Market_Segment-analytics.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Market Segmentation",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Rsg5Zzp8r5v"
      },
      "source": [
        "* Attributes Description\n",
        "\n",
        "Variable | Description\n",
        "--------------|-----------\n",
        "Variable |\tDefinition\n",
        "ID |\tUnique ID\n",
        "Gender|\tGender of the customer\n",
        "Ever_Married\t|Marital status of the customer\n",
        "Age\t|Age of the customer\n",
        "Graduated|\tIs the customer a graduate?\n",
        "Profession|\tProfession of the customer\n",
        "Work_Experience\t|Work Experience in years\n",
        "Spending_Score|\tSpending score of the customer\n",
        "Family_Size|\tNumber of family members for the customer (including the customer)\n",
        "Var_1\t|Anonymised Category for the customer\n",
        "\n",
        "* Based on the attributes group Segment of the customer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OraqCcQC9eLy"
      },
      "source": [
        "* Context\n",
        "\n",
        "An automobile company has plans to enter new markets with their existing products (P1, P2, P3, P4 and P5). After intensive market research, they’ve deduced that the behavior of new market is similar to their existing market.\n",
        "\n",
        "---------------------------------------------------------\n",
        "\n",
        "* Content\n",
        "\n",
        "In their existing market, the sales team has to classify all customers into different number of segments. Then, they can perform segmented outreach and communication for different segment of customers. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jM6APaK_vdkM",
        "outputId": "a6ea2823-f56e-4e28-9901-69296a1b39e8"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l5fBDmmed0iv"
      },
      "source": [
        "### Importing Libraries & Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dsj4LRjZveub"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y_h-Zh0cwDnK"
      },
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/Project 2/Train_customer.csv')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "1SbvXT87wGv4",
        "outputId": "39089918-8d12-4a98-9c1f-edbb9ee1804f"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>ID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Ever_Married</th>\n",
              "      <th>Age</th>\n",
              "      <th>Graduated</th>\n",
              "      <th>Profession</th>\n",
              "      <th>Work_Experience</th>\n",
              "      <th>Spending_Score</th>\n",
              "      <th>Family_Size</th>\n",
              "      <th>Var_1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>462809</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>22</td>\n",
              "      <td>No</td>\n",
              "      <td>Healthcare</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Low</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Cat_4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>462643</td>\n",
              "      <td>Female</td>\n",
              "      <td>Yes</td>\n",
              "      <td>38</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Engineer</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Average</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Cat_4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>466315</td>\n",
              "      <td>Female</td>\n",
              "      <td>Yes</td>\n",
              "      <td>67</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Engineer</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Low</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Cat_6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>461735</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>67</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Lawyer</td>\n",
              "      <td>0.0</td>\n",
              "      <td>High</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Cat_6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>462669</td>\n",
              "      <td>Female</td>\n",
              "      <td>Yes</td>\n",
              "      <td>40</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Entertainment</td>\n",
              "      <td>NaN</td>\n",
              "      <td>High</td>\n",
              "      <td>6.0</td>\n",
              "      <td>Cat_6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       ID  Gender Ever_Married  ...  Spending_Score Family_Size  Var_1\n",
              "0  462809    Male           No  ...             Low         4.0  Cat_4\n",
              "1  462643  Female          Yes  ...         Average         3.0  Cat_4\n",
              "2  466315  Female          Yes  ...             Low         1.0  Cat_6\n",
              "3  461735    Male          Yes  ...            High         2.0  Cat_6\n",
              "4  462669  Female          Yes  ...            High         6.0  Cat_6\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G1YZsBzceCZ3"
      },
      "source": [
        "### Data Wrangling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_DnlAbKYi-Ls"
      },
      "source": [
        "Geeting to know about the data types and other relavant information from data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6N-EY4HNwN6w",
        "outputId": "80c10769-e33f-4de9-b10f-7f40f6f9a1dc"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 9125 entries, 0 to 9124\n",
            "Data columns (total 10 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   ID               9125 non-null   int64  \n",
            " 1   Gender           9125 non-null   object \n",
            " 2   Ever_Married     8970 non-null   object \n",
            " 3   Age              9125 non-null   int64  \n",
            " 4   Graduated        9041 non-null   object \n",
            " 5   Profession       8982 non-null   object \n",
            " 6   Work_Experience  8170 non-null   float64\n",
            " 7   Spending_Score   9125 non-null   object \n",
            " 8   Family_Size      8733 non-null   float64\n",
            " 9   Var_1            9034 non-null   object \n",
            "dtypes: float64(2), int64(2), object(6)\n",
            "memory usage: 713.0+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IRG7eb_1jF0p"
      },
      "source": [
        "Basic Cleaning by dropping the ID column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5B7YeKaP7iTC"
      },
      "source": [
        "df = df.drop(['ID'], axis = 1)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ossl-H28UNB",
        "outputId": "92ecbfe8-3d41-483b-fae7-27de2db18d4c"
      },
      "source": [
        "cat_col = []\n",
        "for x in df.dtypes.index:\n",
        "    if df.dtypes[x]== 'object':\n",
        "        cat_col.append(x)\n",
        "    \n",
        "    \n",
        "for x in cat_col:\n",
        "    print(x)\n",
        "    print(\"-----------------\")\n",
        "    print(df[x].value_counts())\n",
        "    print('-----------------')\n",
        " "
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Gender\n",
            "-----------------\n",
            "Male      5003\n",
            "Female    4122\n",
            "Name: Gender, dtype: int64\n",
            "-----------------\n",
            "Ever_Married\n",
            "-----------------\n",
            "Yes    5213\n",
            "No     3757\n",
            "Name: Ever_Married, dtype: int64\n",
            "-----------------\n",
            "Graduated\n",
            "-----------------\n",
            "Yes    5642\n",
            "No     3399\n",
            "Name: Graduated, dtype: int64\n",
            "-----------------\n",
            "Profession\n",
            "-----------------\n",
            "Artist           2854\n",
            "Healthcare       1500\n",
            "Entertainment    1071\n",
            "Doctor            786\n",
            "Engineer          769\n",
            "Lawyer            727\n",
            "Executive         658\n",
            "Marketing         345\n",
            "Homemaker         272\n",
            "Name: Profession, dtype: int64\n",
            "-----------------\n",
            "Spending_Score\n",
            "-----------------\n",
            "Low        5563\n",
            "Average    2193\n",
            "High       1369\n",
            "Name: Spending_Score, dtype: int64\n",
            "-----------------\n",
            "Var_1\n",
            "-----------------\n",
            "Cat_6    5938\n",
            "Cat_4    1190\n",
            "Cat_3     963\n",
            "Cat_2     469\n",
            "Cat_7     232\n",
            "Cat_1     147\n",
            "Cat_5      95\n",
            "Name: Var_1, dtype: int64\n",
            "-----------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HMXucikt-W7O",
        "outputId": "b6385645-9976-46ad-f4da-f2548af47250"
      },
      "source": [
        "df.nunique()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender              2\n",
              "Ever_Married        2\n",
              "Age                67\n",
              "Graduated           2\n",
              "Profession          9\n",
              "Work_Experience    15\n",
              "Spending_Score      3\n",
              "Family_Size         9\n",
              "Var_1               7\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MNwZ5bPFjMcF"
      },
      "source": [
        "Statistical Discription of Numerical Variables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "FcQls6CS-ypp",
        "outputId": "1afa6b0c-3cc8-4460-da2c-f2c1ec6e442e"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Age</th>\n",
              "      <th>Work_Experience</th>\n",
              "      <th>Family_Size</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>9125.000000</td>\n",
              "      <td>8170.000000</td>\n",
              "      <td>8733.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>43.566795</td>\n",
              "      <td>2.635618</td>\n",
              "      <td>2.841292</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>16.798711</td>\n",
              "      <td>3.405982</td>\n",
              "      <td>1.535801</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>30.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>41.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>53.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>89.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>9.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Age  Work_Experience  Family_Size\n",
              "count  9125.000000      8170.000000  8733.000000\n",
              "mean     43.566795         2.635618     2.841292\n",
              "std      16.798711         3.405982     1.535801\n",
              "min      18.000000         0.000000     1.000000\n",
              "25%      30.000000         0.000000     2.000000\n",
              "50%      41.000000         1.000000     2.000000\n",
              "75%      53.000000         4.000000     4.000000\n",
              "max      89.000000        14.000000     9.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xHpY2RRVeNNn"
      },
      "source": [
        "## Missing Values Treatment"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2VHu1Ltqjgjb"
      },
      "source": [
        "If the missing data is Present in -\n",
        "* Continuous variable feature - Fill Median or mean based on the distriution of feature variale\n",
        "* Categorical Variable feature - Fill mode of the column in place of missing data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "bT6yrpGY_loa",
        "outputId": "cbd63baa-3373-45b8-b35d-7a6094d4f3e8"
      },
      "source": [
        "sns.heatmap(df.isnull())"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ef9c4da10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fVyB2m_g_qEQ",
        "outputId": "272127a0-7225-4554-c0c7-0b5b233eac19"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender               0\n",
              "Ever_Married       155\n",
              "Age                  0\n",
              "Graduated           84\n",
              "Profession         143\n",
              "Work_Experience    955\n",
              "Spending_Score       0\n",
              "Family_Size        392\n",
              "Var_1               91\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wn2YTbMLA9MW",
        "outputId": "a5877869-353c-40b9-c581-db499647148d"
      },
      "source": [
        "df['Work_Experience'].mode()\n"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "7XonMJoTBMI2",
        "outputId": "48ecda00-8a88-4e5e-f3df-e0b5631ff1c5"
      },
      "source": [
        "sns.distplot(df['Work_Experience'])"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ef0e0f450>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aOKXXw5MAUjh"
      },
      "source": [
        "df['Ever_Married'] = df['Ever_Married'].fillna(df['Ever_Married'].mode()[0])\n",
        "df['Graduated']= df['Graduated'].fillna(df['Graduated'].mode()[0])\n",
        "df['Profession'] = df['Profession'].fillna(df['Profession'].mode()[0])\n",
        "df['Work_Experience']= df['Work_Experience'].fillna(df['Work_Experience'].mode()[0])\n",
        "df['Family_Size']= df['Family_Size'].fillna(df['Family_Size'].mode()[0])\n",
        "df['Var_1'] = df['Var_1'].fillna(df['Var_1'].mode()[0])\n"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "8sxIn4zFCEvA",
        "outputId": "d524a07f-8a4d-4102-ea98-de0abd2c8e7a"
      },
      "source": [
        "sns.heatmap(df.isnull())"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8eee2669d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rh2v2j22CIOW",
        "outputId": "bd88e6d2-d27f-4d6b-a983-2103282e1a74"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 9125 entries, 0 to 9124\n",
            "Data columns (total 9 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   Gender           9125 non-null   object \n",
            " 1   Ever_Married     9125 non-null   object \n",
            " 2   Age              9125 non-null   int64  \n",
            " 3   Graduated        9125 non-null   object \n",
            " 4   Profession       9125 non-null   object \n",
            " 5   Work_Experience  9125 non-null   float64\n",
            " 6   Spending_Score   9125 non-null   object \n",
            " 7   Family_Size      9125 non-null   float64\n",
            " 8   Var_1            9125 non-null   object \n",
            "dtypes: float64(2), int64(1), object(6)\n",
            "memory usage: 641.7+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_mKq5NkyCNud"
      },
      "source": [
        "# 2 variable - Numerical (Work Experience & Age)\n",
        "# remaining variable - Categorical\n"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_U-8T2ZQea7m"
      },
      "source": [
        "## Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yFqclGM5kIgD"
      },
      "source": [
        "**1. Variable Identification**\n",
        "* Categorical \n",
        "     - Ordinal\n",
        "     - Nominal\n",
        "* Continuous \n",
        "-----------------------------------\n",
        "**2. Univariate Analysis**\n",
        "* For Categorical Variable - \n",
        "    - Count of data present in the dataset for particular variable\n",
        "* For Continuous Variable - \n",
        "    - Find the Distribution of feature using Histogram\n",
        "    - Outlier detection using Box Plot\n",
        "------------------------\n",
        "**3. Bi-Variate Analysis**\n",
        "* Categorical - Continuous Variables ---> Bar Graph\n",
        "* Continuous - Continuous Variables ---> Scatter Plot to see relationship\n",
        "--------------------------------------------\n",
        "**4. Outlier Detection**\n",
        "* Box plot are the best statistical Measure for Outlier detection\n",
        "\n",
        "5. Missing value Treatment\n",
        "* Depending upon the dataset the missing value treatment can be done before EDA or after EDA process\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SfsoYN6zehAQ"
      },
      "source": [
        "### Univariate analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "WxDrTNOmIhlu",
        "outputId": "70dafe75-b112-458d-c025-813c7d65e563"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Gender</th>\n",
              "      <th>Ever_Married</th>\n",
              "      <th>Age</th>\n",
              "      <th>Graduated</th>\n",
              "      <th>Profession</th>\n",
              "      <th>Work_Experience</th>\n",
              "      <th>Spending_Score</th>\n",
              "      <th>Family_Size</th>\n",
              "      <th>Var_1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>22</td>\n",
              "      <td>No</td>\n",
              "      <td>Healthcare</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Low</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Cat_4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Female</td>\n",
              "      <td>Yes</td>\n",
              "      <td>38</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Engineer</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Average</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Cat_4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Female</td>\n",
              "      <td>Yes</td>\n",
              "      <td>67</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Engineer</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Low</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Cat_6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>67</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Lawyer</td>\n",
              "      <td>0.0</td>\n",
              "      <td>High</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Cat_6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Female</td>\n",
              "      <td>Yes</td>\n",
              "      <td>40</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Entertainment</td>\n",
              "      <td>1.0</td>\n",
              "      <td>High</td>\n",
              "      <td>6.0</td>\n",
              "      <td>Cat_6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Gender Ever_Married  Age  ... Spending_Score Family_Size  Var_1\n",
              "0    Male           No   22  ...            Low         4.0  Cat_4\n",
              "1  Female          Yes   38  ...        Average         3.0  Cat_4\n",
              "2  Female          Yes   67  ...            Low         1.0  Cat_6\n",
              "3    Male          Yes   67  ...           High         2.0  Cat_6\n",
              "4  Female          Yes   40  ...           High         6.0  Cat_6\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "V6TG5lYeI_qd",
        "outputId": "5f6a4270-05a4-41ae-8058-75740c689784"
      },
      "source": [
        "sns.countplot(df['Gender'], hue = df['Ever_Married'])"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8eee17b190>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "id": "cosU_bHOJL-1",
        "outputId": "ab2d0d6e-02ee-4f6f-f151-8485353b6d3b"
      },
      "source": [
        "plt.figure(figsize = (10,6))\n",
        "sns.countplot(df['Profession'], hue = df['Graduated'])"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8eee17b950>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "r48J99ACKf0E",
        "outputId": "1b85b883-1a1a-4033-ca38-9e3363b2da34"
      },
      "source": [
        "sns.countplot(df['Spending_Score'])"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8eee1b4110>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gkgOSPBZYBlw2xTVJ0pwxI05DVdXmJH8IXAHsCpxfVTdNcVlTaU6cbpvFfP9mrjn73s2IC9ySpKk1U05DSZKmkGEhSeoyLKaZJPdPdQ16pCQnJKkkz5rqWrTzbfn/XZKTk3yoTf9Bkjd21n+o/2xlWEgTcxLw9fb8qLThazRDVNVHq+qCqa5jqhkWM0CSg5NcneSGJJckmZ/kaUnWtOXPbZ9692vz/5TkCVNb9eyR5EnAC4FTgGVtUMvPDC1/cZIvtOmlSb6Z5Lokn2nrkuSOJO9Pch3wmiS/l+TaJN9J8rmx9yvJb7T3+sYk7x3+xJvkT9s6NyR592T+G8xlSd6V5E/a9KHt3//bST6Q5LtDXZ+e5PIktyX5qykqd2QMi5nhAuDtVfVbwI3A6VW1HtgtyZOBFwGrgRcl2R9YX1U/m7pyZ53jgcur6nvAvcAm4PAkT2zLXwdcmGQv4M+Ao6vqEAbvyR8Pbefeqjqkqi4EPl9Vh1bVc4FbGAQRwFnAWVX1HOCusRWTLAUOZDBO2sHA85P8qxHt71z0+BYA307ybeCMcfp9HPj9qjoYeHCLZQcz+G/hOcDrkizacuWZzLCY5pI8Bdijqv6+Na0Exv5I/ANwZJv/i/b8IuBrk13nLHcSg8Erac+vAS4HXplkHvBy4FLgCAajIn+j/cFZDuw/tJ2Lhqb/RZKvJbkR+F3g2a39BcDYUcunhvovbY/rgeuAZzEID+0cP6+qg8cewJ9v2SHJHsDuVfXN1vSpLbpcWVX3VdUvgJt5+Hs/482IL+VpXF9lEA77M/hj9XaggL+byqJmkyR7Ai8FnpOkGHwptIA3AacCG4HVVfXTJAFWVdV41zX+39D0J4ATquo7SU4GXtwrBfhPVfW3O7ovGrkHhqYfZJb9ffXIYpqrqvuATUle1JreAIwdZXwNeD1wW1X9isEfruMYXIjVznEi8Mmq2r+qFlfVIuD7wGbgEOD3+PVRx9XAkUmeAZDkiUl+c5zt7g7cneQxDI4sxlwN/E6bXjbUfgXw5qFrIPsmedqj3z1NVFX9GPhpksNb07Jt9Z9tZlXyzRJPSHLX0PxfMzid8dF2EfR2Bp9qqao72qfZr7a+XwcWVtWmySx4ljsJeP8WbZ9j8IfiC8DJDN4fqmpDO0r4dJLHtb5/BnxvK9v9j8C3gA3teffW/jbgvyV5J4NTXfe1bX8pyT8Hvjl4y7mfwQeF9Y96D7U9TgH+a5JfMfjQdt8U1zNpHO5DmkbaB4KfV1UlWQacVFX+0Nc0keRJVXV/mz4N2Keq3jrFZU0Kjyyk6eX5wIfaEeOPgTdPcT16uJcneQeDv513MjiynBM8spAkdXmBW5LUZVhIkroMC0lSl2EhSeoyLDQrJXlnkpuGBn07vL/WDr/W8EBzZyQ5eidvf9L2RRqPt85q1knyAuAVwCFV9UAb4O+xk/HaVfWIMYUejVHsS5J5VbV5pxSoOcMjC81G+wD3VNUDAFV1T1X93zZM+F+14b+vGRqWY0EbJvza9jiytb8ryflJvpLk9iR/NPYC7dP+95J8HXjmUPsnkpzYpu9I8u42XPmNaT+c1F5vVTta+FiSO1sITHhf2nYOTfIPbZjza5LsnmS3JB9vr3d9kpe0vicnuSzJVcCVbSiS89t61yfxi3/aJsNCs9GXgEXtj/lHkvz20LL72vDfHwL+prWdBZxZVYcyGJfpY0P9nwUcw2Bo8NOTPCbJ8xkM93Ewg7G4Dt1GLfe04crPAf6ktZ0OXFVVzwY+C+y3vfuS5LEMRrF9axvm/Gjg5wwGN6y2jycBK5Ps1rZ1CHBiVf028M5Ww2HAS4APDA25Lj2Cp6E061TV/e0P+osY/CG8qA3NAPDpoecz2/TRwEFtzCWAJ48N2Af8XftU/0CS9cDebbuXjP1mSJLLtlHO59vzGuDVbfqFwL9ptV6eZNyxvLaxL2uAu6vq2tbvJ62WFwJnt7Z/THInMDaY4aqq2timlwKvGrvWAuzGILRu2ca+aA4zLDQrVdWDwFeAr7TfjFg+tmi4W3veBTii/Q7BQ1p4PNphp8fW3+Ehq8fZlzU7sKnhIdID/E5V3bojNWnu8TSUZp0kz0wy/MNABzMYxwcGv2Q29jz2IzZfAt4ytP7BnZf4KnBCkscn2R145XaW+A3gte21lgLzx+u4jX25FdgnyaGt3+4Z/BDT12hDnmcwPPp+re+WrgDe0sagIsnztnMfNMd4ZKHZ6EnA2Rn8stlmYC2wgsFdRfOT3MDgE//YjxT9EfDh1j6PQRj8wXgbr6rrklwEfIfBEOHXbmd972YwjPkbGATWD4Gfbs++VNUvk7yuLXs8g+sVRwMfAc5pRyCbgZPbXVRbbvc9DK7Z3JBkFwa/0fGK7dwPzSEOJKg5I8kdwJKqumeK63gc8GBVbW63xp7TfspTmrY8spAm337Axe0T/S8Z/NqeNK15ZCFNA0meCly5lUVHVdW9k12PtCXDQpLU5d1QkqQuw0KS1GVYSJK6DAtJUtf/B/1t4UFodmxHAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "GdZHNi9POuSt",
        "outputId": "e445b650-a7dc-4767-c6e9-ec3da4c4b389"
      },
      "source": [
        "# Profession\n",
        "\n",
        "print(\"Pie plot of Profession\")\n",
        "print('------------------------------------------------------------\\n')\n",
        "\n",
        "\n",
        "plt.subplot(1,1,1)  \n",
        "label = df['Profession'].value_counts().index\n",
        "value = df['Profession'].value_counts().values\n",
        "plt.pie(value, labels = label,autopct='%1.2f%%', shadow=True, explode=None)\n",
        "plt.title(\"Profession\")\n",
        "\n",
        "plt.tight_layout(pad=0.4)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Pie plot of Profession\n",
            "------------------------------------------------------------\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "US7qecQPK1s-",
        "outputId": "49ac5eeb-d9f7-42c8-a392-341eb853bab6"
      },
      "source": [
        "\n",
        "sns.displot(df['Age'], bins = 15)\n"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f8eedfe1150>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "sUTvPcLYLDK9",
        "outputId": "2eadc307-629e-4c6d-e28f-c525bde6065e"
      },
      "source": [
        "sns.displot(df['Age'], kind ='kde')"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f8eede9fe10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "fD2rqPW0kDx9",
        "outputId": "9804cba8-69ca-429d-8274-587fa6bdf9e1"
      },
      "source": [
        "sns.boxplot(df['Age'])"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8eede77c10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEGCAYAAABbzE8LAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAALlUlEQVR4nO3dX4xm9V3H8c+XXdsuVEsXEHHBbps1EERLC6lgscHWP23TtEYxgRjTC2NvzLoYE6MxmjTGiyZGJZtoQkQvqlbTf2oIaYsVjHpBs9uCAgvtaGnLpsAWLKiLrcDPi3MWxwWzzO7OfM/svl7JZOY5z7M8X86eec95fjtzpsYYAWDjndE9AMDpSoABmggwQBMBBmgiwABNtq7lweeee+7YuXPnOo0CcGrav3//18YY5x29fU0B3rlzZ/bt23fypgI4DVTVl15suyUIgCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmqzpd8KR7N27NysrK91jHLeDBw8mSXbs2NE8ycmxa9eu7N69u3sMOC4CvEYrKyu5+94DefbM7d2jHJcth59Mkjzyjc3/V7/l8BPdI8AJ2fyfhQ2ePXN7nr7knd1jHJdtD9yWJJt2/tWO/L/AZmUNGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigyYYEeO/evdm7d+9GPBXAmnT2aetGPMnKyspGPA3AmnX2yRIEQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJlu7BwBYkmuvvfb5j++8884X3D6ZnAEDNBFggNnqs92XcvtEbcgSxMGDB/P0009nz549G/F062plZSVnfHN0j0GSM/7rqays/PspcVzRZ2VlJdu2bWt57mOeAVfV+6pqX1XtO3To0EbMBHBaOOYZ8Bjj5iQ3J8mVV155XKd+O3bsSJLcdNNNx/PHF2XPnj3Z/6+Pdo9Bkude8W3Z9brzT4njij6dr6CsAQM0EWCA2dHfZnas2ydKgAGa+EEMgFXW+6x3NWfAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmmzdiCfZtWvXRjwNwJp19mlDArx79+6NeBqANevskyUIgCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzTZ2j3AZrTl8BPZ9sBt3WMcly2HH0+STTv/alsOP5Hk/O4x4LgJ8Brt2rWre4QTcvDgM0mSHTtOhXCdv+n/Pji9CfAa7d69u3sE4BRhDRigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQpMYYL/3BVYeSfGmdZjk3ydfW6b99spl1fZh1fZh1faxl1teMMc47euOaAryeqmrfGOPK7jleCrOuD7OuD7Ouj5MxqyUIgCYCDNBkSQG+uXuANTDr+jDr+jDr+jjhWRezBgxwulnSGTDAaUWAAZq0BLiqLqqqO6rq/qq6r6r2zNu3V9XtVfWF+f2rO+Y7atZXVNVnquqeedb3z9tfW1V3VdVKVf1FVb2se9YkqaotVfW5qrp1vr3IOZOkqh6qqn+uqrurat+8bYnHwNlV9ZGqeqCqDlTV1Qud8+J5Xx55e6qqblzirElSVb84f07dW1Ufmj/XFnm8VtWeec77qurGedsJ79euM+BnkvzSGOPSJFcl+fmqujTJryT59Bjju5N8er7d7RtJ3jrGeH2Sy5O8vaquSvKBJL87xtiV5N+S/GzjjKvtSXJg1e2lznnED40xLl/1/ZRLPAZuSvKJMcYlSV6faf8ubs4xxoPzvrw8yRVJDif5eBY4a1XtSPILSa4cY1yWZEuS67PA47WqLkvyc0nelOnv/11VtSsnY7+OMdrfkvxVkh9J8mCSC+ZtFyR5sHu2o+Y8M8lnk3x/pp+A2TpvvzrJJxcw34XzgfDWJLcmqSXOuWreh5Kce9S2RR0DSV6V5IuZ/8F6qXO+yNw/muQflzprkh1JvpJke5Kt8/H6Y0s8XpP8VJJbVt3+9SS/fDL2a/sacFXtTPKGJHclOX+M8dX5rkeSnN801v8xv6y/O8ljSW5P8i9Jvj7GeGZ+yMOZDqhuv5fpwHhuvn1OljnnESPJp6pqf1W9b962tGPgtUkOJfnjeWnnD6vqrCxvzqNdn+RD88eLm3WMcTDJbyf5cpKvJnkyyf4s83i9N8kPVtU5VXVmkncmuSgnYb+2BriqXpnko0luHGM8tfq+MX1ZWcT3yI0xnh3Ty7oLM70MuaR5pBeoqncleWyMsb97ljW4ZozxxiTvyLQM9ZbVdy7kGNia5I1J/mCM8YYk/5mjXmouZM7nzeum707y4aPvW8qs83rpezJ9gfvOJGcleXvrUP+PMcaBTEsjn0ryiSR3J3n2qMcc135tC3BVfUum+P7pGONj8+ZHq+qC+f4LMp1xLsYY4+tJ7sj00ujsqto633VhkoNtg03enOTdVfVQkj/PtAxxU5Y35/Pms6CMMR7LtFb5pizvGHg4ycNjjLvm2x/JFOSlzbnaO5J8dozx6Hx7ibP+cJIvjjEOjTH+O8nHMh3Dizxexxi3jDGuGGO8JdPa9OdzEvZr13dBVJJbkhwYY/zOqrv+Osl754/fm2ltuFVVnVdVZ88fb8u0Vn0gU4ivmx/WPusY41fHGBeOMXZmevn5t2OMn87C5jyiqs6qqm898nGmNct7s7BjYIzxSJKvVNXF86a3Jbk/C5vzKDfkf5cfkmXO+uUkV1XVmXMPjuzXpR6v3z6//64kP5Hkz3Iy9mvTovY1mU7X/ynT6fzdmdZVzsn0j0hfSPI3SbYvYAH++5J8bp713iS/MW9/XZLPJFnJ9FLv5d2zrpr52iS3LnnOea575rf7kvzavH2Jx8DlSfbNx8BfJnn1EuecZz0ryeNJXrVq21JnfX+SB+bPqw8mefmCj9e/z/QF4p4kbztZ+9WPIgM0af8uCIDTlQADNBFggCYCDNBEgAGaCDCbQlX9eFWNqlrcTyHC8RJgNosbkvzD/B5OCQLM4s3XDLkm06UJr5+3nVFVvz9fo/f2qrqtqq6b77uiqv5uvsjPJ4/8uCgsjQCzGbwn0/V4P5/k8aq6ItOPg+5McmmSn8l0fY4j1xjZm+S6McYVSf4oyW91DA3HsvXYD4F2N2S6sFAyXWjohkzH7ofHGM8leaSq7pjvvzjJZUluny4xkC2ZLncIiyPALFpVbc90ZbfvraqRKagj09XTXvSPJLlvjHH1Bo0Ix80SBEt3XZIPjjFeM8bYOca4KNNvqHgiyU/Oa8HnZ7oAUTL9loLzqur5JYmq+p6OweFYBJiluyEvPNv9aJLvyHSt3vuT/EmmXxX15Bjjm5mi/YGquifTlfZ+YOPGhZfO1dDYtKrqlWOM/6iqczJdwvDNY7p+L2wK1oDZzG6dL5b/siS/Kb5sNs6AAZpYAwZoIsAATQQYoIkAAzQRYIAm/wOBSnuCO+QYEAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nfW9Xl3em1s2"
      },
      "source": [
        "We can se from the distribution as well as box plot that there are not much outliers which needs attention"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "ABHhfyRuNsG3",
        "outputId": "dffa08d2-be5e-488d-a2ad-0ecd371ce7a9"
      },
      "source": [
        "sns.displot(df['Work_Experience'], bins = 5)\n"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f8eedde8c50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DVp14ruKep1p"
      },
      "source": [
        "### Bivariate Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "9bsd85PWOFpj",
        "outputId": "ed8d8968-9eac-4059-f983-8635121e930a"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Gender</th>\n",
              "      <th>Ever_Married</th>\n",
              "      <th>Age</th>\n",
              "      <th>Graduated</th>\n",
              "      <th>Profession</th>\n",
              "      <th>Work_Experience</th>\n",
              "      <th>Spending_Score</th>\n",
              "      <th>Family_Size</th>\n",
              "      <th>Var_1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>22</td>\n",
              "      <td>No</td>\n",
              "      <td>Healthcare</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Low</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Cat_4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Female</td>\n",
              "      <td>Yes</td>\n",
              "      <td>38</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Engineer</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Average</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Cat_4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Female</td>\n",
              "      <td>Yes</td>\n",
              "      <td>67</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Engineer</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Low</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Cat_6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>67</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Lawyer</td>\n",
              "      <td>0.0</td>\n",
              "      <td>High</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Cat_6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Female</td>\n",
              "      <td>Yes</td>\n",
              "      <td>40</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Entertainment</td>\n",
              "      <td>1.0</td>\n",
              "      <td>High</td>\n",
              "      <td>6.0</td>\n",
              "      <td>Cat_6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Gender Ever_Married  Age  ... Spending_Score Family_Size  Var_1\n",
              "0    Male           No   22  ...            Low         4.0  Cat_4\n",
              "1  Female          Yes   38  ...        Average         3.0  Cat_4\n",
              "2  Female          Yes   67  ...            Low         1.0  Cat_6\n",
              "3    Male          Yes   67  ...           High         2.0  Cat_6\n",
              "4  Female          Yes   40  ...           High         6.0  Cat_6\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "JPDpxhBQTFkC",
        "outputId": "8eebd562-74e8-4ef8-e352-63b4e3cf17b9"
      },
      "source": [
        "a  = df.groupby('Age').Work_Experience.mean()\n",
        "plt.plot(a)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f8eedd35490>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "-NnrOb8xOJaF",
        "outputId": "fb78ce4b-e9ac-4821-caa3-b5d31d4cc3a2"
      },
      "source": [
        "sns.heatmap(df.corr(), annot = True)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8eedcfc8d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "h-U8I--vOYaM",
        "outputId": "91a548ac-ad8f-4311-e1fe-946f38a61388"
      },
      "source": [
        "sns.barplot(x= df['Gender'], y = df['Age'], hue = df['Graduated'])"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8eedc26d50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "rb5-QJBTQiAA",
        "outputId": "711c3585-44ad-40b6-a3b8-6131121d68ef"
      },
      "source": [
        "sns.barplot(x= df['Gender'], y = df['Work_Experience'], hue = df['Graduated'])"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8eedba32d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "GY0-CCiMQ3p8",
        "outputId": "afca137e-3b6c-4587-a5ba-24863e22a7b0"
      },
      "source": [
        "sns.barplot(x= df['Spending_Score'], y = df['Family_Size'], hue = df['Graduated'])"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8eedb148d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "1TGzBMX9RHqH",
        "outputId": "82e336d3-9bc6-4c91-cf42-7ea008eefff4"
      },
      "source": [
        "sns.barplot(x= df['Spending_Score'], y = df['Work_Experience'], hue = df['Ever_Married'])"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8eedaac690>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "crXzDn76RkSE",
        "outputId": "bbe96e34-9e44-454f-d544-c0f7b0143cbf"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Gender</th>\n",
              "      <th>Ever_Married</th>\n",
              "      <th>Age</th>\n",
              "      <th>Graduated</th>\n",
              "      <th>Profession</th>\n",
              "      <th>Work_Experience</th>\n",
              "      <th>Spending_Score</th>\n",
              "      <th>Family_Size</th>\n",
              "      <th>Var_1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>22</td>\n",
              "      <td>No</td>\n",
              "      <td>Healthcare</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Low</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Cat_4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Female</td>\n",
              "      <td>Yes</td>\n",
              "      <td>38</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Engineer</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Average</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Cat_4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Female</td>\n",
              "      <td>Yes</td>\n",
              "      <td>67</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Engineer</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Low</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Cat_6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>67</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Lawyer</td>\n",
              "      <td>0.0</td>\n",
              "      <td>High</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Cat_6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Female</td>\n",
              "      <td>Yes</td>\n",
              "      <td>40</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Entertainment</td>\n",
              "      <td>1.0</td>\n",
              "      <td>High</td>\n",
              "      <td>6.0</td>\n",
              "      <td>Cat_6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Gender Ever_Married  Age  ... Spending_Score Family_Size  Var_1\n",
              "0    Male           No   22  ...            Low         4.0  Cat_4\n",
              "1  Female          Yes   38  ...        Average         3.0  Cat_4\n",
              "2  Female          Yes   67  ...            Low         1.0  Cat_6\n",
              "3    Male          Yes   67  ...           High         2.0  Cat_6\n",
              "4  Female          Yes   40  ...           High         6.0  Cat_6\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "upqY_e-qe2WV"
      },
      "source": [
        "# Data Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XXoeS1BTnDHR"
      },
      "source": [
        "\n",
        "**1. Feature Encoding**\n",
        "* Since the Machine learning model accepts only Numerical values so the categorical variables which have 'object' data types are converted into numerical values depending upon the type of variable\n",
        "   - *Ordinal Categorical variable* - Label Encoding\n",
        "   - *Nominal Categorical Variable* - One Hot Encoding\n",
        "-----------------------\n",
        "**2. Feature Scaling**\n",
        "* Features are scaled to give the good output for some algorithms which depends on some similiarty function like - \n",
        "      - K-Means Clustering algorithm\n",
        "      - K-NN algorithm\n",
        "      - Principal Component Analysis "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bKKxf-NUfAAo"
      },
      "source": [
        "## Feature Encoding\n",
        "* One Hot Encoding\n",
        "* Label Encoding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dmDAxOR6Rx5b"
      },
      "source": [
        "df['Ever_Married']= df['Ever_Married'].replace({'No':0 , 'Yes':1})\n",
        "df['Graduated']= df['Graduated'].replace({'No':0 , 'Yes':1})\n",
        "df['Spending_Score'] = df['Spending_Score'].replace({'Low':0,'Average':1,'High':2})"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AiAEUU1wSfe8",
        "outputId": "ae70559c-a0d2-4f4a-d4d9-a1857afc3dcf"
      },
      "source": [
        "df.Var_1.unique()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Cat_4', 'Cat_6', 'Cat_7', 'Cat_3', 'Cat_1', 'Cat_2', 'Cat_5'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-TszZlWMSmmL"
      },
      "source": [
        "# Replacing Cat_1 with 1, Cat_2 with 2, Cat_3 with 3, Cat4 with 4, Cat_5 with 5, Cat_6 with 6, and Cat_7 with 7 in Var_1 column\n",
        "df['Var_1'] = df['Var_1'].replace({'Cat_1':1,'Cat_2':2,'Cat_3':3,'Cat_4':4,'Cat_5':5,'Cat_6':6,'Cat_7':7})"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vlxy9BdTXKJK"
      },
      "source": [
        "# Replacing Male with 0 and Female with 1 in Gender column\n",
        "df['Gender'] = df['Gender'].replace({'Male':0,'Female':1})"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dq3FnpgEWh2u",
        "outputId": "9f6cd524-f442-4763-8188-a1d92b1dbff7"
      },
      "source": [
        "df['Profession'].value_counts()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Artist           2997\n",
              "Healthcare       1500\n",
              "Entertainment    1071\n",
              "Doctor            786\n",
              "Engineer          769\n",
              "Lawyer            727\n",
              "Executive         658\n",
              "Marketing         345\n",
              "Homemaker         272\n",
              "Name: Profession, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q1BOSJ8gXFHU"
      },
      "source": [
        "df['Profession'] = df['Profession'].replace({'Artist':0,'Healthcare':1,'Entertainment':2,'Engineer':3,\n",
        "                                             'Doctor':4,'Lawyer':5,'Executive':6,'Marketing':7,'Homemaker':8})"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "3zcX9mQVXXOE",
        "outputId": "b85b6828-ed34-4612-fc0c-f4f4742017d4"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Gender</th>\n",
              "      <th>Ever_Married</th>\n",
              "      <th>Age</th>\n",
              "      <th>Graduated</th>\n",
              "      <th>Profession</th>\n",
              "      <th>Work_Experience</th>\n",
              "      <th>Spending_Score</th>\n",
              "      <th>Family_Size</th>\n",
              "      <th>Var_1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>67</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>67</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Gender  Ever_Married  Age  ...  Spending_Score  Family_Size  Var_1\n",
              "0       0             0   22  ...               0          4.0      4\n",
              "1       1             1   38  ...               1          3.0      4\n",
              "2       1             1   67  ...               0          1.0      6\n",
              "3       0             1   67  ...               2          2.0      6\n",
              "4       1             1   40  ...               2          6.0      6\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aRuQhdDyfPVv"
      },
      "source": [
        "## Feature Scaling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CMUQUp4NXYyT"
      },
      "source": [
        "# Obtain the optimal number of clusters"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pcxtrp9eZAWH"
      },
      "source": [
        "\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
        "from sklearn.decomposition import PCA"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kpJE-1rcZjfJ"
      },
      "source": [
        "scaler=StandardScaler()\n",
        "df_scaled=scaler.fit_transform(df)\n"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nR1dNDGRfaG4"
      },
      "source": [
        "# Applying Clustering Technique"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uexCRj5kfnHj"
      },
      "source": [
        "### Elbow Method to obtain Optimal Number of Clusters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "GWQsB9BVZpYE",
        "outputId": "bbc22a04-29c6-41cc-c961-899a2b4511ec"
      },
      "source": [
        "wcss=[]\n",
        "range_values=range(1,15)\n",
        "\n",
        "\n",
        "for i in range_values:\n",
        "    kmeans=KMeans(n_clusters=i)\n",
        "    kmeans.fit(df_scaled)\n",
        "    wcss.append(kmeans.inertia_)\n",
        "\n",
        "plt.plot(wcss, color = 'red', marker = '8')\n",
        "plt.xlabel('K-values')\n",
        "plt.ylabel('WCSS')\n",
        "plt.show()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q91GMPDxaWUb"
      },
      "source": [
        "# We can take the k - value to be 4 or 5"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YbUeKvEef2Sm"
      },
      "source": [
        "### Apply K-mean clustering algorithm considering k = 4"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cdFMh4Fba7Or"
      },
      "source": [
        "kmeans=KMeans(4).fit(df_scaled)"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OWTB8_BNa8Fj",
        "outputId": "76a00baa-76fc-48c1-9dce-4d74ee2aefd6"
      },
      "source": [
        "labels=kmeans.labels_\n",
        "kmeans.cluster_centers_.shape"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4, 9)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        },
        "id": "FwrJKdbMa_0S",
        "outputId": "7db5c216-45fa-40af-9fc4-617f49d590d1"
      },
      "source": [
        "cluster_centers=pd.DataFrame(data=kmeans.cluster_centers_,columns=[df.columns])\n",
        "cluster_centers"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>Gender</th>\n",
              "      <th>Ever_Married</th>\n",
              "      <th>Age</th>\n",
              "      <th>Graduated</th>\n",
              "      <th>Profession</th>\n",
              "      <th>Work_Experience</th>\n",
              "      <th>Spending_Score</th>\n",
              "      <th>Family_Size</th>\n",
              "      <th>Var_1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.300822</td>\n",
              "      <td>-1.166178</td>\n",
              "      <td>-0.347778</td>\n",
              "      <td>0.577608</td>\n",
              "      <td>-0.218778</td>\n",
              "      <td>0.329784</td>\n",
              "      <td>-0.729699</td>\n",
              "      <td>-0.568415</td>\n",
              "      <td>0.187922</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.068328</td>\n",
              "      <td>0.836593</td>\n",
              "      <td>0.237367</td>\n",
              "      <td>0.538177</td>\n",
              "      <td>-0.644825</td>\n",
              "      <td>-0.053636</td>\n",
              "      <td>0.304638</td>\n",
              "      <td>-0.104254</td>\n",
              "      <td>0.084056</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.028047</td>\n",
              "      <td>-0.924983</td>\n",
              "      <td>-0.977192</td>\n",
              "      <td>-1.100655</td>\n",
              "      <td>0.011332</td>\n",
              "      <td>-0.067982</td>\n",
              "      <td>-0.657578</td>\n",
              "      <td>0.782035</td>\n",
              "      <td>-0.453011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.159334</td>\n",
              "      <td>0.821316</td>\n",
              "      <td>1.000855</td>\n",
              "      <td>-0.239369</td>\n",
              "      <td>1.197992</td>\n",
              "      <td>-0.168384</td>\n",
              "      <td>0.934700</td>\n",
              "      <td>-0.104849</td>\n",
              "      <td>0.162489</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Gender Ever_Married       Age  ... Spending_Score Family_Size     Var_1\n",
              "0  0.300822    -1.166178 -0.347778  ...      -0.729699   -0.568415  0.187922\n",
              "1 -0.068328     0.836593  0.237367  ...       0.304638   -0.104254  0.084056\n",
              "2 -0.028047    -0.924983 -0.977192  ...      -0.657578    0.782035 -0.453011\n",
              "3 -0.159334     0.821316  1.000855  ...       0.934700   -0.104849  0.162489\n",
              "\n",
              "[4 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NUYCLEXQbH3a"
      },
      "source": [
        "\n",
        "cluster_centers=scaler.inverse_transform(cluster_centers)"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        },
        "id": "purU4xRGbLSK",
        "outputId": "3110933a-df3c-4af7-cb16-ebb2a9a19e79"
      },
      "source": [
        "cluster_centers=pd.DataFrame(data=cluster_centers,columns=[df.columns])\n",
        "cluster_centers"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>Gender</th>\n",
              "      <th>Ever_Married</th>\n",
              "      <th>Age</th>\n",
              "      <th>Graduated</th>\n",
              "      <th>Profession</th>\n",
              "      <th>Work_Experience</th>\n",
              "      <th>Spending_Score</th>\n",
              "      <th>Family_Size</th>\n",
              "      <th>Var_1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.601434</td>\n",
              "      <td>0.014344</td>\n",
              "      <td>37.724898</td>\n",
              "      <td>0.906762</td>\n",
              "      <td>1.808914</td>\n",
              "      <td>3.539959</td>\n",
              "      <td>-1.787459e-14</td>\n",
              "      <td>1.945697</td>\n",
              "      <td>5.418033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.417722</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>47.554041</td>\n",
              "      <td>0.887699</td>\n",
              "      <td>0.792924</td>\n",
              "      <td>2.289516</td>\n",
              "      <td>7.659851e-01</td>\n",
              "      <td>2.647517</td>\n",
              "      <td>5.270691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.437768</td>\n",
              "      <td>0.133047</td>\n",
              "      <td>27.152122</td>\n",
              "      <td>0.095374</td>\n",
              "      <td>2.357654</td>\n",
              "      <td>2.242728</td>\n",
              "      <td>5.340963e-02</td>\n",
              "      <td>3.987601</td>\n",
              "      <td>4.508822</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.372431</td>\n",
              "      <td>0.992481</td>\n",
              "      <td>60.378947</td>\n",
              "      <td>0.511779</td>\n",
              "      <td>5.187469</td>\n",
              "      <td>1.915288</td>\n",
              "      <td>1.232581e+00</td>\n",
              "      <td>2.646617</td>\n",
              "      <td>5.381955</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Gender Ever_Married        Age  ... Spending_Score Family_Size     Var_1\n",
              "0  0.601434     0.014344  37.724898  ...  -1.787459e-14    1.945697  5.418033\n",
              "1  0.417722     1.000000  47.554041  ...   7.659851e-01    2.647517  5.270691\n",
              "2  0.437768     0.133047  27.152122  ...   5.340963e-02    3.987601  4.508822\n",
              "3  0.372431     0.992481  60.378947  ...   1.232581e+00    2.646617  5.381955\n",
              "\n",
              "[4 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oi7yZntabO_a"
      },
      "source": [
        "y_kmeans=kmeans.fit_predict(df_scaled)"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "VTJY4G7zbV-M",
        "outputId": "10c82bc4-0b2e-497e-a6ff-304c7df55415"
      },
      "source": [
        "df_cluster=pd.concat([df,pd.DataFrame({'cluster':labels})],axis=1)\n",
        "\n",
        "df_cluster.head(9)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Gender</th>\n",
              "      <th>Ever_Married</th>\n",
              "      <th>Age</th>\n",
              "      <th>Graduated</th>\n",
              "      <th>Profession</th>\n",
              "      <th>Work_Experience</th>\n",
              "      <th>Spending_Score</th>\n",
              "      <th>Family_Size</th>\n",
              "      <th>Var_1</th>\n",
              "      <th>cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>67</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>67</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>56</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>61</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Gender  Ever_Married  Age  ...  Family_Size  Var_1  cluster\n",
              "0       0             0   22  ...          4.0      4        2\n",
              "1       1             1   38  ...          3.0      4        1\n",
              "2       1             1   67  ...          1.0      6        1\n",
              "3       0             1   67  ...          2.0      6        3\n",
              "4       1             1   40  ...          6.0      6        1\n",
              "5       0             1   56  ...          2.0      6        1\n",
              "6       0             0   32  ...          3.0      6        0\n",
              "7       1             0   33  ...          3.0      6        0\n",
              "8       1             1   61  ...          3.0      7        1\n",
              "\n",
              "[9 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D_Ck7iA-bb1b"
      },
      "source": [
        "## Apply Principal Component Analysis (PCA) technique to perform dimensionality reduction and data visualization"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fsc8fjdBf-X8"
      },
      "source": [
        "# Principal Component Analysis (PCA) technique\n",
        "\n",
        "* To Perform Dimensionality Reduction\n",
        "* Data Visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XmN6H1yZbgWi",
        "outputId": "71c33f28-f36c-4be9-f9be-69a470c4ab8b"
      },
      "source": [
        "pca=PCA(n_components=2)\n",
        "principal_comp=pca.fit_transform(df_scaled)\n",
        "principal_comp"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-2.25066672,  1.18725021],\n",
              "       [ 0.62433438,  0.19540444],\n",
              "       [ 1.35713644, -1.47785023],\n",
              "       ...,\n",
              "       [ 1.46978324,  0.75076587],\n",
              "       [-2.39242844,  1.24604064],\n",
              "       [-2.38273198,  1.61847844]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "rmUjEMIDbiVW",
        "outputId": "42e54269-c668-40d4-9258-e626cbb69c80"
      },
      "source": [
        "pca_df=pd.DataFrame(data=principal_comp,columns=['pca1','pca2'])\n",
        "pca_df.head()"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>pca1</th>\n",
              "      <th>pca2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-2.250667</td>\n",
              "      <td>1.187250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.624334</td>\n",
              "      <td>0.195404</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.357136</td>\n",
              "      <td>-1.477850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.897506</td>\n",
              "      <td>0.436680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.215228</td>\n",
              "      <td>1.027937</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       pca1      pca2\n",
              "0 -2.250667  1.187250\n",
              "1  0.624334  0.195404\n",
              "2  1.357136 -1.477850\n",
              "3  2.897506  0.436680\n",
              "4  1.215228  1.027937"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "O3wSHM-Lbmn6",
        "outputId": "3f24374d-188f-436d-e3d1-1c1ae8944181"
      },
      "source": [
        "pca_df=pd.concat([pca_df,pd.DataFrame({'cluster':labels})],axis=1)\n",
        "pca_df.head()"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>pca1</th>\n",
              "      <th>pca2</th>\n",
              "      <th>cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-2.250667</td>\n",
              "      <td>1.187250</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.624334</td>\n",
              "      <td>0.195404</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.357136</td>\n",
              "      <td>-1.477850</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.897506</td>\n",
              "      <td>0.436680</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.215228</td>\n",
              "      <td>1.027937</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       pca1      pca2  cluster\n",
              "0 -2.250667  1.187250        2\n",
              "1  0.624334  0.195404        1\n",
              "2  1.357136 -1.477850        1\n",
              "3  2.897506  0.436680        3\n",
              "4  1.215228  1.027937        1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "Zad3pfBwbqgq",
        "outputId": "d465874f-fae6-4078-e412-56f2ea337ca7"
      },
      "source": [
        "plt.figure(figsize=(10,8))\n",
        "ax=sns.scatterplot(x='pca1',y='pca2',hue='cluster',data=pca_df,palette=['red','green','blue','yellow'])\n",
        "plt.show()"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8HxmkUnCb0H7"
      },
      "source": [
        ""
      ],
      "execution_count": 58,
      "outputs": []
    }
  ]
}