rainfall_prediction.ipynb

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        "<a href=\"https://colab.research.google.com/github/chan-98/data_science/blob/main/rainfall_prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "# Rainfall Prediction Using Linear Regression"
      ],
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        "id": "lKwv0ouN9pK5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "DATA:\n",
        "\n",
        "> \"/content/austin_weather.csv\""
      ],
      "metadata": {
        "id": "EdbLHQnk9wut"
      }
    },
    {
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      "metadata": {
        "id": "tyLULBwN9eNk"
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      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import sklearn as sk\n",
        "from sklearn.linear_model import LinearRegression\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(\"/content/austin_weather.csv\")\n",
        "data"
      ],
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          "height": 461
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        "outputId": "26f33090-5b1e-43b6-c286-96d31dd85d02"
      },
      "execution_count": null,
      "outputs": [
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            "text/plain": [
              "            Date  TempHighF  TempAvgF  TempLowF DewPointHighF DewPointAvgF  \\\n",
              "0     2013-12-21         74        60        45            67           49   \n",
              "1     2013-12-22         56        48        39            43           36   \n",
              "2     2013-12-23         58        45        32            31           27   \n",
              "3     2013-12-24         61        46        31            36           28   \n",
              "4     2013-12-25         58        50        41            44           40   \n",
              "...          ...        ...       ...       ...           ...          ...   \n",
              "1314  2017-07-27        103        89        75            71           67   \n",
              "1315  2017-07-28        105        91        76            71           64   \n",
              "1316  2017-07-29        107        92        77            72           64   \n",
              "1317  2017-07-30        106        93        79            70           68   \n",
              "1318  2017-07-31         99        88        77            66           61   \n",
              "\n",
              "     DewPointLowF HumidityHighPercent HumidityAvgPercent HumidityLowPercent  \\\n",
              "0              43                  93                 75                 57   \n",
              "1              28                  93                 68                 43   \n",
              "2              23                  76                 52                 27   \n",
              "3              21                  89                 56                 22   \n",
              "4              36                  86                 71                 56   \n",
              "...           ...                 ...                ...                ...   \n",
              "1314           61                  82                 54                 25   \n",
              "1315           55                  87                 54                 20   \n",
              "1316           55                  82                 51                 19   \n",
              "1317           63                  69                 48                 27   \n",
              "1318           54                  64                 43                 22   \n",
              "\n",
              "      ... SeaLevelPressureAvgInches SeaLevelPressureLowInches  \\\n",
              "0     ...                     29.68                     29.59   \n",
              "1     ...                     30.13                     29.87   \n",
              "2     ...                     30.49                     30.41   \n",
              "3     ...                     30.45                      30.3   \n",
              "4     ...                     30.33                     30.27   \n",
              "...   ...                       ...                       ...   \n",
              "1314  ...                     29.97                     29.88   \n",
              "1315  ...                      29.9                     29.81   \n",
              "1316  ...                     29.86                     29.79   \n",
              "1317  ...                     29.91                     29.87   \n",
              "1318  ...                     29.97                     29.91   \n",
              "\n",
              "     VisibilityHighMiles VisibilityAvgMiles VisibilityLowMiles WindHighMPH  \\\n",
              "0                     10                  7                  2          20   \n",
              "1                     10                 10                  5          16   \n",
              "2                     10                 10                 10           8   \n",
              "3                     10                 10                  7          12   \n",
              "4                     10                 10                  7          10   \n",
              "...                  ...                ...                ...         ...   \n",
              "1314                  10                 10                 10          12   \n",
              "1315                  10                 10                 10          14   \n",
              "1316                  10                 10                 10          12   \n",
              "1317                  10                 10                 10          13   \n",
              "1318                  10                 10                 10          12   \n",
              "\n",
              "     WindAvgMPH WindGustMPH PrecipitationSumInches               Events  \n",
              "0             4          31                   0.46  Rain , Thunderstorm  \n",
              "1             6          25                      0                       \n",
              "2             3          12                      0                       \n",
              "3             4          20                      0                       \n",
              "4             2          16                      T                       \n",
              "...         ...         ...                    ...                  ...  \n",
              "1314          5          21                      0                       \n",
              "1315          5          20                      0                       \n",
              "1316          4          17                      0                       \n",
              "1317          4          20                      0                       \n",
              "1318          4          20                      0                       \n",
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              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>TempHighF</th>\n",
              "      <th>TempAvgF</th>\n",
              "      <th>TempLowF</th>\n",
              "      <th>DewPointHighF</th>\n",
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              "      <th>VisibilityLowMiles</th>\n",
              "      <th>WindHighMPH</th>\n",
              "      <th>WindAvgMPH</th>\n",
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              "      <th>0</th>\n",
              "      <td>2013-12-21</td>\n",
              "      <td>74</td>\n",
              "      <td>60</td>\n",
              "      <td>45</td>\n",
              "      <td>67</td>\n",
              "      <td>49</td>\n",
              "      <td>43</td>\n",
              "      <td>93</td>\n",
              "      <td>75</td>\n",
              "      <td>57</td>\n",
              "      <td>...</td>\n",
              "      <td>29.68</td>\n",
              "      <td>29.59</td>\n",
              "      <td>10</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
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              "      <td>0.46</td>\n",
              "      <td>Rain , Thunderstorm</td>\n",
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              "      <th>1</th>\n",
              "      <td>2013-12-22</td>\n",
              "      <td>56</td>\n",
              "      <td>48</td>\n",
              "      <td>39</td>\n",
              "      <td>43</td>\n",
              "      <td>36</td>\n",
              "      <td>28</td>\n",
              "      <td>93</td>\n",
              "      <td>68</td>\n",
              "      <td>43</td>\n",
              "      <td>...</td>\n",
              "      <td>30.13</td>\n",
              "      <td>29.87</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>16</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2013-12-23</td>\n",
              "      <td>58</td>\n",
              "      <td>45</td>\n",
              "      <td>32</td>\n",
              "      <td>31</td>\n",
              "      <td>27</td>\n",
              "      <td>23</td>\n",
              "      <td>76</td>\n",
              "      <td>52</td>\n",
              "      <td>27</td>\n",
              "      <td>...</td>\n",
              "      <td>30.49</td>\n",
              "      <td>30.41</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td></td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2013-12-24</td>\n",
              "      <td>61</td>\n",
              "      <td>46</td>\n",
              "      <td>31</td>\n",
              "      <td>36</td>\n",
              "      <td>28</td>\n",
              "      <td>21</td>\n",
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              "      <td>30.3</td>\n",
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              "      <td>7</td>\n",
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              "      <td>2013-12-25</td>\n",
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              "      <td>71</td>\n",
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              "      <td>30.33</td>\n",
              "      <td>30.27</td>\n",
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              "      <td>10</td>\n",
              "      <td>7</td>\n",
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              "      <th>1314</th>\n",
              "      <td>2017-07-27</td>\n",
              "      <td>103</td>\n",
              "      <td>89</td>\n",
              "      <td>75</td>\n",
              "      <td>71</td>\n",
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              "      <td>2017-07-28</td>\n",
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              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
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              "      <th>1316</th>\n",
              "      <td>2017-07-29</td>\n",
              "      <td>107</td>\n",
              "      <td>92</td>\n",
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              "      <td>72</td>\n",
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              "      <td>55</td>\n",
              "      <td>82</td>\n",
              "      <td>51</td>\n",
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              "      <td>29.79</td>\n",
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              "      <td>63</td>\n",
              "      <td>69</td>\n",
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              "      <td>29.91</td>\n",
              "      <td>29.87</td>\n",
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              "      <td>10</td>\n",
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              "      <th>1318</th>\n",
              "      <td>2017-07-31</td>\n",
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              "      --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-8d05a883-f30a-4172-aab3-9bb5ecc3d10a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_67ddeb4e-876b-48df-8d5b-a90ab65c6e6f\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('data')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_67ddeb4e-876b-48df-8d5b-a90ab65c6e6f button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('data');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Cleaning the dataset.\n",
        "\n",
        "> Remove text fields, only numbers are required"
      ],
      "metadata": {
        "id": "0MdGc0aN_sog"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# cleaning\n",
        "\n",
        "data = data.drop([\"Events\", \"Date\", \"SeaLevelPressureLowInches\"], axis=1)"
      ],
      "metadata": {
        "id": "b0ISmXE69wX1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.replace(\"T\", 0.0)\n",
        "data = data.replace(\"-\", 0.0)"
      ],
      "metadata": {
        "id": "oyDA-3fY9wVP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "id": "LVL-ZG4O9wPH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "outputId": "2ffa3e08-72e5-4506-a891-75feca9ab804"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      TempHighF  TempAvgF  TempLowF DewPointHighF DewPointAvgF DewPointLowF  \\\n",
              "0            74        60        45            67           49           43   \n",
              "1            56        48        39            43           36           28   \n",
              "2            58        45        32            31           27           23   \n",
              "3            61        46        31            36           28           21   \n",
              "4            58        50        41            44           40           36   \n",
              "...         ...       ...       ...           ...          ...          ...   \n",
              "1314        103        89        75            71           67           61   \n",
              "1315        105        91        76            71           64           55   \n",
              "1316        107        92        77            72           64           55   \n",
              "1317        106        93        79            70           68           63   \n",
              "1318         99        88        77            66           61           54   \n",
              "\n",
              "     HumidityHighPercent HumidityAvgPercent HumidityLowPercent  \\\n",
              "0                     93                 75                 57   \n",
              "1                     93                 68                 43   \n",
              "2                     76                 52                 27   \n",
              "3                     89                 56                 22   \n",
              "4                     86                 71                 56   \n",
              "...                  ...                ...                ...   \n",
              "1314                  82                 54                 25   \n",
              "1315                  87                 54                 20   \n",
              "1316                  82                 51                 19   \n",
              "1317                  69                 48                 27   \n",
              "1318                  64                 43                 22   \n",
              "\n",
              "     SeaLevelPressureHighInches SeaLevelPressureAvgInches VisibilityHighMiles  \\\n",
              "0                         29.86                     29.68                  10   \n",
              "1                         30.41                     30.13                  10   \n",
              "2                         30.56                     30.49                  10   \n",
              "3                         30.56                     30.45                  10   \n",
              "4                         30.41                     30.33                  10   \n",
              "...                         ...                       ...                 ...   \n",
              "1314                      30.04                     29.97                  10   \n",
              "1315                      29.97                      29.9                  10   \n",
              "1316                      29.91                     29.86                  10   \n",
              "1317                      29.96                     29.91                  10   \n",
              "1318                      30.04                     29.97                  10   \n",
              "\n",
              "     VisibilityAvgMiles VisibilityLowMiles WindHighMPH WindAvgMPH WindGustMPH  \\\n",
              "0                     7                  2          20          4          31   \n",
              "1                    10                  5          16          6          25   \n",
              "2                    10                 10           8          3          12   \n",
              "3                    10                  7          12          4          20   \n",
              "4                    10                  7          10          2          16   \n",
              "...                 ...                ...         ...        ...         ...   \n",
              "1314                 10                 10          12          5          21   \n",
              "1315                 10                 10          14          5          20   \n",
              "1316                 10                 10          12          4          17   \n",
              "1317                 10                 10          13          4          20   \n",
              "1318                 10                 10          12          4          20   \n",
              "\n",
              "     PrecipitationSumInches  \n",
              "0                      0.46  \n",
              "1                         0  \n",
              "2                         0  \n",
              "3                         0  \n",
              "4                       0.0  \n",
              "...                     ...  \n",
              "1314                      0  \n",
              "1315                      0  \n",
              "1316                      0  \n",
              "1317                      0  \n",
              "1318                      0  \n",
              "\n",
              "[1319 rows x 18 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-35c83fa9-0aee-4333-9148-0c27308c76a9\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TempHighF</th>\n",
              "      <th>TempAvgF</th>\n",
              "      <th>TempLowF</th>\n",
              "      <th>DewPointHighF</th>\n",
              "      <th>DewPointAvgF</th>\n",
              "      <th>DewPointLowF</th>\n",
              "      <th>HumidityHighPercent</th>\n",
              "      <th>HumidityAvgPercent</th>\n",
              "      <th>HumidityLowPercent</th>\n",
              "      <th>SeaLevelPressureHighInches</th>\n",
              "      <th>SeaLevelPressureAvgInches</th>\n",
              "      <th>VisibilityHighMiles</th>\n",
              "      <th>VisibilityAvgMiles</th>\n",
              "      <th>VisibilityLowMiles</th>\n",
              "      <th>WindHighMPH</th>\n",
              "      <th>WindAvgMPH</th>\n",
              "      <th>WindGustMPH</th>\n",
              "      <th>PrecipitationSumInches</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>74</td>\n",
              "      <td>60</td>\n",
              "      <td>45</td>\n",
              "      <td>67</td>\n",
              "      <td>49</td>\n",
              "      <td>43</td>\n",
              "      <td>93</td>\n",
              "      <td>75</td>\n",
              "      <td>57</td>\n",
              "      <td>29.86</td>\n",
              "      <td>29.68</td>\n",
              "      <td>10</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>4</td>\n",
              "      <td>31</td>\n",
              "      <td>0.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>56</td>\n",
              "      <td>48</td>\n",
              "      <td>39</td>\n",
              "      <td>43</td>\n",
              "      <td>36</td>\n",
              "      <td>28</td>\n",
              "      <td>93</td>\n",
              "      <td>68</td>\n",
              "      <td>43</td>\n",
              "      <td>30.41</td>\n",
              "      <td>30.13</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "      <td>25</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>58</td>\n",
              "      <td>45</td>\n",
              "      <td>32</td>\n",
              "      <td>31</td>\n",
              "      <td>27</td>\n",
              "      <td>23</td>\n",
              "      <td>76</td>\n",
              "      <td>52</td>\n",
              "      <td>27</td>\n",
              "      <td>30.56</td>\n",
              "      <td>30.49</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>61</td>\n",
              "      <td>46</td>\n",
              "      <td>31</td>\n",
              "      <td>36</td>\n",
              "      <td>28</td>\n",
              "      <td>21</td>\n",
              "      <td>89</td>\n",
              "      <td>56</td>\n",
              "      <td>22</td>\n",
              "      <td>30.56</td>\n",
              "      <td>30.45</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>7</td>\n",
              "      <td>12</td>\n",
              "      <td>4</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>58</td>\n",
              "      <td>50</td>\n",
              "      <td>41</td>\n",
              "      <td>44</td>\n",
              "      <td>40</td>\n",
              "      <td>36</td>\n",
              "      <td>86</td>\n",
              "      <td>71</td>\n",
              "      <td>56</td>\n",
              "      <td>30.41</td>\n",
              "      <td>30.33</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>7</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>16</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1314</th>\n",
              "      <td>103</td>\n",
              "      <td>89</td>\n",
              "      <td>75</td>\n",
              "      <td>71</td>\n",
              "      <td>67</td>\n",
              "      <td>61</td>\n",
              "      <td>82</td>\n",
              "      <td>54</td>\n",
              "      <td>25</td>\n",
              "      <td>30.04</td>\n",
              "      <td>29.97</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>12</td>\n",
              "      <td>5</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1315</th>\n",
              "      <td>105</td>\n",
              "      <td>91</td>\n",
              "      <td>76</td>\n",
              "      <td>71</td>\n",
              "      <td>64</td>\n",
              "      <td>55</td>\n",
              "      <td>87</td>\n",
              "      <td>54</td>\n",
              "      <td>20</td>\n",
              "      <td>29.97</td>\n",
              "      <td>29.9</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>14</td>\n",
              "      <td>5</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1316</th>\n",
              "      <td>107</td>\n",
              "      <td>92</td>\n",
              "      <td>77</td>\n",
              "      <td>72</td>\n",
              "      <td>64</td>\n",
              "      <td>55</td>\n",
              "      <td>82</td>\n",
              "      <td>51</td>\n",
              "      <td>19</td>\n",
              "      <td>29.91</td>\n",
              "      <td>29.86</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>12</td>\n",
              "      <td>4</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1317</th>\n",
              "      <td>106</td>\n",
              "      <td>93</td>\n",
              "      <td>79</td>\n",
              "      <td>70</td>\n",
              "      <td>68</td>\n",
              "      <td>63</td>\n",
              "      <td>69</td>\n",
              "      <td>48</td>\n",
              "      <td>27</td>\n",
              "      <td>29.96</td>\n",
              "      <td>29.91</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>13</td>\n",
              "      <td>4</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1318</th>\n",
              "      <td>99</td>\n",
              "      <td>88</td>\n",
              "      <td>77</td>\n",
              "      <td>66</td>\n",
              "      <td>61</td>\n",
              "      <td>54</td>\n",
              "      <td>64</td>\n",
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.to_csv(\"autin_weather_final.csv\")"
      ],
      "metadata": {
        "id": "_GTYBq7E9wLY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(\"autin_weather_final.csv\")\n",
        "data.head()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
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        "id": "w1DhOj4RA0xU",
        "outputId": "e4c8226d-6572-45fa-bde9-41ac3e097e72"
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      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0  TempHighF  TempAvgF  TempLowF  DewPointHighF  DewPointAvgF  \\\n",
              "0           0         74        60        45           67.0          49.0   \n",
              "1           1         56        48        39           43.0          36.0   \n",
              "2           2         58        45        32           31.0          27.0   \n",
              "3           3         61        46        31           36.0          28.0   \n",
              "4           4         58        50        41           44.0          40.0   \n",
              "\n",
              "   DewPointLowF  HumidityHighPercent  HumidityAvgPercent  HumidityLowPercent  \\\n",
              "0          43.0                 93.0                75.0                57.0   \n",
              "1          28.0                 93.0                68.0                43.0   \n",
              "2          23.0                 76.0                52.0                27.0   \n",
              "3          21.0                 89.0                56.0                22.0   \n",
              "4          36.0                 86.0                71.0                56.0   \n",
              "\n",
              "   SeaLevelPressureHighInches  SeaLevelPressureAvgInches  VisibilityHighMiles  \\\n",
              "0                       29.86                      29.68                 10.0   \n",
              "1                       30.41                      30.13                 10.0   \n",
              "2                       30.56                      30.49                 10.0   \n",
              "3                       30.56                      30.45                 10.0   \n",
              "4                       30.41                      30.33                 10.0   \n",
              "\n",
              "   VisibilityAvgMiles  VisibilityLowMiles  WindHighMPH  WindAvgMPH  \\\n",
              "0                 7.0                 2.0         20.0         4.0   \n",
              "1                10.0                 5.0         16.0         6.0   \n",
              "2                10.0                10.0          8.0         3.0   \n",
              "3                10.0                 7.0         12.0         4.0   \n",
              "4                10.0                 7.0         10.0         2.0   \n",
              "\n",
              "   WindGustMPH  PrecipitationSumInches  \n",
              "0         31.0                    0.46  \n",
              "1         25.0                    0.00  \n",
              "2         12.0                    0.00  \n",
              "3         20.0                    0.00  \n",
              "4         16.0                    0.00  "
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              "      <th>Unnamed: 0</th>\n",
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              "      <th>SeaLevelPressureAvgInches</th>\n",
              "      <th>VisibilityHighMiles</th>\n",
              "      <th>VisibilityAvgMiles</th>\n",
              "      <th>VisibilityLowMiles</th>\n",
              "      <th>WindHighMPH</th>\n",
              "      <th>WindAvgMPH</th>\n",
              "      <th>WindGustMPH</th>\n",
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              "      <td>8.0</td>\n",
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              "      <td>12.0</td>\n",
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              "      <th>3</th>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The last column \"PrecipitationSumInches\" will be the label based on which the model will be trained."
      ],
      "metadata": {
        "id": "3Ah83PutBcjZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = data.drop(\"PrecipitationSumInches\", axis=1)"
      ],
      "metadata": {
        "id": "QywdQgVwA0uw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "X0AoNZ2yA0sI",
        "outputId": "ba74e327-dc82-404e-a18c-736f3b5ed33e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Unnamed: 0  TempHighF  TempAvgF  TempLowF  DewPointHighF  DewPointAvgF  \\\n",
              "0              0         74        60        45           67.0          49.0   \n",
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              "4              4         58        50        41           44.0          40.0   \n",
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              "\n",
              "      DewPointLowF  HumidityHighPercent  HumidityAvgPercent  \\\n",
              "0             43.0                 93.0                75.0   \n",
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              "      HumidityLowPercent  SeaLevelPressureHighInches  \\\n",
              "0                   57.0                       29.86   \n",
              "1                   43.0                       30.41   \n",
              "2                   27.0                       30.56   \n",
              "3                   22.0                       30.56   \n",
              "4                   56.0                       30.41   \n",
              "...                  ...                         ...   \n",
              "1314                25.0                       30.04   \n",
              "1315                20.0                       29.97   \n",
              "1316                19.0                       29.91   \n",
              "1317                27.0                       29.96   \n",
              "1318                22.0                       30.04   \n",
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              "      SeaLevelPressureAvgInches  VisibilityHighMiles  VisibilityAvgMiles  \\\n",
              "0                         29.68                 10.0                 7.0   \n",
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              "2                         30.49                 10.0                10.0   \n",
              "3                         30.45                 10.0                10.0   \n",
              "4                         30.33                 10.0                10.0   \n",
              "...                         ...                  ...                 ...   \n",
              "1314                      29.97                 10.0                10.0   \n",
              "1315                      29.90                 10.0                10.0   \n",
              "1316                      29.86                 10.0                10.0   \n",
              "1317                      29.91                 10.0                10.0   \n",
              "1318                      29.97                 10.0                10.0   \n",
              "\n",
              "      VisibilityLowMiles  WindHighMPH  WindAvgMPH  WindGustMPH  \n",
              "0                    2.0         20.0         4.0         31.0  \n",
              "1                    5.0         16.0         6.0         25.0  \n",
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              "3                    7.0         12.0         4.0         20.0  \n",
              "4                    7.0         10.0         2.0         16.0  \n",
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              "1317                10.0         13.0         4.0         20.0  \n",
              "1318                10.0         12.0         4.0         20.0  \n",
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              "      <td>55.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>29.97</td>\n",
              "      <td>29.90</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1316</th>\n",
              "      <td>1316</td>\n",
              "      <td>107</td>\n",
              "      <td>92</td>\n",
              "      <td>77</td>\n",
              "      <td>72.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>29.91</td>\n",
              "      <td>29.86</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>17.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1317</th>\n",
              "      <td>1317</td>\n",
              "      <td>106</td>\n",
              "      <td>93</td>\n",
              "      <td>79</td>\n",
              "      <td>70.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>29.96</td>\n",
              "      <td>29.91</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1318</th>\n",
              "      <td>1318</td>\n",
              "      <td>99</td>\n",
              "      <td>88</td>\n",
              "      <td>77</td>\n",
              "      <td>66.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>30.04</td>\n",
              "      <td>29.97</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1319 rows × 18 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y = data[\"PrecipitationSumInches\"]\n",
        "Y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p4RLZw8sA0pR",
        "outputId": "caf118ec-7c1c-41e5-87ba-0712ef2d9691"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       0.46\n",
              "1       0.00\n",
              "2       0.00\n",
              "3       0.00\n",
              "4       0.00\n",
              "        ... \n",
              "1314    0.00\n",
              "1315    0.00\n",
              "1316    0.00\n",
              "1317    0.00\n",
              "1318    0.00\n",
              "Name: PrecipitationSumInches, Length: 1319, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Reshape into a 2D vector"
      ],
      "metadata": {
        "id": "Bcf1PEZRCM5y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Y = Y.values.reshape(-1, 1)\n",
        "Y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VucrBq-ICP2w",
        "outputId": "284b899e-3a41-4ea6-e0e4-b4c8eedf9c2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.46],\n",
              "       [0.  ],\n",
              "       [0.  ],\n",
              "       ...,\n",
              "       [0.  ],\n",
              "       [0.  ],\n",
              "       [0.  ]])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pick any random day from X and plot graph to observe the selected day"
      ],
      "metadata": {
        "id": "rRi5G18VCcnz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "day_index = 798\n",
        "days = [i for i in range(Y.size)]\n",
        "days"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4l91nvcvCQNK",
        "outputId": "ae0943f3-6cb9-4e20-8310-bb9fdd8214e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              " 999,\n",
              " ...]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialise the LR classifier\n",
        "\n",
        "classifier = LinearRegression()\n",
        "\n",
        "# train the classifier\n",
        "\n",
        "classifier.fit(X, Y)    # pass labels to train the model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "5u7eXO3BCQGI",
        "outputId": "e9aee96a-7f2c-42d0-b568-3af41667f36c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot graph of precipitation vs total num of days\n",
        "\n",
        "print(\"Precipitation Trend Graph: \\n\")\n",
        "plt.scatter(days, Y, color = 'g')\n",
        "plt.scatter(days[day_index], Y[day_index], color = 'red')\n",
        "plt.title(\"Precipitation Levels\")\n",
        "plt.xlabel(\"Days\")\n",
        "plt.ylabel(\"Precipitation in inches\")\n",
        "plt.show()\n",
        "# filter out\n",
        "x_vis = X.filter(['TempAvgF', 'DewPointAvgF', 'HumidityAvgPercent', 'SeaLevelPressureAvgInches', 'VisibilityAvgMiles', 'WindAvgMPH'])\n",
        "x_vis"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 934
        },
        "id": "u3kZkXoMCP8w",
        "outputId": "f4e8363c-e42f-4ddb-8282-692680be30df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precipitation Trend Graph: \n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      TempAvgF  DewPointAvgF  HumidityAvgPercent  SeaLevelPressureAvgInches  \\\n",
              "0           60          49.0                75.0                      29.68   \n",
              "1           48          36.0                68.0                      30.13   \n",
              "2           45          27.0                52.0                      30.49   \n",
              "3           46          28.0                56.0                      30.45   \n",
              "4           50          40.0                71.0                      30.33   \n",
              "...        ...           ...                 ...                        ...   \n",
              "1314        89          67.0                54.0                      29.97   \n",
              "1315        91          64.0                54.0                      29.90   \n",
              "1316        92          64.0                51.0                      29.86   \n",
              "1317        93          68.0                48.0                      29.91   \n",
              "1318        88          61.0                43.0                      29.97   \n",
              "\n",
              "      VisibilityAvgMiles  WindAvgMPH  \n",
              "0                    7.0         4.0  \n",
              "1                   10.0         6.0  \n",
              "2                   10.0         3.0  \n",
              "3                   10.0         4.0  \n",
              "4                   10.0         2.0  \n",
              "...                  ...         ...  \n",
              "1314                10.0         5.0  \n",
              "1315                10.0         5.0  \n",
              "1316                10.0         4.0  \n",
              "1317                10.0         4.0  \n",
              "1318                10.0         4.0  \n",
              "\n",
              "[1319 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a8ac6514-3698-4ff5-97e0-ec22382db8e8\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TempAvgF</th>\n",
              "      <th>DewPointAvgF</th>\n",
              "      <th>HumidityAvgPercent</th>\n",
              "      <th>SeaLevelPressureAvgInches</th>\n",
              "      <th>VisibilityAvgMiles</th>\n",
              "      <th>WindAvgMPH</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>60</td>\n",
              "      <td>49.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>29.68</td>\n",
              "      <td>7.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>48</td>\n",
              "      <td>36.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>30.13</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>45</td>\n",
              "      <td>27.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>30.49</td>\n",
              "      <td>10.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>46</td>\n",
              "      <td>28.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>30.45</td>\n",
              "      <td>10.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>50</td>\n",
              "      <td>40.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>30.33</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1314</th>\n",
              "      <td>89</td>\n",
              "      <td>67.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>29.97</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1315</th>\n",
              "      <td>91</td>\n",
              "      <td>64.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>29.90</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1316</th>\n",
              "      <td>92</td>\n",
              "      <td>64.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>29.86</td>\n",
              "      <td>10.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1317</th>\n",
              "      <td>93</td>\n",
              "      <td>68.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>29.91</td>\n",
              "      <td>10.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1318</th>\n",
              "      <td>88</td>\n",
              "      <td>61.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>29.97</td>\n",
              "      <td>10.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1319 rows × 6 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a8ac6514-3698-4ff5-97e0-ec22382db8e8')\"\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "          '<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",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
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              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
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              "    width: 32px;\n",
              "  }\n",
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              "  .colab-df-quickchart:hover {\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      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",
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              "\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",
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              "        document.querySelector('#df-fdb642bd-a37a-4f83-bec2-a71ed3edfa01 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "  <div id=\"id_98bcac88-4696-4c1d-b31b-ec7d8992bcad\">\n",
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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('x_vis')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('x_vis');\n",
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              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Precipitation vs. Attributes Trend Graph: \\n\")\n",
        "\n",
        "for i in range(x_vis.columns.size):\n",
        "  plt.subplot(3,2,i+1)\n",
        "  plt.scatter(days, x_vis[x_vis.columns.values[i][:100]], color = 'g')\n",
        "  plt.scatter(days[day_index], x_vis[x_vis.columns.values[i]][day_index], color = 'red')\n",
        "  plt.title(x_vis.columns.values[i])\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "bOm2TmXoA0mo",
        "outputId": "e3aa9290-296b-4cf7-df04-70c912e2e065"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precipitation vs. Attributes Trend Graph: \n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The rainfall can be expected when temp and humidity are high. Not the scale of X and Y axes are different for different plots."
      ],
      "metadata": {
        "id": "5ev9GKYUGAyj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lamb = lambda x: x ** 3\n",
        "print(lamb(5))"
      ],
      "metadata": {
        "id": "fCyckUOdDgBy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd30b810-77a5-4c83-f864-d87f75c194b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "125\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kSF5hVEsaz5Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7DlySmcmDf-p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OY44XDFpKb8C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "R6mgYAunDf3S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hWfNDGl0Df0E"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}