Customer_Segmentation_using_K_Means_Clustering.ipynb

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  "metadata": {
    "colab": {
      "name": "Customer Segmentation using K-Means Clustering.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPj+6DEveAB8J1Fu0lLITWM",
      "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/nandikonda-vishal21/Machine-learning-projects/blob/main/Customer_Segmentation_using_K_Means_Clustering.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G6opdwW6CIDy"
      },
      "source": [
        "Importing the Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tLA34mW1B46-"
      },
      "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": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jICr8_y3CQOI"
      },
      "source": [
        "Data Collection & Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gX2B9gWOCRS2"
      },
      "source": [
        "# loading the data from csv file to a Pandas DataFrame\n",
        "customer_data = pd.read_csv('/content/Mall_Customers.csv')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "2GzrqkbNCtvr",
        "outputId": "b254934d-c64c-4e1d-b2aa-a10814d4433a"
      },
      "source": [
        "# first 5 rows in the dataframe\n",
        "customer_data.head()"
      ],
      "execution_count": 3,
      "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": "8psxO_mJCzOu",
        "outputId": "e901ce13-7aed-480c-dba7-03093186aee4"
      },
      "source": [
        "# finding the number of rows and columns\n",
        "customer_data.shape"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(200, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zCvxgs1SC3NI",
        "outputId": "100d313b-1ea7-4de2-a8fb-4ff98818a02b"
      },
      "source": [
        "# getting some informations about the dataset\n",
        "customer_data.info()"
      ],
      "execution_count": 5,
      "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$)      200 non-null    int64 \n",
            " 4   Spending Score (1-100)  200 non-null    int64 \n",
            "dtypes: int64(4), object(1)\n",
            "memory usage: 7.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qrZ3BGW8C7Ah",
        "outputId": "2052dfa3-325d-4f8c-9f99-3291da3c5243"
      },
      "source": [
        "# checking for missing values\n",
        "customer_data.isnull().sum()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CustomerID                0\n",
              "Gender                    0\n",
              "Age                       0\n",
              "Annual Income (k$)        0\n",
              "Spending Score (1-100)    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tGQ4OsgGC_H1"
      },
      "source": [
        "Choosing the Annual Income Column & Spending Score column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CCE01HU2DAEZ"
      },
      "source": [
        "X = customer_data.iloc[:,[3,4]].values"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1_ss4zWADGND",
        "outputId": "7030ca9f-752e-498f-b65e-efaf4408e5b3"
      },
      "source": [
        "print(X)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1xlC8RmuDMpN"
      },
      "source": [
        "Choosing the number of clusters\n",
        "\n",
        "WCSS -> Within Clusters Sum of Squares"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oePNgyxJDNm8"
      },
      "source": [
        "# finding wcss value for different number of clusters\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": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "TRtzHGE7DUX0",
        "outputId": "a1a3e015-b47c-4392-c93d-09b089335ae1"
      },
      "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": 10,
      "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": "g_8Is81QDZIr"
      },
      "source": [
        "Training the k-Means Clustering Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GU6HLFdTDblE",
        "outputId": "6359a38b-051a-486d-b862-d918d2d4982d"
      },
      "source": [
        "kmeans = KMeans(n_clusters=5, init='k-means++', random_state=0)\n",
        "\n",
        "# return a label for each data point based on their cluster\n",
        "Y = kmeans.fit_predict(X)\n",
        "\n",
        "print(Y)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3\n",
            " 1 3 1 3 1 3 0 3 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 2 4 2 0 2 4 2 4 2 0 2 4 2 4 2 4 2 4 2 0 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": "ezLG_91CDh5U"
      },
      "source": [
        "5 Clusters - 0, 1, 2, 3, 4\n",
        "\n",
        "Visualizing all the Clusters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "gOOna7D5DjKF",
        "outputId": "86875724-1358-4650-c59f-c410ce304384"
      },
      "source": [
        "# plotting all the clusters 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": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}