Customer_Segmentation_using_K_Means_Clustering.ipynb

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      "name": " Customer Segmentation using K-Means Clustering.ipynb",
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      "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"
      ]
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      "cell_type": "code",
      "source": [
        "# loading the data from csv file to a Pandas DataFrame\n",
        "customer_data = pd.read_csv('/Mall_Customers.csv')"
      ],
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        "customer_data.head(3)"
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              "  <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$)</th>\n",
              "      <th>Spending Score (1-100)</th>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Male</td>\n",
              "      <td>19</td>\n",
              "      <td>15</td>\n",
              "      <td>39</td>\n",
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              "      <th>1</th>\n",
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              "      <td>Male</td>\n",
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              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Female</td>\n",
              "      <td>20</td>\n",
              "      <td>16</td>\n",
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            "text/plain": [
              "   CustomerID  Gender  Age  Annual Income (k$)  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"
            ]
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      "cell_type": "code",
      "source": [
        "# checking for missing values\n",
        "customer_data.isnull().sum()"
      ],
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      "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
        }
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    {
      "cell_type": "markdown",
      "source": [
        "Choosing the Annual Income Column & Spending Score column"
      ],
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    {
      "cell_type": "code",
      "source": [
        "X = customer_data.iloc[:,[3,4]].values"
      ],
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      "execution_count": 7,
      "outputs": []
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      "source": [
        "print(X)"
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      "execution_count": 8,
      "outputs": [
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          "text": [
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          ]
        }
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Choosing the number of clusters\n",
        "\n",
        "WCSS -> Within Clusters Sum of Squares"
      ],
      "metadata": {
        "id": "ef1aZeroa4yb"
      }
    },
    {
      "cell_type": "code",
      "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_)"
      ],
      "metadata": {
        "id": "EWuIrxDpa55m"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "6WNi1oLpbHhs",
        "outputId": "7906bade-6232-437f-92ed-8df05f815a92"
      },
      "execution_count": 10,
      "outputs": [
        {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Optimum Number of Clusters = 5\n",
        "\n",
        "Training the k-Means Clustering Model"
      ],
      "metadata": {
        "id": "avOLeSRMbRHt"
      }
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WiYL2uW6bSC6",
        "outputId": "a6c8ef9b-036e-4df2-9800-cdc375abe0f8"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4\n",
            " 3 4 3 4 3 4 1 4 3 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 0 2 1 2 0 2 0 2 1 2 0 2 0 2 0 2 0 2 1 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 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0\n",
            " 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "5 Clusters - 0, 1, 2, 3, 4\n",
        "\n",
        "Visualizing all the Clusters"
      ],
      "metadata": {
        "id": "RdI5oK63bioj"
      }
    },
    {
      "cell_type": "code",
      "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()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "cuoCtlXubjvb",
        "outputId": "4b858adc-d2b0-477a-be0b-3fa88cb71a0d"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}