Customer_Segmentation_using_K_Means_Clustering.ipynb

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      "source": [
        "## Customer Segmentation using K-Means Clustering"
      ],
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      "source": [
        "This project demonstrates the application of unsupervised learning, specifically K-Means clustering, to segment customers based on their annual income and spending ratio. The dataset used for this analysis is obtained from Kaggle and consists of customer information. The primary objective is to group customers into distinct clusters, allowing businesses to tailor their marketing strategies and services to each segment effectively. K-Means clustering is utilized to identify patterns and similarities among customers, enabling data-driven decision-making for improved customer satisfaction and revenue optimization."
      ],
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      "source": [
        "# importing required libraries\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.cluster import KMeans"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# reading dateset\n",
        "\n",
        "df = pd.read_csv('/content/Mall_Customers.csv')"
      ],
      "metadata": {
        "id": "r2YEUuy6-4_Y"
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      "execution_count": 3,
      "outputs": []
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      "source": [
        "# explore the dataset\n",
        "\n",
        "df.head()"
      ],
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      "execution_count": 4,
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            "text/plain": [
              "   CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)\n",
              "0           1    Male   19                  15                      39\n",
              "1           2    Male   21                  15                      81\n",
              "2           3  Female   20                  16                       6\n",
              "3           4  Female   23                  16                      77\n",
              "4           5  Female   31                  17                      40"
            ],
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              "      <th>CustomerID</th>\n",
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        "df.shape"
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          "data": {
            "text/plain": [
              "(200, 5)"
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          "execution_count": 5
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 200 entries, 0 to 199\n",
            "Data columns (total 5 columns):\n",
            " #   Column                  Non-Null Count  Dtype \n",
            "---  ------                  --------------  ----- \n",
            " 0   CustomerID              200 non-null    int64 \n",
            " 1   Gender                  200 non-null    object\n",
            " 2   Age                     200 non-null    int64 \n",
            " 3   Annual Income (k$)      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"
          ]
        }
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      "source": [
        "df.isnull().sum()"
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      },
      "execution_count": 8,
      "outputs": [
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          "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"
            ]
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          "execution_count": 8
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        "correlation_matrix = df.corr(numeric_only=True)\n"
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        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap=\"coolwarm\", fmt=\".2f\", linewidths=0.5)\n",
        "plt.title(\"Correlation Heatmap\")\n",
        "plt.show()\n"
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.iloc[:,[3,4]].values\n",
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r0wcoRfqASHM",
        "outputId": "e77f49c1-d331-4733-d2e6-9e893d790feb"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 15,  39],\n",
              "       [ 15,  81],\n",
              "       [ 16,   6],\n",
              "       [ 16,  77],\n",
              "       [ 17,  40],\n",
              "       [ 17,  76],\n",
              "       [ 18,   6],\n",
              "       [ 18,  94],\n",
              "       [ 19,   3],\n",
              "       [ 19,  72],\n",
              "       [ 19,  14],\n",
              "       [ 19,  99],\n",
              "       [ 20,  15],\n",
              "       [ 20,  77],\n",
              "       [ 20,  13],\n",
              "       [ 20,  79],\n",
              "       [ 21,  35],\n",
              "       [ 21,  66],\n",
              "       [ 23,  29],\n",
              "       [ 23,  98],\n",
              "       [ 24,  35],\n",
              "       [ 24,  73],\n",
              "       [ 25,   5],\n",
              "       [ 25,  73],\n",
              "       [ 28,  14],\n",
              "       [ 28,  82],\n",
              "       [ 28,  32],\n",
              "       [ 28,  61],\n",
              "       [ 29,  31],\n",
              "       [ 29,  87],\n",
              "       [ 30,   4],\n",
              "       [ 30,  73],\n",
              "       [ 33,   4],\n",
              "       [ 33,  92],\n",
              "       [ 33,  14],\n",
              "       [ 33,  81],\n",
              "       [ 34,  17],\n",
              "       [ 34,  73],\n",
              "       [ 37,  26],\n",
              "       [ 37,  75],\n",
              "       [ 38,  35],\n",
              "       [ 38,  92],\n",
              "       [ 39,  36],\n",
              "       [ 39,  61],\n",
              "       [ 39,  28],\n",
              "       [ 39,  65],\n",
              "       [ 40,  55],\n",
              "       [ 40,  47],\n",
              "       [ 40,  42],\n",
              "       [ 40,  42],\n",
              "       [ 42,  52],\n",
              "       [ 42,  60],\n",
              "       [ 43,  54],\n",
              "       [ 43,  60],\n",
              "       [ 43,  45],\n",
              "       [ 43,  41],\n",
              "       [ 44,  50],\n",
              "       [ 44,  46],\n",
              "       [ 46,  51],\n",
              "       [ 46,  46],\n",
              "       [ 46,  56],\n",
              "       [ 46,  55],\n",
              "       [ 47,  52],\n",
              "       [ 47,  59],\n",
              "       [ 48,  51],\n",
              "       [ 48,  59],\n",
              "       [ 48,  50],\n",
              "       [ 48,  48],\n",
              "       [ 48,  59],\n",
              "       [ 48,  47],\n",
              "       [ 49,  55],\n",
              "       [ 49,  42],\n",
              "       [ 50,  49],\n",
              "       [ 50,  56],\n",
              "       [ 54,  47],\n",
              "       [ 54,  54],\n",
              "       [ 54,  53],\n",
              "       [ 54,  48],\n",
              "       [ 54,  52],\n",
              "       [ 54,  42],\n",
              "       [ 54,  51],\n",
              "       [ 54,  55],\n",
              "       [ 54,  41],\n",
              "       [ 54,  44],\n",
              "       [ 54,  57],\n",
              "       [ 54,  46],\n",
              "       [ 57,  58],\n",
              "       [ 57,  55],\n",
              "       [ 58,  60],\n",
              "       [ 58,  46],\n",
              "       [ 59,  55],\n",
              "       [ 59,  41],\n",
              "       [ 60,  49],\n",
              "       [ 60,  40],\n",
              "       [ 60,  42],\n",
              "       [ 60,  52],\n",
              "       [ 60,  47],\n",
              "       [ 60,  50],\n",
              "       [ 61,  42],\n",
              "       [ 61,  49],\n",
              "       [ 62,  41],\n",
              "       [ 62,  48],\n",
              "       [ 62,  59],\n",
              "       [ 62,  55],\n",
              "       [ 62,  56],\n",
              "       [ 62,  42],\n",
              "       [ 63,  50],\n",
              "       [ 63,  46],\n",
              "       [ 63,  43],\n",
              "       [ 63,  48],\n",
              "       [ 63,  52],\n",
              "       [ 63,  54],\n",
              "       [ 64,  42],\n",
              "       [ 64,  46],\n",
              "       [ 65,  48],\n",
              "       [ 65,  50],\n",
              "       [ 65,  43],\n",
              "       [ 65,  59],\n",
              "       [ 67,  43],\n",
              "       [ 67,  57],\n",
              "       [ 67,  56],\n",
              "       [ 67,  40],\n",
              "       [ 69,  58],\n",
              "       [ 69,  91],\n",
              "       [ 70,  29],\n",
              "       [ 70,  77],\n",
              "       [ 71,  35],\n",
              "       [ 71,  95],\n",
              "       [ 71,  11],\n",
              "       [ 71,  75],\n",
              "       [ 71,   9],\n",
              "       [ 71,  75],\n",
              "       [ 72,  34],\n",
              "       [ 72,  71],\n",
              "       [ 73,   5],\n",
              "       [ 73,  88],\n",
              "       [ 73,   7],\n",
              "       [ 73,  73],\n",
              "       [ 74,  10],\n",
              "       [ 74,  72],\n",
              "       [ 75,   5],\n",
              "       [ 75,  93],\n",
              "       [ 76,  40],\n",
              "       [ 76,  87],\n",
              "       [ 77,  12],\n",
              "       [ 77,  97],\n",
              "       [ 77,  36],\n",
              "       [ 77,  74],\n",
              "       [ 78,  22],\n",
              "       [ 78,  90],\n",
              "       [ 78,  17],\n",
              "       [ 78,  88],\n",
              "       [ 78,  20],\n",
              "       [ 78,  76],\n",
              "       [ 78,  16],\n",
              "       [ 78,  89],\n",
              "       [ 78,   1],\n",
              "       [ 78,  78],\n",
              "       [ 78,   1],\n",
              "       [ 78,  73],\n",
              "       [ 79,  35],\n",
              "       [ 79,  83],\n",
              "       [ 81,   5],\n",
              "       [ 81,  93],\n",
              "       [ 85,  26],\n",
              "       [ 85,  75],\n",
              "       [ 86,  20],\n",
              "       [ 86,  95],\n",
              "       [ 87,  27],\n",
              "       [ 87,  63],\n",
              "       [ 87,  13],\n",
              "       [ 87,  75],\n",
              "       [ 87,  10],\n",
              "       [ 87,  92],\n",
              "       [ 88,  13],\n",
              "       [ 88,  86],\n",
              "       [ 88,  15],\n",
              "       [ 88,  69],\n",
              "       [ 93,  14],\n",
              "       [ 93,  90],\n",
              "       [ 97,  32],\n",
              "       [ 97,  86],\n",
              "       [ 98,  15],\n",
              "       [ 98,  88],\n",
              "       [ 99,  39],\n",
              "       [ 99,  97],\n",
              "       [101,  24],\n",
              "       [101,  68],\n",
              "       [103,  17],\n",
              "       [103,  85],\n",
              "       [103,  23],\n",
              "       [103,  69],\n",
              "       [113,   8],\n",
              "       [113,  91],\n",
              "       [120,  16],\n",
              "       [120,  79],\n",
              "       [126,  28],\n",
              "       [126,  74],\n",
              "       [137,  18],\n",
              "       [137,  83]])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# applying k-mean clustering and selecting the best value of k\n",
        "\n",
        "wcss = []\n",
        "\n",
        "for i in range(1,11):\n",
        "  k_means = KMeans(n_clusters=i, init='k-means++',\n",
        "                  random_state=42)\n",
        "  k_means.fit(X)\n",
        "\n",
        "  wcss.append(k_means.inertia_)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RNpYL7VOCF37",
        "outputId": "462793b3-979f-45aa-d9c4-642288fd0e1b"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# apply elbow method to find the best number of clusters\n",
        "\n",
        "sns.set()\n",
        "plt.plot(range(1,11), wcss)\n",
        "plt.title('The Elbow method for K value')\n",
        "plt.xlabel('Number of clusters')\n",
        "plt.ylabel('WCSS')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "7u17OC6dD3xp",
        "outputId": "52f59afe-9af6-4367-89bd-cf131214c3d8"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "k_means = KMeans(n_clusters=5, init= 'k-means++', random_state=0)"
      ],
      "metadata": {
        "id": "n76eBJV5EZ_w"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = k_means.fit_predict(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fVAx9UchFRU-",
        "outputId": "a5684feb-b1d8-4066-90f6-f3abb5620955"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hNgIdQQ0F_TM",
        "outputId": "04c559ff-8f84-45f0-b229-ab42d5c86c22"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3,\n",
              "       4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 1,\n",
              "       4, 3, 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,\n",
              "       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, 2, 0, 2, 1, 2, 0, 2, 0, 2,\n",
              "       1, 2, 0, 2, 0, 2, 0, 2, 0, 2, 1, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2,\n",
              "       0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2,\n",
              "       0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2,\n",
              "       0, 2], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# visualization\n",
        "\n",
        "plt.figure(figsize=(8,8))\n",
        "plt.scatter(X[y==0,0], X[y==0,1], s=50, c='yellow', label='Cluster 1')\n",
        "plt.scatter(X[y==1,0], X[y==1,1], s=50, c='green', label='Cluster 2')\n",
        "plt.scatter(X[y==2,0], X[y==2,1], s=50, c='blue', label='Cluster 3')\n",
        "plt.scatter(X[y==3,0], X[y==3,1], s=50, c='cyan', label='Cluster 4')\n",
        "plt.scatter(X[y==4,0], X[y==4,1], s=50, c='violet', label='Cluster 5')\n",
        "\n",
        "plt.scatter(k_means.cluster_centers_[:,0], k_means.cluster_centers_[:,1], s=100, c='red', label='Centriod')\n",
        "\n",
        "plt.title('Customer clusters')\n",
        "plt.xlabel('Annual Income')\n",
        "plt.ylabel('Spending Score')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 727
        },
        "id": "koqTdQo5GBqI",
        "outputId": "f4d19591-cee7-4bd2-f31d-6000dc1ed2bb"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uBz9omR2JEIW"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}