Project_1_Customer_Segmentation_using_K_Means_Clustering_with_Python.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Project 1 - Customer Segmentation using K-Means Clustering with Python.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOuYK5UHHyxo9aTvlnn5NjI",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/DeveloperAkram/Colab-Notebooks/blob/master/Project_1_Customer_Segmentation_using_K_Means_Clustering_with_Python.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JvC03DEIHoba"
      },
      "source": [
        "Importing  the Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UHhmgtWuGjJg"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.cluster import KMeans"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rqPe_-Z2IGYK"
      },
      "source": [
        "Data Collection and Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3hpfoV1MIDkI"
      },
      "source": [
        "# Loading the data from csv to a Pandas DataFrame\n",
        "\n",
        "customer_data = pd.read_csv('/content/Mall_Customers.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "SuoY9ZNGIkxJ",
        "outputId": "e5941c5a-c4c8-4d2c-c799-4fc15389a1b4"
      },
      "source": [
        "# First 5 rows in the datafame\n",
        "\n",
        "customer_data.head()"
      ],
      "execution_count": null,
      "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>CustomerID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Annual Income (k$)</th>\n",
              "      <th>Spending Score (1-100)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Male</td>\n",
              "      <td>19</td>\n",
              "      <td>15</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Male</td>\n",
              "      <td>21</td>\n",
              "      <td>15</td>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Female</td>\n",
              "      <td>20</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Female</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Female</td>\n",
              "      <td>31</td>\n",
              "      <td>17</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)\n",
              "0           1    Male   19                  15                      39\n",
              "1           2    Male   21                  15                      81\n",
              "2           3  Female   20                  16                       6\n",
              "3           4  Female   23                  16                      77\n",
              "4           5  Female   31                  17                      40"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bt_BWuf3Iu-s",
        "outputId": "6ca26cdf-48dc-479c-db4c-d0d8631bf0da"
      },
      "source": [
        "# total no of rows and columns\n",
        "\n",
        "customer_data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(200, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oUcZidTtIyn3",
        "outputId": "647de243-7d21-48a8-d4ce-90adf4ac3c41"
      },
      "source": [
        "# getting some informations about dataset\n",
        "\n",
        "customer_data.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 200 entries, 0 to 199\n",
            "Data columns (total 5 columns):\n",
            " #   Column                  Non-Null Count  Dtype \n",
            "---  ------                  --------------  ----- \n",
            " 0   CustomerID              200 non-null    int64 \n",
            " 1   Gender                  200 non-null    object\n",
            " 2   Age                     200 non-null    int64 \n",
            " 3   Annual Income (k$)      200 non-null    int64 \n",
            " 4   Spending Score (1-100)  200 non-null    int64 \n",
            "dtypes: int64(4), object(1)\n",
            "memory usage: 7.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "B7UIVuFSJJfi",
        "outputId": "a1fe8d7f-eff7-46d8-df6a-c36d093d9d46"
      },
      "source": [
        "# getting numeric values at a glance\n",
        "\n",
        "customer_data.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Age</th>\n",
              "      <th>Annual Income (k$)</th>\n",
              "      <th>Spending Score (1-100)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>200.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>200.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>100.500000</td>\n",
              "      <td>38.850000</td>\n",
              "      <td>60.560000</td>\n",
              "      <td>50.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>57.879185</td>\n",
              "      <td>13.969007</td>\n",
              "      <td>26.264721</td>\n",
              "      <td>25.823522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>50.750000</td>\n",
              "      <td>28.750000</td>\n",
              "      <td>41.500000</td>\n",
              "      <td>34.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>100.500000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>61.500000</td>\n",
              "      <td>50.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>150.250000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>78.000000</td>\n",
              "      <td>73.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>200.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>137.000000</td>\n",
              "      <td>99.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       CustomerID         Age  Annual Income (k$)  Spending Score (1-100)\n",
              "count  200.000000  200.000000          200.000000              200.000000\n",
              "mean   100.500000   38.850000           60.560000               50.200000\n",
              "std     57.879185   13.969007           26.264721               25.823522\n",
              "min      1.000000   18.000000           15.000000                1.000000\n",
              "25%     50.750000   28.750000           41.500000               34.750000\n",
              "50%    100.500000   36.000000           61.500000               50.000000\n",
              "75%    150.250000   49.000000           78.000000               73.000000\n",
              "max    200.000000   70.000000          137.000000               99.000000"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GY8wkQBgJSFD",
        "outputId": "8933d98e-ab97-431b-e5dd-86b7c7f4d897"
      },
      "source": [
        "# checking for missing values\n",
        "\n",
        "customer_data.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CustomerID                0\n",
              "Gender                    0\n",
              "Age                       0\n",
              "Annual Income (k$)        0\n",
              "Spending Score (1-100)    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bxSoGrRXJuF7"
      },
      "source": [
        "Choosing the Anual Income Column & Spending Score Column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LmVzYkuXJfkA"
      },
      "source": [
        "X = customer_data.iloc[:, [3,4]].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2a2l_so9KKDj"
      },
      "source": [
        "print(X)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ilb2d22BKdfq"
      },
      "source": [
        "Choosing the number of cluster"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tq4kOlF7LIlN"
      },
      "source": [
        "WCSS -> Within Clusters Sum of Square"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cWyZGz4GKQrc"
      },
      "source": [
        "# finding wcss value for different number of cluster\n",
        "\n",
        "wcss = []\n",
        "\n",
        "for i in range(1, 11):\n",
        "  kmeans = KMeans(n_clusters=i, init='k-means++', random_state=42)\n",
        "  kmeans.fit(X)\n",
        "\n",
        "  wcss.append(kmeans.inertia_)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "9ffWgCtKMiVQ",
        "outputId": "ee1cf950-5f63-4abb-f5af-69f09655dcd0"
      },
      "source": [
        "# Plot an elbow graph\n",
        "\n",
        "sns.set()\n",
        "plt.plot(range(1,11), wcss)\n",
        "plt.title('The Elbow Point Graph')\n",
        "plt.xlabel('Number of clusters')\n",
        "plt.ylabel('WCSS')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zv40TwjnNZBt"
      },
      "source": [
        "Optimum Number of Cluster = 5"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iqzNuwRnNgms"
      },
      "source": [
        "Training the K-Means Clustering Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V4TOeR6qNHf8",
        "outputId": "a2cebd98-4a1c-45c7-8d36-8b226fb44b60"
      },
      "source": [
        "kmeans = KMeans(n_clusters=5, init='k-means++', random_state=42)\n",
        "\n",
        "# return a label for each data point based on their cluster\n",
        "\n",
        "Y = kmeans.fit_predict(X)\n",
        "\n",
        "print(Y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3\n",
            " 0 3 0 3 0 3 1 3 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 2 4 2 1 2 4 2 4 2 1 2 4 2 4 2 4 2 4 2 1 2 4 2 4 2\n",
            " 4 2 4 2 4 2 4 2 4 2 4 2 4 2 4 2 4 2 4 2 4 2 4 2 4 2 4 2 4 2 4 2 4 2 4 2 4\n",
            " 2 4 2 4 2 4 2 4 2 4 2 4 2 4 2]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NphxjkmDTfQt"
      },
      "source": [
        "5 Clusters -> 0,1,2,3,4"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k26OOYr4RqR_"
      },
      "source": [
        "Visualizing all the Clusters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "xd3a7fIlR9Rf",
        "outputId": "24b2fc91-06f3-4ca6-923c-12feb03abcdc"
      },
      "source": [
        "# plotting all the cluters and their Centroids\n",
        "\n",
        "plt.figure(figsize=(8,8))\n",
        "plt.scatter(X[Y == 0,0], X[Y == 0,1], s=50, c='green', label='Cluster 1')\n",
        "plt.scatter(X[Y == 1,0], X[Y == 1,1], s=50, c='red', label='Cluster 2')\n",
        "plt.scatter(X[Y == 2,0], X[Y == 2,1], s=50, c='yellow', label='Cluster 3')\n",
        "plt.scatter(X[Y == 3,0], X[Y == 3,1], s=50, c='violet', label='Cluster 4')\n",
        "plt.scatter(X[Y == 4,0], X[Y == 4,1], s=50, c='blue', label='Cluster 5')\n",
        "\n",
        "# plot the centroids\n",
        "plt.scatter(kmeans.cluster_centers_[:,0], kmeans.cluster_centers_[:, 1], s=100, c='cyan', label='Centroids')\n",
        "\n",
        "plt.title('Customer Groups')\n",
        "plt.xlabel('Annual Income')\n",
        "plt.ylabel('Spending Score')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kAz48e7zTUaW"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}