A3_DataPreprocessing.ipynb

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      "cell_type": "markdown",
      "source": [
        "**ML ASSIGNMENT 3**"
      ],
      "metadata": {
        "id": "kuz-Qm5MqUAs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*   **Lakshay Singla**\n",
        "*   **102003371**\n",
        "*   **3CO15**\n",
        "\n"
      ],
      "metadata": {
        "id": "KxHZdB3o5Fu4"
      }
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    {
      "cell_type": "markdown",
      "source": [
        "**Importing Libraries**"
      ],
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        "id": "RucsbIxp0cAF"
      }
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      "cell_type": "code",
      "execution_count": null,
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        "id": "z7pVUTxo2zQ6"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Merging the two given datasets**"
      ],
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        "id": "DLa7HSoN0XXA"
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    {
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      "source": [
        "df = pd.read_csv('AWCustomers.csv')\n",
        "extra = pd.read_csv('AWSales.csv')\n",
        "df = pd.merge(df,extra)\n",
        "df.head()"
      ],
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              "   CustomerID Title FirstName MiddleName  LastName Suffix  \\\n",
              "0       21173   NaN      Chad          C      Yuan    NaN   \n",
              "1       13249   NaN      Ryan        NaN     Perry    NaN   \n",
              "2       29350   NaN     Julia        NaN  Thompson    NaN   \n",
              "3       13503   NaN  Theodore        NaN     Gomez    NaN   \n",
              "4       22803   NaN  Marshall          J      Shan    NaN   \n",
              "\n",
              "             AddressLine1 AddressLine2         City    StateProvinceName  ...  \\\n",
              "0      7090 C. Mount Hood          NaN   Wollongong      New South Wales  ...   \n",
              "1     3651 Willow Lake Rd          NaN      Shawnee     British Columbia  ...   \n",
              "2  1774 Tice Valley Blvd.          NaN  West Covina           California  ...   \n",
              "3         2103 Baldwin Dr          NaN    Liverpool              England  ...   \n",
              "4         Am Gallberg 234          NaN        Werne  Nordrhein-Westfalen  ...   \n",
              "\n",
              "  Gender MaritalStatus HomeOwnerFlag NumberCarsOwned NumberChildrenAtHome  \\\n",
              "0      M             M             1               3                    0   \n",
              "1      M             M             1               2                    1   \n",
              "2      F             S             0               3                    0   \n",
              "3      M             M             1               2                    1   \n",
              "4      M             S             1               1                    0   \n",
              "\n",
              "  TotalChildren YearlyIncome LastUpdated  BikeBuyer  AvgMonthSpend  \n",
              "0             1        81916  2017-03-06          1          50.97  \n",
              "1             2        81076  2017-03-06          1          53.11  \n",
              "2             0        86387  2017-03-06          1          54.08  \n",
              "3             2        61481  2017-03-06          1          56.93  \n",
              "4             0        51804  2017-03-06          1          55.41  \n",
              "\n",
              "[5 rows x 26 columns]"
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              "      <td>Gomez</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2103 Baldwin Dr</td>\n",
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              "      <td>Liverpool</td>\n",
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              "      <td>M</td>\n",
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              "      <td>2</td>\n",
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              "      <td>61481</td>\n",
              "      <td>2017-03-06</td>\n",
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              "      <td>Shan</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Am Gallberg 234</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Werne</td>\n",
              "      <td>Nordrhein-Westfalen</td>\n",
              "      <td>...</td>\n",
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              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>51804</td>\n",
              "      <td>2017-03-06</td>\n",
              "      <td>1</td>\n",
              "      <td>55.41</td>\n",
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              "<p>5 rows × 26 columns</p>\n",
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          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Dropping duplicate ids present if any**"
      ],
      "metadata": {
        "id": "8UKGM9XRX-dt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop_duplicates(subset=['CustomerID'])"
      ],
      "metadata": {
        "id": "dhZu-Q-Z7qom"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Removing the extra columns**"
      ],
      "metadata": {
        "id": "nnYKdS9D0R_H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop(['CustomerID','Title','FirstName','MiddleName','LastName','Suffix','AddressLine1','AddressLine2','PostalCode','PhoneNumber','LastUpdated','City','StateProvinceName'],axis = 1)\n",
        "df.info()"
      ],
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        "outputId": "1e9c5a4d-0e64-4ff4-ce6b-21dde3c3d02a"
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 18355 entries, 0 to 18360\n",
            "Data columns (total 13 columns):\n",
            " #   Column                Non-Null Count  Dtype  \n",
            "---  ------                --------------  -----  \n",
            " 0   CountryRegionName     18355 non-null  object \n",
            " 1   BirthDate             18355 non-null  object \n",
            " 2   Education             18355 non-null  object \n",
            " 3   Occupation            18355 non-null  object \n",
            " 4   Gender                18355 non-null  object \n",
            " 5   MaritalStatus         18355 non-null  object \n",
            " 6   HomeOwnerFlag         18355 non-null  int64  \n",
            " 7   NumberCarsOwned       18355 non-null  int64  \n",
            " 8   NumberChildrenAtHome  18355 non-null  int64  \n",
            " 9   TotalChildren         18355 non-null  int64  \n",
            " 10  YearlyIncome          18355 non-null  int64  \n",
            " 11  BikeBuyer             18355 non-null  int64  \n",
            " 12  AvgMonthSpend         18355 non-null  float64\n",
            "dtypes: float64(1), int64(6), object(6)\n",
            "memory usage: 2.0+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
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      "execution_count": null,
      "outputs": [
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              "  CountryRegionName   BirthDate        Education      Occupation Gender  \\\n",
              "0         Australia  1987-11-13        Bachelors        Clerical      M   \n",
              "1            Canada  1972-07-21  Partial College        Clerical      M   \n",
              "2     United States  1985-11-09        Bachelors        Clerical      F   \n",
              "3    United Kingdom  1977-10-18  Partial College  Skilled Manual      M   \n",
              "4           Germany  1975-02-05  Partial College  Skilled Manual      M   \n",
              "\n",
              "  MaritalStatus  HomeOwnerFlag  NumberCarsOwned  NumberChildrenAtHome  \\\n",
              "0             M              1                3                     0   \n",
              "1             M              1                2                     1   \n",
              "2             S              0                3                     0   \n",
              "3             M              1                2                     1   \n",
              "4             S              1                1                     0   \n",
              "\n",
              "   TotalChildren  YearlyIncome  BikeBuyer  AvgMonthSpend  \n",
              "0              1         81916          1          50.97  \n",
              "1              2         81076          1          53.11  \n",
              "2              0         86387          1          54.08  \n",
              "3              2         61481          1          56.93  \n",
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              "      <td>United Kingdom</td>\n",
              "      <td>1977-10-18</td>\n",
              "      <td>Partial College</td>\n",
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          "metadata": {},
          "execution_count": 126
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    },
    {
      "cell_type": "markdown",
      "source": [
        "**Calculating Age from DOB**"
      ],
      "metadata": {
        "id": "kjyhZeE00NUJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datetime import datetime\n",
        "from datetime import date\n",
        "def calculate_age(born):\n",
        "    born = datetime.strptime(born, \"%Y-%m-%d\").date()\n",
        "    today = date.today()\n",
        "    return today.year - born.year - ((today.month, today.day) < (born.month, born.day))\n",
        "\n",
        "df['age'] = df['BirthDate'].apply(calculate_age)\n",
        "df = df.drop('BirthDate',axis = 1)\n",
        "df.head()"
      ],
      "metadata": {
        "id": "u2fMgf2B-On8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "outputId": "5bdcdae5-6909-4791-b483-e056494f53fd"
      },
      "execution_count": null,
      "outputs": [
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            "text/plain": [
              "  CountryRegionName        Education      Occupation Gender MaritalStatus  \\\n",
              "0         Australia        Bachelors        Clerical      M             M   \n",
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              "\n",
              "   HomeOwnerFlag  NumberCarsOwned  NumberChildrenAtHome  TotalChildren  \\\n",
              "0              1                3                     0              1   \n",
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              "   YearlyIncome  BikeBuyer  AvgMonthSpend  age  \n",
              "0         81916          1          50.97   34  \n",
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              "2         86387          1          54.08   36  \n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Checking for NULL values**"
      ],
      "metadata": {
        "id": "pX8SVPbV0t2q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OJzv1pL9-zza",
        "outputId": "7ba99449-4006-41fb-e646-cb81c5536144"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CountryRegionName       0\n",
              "Education               0\n",
              "Occupation              0\n",
              "Gender                  0\n",
              "MaritalStatus           0\n",
              "HomeOwnerFlag           0\n",
              "NumberCarsOwned         0\n",
              "NumberChildrenAtHome    0\n",
              "TotalChildren           0\n",
              "YearlyIncome            0\n",
              "BikeBuyer               0\n",
              "AvgMonthSpend           0\n",
              "age                     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Converting into numerical form for further processing**\n",
        "\n",
        "*   **One Hot Encoding**\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "7FgpQFyw0ynx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Genderr = pd.get_dummies(df.Gender)\n",
        "Genderr = Genderr.drop('F',axis = 1)\n",
        "df = pd.concat([df,Genderr],axis = 'columns')\n",
        "df = df.drop('Gender',axis = 'columns')\n",
        "df.rename(columns = {'M':'Male'},inplace = True)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "B8hNJKKxid9Q",
        "outputId": "43c77916-1077-41a7-9b07-3a6419c063ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "  CountryRegionName        Education      Occupation MaritalStatus  \\\n",
              "0         Australia        Bachelors        Clerical             M   \n",
              "1            Canada  Partial College        Clerical             M   \n",
              "2     United States        Bachelors        Clerical             S   \n",
              "3    United Kingdom  Partial College  Skilled Manual             M   \n",
              "4           Germany  Partial College  Skilled Manual             S   \n",
              "\n",
              "   HomeOwnerFlag  NumberCarsOwned  NumberChildrenAtHome  TotalChildren  \\\n",
              "0              1                3                     0              1   \n",
              "1              1                2                     1              2   \n",
              "2              0                3                     0              0   \n",
              "3              1                2                     1              2   \n",
              "4              1                1                     0              0   \n",
              "\n",
              "   YearlyIncome  BikeBuyer  AvgMonthSpend  age  Male  \n",
              "0         81916          1          50.97   34     1  \n",
              "1         81076          1          53.11   50     1  \n",
              "2         86387          1          54.08   36     0  \n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "Status = pd.get_dummies(df.MaritalStatus,drop_first=True)\n",
        "df = pd.concat([df,Status],axis = 'columns')\n",
        "df = df.drop('MaritalStatus',axis = 1)\n",
        "df.head()"
      ],
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        "id": "0ndirIMqktda",
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      "execution_count": null,
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              "  CountryRegionName        Education      Occupation  HomeOwnerFlag  \\\n",
              "0         Australia        Bachelors        Clerical              1   \n",
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              "\n",
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              "0                3                     0              1         81916   \n",
              "1                2                     1              2         81076   \n",
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              "3                2                     1              2         61481   \n",
              "4                1                     0              0         51804   \n",
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              "0          1          50.97   34     1  0  \n",
              "1          1          53.11   50     1  0  \n",
              "2          1          54.08   36     0  1  \n",
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    {
      "cell_type": "markdown",
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        "**Mapping the Education with labels More the education More is the weightage Similar with Occupation**"
      ],
      "metadata": {
        "id": "G-_W2PSm1EbB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Occupation']=df['Occupation'].map({'Manual':1,'Skilled Manual':2,'Clerical':3,'Management':4,'Professional':5})\n",
        "df['Education']=df['Education'].map({'Partial High School':1,'High School':2,'Partial College':3,'Bachelors':4,'Graduate Degree':5})\n",
        "df.head()"
      ],
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              "  CountryRegionName  Education  Occupation  HomeOwnerFlag  NumberCarsOwned  \\\n",
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              "2     United States          4           3              0                3   \n",
              "3    United Kingdom          3           2              1                2   \n",
              "4           Germany          3           2              1                1   \n",
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              "0                     0              1         81916          1   \n",
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              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Germany</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "          const element = document.querySelector('#df-48349a06-f99f-4984-9405-3301c3d29af7');\n",
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              "                                                     [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",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**PERFORMING ONE HOT ENCODING**"
      ],
      "metadata": {
        "id": "56w4StVc1gG0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "CountryRegionName = pd.get_dummies(df.CountryRegionName,drop_first=True)\n",
        "df = pd.concat([df,CountryRegionName],axis = 'columns')\n",
        "df = df.drop('CountryRegionName',axis = 1)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "7GAqS6IPAkYB",
        "outputId": "60bfead9-7f92-4e51-d280-f3bb9e08a384"
      },
      "execution_count": null,
      "outputs": [
        {
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            "text/plain": [
              "   Education  Occupation  HomeOwnerFlag  NumberCarsOwned  \\\n",
              "0          4           3              1                3   \n",
              "1          3           3              1                2   \n",
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              "   NumberChildrenAtHome  TotalChildren  YearlyIncome  BikeBuyer  \\\n",
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              "3                     1              2         61481          1   \n",
              "4                     0              0         51804          1   \n",
              "\n",
              "   AvgMonthSpend  age  Male  S  Canada  France  Germany  United Kingdom  \\\n",
              "0          50.97   34     1  0       0       0        0               0   \n",
              "1          53.11   50     1  0       1       0        0               0   \n",
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              "\n",
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              "          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",
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              "  "
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          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['4 bins'] = pd.qcut(df['age'], q=4)\n",
        "df.head()"
      ],
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          "height": 374
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        "id": "d__AOkx_9T4i",
        "outputId": "2145399a-beea-4e73-f414-9e9d6a5746dd"
      },
      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Education  Occupation  HomeOwnerFlag  NumberCarsOwned  \\\n",
              "0          4           3              1                3   \n",
              "1          3           3              1                2   \n",
              "2          4           3              0                3   \n",
              "3          3           2              1                2   \n",
              "4          3           2              1                1   \n",
              "\n",
              "   NumberChildrenAtHome  TotalChildren  YearlyIncome  BikeBuyer  \\\n",
              "0                     0              1         81916          1   \n",
              "1                     1              2         81076          1   \n",
              "2                     0              0         86387          1   \n",
              "3                     1              2         61481          1   \n",
              "4                     0              0         51804          1   \n",
              "\n",
              "   AvgMonthSpend  age  Male  S  Canada  France  Germany  United Kingdom  \\\n",
              "0          50.97   34     1  0       0       0        0               0   \n",
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              "4          55.41   47     1  1       0       0        1               0   \n",
              "\n",
              "   United States        4 bins  \n",
              "0              0  (31.0, 39.0]  \n",
              "1              0  (47.0, 92.0]  \n",
              "2              1  (31.0, 39.0]  \n",
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              "4              0  (39.0, 47.0]  "
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            ]
          },
          "metadata": {},
          "execution_count": 145
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**SCALING AND NORMALIZING THE DATA**"
      ],
      "metadata": {
        "id": "Tj_5vWPd1Yeh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler=MinMaxScaler()\n",
        "scaled=scaler.fit_transform(df[['YearlyIncome','age','AvgMonthSpend']])\n",
        "df['YearlyIncome_scaled']=scaled[:,0]\n",
        "df['Age_scaled']=scaled[:,1]\n",
        "df['AvgMonthSpend_Scaled']=scaled[:,2]\n",
        "df.drop(['YearlyIncome','age'],axis=1,inplace=True)\n",
        "df = df.drop('AvgMonthSpend',axis=1)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 678
        },
        "id": "OBh_VCFfzhsW",
        "outputId": "226a808d-f960-4716-ce25-2ac47f235cae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Education  Occupation  HomeOwnerFlag  NumberCarsOwned  \\\n",
              "0              4           3              1                3   \n",
              "1              3           3              1                2   \n",
              "2              4           3              0                3   \n",
              "3              3           2              1                2   \n",
              "4              3           2              1                1   \n",
              "...          ...         ...            ...              ...   \n",
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              "18359          2           3              0                0   \n",
              "18360          5           5              1                2   \n",
              "\n",
              "       NumberChildrenAtHome  TotalChildren  BikeBuyer  Male  S  Canada  \\\n",
              "0                         0              1          1     1  0       0   \n",
              "1                         1              2          1     1  0       1   \n",
              "2                         0              0          1     0  1       0   \n",
              "3                         1              2          1     1  0       0   \n",
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              "18360                     0              0          1     0  0       0   \n",
              "\n",
              "       France  Germany  United Kingdom  United States          4 bins  \\\n",
              "0           0        0               0              0    (31.0, 39.0]   \n",
              "1           0        0               0              0    (47.0, 92.0]   \n",
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              "3           0        0               1              0    (39.0, 47.0]   \n",
              "4           0        1               0              0    (39.0, 47.0]   \n",
              "...       ...      ...             ...            ...             ...   \n",
              "18356       0        0               0              1  (20.999, 31.0]   \n",
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              "18359       0        0               0              0  (20.999, 31.0]   \n",
              "18360       0        0               0              1    (31.0, 39.0]   \n",
              "\n",
              "       YearlyIncome_scaled  Age_scaled  AvgMonthSpend_Scaled  \n",
              "0                 0.496842    0.183099              0.324210  \n",
              "1                 0.489453    0.408451              0.425201  \n",
              "2                 0.536172    0.211268              0.470977  \n",
              "3                 0.317083    0.323944              0.605474  \n",
              "4                 0.231958    0.366197              0.533742  \n",
              "...                    ...         ...                   ...  \n",
              "18356             0.242065    0.140845              0.190656  \n",
              "18357             0.312781    0.112676              0.289287  \n",
              "18358             0.232442    0.239437              0.257669  \n",
              "18359             0.543121    0.084507              0.345446  \n",
              "18360             0.980128    0.211268              0.385559  \n",
              "\n",
              "[18355 rows x 18 columns]"
            ],
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              "      <th></th>\n",
              "      <th>Education</th>\n",
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              "      <td>1</td>\n",
              "      <td>(31.0, 39.0]</td>\n",
              "      <td>0.980128</td>\n",
              "      <td>0.211268</td>\n",
              "      <td>0.385559</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>18355 rows × 18 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-428841ed-06d9-402c-bb26-4ae39fb22102')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\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",
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              "\n",
              "    .colab-df-convert: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-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert: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",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-428841ed-06d9-402c-bb26-4ae39fb22102 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-428841ed-06d9-402c-bb26-4ae39fb22102');\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",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**COSINE**"
      ],
      "metadata": {
        "id": "tNvzEuKh2kFV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy.linalg import norm\n",
        "A = df['YearlyIncome_scaled']\n",
        "B = df['Education']\n",
        "cosine = np.dot(A,B)/(norm(A)*norm(B))\n",
        "print(\"Cosine Similarity between YearlyIncome_Scaled and Education\", cosine)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gNrKp-Gx-U9u",
        "outputId": "e67e993f-77ec-48af-9329-9dfaec17c0d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cosine Similarity between YearlyIncome_Scaled and Education 0.87552000634723\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.spatial import distance\n",
        "distance.cosine(df['Education'].values,df['YearlyIncome_scaled'].values)\n",
        "distance.cosine(df['Education'].values,df['AvgMonthSpend_Scaled'].values)\n",
        "distance.cosine(df['TotalChildren'].values,df['BikeBuyer'].values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MhYxUjwWHTBa",
        "outputId": "57d1506d-d56d-440f-92f6-e13faa6daff9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.39451704519752906"
            ]
          },
          "metadata": {},
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**JACCARD SCORE**"
      ],
      "metadata": {
        "id": "R44bIEua3rsB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import jaccard_score\n",
        "print(jaccard_score(df['Canada'], df['Male']))\n",
        "print(jaccard_score(df['Germany'], df['Male']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "89OO33Vc3dlr",
        "outputId": "e7730db2-9b4e-4f4a-a4fb-e9b9adcaf5de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.07924340467894475\n",
            "0.08854525755934206\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**PEARSON CORRELATION**"
      ],
      "metadata": {
        "id": "NSTDbjkn4xuE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(np.corrcoef(df['Education'], df['YearlyIncome_scaled']))\n",
        "print(np.corrcoef(df['BikeBuyer'], df['TotalChildren']))\n",
        "print(np.corrcoef(df['BikeBuyer'], df['Age_scaled']))\n",
        "print(np.corrcoef(df['Age_scaled'], df['TotalChildren']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0O4Qd2tPHTXX",
        "outputId": "32ed080d-718d-4484-c6a3-68ef78d8d73a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1.         0.46768437]\n",
            " [0.46768437 1.        ]]\n",
            "[[1.         0.20962836]\n",
            " [0.20962836 1.        ]]\n",
            "[[1.         0.09735391]\n",
            " [0.09735391 1.        ]]\n",
            "[[1.         0.54791876]\n",
            " [0.54791876 1.        ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " --------------------**SUBMITTED BY LAKSHAY SINGLA** --------------------           "
      ],
      "metadata": {
        "id": "VSmb8Nv340Z5"
      }
    }
  ]
}