052376  \n",
              "references_count_per_method       0.179455        0.186221  \n",
              "image_count                       0.057205        0.016159  \n",
              "image_count_per_method            0.053105        0.014499  \n",
              "num_votes                         0.008868       -0.017895  \n",
              "is_expert                         0.091426        0.057637  \n",
              "views                            -0.040506       -0.068407  \n",
              "co_authors                        0.016866       -0.093738  \n",
              "warnings                         -0.048833       -0.055784  \n",
              "tips                             -0.103348       -0.114247  \n",
              "to_be_verb                        0.440169        0.144546  \n",
              "aux_verb                          0.374963        0.075012  \n",
              "conjunction                       0.385959        0.159861  \n",
              "pronoun                           0.262445       -0.096916  \n",
              "preposition                       0.256682        0.005565  \n",
              "nominalization                    0.666887        0.414715  \n",
              "sentence_beginning_pronoun        0.146619       -0.041067  \n",
              "sentence_beginning_interrogative  0.071785       -0.090217  \n",
              "sentence_beginning_article        0.204896        0.204228  \n",
              "sentence_beginning_subordination  0.180945       -0.029170  \n",
              "sentence_beginning_conjunction    0.057783       -0.062901  \n",
              "sentence_beginning_preposition    0.270403        0.161667  \n",
              "Kincaid                           0.936354        0.629652  \n",
              "ARI                               0.946545        0.638286  \n",
              "Coleman_Liau                      0.889974        0.689721  \n",
              "FleschReadingEase                -0.884963       -0.643707  \n",
              "GunningFogIndex                   0.917994        0.623847  \n",
              "LIX                               0.995693        0.681521  \n",
              "SMOGIndex                         0.912010        0.648232  \n",
              "RIX                               1.000000        0.669389  \n",
              "DaleChallIndex                    0.669389        1.000000  \n",
              "\n",
              "[44 rows x 44 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9390435b-0b8c-4346-bd07-3673e121eb58\" class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>character_count</th>\n",
              "      <th>word_count</th>\n",
              "      <th>method_count</th>\n",
              "      <th>mean_method_size</th>\n",
              "      <th>mean_paragraph_size</th>\n",
              "      <th>size_largest_method</th>\n",
              "      <th>size_shortest_method</th>\n",
              "      <th>std_method_size</th>\n",
              "      <th>step_count</th>\n",
              "      <th>mean_steps_per_method</th>\n",
              "      <th>...</th>\n",
              "      <th>sentence_beginning_preposition</th>\n",
              "      <th>Kincaid</th>\n",
              "      <th>ARI</th>\n",
              "      <th>Coleman_Liau</th>\n",
              "      <th>FleschReadingEase</th>\n",
              "      <th>GunningFogIndex</th>\n",
              "      <th>LIX</th>\n",
              "      <th>SMOGIndex</th>\n",
              "      <th>RIX</th>\n",
              "      <th>DaleChallIndex</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>character_count</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.987185</td>\n",
              "      <td>0.382224</td>\n",
              "      <td>0.809764</td>\n",
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              "      <td>0.552871</td>\n",
              "      <td>0.568180</td>\n",
              "      <td>0.302967</td>\n",
              "      <td>...</td>\n",
              "      <td>0.517851</td>\n",
              "      <td>0.460554</td>\n",
              "      <td>0.462206</td>\n",
              "      <td>0.369371</td>\n",
              "      <td>-0.398505</td>\n",
              "      <td>0.471542</td>\n",
              "      <td>0.444953</td>\n",
              "      <td>0.447958</td>\n",
              "      <td>0.455152</td>\n",
              "      <td>0.211985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>word_count</th>\n",
              "      <td>0.987185</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.389709</td>\n",
              "      <td>0.792921</td>\n",
              "      <td>0.675493</td>\n",
              "      <td>0.817735</td>\n",
              "      <td>0.598459</td>\n",
              "      <td>0.542369</td>\n",
              "      <td>0.595421</td>\n",
              "      <td>0.327925</td>\n",
              "      <td>...</td>\n",
              "      <td>0.504511</td>\n",
              "      <td>0.352038</td>\n",
              "      <td>0.358192</td>\n",
              "      <td>0.233605</td>\n",
              "      <td>-0.272668</td>\n",
              "      <td>0.377294</td>\n",
              "      <td>0.331519</td>\n",
              "      <td>0.341655</td>\n",
              "      <td>0.345419</td>\n",
              "      <td>0.116210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>method_count</th>\n",
              "      <td>0.382224</td>\n",
              "      <td>0.389709</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.171152</td>\n",
              "      <td>-0.000587</td>\n",
              "      <td>-0.040237</td>\n",
              "      <td>-0.295216</td>\n",
              "      <td>0.124530</td>\n",
              "      <td>0.557108</td>\n",
              "      <td>-0.247584</td>\n",
              "      <td>...</td>\n",
              "      <td>0.174328</td>\n",
              "      <td>0.000738</td>\n",
              "      <td>-0.011675</td>\n",
              "      <td>0.036408</td>\n",
              "      <td>-0.029093</td>\n",
              "      <td>-0.007234</td>\n",
              "      <td>0.009015</td>\n",
              "      <td>0.013469</td>\n",
              "      <td>0.006244</td>\n",
              "      <td>-0.001376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean_method_size</th>\n",
              "      <td>0.809764</td>\n",
              "      <td>0.792921</td>\n",
              "      <td>-0.171152</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.763606</td>\n",
              "      <td>0.934045</td>\n",
              "      <td>0.856823</td>\n",
              "      <td>0.519212</td>\n",
              "      <td>0.276491</td>\n",
              "      <td>0.484521</td>\n",
              "      <td>...</td>\n",
              "      <td>0.440223</td>\n",
              "      <td>0.480912</td>\n",
              "      <td>0.490825</td>\n",
              "      <td>0.363550</td>\n",
              "      <td>-0.398072</td>\n",
              "      <td>0.498028</td>\n",
              "      <td>0.460032</td>\n",
              "      <td>0.460364</td>\n",
              "      <td>0.472245</td>\n",
              "      <td>0.220874</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean_paragraph_size</th>\n",
              "      <td>0.716238</td>\n",
              "      <td>0.675493</td>\n",
              "      <td>-0.000587</td>\n",
              "      <td>0.763606</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.721740</td>\n",
              "      <td>0.659438</td>\n",
              "      <td>0.406629</td>\n",
              "      <td>-0.094127</td>\n",
              "      <td>-0.113931</td>\n",
              "      <td>...</td>\n",
              "      <td>0.441233</td>\n",
              "      <td>0.622858</td>\n",
              "      <td>0.617050</td>\n",
              "      <td>0.491036</td>\n",
              "      <td>-0.541363</td>\n",
              "      <td>0.622616</td>\n",
              "      <td>0.593999</td>\n",
              "      <td>0.588919</td>\n",
              "      <td>0.605884</td>\n",
              "      <td>0.301680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>size_largest_method</th>\n",
              "      <td>0.834141</td>\n",
              "      <td>0.817735</td>\n",
              "      <td>-0.040237</td>\n",
              "      <td>0.934045</td>\n",
              "      <td>0.721740</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.666877</td>\n",
              "      <td>0.753108</td>\n",
              "      <td>0.353426</td>\n",
              "      <td>0.452145</td>\n",
              "      <td>...</td>\n",
              "      <td>0.450770</td>\n",
              "      <td>0.465054</td>\n",
              "      <td>0.474834</td>\n",
              "      <td>0.362493</td>\n",
              "      <td>-0.389620</td>\n",
              "      <td>0.482204</td>\n",
              "      <td>0.448693</td>\n",
              "      <td>0.450394</td>\n",
              "      <td>0.459525</td>\n",
              "      <td>0.225167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>size_shortest_method</th>\n",
              "      <td>0.611749</td>\n",
              "      <td>0.598459</td>\n",
              "      <td>-0.295216</td>\n",
              "      <td>0.856823</td>\n",
              "      <td>0.659438</td>\n",
              "      <td>0.666877</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.101433</td>\n",
              "      <td>0.119515</td>\n",
              "      <td>0.408204</td>\n",
              "      <td>...</td>\n",
              "      <td>0.339786</td>\n",
              "      <td>0.405056</td>\n",
              "      <td>0.413057</td>\n",
              "      <td>0.290399</td>\n",
              "      <td>-0.328837</td>\n",
              "      <td>0.418159</td>\n",
              "      <td>0.382329</td>\n",
              "      <td>0.379714</td>\n",
              "      <td>0.394456</td>\n",
              "      <td>0.168289</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std_method_size</th>\n",
              "      <td>0.552871</td>\n",
              "      <td>0.542369</td>\n",
              "      <td>0.124530</td>\n",
              "      <td>0.519212</td>\n",
              "      <td>0.406629</td>\n",
              "      <td>0.753108</td>\n",
              "      <td>0.101433</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.323305</td>\n",
              "      <td>0.267492</td>\n",
              "      <td>...</td>\n",
              "      <td>0.300630</td>\n",
              "      <td>0.278389</td>\n",
              "      <td>0.283946</td>\n",
              "      <td>0.234836</td>\n",
              "      <td>-0.241186</td>\n",
              "      <td>0.288819</td>\n",
              "      <td>0.273283</td>\n",
              "      <td>0.277927</td>\n",
              "      <td>0.278693</td>\n",
              "      <td>0.152996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>step_count</th>\n",
              "      <td>0.568180</td>\n",
              "      <td>0.595421</td>\n",
              "      <td>0.557108</td>\n",
              "      <td>0.276491</td>\n",
              "      <td>-0.094127</td>\n",
              "      <td>0.353426</td>\n",
              "      <td>0.119515</td>\n",
              "      <td>0.323305</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.614877</td>\n",
              "      <td>...</td>\n",
              "      <td>0.234037</td>\n",
              "      <td>-0.066457</td>\n",
              "      <td>-0.064476</td>\n",
              "      <td>-0.039351</td>\n",
              "      <td>0.051266</td>\n",
              "      <td>-0.052623</td>\n",
              "      <td>-0.056741</td>\n",
              "      <td>-0.040669</td>\n",
              "      <td>-0.056547</td>\n",
              "      <td>-0.057019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean_steps_per_method</th>\n",
              "      <td>0.302967</td>\n",
              "      <td>0.327925</td>\n",
              "      <td>-0.247584</td>\n",
              "      <td>0.484521</td>\n",
              "      <td>-0.113931</td>\n",
              "      <td>0.452145</td>\n",
              "      <td>0.408204</td>\n",
              "      <td>0.267492</td>\n",
              "      <td>0.614877</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.107265</td>\n",
              "      <td>-0.082780</td>\n",
              "      <td>-0.069218</td>\n",
              "      <td>-0.084701</td>\n",
              "      <td>0.091902</td>\n",
              "      <td>-0.059251</td>\n",
              "      <td>-0.079327</td>\n",
              "      <td>-0.064863</td>\n",
              "      <td>-0.076768</td>\n",
              "      <td>-0.070396</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>introduction_size</th>\n",
              "      <td>0.292313</td>\n",
              "      <td>0.257404</td>\n",
              "      <td>0.032412</td>\n",
              "      <td>0.255630</td>\n",
              "      <td>0.352897</td>\n",
              "      <td>0.241851</td>\n",
              "      <td>0.236813</td>\n",
              "      <td>0.137888</td>\n",
              "      <td>0.008033</td>\n",
              "      <td>-0.027799</td>\n",
              "      <td>...</td>\n",
              "      <td>0.214906</td>\n",
              "      <td>0.389224</td>\n",
              "      <td>0.390969</td>\n",
              "      <td>0.337053</td>\n",
              "      <td>-0.353384</td>\n",
              "      <td>0.394831</td>\n",
              "      <td>0.381819</td>\n",
              "      <td>0.379357</td>\n",
              "      <td>0.386465</td>\n",
              "      <td>0.281860</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>summary_size</th>\n",
              "      <td>0.094198</td>\n",
              "      <td>0.095883</td>\n",
              "      <td>0.001417</td>\n",
              "      <td>0.097710</td>\n",
              "      <td>0.093407</td>\n",
              "      <td>0.101310</td>\n",
              "      <td>0.080603</td>\n",
              "      <td>0.072140</td>\n",
              "      <td>0.033925</td>\n",
              "      <td>0.038214</td>\n",
              "      <td>...</td>\n",
              "      <td>0.052667</td>\n",
              "      <td>0.027438</td>\n",
              "      <td>0.020019</td>\n",
              "      <td>0.014656</td>\n",
              "      <td>-0.024765</td>\n",
              "      <td>0.031225</td>\n",
              "      <td>0.017415</td>\n",
              "      <td>0.032274</td>\n",
              "      <td>0.019565</td>\n",
              "      <td>-0.018722</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>references_count</th>\n",
              "      <td>0.384823</td>\n",
              "      <td>0.373089</td>\n",
              "      <td>0.193179</td>\n",
              "      <td>0.284730</td>\n",
              "      <td>0.205533</td>\n",
              "      <td>0.290488</td>\n",
              "      <td>0.225244</td>\n",
              "      <td>0.194210</td>\n",
              "      <td>0.295259</td>\n",
              "      <td>0.166310</td>\n",
              "      <td>...</td>\n",
              "      <td>0.223700</td>\n",
              "      <td>0.199755</td>\n",
              "      <td>0.184188</td>\n",
              "      <td>0.189941</td>\n",
              "      <td>-0.204740</td>\n",
              "      <td>0.187467</td>\n",
              "      <td>0.188861</td>\n",
              "      <td>0.190567</td>\n",
              "      <td>0.187896</td>\n",
              "      <td>0.187717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>references_count_per_text_length</th>\n",
              "      <td>-0.273089</td>\n",
              "      <td>-0.275521</td>\n",
              "      <td>-0.071759</td>\n",
              "      <td>-0.255564</td>\n",
              "      <td>-0.276541</td>\n",
              "      <td>-0.263961</td>\n",
              "      <td>-0.185618</td>\n",
              "      <td>-0.177219</td>\n",
              "      <td>-0.105790</td>\n",
              "      <td>-0.051552</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.118412</td>\n",
              "      <td>-0.108469</td>\n",
              "      <td>-0.123314</td>\n",
              "      <td>-0.064305</td>\n",
              "      <td>0.069098</td>\n",
              "      <td>-0.127306</td>\n",
              "      <td>-0.112871</td>\n",
              "      <td>-0.111923</td>\n",
              "      <td>-0.119976</td>\n",
              "      <td>0.052376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>references_count_per_method</th>\n",
              "      <td>0.223879</td>\n",
              "      <td>0.209800</td>\n",
              "      <td>-0.199810</td>\n",
              "      <td>0.363008</td>\n",
              "      <td>0.197536</td>\n",
              "      <td>0.317515</td>\n",
              "      <td>0.350759</td>\n",
              "      <td>0.155672</td>\n",
              "      <td>0.077929</td>\n",
              "      <td>0.279013</td>\n",
              "      <td>...</td>\n",
              "      <td>0.147859</td>\n",
              "      <td>0.192993</td>\n",
              "      <td>0.184716</td>\n",
              "      <td>0.168947</td>\n",
              "      <td>-0.184855</td>\n",
              "      <td>0.185683</td>\n",
              "      <td>0.179004</td>\n",
              "      <td>0.179687</td>\n",
              "      <td>0.179455</td>\n",
              "      <td>0.186221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>image_count</th>\n",
              "      <td>0.560916</td>\n",
              "      <td>0.570175</td>\n",
              "      <td>0.511036</td>\n",
              "      <td>0.292402</td>\n",
              "      <td>0.003666</td>\n",
              "      <td>0.362538</td>\n",
              "      <td>0.141318</td>\n",
              "      <td>0.322343</td>\n",
              "      <td>0.867587</td>\n",
              "      <td>0.510876</td>\n",
              "      <td>...</td>\n",
              "      <td>0.250690</td>\n",
              "      <td>0.048745</td>\n",
              "      <td>0.039653</td>\n",
              "      <td>0.069664</td>\n",
              "      <td>-0.067423</td>\n",
              "      <td>0.053604</td>\n",
              "      <td>0.057857</td>\n",
              "      <td>0.071113</td>\n",
              "      <td>0.057205</td>\n",
              "      <td>0.016159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>image_count_per_method</th>\n",
              "      <td>0.336932</td>\n",
              "      <td>0.342589</td>\n",
              "      <td>-0.179406</td>\n",
              "      <td>0.476424</td>\n",
              "      <td>-0.001990</td>\n",
              "      <td>0.452723</td>\n",
              "      <td>0.393312</td>\n",
              "      <td>0.278499</td>\n",
              "      <td>0.532216</td>\n",
              "      <td>0.831480</td>\n",
              "      <td>...</td>\n",
              "      <td>0.147151</td>\n",
              "      <td>0.046841</td>\n",
              "      <td>0.047274</td>\n",
              "      <td>0.043655</td>\n",
              "      <td>-0.045149</td>\n",
              "      <td>0.058185</td>\n",
              "      <td>0.051549</td>\n",
              "      <td>0.061511</td>\n",
              "      <td>0.053105</td>\n",
              "      <td>0.014499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num_votes</th>\n",
              "      <td>-0.023086</td>\n",
              "      <td>-0.026648</td>\n",
              "      <td>-0.024069</td>\n",
              "      <td>-0.000834</td>\n",
              "      <td>0.003152</td>\n",
              "      <td>0.007239</td>\n",
              "      <td>-0.018989</td>\n",
              "      <td>0.020500</td>\n",
              "      <td>-0.024617</td>\n",
              "      <td>-0.004358</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.014098</td>\n",
              "      <td>0.003334</td>\n",
              "      <td>0.000063</td>\n",
              "      <td>0.009810</td>\n",
              "      <td>-0.006634</td>\n",
              "      <td>0.003413</td>\n",
              "      <td>0.009625</td>\n",
              "      <td>0.010351</td>\n",
              "      <td>0.008868</td>\n",
              "      <td>-0.017895</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>is_expert</th>\n",
              "      <td>0.109111</td>\n",
              "      <td>0.105519</td>\n",
              "      <td>-0.001914</td>\n",
              "      <td>0.101764</td>\n",
              "      <td>0.147187</td>\n",
              "      <td>0.067274</td>\n",
              "      <td>0.153488</td>\n",
              "      <td>-0.009655</td>\n",
              "      <td>-0.040773</td>\n",
              "      <td>-0.049513</td>\n",
              "      <td>...</td>\n",
              "      <td>0.096967</td>\n",
              "      <td>0.104017</td>\n",
              "      <td>0.089190</td>\n",
              "      <td>0.083653</td>\n",
              "      <td>-0.104440</td>\n",
              "      <td>0.094332</td>\n",
              "      <td>0.089313</td>\n",
              "      <td>0.089246</td>\n",
              "      <td>0.091426</td>\n",
              "      <td>0.057637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>views</th>\n",
              "      <td>0.095042</td>\n",
              "      <td>0.108131</td>\n",
              "      <td>0.028187</td>\n",
              "      <td>0.089640</td>\n",
              "      <td>0.049008</td>\n",
              "      <td>0.106485</td>\n",
              "      <td>0.044283</td>\n",
              "      <td>0.096399</td>\n",
              "      <td>0.100273</td>\n",
              "      <td>0.095685</td>\n",
              "      <td>...</td>\n",
              "      <td>0.020006</td>\n",
              "      <td>-0.047269</td>\n",
              "      <td>-0.046107</td>\n",
              "      <td>-0.057216</td>\n",
              "      <td>0.054299</td>\n",
              "      <td>-0.030242</td>\n",
              "      <td>-0.044317</td>\n",
              "      <td>-0.029002</td>\n",
              "      <td>-0.040506</td>\n",
              "      <td>-0.068407</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>co_authors</th>\n",
              "      <td>0.215656</td>\n",
              "      <td>0.222958</td>\n",
              "      <td>0.093407</td>\n",
              "      <td>0.179872</td>\n",
              "      <td>0.156238</td>\n",
              "      <td>0.204423</td>\n",
              "      <td>0.105391</td>\n",
              "      <td>0.170345</td>\n",
              "      <td>0.157974</td>\n",
              "      <td>0.102091</td>\n",
              "      <td>...</td>\n",
              "      <td>0.059849</td>\n",
              "      <td>0.028987</td>\n",
              "      <td>0.002276</td>\n",
              "      <td>-0.001588</td>\n",
              "      <td>-0.031653</td>\n",
              "      <td>0.021541</td>\n",
              "      <td>0.014627</td>\n",
              "      <td>0.028093</td>\n",
              "      <td>0.016866</td>\n",
              "      <td>-0.093738</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>warnings</th>\n",
              "      <td>0.044575</td>\n",
              "      <td>0.056406</td>\n",
              "      <td>0.023138</td>\n",
              "      <td>0.038818</td>\n",
              "      <td>-0.000460</td>\n",
              "      <td>0.051225</td>\n",
              "      <td>0.009062</td>\n",
              "      <td>0.053788</td>\n",
              "      <td>0.074928</td>\n",
              "      <td>0.074117</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.002986</td>\n",
              "      <td>-0.039776</td>\n",
              "      <td>-0.041703</td>\n",
              "      <td>-0.065642</td>\n",
              "      <td>0.053905</td>\n",
              "      <td>-0.036499</td>\n",
              "      <td>-0.054792</td>\n",
              "      <td>-0.039734</td>\n",
              "      <td>-0.048833</td>\n",
              "      <td>-0.055784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tips</th>\n",
              "      <td>0.051980</td>\n",
              "      <td>0.069968</td>\n",
              "      <td>0.025661</td>\n",
              "      <td>0.050147</td>\n",
              "      <td>-0.004811</td>\n",
              "      <td>0.057152</td>\n",
              "      <td>0.020321</td>\n",
              "      <td>0.045327</td>\n",
              "      <td>0.095184</td>\n",
              "      <td>0.092066</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.014118</td>\n",
              "      <td>-0.104963</td>\n",
              "      <td>-0.100776</td>\n",
              "      <td>-0.114199</td>\n",
              "      <td>0.112555</td>\n",
              "      <td>-0.102248</td>\n",
              "      <td>-0.108426</td>\n",
              "      <td>-0.102687</td>\n",
              "      <td>-0.103348</td>\n",
              "      <td>-0.114247</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>to_be_verb</th>\n",
              "      <td>0.778161</td>\n",
              "      <td>0.756602</td>\n",
              "      <td>0.260915</td>\n",
              "      <td>0.661982</td>\n",
              "      <td>0.664579</td>\n",
              "      <td>0.679973</td>\n",
              "      <td>0.503997</td>\n",
              "      <td>0.454905</td>\n",
              "      <td>0.344502</td>\n",
              "      <td>0.155957</td>\n",
              "      <td>...</td>\n",
              "      <td>0.425630</td>\n",
              "      <td>0.455553</td>\n",
              "      <td>0.425473</td>\n",
              "      <td>0.366134</td>\n",
              "      <td>-0.422117</td>\n",
              "      <td>0.466433</td>\n",
              "      <td>0.432724</td>\n",
              "      <td>0.460627</td>\n",
              "      <td>0.440169</td>\n",
              "      <td>0.144546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>aux_verb</th>\n",
              "      <td>0.789276</td>\n",
              "      <td>0.776047</td>\n",
              "      <td>0.296747</td>\n",
              "      <td>0.647562</td>\n",
              "      <td>0.613876</td>\n",
              "      <td>0.666082</td>\n",
              "      <td>0.498034</td>\n",
              "      <td>0.444273</td>\n",
              "      <td>0.414070</td>\n",
              "      <td>0.203667</td>\n",
              "      <td>...</td>\n",
              "      <td>0.437171</td>\n",
              "      <td>0.408653</td>\n",
              "      <td>0.374105</td>\n",
              "      <td>0.318838</td>\n",
              "      <td>-0.378152</td>\n",
              "      <td>0.403346</td>\n",
              "      <td>0.367912</td>\n",
              "      <td>0.393910</td>\n",
              "      <td>0.374963</td>\n",
              "      <td>0.075012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conjunction</th>\n",
              "      <td>0.852892</td>\n",
              "      <td>0.842742</td>\n",
              "      <td>0.336828</td>\n",
              "      <td>0.690273</td>\n",
              "      <td>0.610257</td>\n",
              "      <td>0.709729</td>\n",
              "      <td>0.528575</td>\n",
              "      <td>0.473649</td>\n",
              "      <td>0.498595</td>\n",
              "      <td>0.270001</td>\n",
              "      <td>...</td>\n",
              "      <td>0.385742</td>\n",
              "      <td>0.424518</td>\n",
              "      <td>0.403944</td>\n",
              "      <td>0.314488</td>\n",
              "      <td>-0.372402</td>\n",
              "      <td>0.402427</td>\n",
              "      <td>0.372656</td>\n",
              "      <td>0.379792</td>\n",
              "      <td>0.385959</td>\n",
              "      <td>0.159861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pronoun</th>\n",
              "      <td>0.805499</td>\n",
              "      <td>0.823078</td>\n",
              "      <td>0.303460</td>\n",
              "      <td>0.663490</td>\n",
              "      <td>0.618479</td>\n",
              "      <td>0.670577</td>\n",
              "      <td>0.527209</td>\n",
              "      <td>0.429459</td>\n",
              "      <td>0.428764</td>\n",
              "      <td>0.221409</td>\n",
              "      <td>...</td>\n",
              "      <td>0.416311</td>\n",
              "      <td>0.293552</td>\n",
              "      <td>0.261806</td>\n",
              "      <td>0.156610</td>\n",
              "      <td>-0.236912</td>\n",
              "      <td>0.287580</td>\n",
              "      <td>0.249550</td>\n",
              "      <td>0.258816</td>\n",
              "      <td>0.262445</td>\n",
              "      <td>-0.096916</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>preposition</th>\n",
              "      <td>0.918870</td>\n",
              "      <td>0.947993</td>\n",
              "      <td>0.371137</td>\n",
              "      <td>0.736368</td>\n",
              "      <td>0.605971</td>\n",
              "      <td>0.753671</td>\n",
              "      <td>0.566268</td>\n",
              "      <td>0.492323</td>\n",
              "      <td>0.582904</td>\n",
              "      <td>0.330700</td>\n",
              "      <td>...</td>\n",
              "      <td>0.496158</td>\n",
              "      <td>0.261338</td>\n",
              "      <td>0.284635</td>\n",
              "      <td>0.130372</td>\n",
              "      <td>-0.166600</td>\n",
              "      <td>0.290482</td>\n",
              "      <td>0.238923</td>\n",
              "      <td>0.239356</td>\n",
              "      <td>0.256682</td>\n",
              "      <td>0.005565</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nominalization</th>\n",
              "      <td>0.653818</td>\n",
              "      <td>0.580382</td>\n",
              "      <td>0.183980</td>\n",
              "      <td>0.575944</td>\n",
              "      <td>0.625134</td>\n",
              "      <td>0.585165</td>\n",
              "      <td>0.448970</td>\n",
              "      <td>0.387524</td>\n",
              "      <td>0.217509</td>\n",
              "      <td>0.080209</td>\n",
              "      <td>...</td>\n",
              "      <td>0.411470</td>\n",
              "      <td>0.653431</td>\n",
              "      <td>0.622247</td>\n",
              "      <td>0.638831</td>\n",
              "      <td>-0.660867</td>\n",
              "      <td>0.660646</td>\n",
              "      <td>0.671677</td>\n",
              "      <td>0.680890</td>\n",
              "      <td>0.666887</td>\n",
              "      <td>0.414715</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sentence_beginning_pronoun</th>\n",
              "      <td>0.647870</td>\n",
              "      <td>0.653885</td>\n",
              "      <td>0.272335</td>\n",
              "      <td>0.519260</td>\n",
              "      <td>0.461393</td>\n",
              "      <td>0.542095</td>\n",
              "      <td>0.389600</td>\n",
              "      <td>0.372587</td>\n",
              "      <td>0.398590</td>\n",
              "      <td>0.212507</td>\n",
              "      <td>...</td>\n",
              "      <td>0.326565</td>\n",
              "      <td>0.167180</td>\n",
              "      <td>0.116124</td>\n",
              "      <td>0.128197</td>\n",
              "      <td>-0.180102</td>\n",
              "      <td>0.163006</td>\n",
              "      <td>0.147819</td>\n",
              "      <td>0.175940</td>\n",
              "      <td>0.146619</td>\n",
              "      <td>-0.041067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sentence_beginning_interrogative</th>\n",
              "      <td>0.366704</td>\n",
              "      <td>0.377704</td>\n",
              "      <td>0.160374</td>\n",
              "      <td>0.294946</td>\n",
              "      <td>0.279035</td>\n",
              "      <td>0.303726</td>\n",
              "      <td>0.224559</td>\n",
              "      <td>0.207513</td>\n",
              "      <td>0.209583</td>\n",
              "      <td>0.097258</td>\n",
              "      <td>...</td>\n",
              "      <td>0.272818</td>\n",
              "      <td>0.060239</td>\n",
              "      <td>0.058969</td>\n",
              "      <td>0.039785</td>\n",
              "      <td>-0.049019</td>\n",
              "      <td>0.072128</td>\n",
              "      <td>0.067887</td>\n",
              "      <td>0.069954</td>\n",
              "      <td>0.071785</td>\n",
              "      <td>-0.090217</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sentence_beginning_article</th>\n",
              "      <td>0.440532</td>\n",
              "      <td>0.430477</td>\n",
              "      <td>0.167311</td>\n",
              "      <td>0.366580</td>\n",
              "      <td>0.325093</td>\n",
              "      <td>0.386444</td>\n",
              "      <td>0.265852</td>\n",
              "      <td>0.273167</td>\n",
              "      <td>0.247474</td>\n",
              "      <td>0.130303</td>\n",
              "      <td>...</td>\n",
              "      <td>0.301665</td>\n",
              "      <td>0.176010</td>\n",
              "      <td>0.197459</td>\n",
              "      <td>0.191239</td>\n",
              "      <td>-0.164292</td>\n",
              "      <td>0.224624</td>\n",
              "      <td>0.203828</td>\n",
              "      <td>0.226421</td>\n",
              "      <td>0.204896</td>\n",
              "      <td>0.204228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sentence_beginning_subordination</th>\n",
              "      <td>0.603594</td>\n",
              "      <td>0.623814</td>\n",
              "      <td>0.227928</td>\n",
              "      <td>0.498594</td>\n",
              "      <td>0.436844</td>\n",
              "      <td>0.499106</td>\n",
              "      <td>0.408758</td>\n",
              "      <td>0.311460</td>\n",
              "      <td>0.342024</td>\n",
              "      <td>0.192391</td>\n",
              "      <td>...</td>\n",
              "      <td>0.386923</td>\n",
              "      <td>0.192617</td>\n",
              "      <td>0.175341</td>\n",
              "      <td>0.076547</td>\n",
              "      <td>-0.137493</td>\n",
              "      <td>0.222403</td>\n",
              "      <td>0.167824</td>\n",
              "      <td>0.197384</td>\n",
              "      <td>0.180945</td>\n",
              "      <td>-0.029170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sentence_beginning_conjunction</th>\n",
              "      <td>0.214157</td>\n",
              "      <td>0.220255</td>\n",
              "      <td>0.076081</td>\n",
              "      <td>0.185495</td>\n",
              "      <td>0.202888</td>\n",
              "      <td>0.193709</td>\n",
              "      <td>0.134992</td>\n",
              "      <td>0.135675</td>\n",
              "      <td>0.087523</td>\n",
              "      <td>0.037050</td>\n",
              "      <td>...</td>\n",
              "      <td>0.162046</td>\n",
              "      <td>0.068954</td>\n",
              "      <td>0.042657</td>\n",
              "      <td>0.014364</td>\n",
              "      <td>-0.057662</td>\n",
              "      <td>0.071008</td>\n",
              "      <td>0.054187</td>\n",
              "      <td>0.069066</td>\n",
              "      <td>0.057783</td>\n",
              "      <td>-0.062901</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sentence_beginning_preposition</th>\n",
              "      <td>0.517851</td>\n",
              "      <td>0.504511</td>\n",
              "      <td>0.174328</td>\n",
              "      <td>0.440223</td>\n",
              "      <td>0.441233</td>\n",
              "      <td>0.450770</td>\n",
              "      <td>0.339786</td>\n",
              "      <td>0.300630</td>\n",
              "      <td>0.234037</td>\n",
              "      <td>0.107265</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.259970</td>\n",
              "      <td>0.268751</td>\n",
              "      <td>0.235175</td>\n",
              "      <td>-0.237823</td>\n",
              "      <td>0.276105</td>\n",
              "      <td>0.266106</td>\n",
              "      <td>0.269749</td>\n",
              "      <td>0.270403</td>\n",
              "      <td>0.161667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kincaid</th>\n",
              "      <td>0.460554</td>\n",
              "      <td>0.352038</td>\n",
              "      <td>0.000738</td>\n",
              "      <td>0.480912</td>\n",
              "      <td>0.622858</td>\n",
              "      <td>0.465054</td>\n",
              "      <td>0.405056</td>\n",
              "      <td>0.278389</td>\n",
              "      <td>-0.066457</td>\n",
              "      <td>-0.082780</td>\n",
              "      <td>...</td>\n",
              "      <td>0.259970</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.955026</td>\n",
              "      <td>0.883096</td>\n",
              "      <td>-0.949888</td>\n",
              "      <td>0.939074</td>\n",
              "      <td>0.931811</td>\n",
              "      <td>0.927111</td>\n",
              "      <td>0.936354</td>\n",
              "      <td>0.629652</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ARI</th>\n",
              "      <td>0.462206</td>\n",
              "      <td>0.358192</td>\n",
              "      <td>-0.011675</td>\n",
              "      <td>0.490825</td>\n",
              "      <td>0.617050</td>\n",
              "      <td>0.474834</td>\n",
              "      <td>0.413057</td>\n",
              "      <td>0.283946</td>\n",
              "      <td>-0.064476</td>\n",
              "      <td>-0.069218</td>\n",
              "      <td>...</td>\n",
              "      <td>0.268751</td>\n",
              "      <td>0.955026</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.875046</td>\n",
              "      <td>-0.859583</td>\n",
              "      <td>0.922081</td>\n",
              "      <td>0.937217</td>\n",
              "      <td>0.881645</td>\n",
              "      <td>0.946545</td>\n",
              "      <td>0.638286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Coleman_Liau</th>\n",
              "      <td>0.369371</td>\n",
              "      <td>0.233605</td>\n",
              "      <td>0.036408</td>\n",
              "      <td>0.363550</td>\n",
              "      <td>0.491036</td>\n",
              "      <td>0.362493</td>\n",
              "      <td>0.290399</td>\n",
              "      <td>0.234836</td>\n",
              "      <td>-0.039351</td>\n",
              "      <td>-0.084701</td>\n",
              "      <td>...</td>\n",
              "      <td>0.235175</td>\n",
              "      <td>0.883096</td>\n",
              "      <td>0.875046</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.938742</td>\n",
              "      <td>0.801821</td>\n",
              "      <td>0.904432</td>\n",
              "      <td>0.849988</td>\n",
              "      <td>0.889974</td>\n",
              "      <td>0.689721</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FleschReadingEase</th>\n",
              "      <td>-0.398505</td>\n",
              "      <td>-0.272668</td>\n",
              "      <td>-0.029093</td>\n",
              "      <td>-0.398072</td>\n",
              "      <td>-0.541363</td>\n",
              "      <td>-0.389620</td>\n",
              "      <td>-0.328837</td>\n",
              "      <td>-0.241186</td>\n",
              "      <td>0.051266</td>\n",
              "      <td>0.091902</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.237823</td>\n",
              "      <td>-0.949888</td>\n",
              "      <td>-0.859583</td>\n",
              "      <td>-0.938742</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.857217</td>\n",
              "      <td>-0.895579</td>\n",
              "      <td>-0.900873</td>\n",
              "      <td>-0.884963</td>\n",
              "      <td>-0.643707</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GunningFogIndex</th>\n",
              "      <td>0.471542</td>\n",
              "      <td>0.377294</td>\n",
              "      <td>-0.007234</td>\n",
              "      <td>0.498028</td>\n",
              "      <td>0.622616</td>\n",
              "      <td>0.482204</td>\n",
              "      <td>0.418159</td>\n",
              "      <td>0.288819</td>\n",
              "      <td>-0.052623</td>\n",
              "      <td>-0.059251</td>\n",
              "      <td>...</td>\n",
              "      <td>0.276105</td>\n",
              "      <td>0.939074</td>\n",
              "      <td>0.922081</td>\n",
              "      <td>0.801821</td>\n",
              "      <td>-0.857217</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.910654</td>\n",
              "      <td>0.978758</td>\n",
              "      <td>0.917994</td>\n",
              "      <td>0.623847</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LIX</th>\n",
              "      <td>0.444953</td>\n",
              "      <td>0.331519</td>\n",
              "      <td>0.009015</td>\n",
              "      <td>0.460032</td>\n",
              "      <td>0.593999</td>\n",
              "      <td>0.448693</td>\n",
              "      <td>0.382329</td>\n",
              "      <td>0.273283</td>\n",
              "      <td>-0.056741</td>\n",
              "      <td>-0.079327</td>\n",
              "      <td>...</td>\n",
              "      <td>0.266106</td>\n",
              "      <td>0.931811</td>\n",
              "      <td>0.937217</td>\n",
              "      <td>0.904432</td>\n",
              "      <td>-0.895579</td>\n",
              "      <td>0.910654</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.909188</td>\n",
              "      <td>0.995693</td>\n",
              "      <td>0.681521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SMOGIndex</th>\n",
              "      <td>0.447958</td>\n",
              "      <td>0.341655</td>\n",
              "      <td>0.013469</td>\n",
              "      <td>0.460364</td>\n",
              "      <td>0.588919</td>\n",
              "      <td>0.450394</td>\n",
              "      <td>0.379714</td>\n",
              "      <td>0.277927</td>\n",
              "      <td>-0.040669</td>\n",
              "      <td>-0.064863</td>\n",
              "      <td>...</td>\n",
              "      <td>0.269749</td>\n",
              "      <td>0.927111</td>\n",
              "      <td>0.881645</td>\n",
              "      <td>0.849988</td>\n",
              "      <td>-0.900873</td>\n",
              "      <td>0.978758</td>\n",
              "      <td>0.909188</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.912010</td>\n",
              "      <td>0.648232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RIX</th>\n",
              "      <td>0.455152</td>\n",
              "      <td>0.345419</td>\n",
              "      <td>0.006244</td>\n",
              "      <td>0.472245</td>\n",
              "      <td>0.605884</td>\n",
              "      <td>0.459525</td>\n",
              "      <td>0.394456</td>\n",
              "      <td>0.278693</td>\n",
              "      <td>-0.056547</td>\n",
              "      <td>-0.076768</td>\n",
              "      <td>...</td>\n",
              "      <td>0.270403</td>\n",
              "      <td>0.936354</td>\n",
              "      <td>0.946545</td>\n",
              "      <td>0.889974</td>\n",
              "      <td>-0.884963</td>\n",
              "      <td>0.917994</td>\n",
              "      <td>0.995693</td>\n",
              "      <td>0.912010</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.669389</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DaleChallIndex</th>\n",
              "      <td>0.211985</td>\n",
              "      <td>0.116210</td>\n",
              "      <td>-0.001376</td>\n",
              "      <td>0.220874</td>\n",
              "      <td>0.301680</td>\n",
              "      <td>0.225167</td>\n",
              "      <td>0.168289</td>\n",
              "      <td>0.152996</td>\n",
              "      <td>-0.057019</td>\n",
              "      <td>-0.070396</td>\n",
              "      <td>...</td>\n",
              "      <td>0.161667</td>\n",
              "      <td>0.629652</td>\n",
              "      <td>0.638286</td>\n",
              "      <td>0.689721</td>\n",
              "      <td>-0.643707</td>\n",
              "      <td>0.623847</td>\n",
              "      <td>0.681521</td>\n",
              "      <td>0.648232</td>\n",
              "      <td>0.669389</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>44 rows × 44 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "  <style>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-9390435b-0b8c-4346-bd07-3673e121eb58 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-9390435b-0b8c-4346-bd07-3673e121eb58');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-31d6bc8b-952c-4b67-aa0e-9a19c56b9342\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-31d6bc8b-952c-4b67-aa0e-9a19c56b9342')\"\n",
              "            title=\"Suggest charts.\"\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-31d6bc8b-952c-4b67-aa0e-9a19c56b9342 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(6, 6))\n",
        "\n",
        "plt.title(\"Correlation Plot\\nAbsolute value of Pearson's Correlation Coefficient\\n\\n\")\n",
        "sns.heatmap(spearman_corr,\n",
        "            cmap=sns.diverging_palette(230, 10, as_cmap=True),\n",
        "            square=True,\n",
        "            vmin=0,\n",
        "            vmax=1,\n",
        "            ax=ax)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 745
        },
        "id": "22w3925cobUr",
        "outputId": "5a1da2a9-54b6-46a2-f32c-a1d1335059fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Feature-Target Correlation"
      ],
      "metadata": {
        "id": "tu0RHZ1Ht8p0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pearson_corr_target = df.drop('percent_helpful', axis=1).corrwith(df['percent_helpful'], method='pearson')\n",
        "pearson_corr_target.sort_values()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IRjeCJx_ojks",
        "outputId": "bd89c1f8-3c16-4e21-f042-c181437766d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "co_authors                         -0.110067\n",
              "num_votes                          -0.067924\n",
              "tips                               -0.067206\n",
              "FleschReadingEase                  -0.053102\n",
              "sentence_beginning_conjunction     -0.052129\n",
              "views                              -0.043096\n",
              "to_be_verb                         -0.030818\n",
              "pronoun                            -0.028787\n",
              "warnings                           -0.022456\n",
              "mean_steps_per_method              -0.009412\n",
              "sentence_beginning_interrogative   -0.006815\n",
              "step_count                         -0.006530\n",
              "std_method_size                    -0.003368\n",
              "sentence_beginning_pronoun         -0.002845\n",
              "image_count_per_method              0.001668\n",
              "preposition                         0.002750\n",
              "image_count                         0.003376\n",
              "size_largest_method                 0.003840\n",
              "sentence_beginning_subordination    0.003871\n",
              "word_count                          0.006774\n",
              "mean_method_size                    0.007221\n",
              "sentence_beginning_preposition      0.007369\n",
              "mean_paragraph_size                 0.008495\n",
              "size_shortest_method                0.009278\n",
              "nominalization                      0.010112\n",
              "conjunction                         0.011130\n",
              "method_count                        0.012469\n",
              "summary_size                        0.013293\n",
              "character_count                     0.014474\n",
              "sentence_beginning_article          0.018202\n",
              "aux_verb                            0.023540\n",
              "RIX                                 0.034768\n",
              "LIX                                 0.036858\n",
              "GunningFogIndex                     0.044778\n",
              "ARI                                 0.045787\n",
              "SMOGIndex                           0.046900\n",
              "Kincaid                             0.050823\n",
              "Coleman_Liau                        0.052180\n",
              "introduction_size                   0.067219\n",
              "references_count_per_method         0.069955\n",
              "references_count                    0.074219\n",
              "DaleChallIndex                      0.077930\n",
              "references_count_per_text_length    0.086065\n",
              "is_expert                           0.121299\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The closer the value to 1 the more important the feature is in predicting the result. So we can colclude, `is_expert` is most important and `co_authors` is least important feature"
      ],
      "metadata": {
        "id": "RYtebg80vIiL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "spearman_corr_target = df.drop('percent_helpful', axis=1).corrwith(df['percent_helpful'], method='spearman')\n",
        "spearman_corr_target.sort_values()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KpqshsWBuCjA",
        "outputId": "e27a9f21-0122-4c82-d054-91734da15405"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "num_votes                          -0.188499\n",
              "co_authors                         -0.133895\n",
              "views                              -0.081796\n",
              "tips                               -0.053699\n",
              "sentence_beginning_conjunction     -0.052846\n",
              "FleschReadingEase                  -0.052516\n",
              "to_be_verb                         -0.030203\n",
              "pronoun                            -0.025295\n",
              "warnings                           -0.018203\n",
              "sentence_beginning_interrogative   -0.014812\n",
              "nominalization                     -0.002250\n",
              "sentence_beginning_pronoun         -0.001815\n",
              "std_method_size                    -0.001593\n",
              "size_largest_method                 0.005722\n",
              "mean_steps_per_method               0.005787\n",
              "preposition                         0.005801\n",
              "mean_paragraph_size                 0.008202\n",
              "step_count                          0.008600\n",
              "mean_method_size                    0.008891\n",
              "word_count                          0.009642\n",
              "method_count                        0.011820\n",
              "image_count_per_method              0.013298\n",
              "size_shortest_method                0.013974\n",
              "summary_size                        0.013989\n",
              "character_count                     0.015671\n",
              "image_count                         0.015802\n",
              "conjunction                         0.016914\n",
              "sentence_beginning_subordination    0.018649\n",
              "sentence_beginning_preposition      0.020603\n",
              "aux_verb                            0.024879\n",
              "sentence_beginning_article          0.027147\n",
              "LIX                                 0.036778\n",
              "RIX                                 0.037271\n",
              "SMOGIndex                           0.046420\n",
              "GunningFogIndex                     0.046532\n",
              "ARI                                 0.048297\n",
              "Kincaid                             0.050914\n",
              "Coleman_Liau                        0.051620\n",
              "introduction_size                   0.074835\n",
              "references_count_per_method         0.078637\n",
              "references_count                    0.085700\n",
              "references_count_per_text_length    0.087161\n",
              "DaleChallIndex                      0.092504\n",
              "is_expert                           0.123549\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The closer the value to 1 the more important the feature is in predicting the result. So we can colclude, `is_expert` is most important and `num_votes` is least important feature"
      ],
      "metadata": {
        "id": "Z8iHwrRwupUR"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6ExPvqCsuExO"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}