KMeansClustering.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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      "source": [
        "Analysing Data!"
      ],
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      "source": [
        "customer_data = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/KMeans Clustering/Mall_Customers.csv')"
      ],
      "metadata": {
        "id": "2RZZeTAFpgEb"
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      "execution_count": 3,
      "outputs": []
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      "source": [
        "#Loading some sample rows ( the first 5)\n",
        "customer_data.head()"
      ],
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            "text/plain": [
              "   CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)\n",
              "0           1    Male   19                  15                      39\n",
              "1           2    Male   21                  15                      81\n",
              "2           3  Female   20                  16                       6\n",
              "3           4  Female   23                  16                      77\n",
              "4           5  Female   31                  17                      40"
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      "cell_type": "code",
      "source": [
        "#Checking if some data is left empty.\n",
        "customer_data.isnull().sum()"
      ],
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      "execution_count": 5,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CustomerID                0\n",
              "Gender                    0\n",
              "Age                       0\n",
              "Annual Income (k$)        0\n",
              "Spending Score (1-100)    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
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    {
      "cell_type": "markdown",
      "source": [
        "In this example I have chosen to make observations from my dataset only on the basis of Annual Income and Spending Score. I have to slice  my dataset  using the iloc function."
      ],
      "metadata": {
        "id": "KfGKP6mKquap"
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      "cell_type": "code",
      "source": [
        "X = customer_data.iloc[:,[3,4]].values"
      ],
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        "id": "ORzGPR2Pq7xA"
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      "execution_count": 6,
      "outputs": []
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      "source": [
        "print(X)"
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      "execution_count": 7,
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          "text": [
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    {
      "cell_type": "markdown",
      "source": [
        "Choosing the 'K' in our K Means Clustering Algorithm. Now we will we using the WCSS method for the same"
      ],
      "metadata": {
        "id": "k77IxthTrMkx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "wcss = []\n",
        "\n",
        "for i in range(1,11):\n",
        "   kmeans = KMeans(n_clusters = i , init = 'k-means++')\n",
        "   kmeans.fit(X)\n",
        "\n",
        "   wcss.append(kmeans.inertia_)\n",
        "   "
      ],
      "metadata": {
        "id": "lZWyWlfMrRSy"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set()\n",
        "plt.plot(range(1,11), wcss)\n",
        "plt.xlabel('Number Of Clusters')\n",
        "plt.ylabel('WCSS')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "A1owQG5EXXTj",
        "outputId": "f9772c83-6f2a-4c01-d73b-ed9e720172a6"
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      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
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            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "I choose the number of clusters to be 5 as it is the elbow point in my graph."
      ],
      "metadata": {
        "id": "xj1X-tC8g0RL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans = KMeans(n_clusters = 5 , init = 'k-means++')\n",
        "Y = kmeans.fit_predict(X)\n",
        "print(Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GUnSrwORcTSn",
        "outputId": "090f2220-365a-4ecd-8acf-d819d887b505"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0\n",
            " 4 0 4 0 4 0 3 0 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
            " 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
            " 3 3 3 3 3 3 3 3 3 3 3 3 1 2 1 3 1 2 1 2 1 3 1 2 1 2 1 2 1 2 1 3 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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (6,6))\n",
        "plt.scatter(X[Y == 0,0] , X[Y == 0,1] , s = 50 , c = 'blue' , label =' Cluster I')\n",
        "plt.scatter(X[Y == 1,0] , X[Y == 1,1] , s = 50 , c = 'green' , label =' Cluster II')\n",
        "plt.scatter(X[Y == 2,0] , X[Y == 2,1] , s = 50 , c = 'red' , label =' Cluster III')\n",
        "plt.scatter(X[Y == 3,0] , X[Y == 3,1] , s = 50 , c = 'pink' , label =' Cluster IV')\n",
        "plt.scatter(X[Y == 4,0] , X[Y == 4,1] , s = 50 , c = 'orange' , label =' Cluster V')\n",
        "\n",
        "plt.scatter(kmeans.cluster_centers_[:,0] , kmeans.cluster_centers_[:,1] , s =150 , c = 'yellow')\n",
        "\n",
        "plt.xlabel('Annual Income')\n",
        "plt.ylabel('Spending Score')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "wDqgws6Cg7Ay",
        "outputId": "665ab2e8-3749-41aa-c788-7b68f0519622"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the graph we can conclude that offers and membership cards should be made for the customers of the blue cluster(less annual income , less spends) and the red cluster(more annual income , less spends). Now we can form a marketing strategy for our product."
      ],
      "metadata": {
        "id": "sKPEKsRCh8Op"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-sjnNw96ifgA"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}