KMeans_Clustering.ipynb

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      "source": [
        "#understanding K-Means\n",
        "from sklearn.cluster import KMeans\n",
        "import numpy as np"
      ]
    },
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      "cell_type": "code",
      "source": [
        "X = np.array([[1, 2], [1, 4], [1, 0], [10, 2], [10, 4], [10, 0]])\n",
        "kmeans = KMeans(n_clusters=2, random_state=0).fit(X)"
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        "kmeans.predict([[0, 0], [12, 3]])"
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              "array([[10.,  2.],\n",
              "       [ 1.,  2.]])"
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      "source": [
        "### K-Means Clustering\n",
        "\n",
        "use the Mall Customer dataset to segment the customers in clusters based on their Age, Annual Income, Spending Score, etc."
      ],
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      "source": [
        "from sklearn.cluster import KMeans\n",
        "from sklearn import preprocessing\n",
        "import sklearn.cluster as cluster\n",
        "import sklearn.metrics as metrics\n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "import seaborn as sns\n",
        "from matplotlib import pyplot as plt\n",
        "%matplotlib inline"
      ],
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      "source": [
        "#Load the dataset\n",
        "df = pd.read_csv(\"Mall_Customers.csv\")\n",
        "df.head()"
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              "   CustomerID   Genre  Age  Annual Income (k$)  Spending Score (1-100)\n",
              "0           1    Male   19                  15                      39\n",
              "1           2    Male   21                  15                      81\n",
              "2           3  Female   20                  16                       6\n",
              "3           4  Female   23                  16                      77\n",
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        "df.shape"
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              "(200, 5)"
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      "source": [
        "#### Objective\n",
        "Customer segmentation deals with grouping clusters together based on some common patterns within their attributes. To keep the example simple and to visualize the clustering on a 2-D graph we will use only two attributes Annual Income and Spending Score. "
      ],
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      "source": [
        "#### Apply Feature Scaling\n",
        "Clustering algorithms like K-means require feature scaling of the data as part of data preprocessing to produce good results. This is because clustering techniques use distance calculation between the data points. Hence it is proper to bring data of different units under a common scale."
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        "scaler = MinMaxScaler()\n",
        "scale = scaler.fit_transform(df[['Annual Income (k$)','Spending Score (1-100)']])\n",
        "df_scale = pd.DataFrame(scale, columns = ['Annual Income (k$)','Spending Score (1-100)']);\n",
        "df_scale.head(5)"
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              "   Annual Income (k$)  Spending Score (1-100)\n",
              "0            0.000000                0.387755\n",
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      "source": [
        "#### Applying Kmeans with 2 Clusters (K=2)\n",
        "Let us see how to apply K-Means in Sklearn to group the dataset into 2 clusters (0 and 1). The output shows the cluster (0th or 1st) corresponding to the data points in the dataset."
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        "km=KMeans(n_clusters=2)\n",
        "y_predicted = km.fit_predict(df_scale[['Annual Income (k$)','Spending Score (1-100)']])\n",
        "y_predicted"
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              "       0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1,\n",
              "       1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0,\n",
              "       1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,\n",
              "       1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,\n",
              "       1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,\n",
              "       1, 0], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "km.cluster_centers_\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "okCpnv7SPTO8",
        "outputId": "f0f05de2-659b-46f1-a1e9-3ae48a297310"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.37861485, 0.73950929],\n",
              "       [0.36929553, 0.31163817]])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finally, let us visualize the results. From the graph, it is evident that there is a scope for data to be grouped into more clusters than only 2. But how to know how many clusters? Let us understand this in the next section."
      ],
      "metadata": {
        "id": "PH52Lb1JPyOf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Clusters'] = km.labels_\n",
        "sns.scatterplot(x=\"Spending Score (1-100)\", y=\"Annual Income (k$)\",hue = 'Clusters',  data=df,palette='viridis')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "mew1BLL6PY6q",
        "outputId": "f6713f65-6b66-49be-8247-9fa28b75e09d"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5b019223d0>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Finding Optimum number of Clusters in K Means\n",
        "\n",
        "The tricky part with K-Means clustering is you do not know in advance that in how many clusters the given data can be divided (hence it is an unsupervised learning algorithm). It can be done with the trial and error method but let us see a more proper technique for this.\n",
        "\n",
        "**i) Elbow Method with Within-Cluster-Sum of Squared Error (WCSS)**\n",
        "\n",
        "The Elbow Method is a popular technique for determining the optimal number of clusters. Here, we calculate the Within-Cluster-Sum of Squared Errors (WCSS) for various values of k and choose the k for which WSS first starts to diminish. In the plot of WSS-versus-k, this can be observed as an elbow.\n",
        "\n",
        "- The Squared Error for a data point is the square of the distance of a point from its cluster center.\n",
        "- The WSS score is the summation of Squared Errors for all given data points.\n",
        "- Distance metrics like Euclidean Distance or the Manhattan Distance can be used.\n",
        "\n",
        "Continuing with our example, we calculate the WCSS for K=2 to k=12 and calculate the WCSS in each iteration."
      ],
      "metadata": {
        "id": "TJcnSg5IQt33"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "K=range(2,12)\n",
        "wss = []\n",
        "\n",
        "for k in K:\n",
        "    kmeans=cluster.KMeans(n_clusters=k)\n",
        "    kmeans=kmeans.fit(df_scale)\n",
        "    wss_iter = kmeans.inertia_\n",
        "    wss.append(wss_iter)"
      ],
      "metadata": {
        "id": "JSMbMn-pP23g"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let us now plot the WCSS vs K cluster graph. It can be seen below that there is an elbow bend at K=5 i.e. it is the point after which WCSS does not diminish much with the increase in value of K."
      ],
      "metadata": {
        "id": "4sTmEczKRBvY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.xlabel('K')\n",
        "plt.ylabel('Within-Cluster-Sum of Squared Errors (WSS)')\n",
        "plt.plot(K,wss)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "A6UppjmvQ9x2",
        "outputId": "2bc133b8-4cec-4904-aa99-3cfa6b08ad88"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f5afeb88450>]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Applying Kmeans with 5 Clusters (K=5)\n",
        "Now that we have identified that the optimum value of K is 5"
      ],
      "metadata": {
        "id": "Jx7Y1gyCROhh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "km=KMeans(n_clusters=5)\n",
        "y_predicted = km.fit_predict(df_scale[['Annual Income (k$)','Spending Score (1-100)']])\n",
        "y_predicted"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aXbpAqzzREWQ",
        "outputId": "eff9a2e7-ca87-455c-b153-09c1275d05f3"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3,\n",
              "       4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 0,\n",
              "       4, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 1, 0, 1, 2, 1, 2, 1,\n",
              "       0, 1, 2, 1, 2, 1, 2, 1, 2, 1, 0, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1,\n",
              "       2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1,\n",
              "       2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1,\n",
              "       2, 1], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Clusters'] = km.labels_\n",
        "\n",
        "sns.scatterplot(x=\"Spending Score (1-100)\", y=\"Annual Income (k$)\",hue = 'Clusters',  data=df,palette='viridis')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "hEbaYe0CRXqF",
        "outputId": "b70c6eb3-732e-4cfa-f3d7-d37748df3e13"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5afe967650>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "zxPjNdZsXrJ_"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}