Clusters_Customer_using_K_Means_.ipynb

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        "Importing the Dependencies"
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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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      "source": [
        "Data Collection & Analysis"
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      "source": [
        "# loading the data from csv file to a Pandas DataFrame\n",
        "customer_data = pd.read_csv('/content/Mall_Customers.csv')"
      ],
      "metadata": {
        "id": "Rz6q_WVGyYhn"
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      "execution_count": 3,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "# first 5 rows in the dataframe\n",
        "customer_data.head()"
      ],
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            "text/plain": [
              "   CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)\n",
              "0           1    Male   19                  15                      39\n",
              "1           2    Male   21                  15                      81\n",
              "2           3  Female   20                  16                       6\n",
              "3           4  Female   23                  16                      77\n",
              "4           5  Female   31                  17                      40"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "customer_data",
              "summary": "{\n  \"name\": \"customer_data\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"CustomerID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 57,\n        \"min\": 1,\n        \"max\": 200,\n        \"num_unique_values\": 200,\n        \"samples\": [\n          96,\n          16,\n          31\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 18,\n        \"max\": 70,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          55,\n          26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Annual Income (k$)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26,\n        \"min\": 15,\n        \"max\": 137,\n        \"num_unique_values\": 64,\n        \"samples\": [\n          87,\n          101\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Spending Score (1-100)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25,\n        \"min\": 1,\n        \"max\": 99,\n        \"num_unique_values\": 84,\n        \"samples\": [\n          83,\n          39\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# finding the number of rows and columns\n",
        "customer_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-b1_tvQtyvo4",
        "outputId": "e624a901-41e0-400f-dd25-a47d133ed9eb"
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      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(200, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
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    {
      "cell_type": "code",
      "source": [
        "# getting some informations about the dataset\n",
        "customer_data.info()"
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        },
        "id": "l2CgyNbBy1Qb",
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      "execution_count": 6,
      "outputs": [
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          "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$)      200 non-null    int64 \n",
            " 4   Spending Score (1-100)  200 non-null    int64 \n",
            "dtypes: int64(4), object(1)\n",
            "memory usage: 7.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# checking for missing values\n",
        "customer_data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8lebIjmXy10r",
        "outputId": "7af32752-dad0-4001-e930-cc6886f309c4"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CustomerID                0\n",
              "Gender                    0\n",
              "Age                       0\n",
              "Annual Income (k$)        0\n",
              "Spending Score (1-100)    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Choosing the Annual Income Column & Spending Score column"
      ],
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        "id": "Sf_1_W9_zCHz"
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    {
      "cell_type": "code",
      "source": [
        "X = customer_data.iloc[:,[3,4]].values"
      ],
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        "id": "yTDh4jUmy8WP"
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      "execution_count": 8,
      "outputs": []
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      "source": [
        "print(X)"
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        "id": "5LSjsgeczF6O",
        "outputId": "2f4ff7a6-86ec-423c-ccbe-7d37ef914fb0"
      },
      "execution_count": 9,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
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            " [ 54  42]\n",
            " [ 54  51]\n",
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            " [ 78   1]\n",
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            " [113   8]\n",
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            " [120  16]\n",
            " [120  79]\n",
            " [126  28]\n",
            " [126  74]\n",
            " [137  18]\n",
            " [137  83]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Choosing the number of clusters"
      ],
      "metadata": {
        "id": "FHypINcezPo3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "WCSS -> Within Clusters Sum of Squares"
      ],
      "metadata": {
        "id": "n7pcBqzdzPlz"
      }
    },
    {
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OVDQmuCpzLIu",
        "outputId": "6e703564-e1ab-4648-8c1f-105ab79aa38f"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: 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",
            "  super()._check_params_vs_input(X, default_n_init=10)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: 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",
            "  super()._check_params_vs_input(X, default_n_init=10)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: 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",
            "  super()._check_params_vs_input(X, default_n_init=10)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: 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",
            "  super()._check_params_vs_input(X, default_n_init=10)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: 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",
            "  super()._check_params_vs_input(X, default_n_init=10)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: 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",
            "  super()._check_params_vs_input(X, default_n_init=10)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: 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",
            "  super()._check_params_vs_input(X, default_n_init=10)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: 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",
            "  super()._check_params_vs_input(X, default_n_init=10)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: 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",
            "  super()._check_params_vs_input(X, default_n_init=10)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: 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",
            "  super()._check_params_vs_input(X, default_n_init=10)\n"
          ]
        }
      ]
    },
    {
      "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": 480
        },
        "id": "8cJOCRkiPv_e",
        "outputId": "503b4bda-7986-41cf-9b59-2e332aab2390"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Optimum Number of Clusters = 5"
      ],
      "metadata": {
        "id": "K8uBimveQJM0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Training the k-Means Clustering Model"
      ],
      "metadata": {
        "id": "X5Wncq-gQJIu"
      }
    },
    {
      "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": "im9CeWsrQFhl",
        "outputId": "70c83a2c-66ec-4e0e-a250-b69f9bd32709"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[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 3\n",
            " 4 3 4 3 4 3 0 3 4 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 1 2 1 0 1 2 1 2 1 0 1 2 1 2 1 2 1 2 1 0 1 2 1 2 1\n",
            " 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2\n",
            " 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: 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",
            "  super()._check_params_vs_input(X, default_n_init=10)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "5 Clusters - 0, 1, 2, 3, 4"
      ],
      "metadata": {
        "id": "NHQVpM1JQYQW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualizing all the Clusters"
      ],
      "metadata": {
        "id": "vuCQ39M2QcSX"
      }
    },
    {
      "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": 727
        },
        "id": "C7L9ruuxQTpG",
        "outputId": "bc5b8121-80f3-4f26-e664-0d9959c60569"
      },
      "execution_count": 13,
      "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": "xCiKHqO8QhSI"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}