Crime_Data_Analysis.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Crime Data Analysis.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VGU6jOdp0Mjc"
      },
      "source": [
        "# Data Preprocessing on the training data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pUe5pzwE0VLq"
      },
      "source": [
        "## Importing Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZLMJ8F870Yjl"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SiWlHYLd0i7i"
      },
      "source": [
        "## Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gEZlQGXc0m0R"
      },
      "source": [
        "train_dataset = pd.read_csv('Crime Data_2001_2012.csv')\n",
        "test_dataset = pd.read_csv('Crime Data_2013.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y464hboJ9QcR"
      },
      "source": [
        "##Removing Total value Rows from the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jIQ8m74r2wkB",
        "outputId": "0c5ad6d4-cf6f-4084-a587-196ddefb11e8"
      },
      "source": [
        "total_vals = (train_dataset == 'TOTAL').any(axis=1)\n",
        "for i in reversed(range(len(total_vals))):\n",
        "  if total_vals[i]:\n",
        "    train_dataset.drop(train_dataset.index[i],inplace=True)\n",
        "train_dataset.reset_index(drop=True,inplace=True)\n",
        "print(train_dataset)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "            STATE/UT       DISTRICT  ...  OTHER IPC CRIMES  TOTAL IPC CRIMES\n",
            "0     ANDHRA PRADESH       ADILABAD  ...              1518              4154\n",
            "1     ANDHRA PRADESH      ANANTAPUR  ...               754              4125\n",
            "2     ANDHRA PRADESH       CHITTOOR  ...              1262              5818\n",
            "3     ANDHRA PRADESH       CUDDAPAH  ...              1181              3140\n",
            "4     ANDHRA PRADESH  EAST GODAVARI  ...              2313              6507\n",
            "...              ...            ...  ...               ...               ...\n",
            "8604        DELHI UT            STF  ...                 1                 6\n",
            "8605        DELHI UT           WEST  ...              1144              5558\n",
            "8606     LAKSHADWEEP    LAKSHADWEEP  ...                32                60\n",
            "8607      PUDUCHERRY       KARAIKAL  ...               392               787\n",
            "8608      PUDUCHERRY     PUDUCHERRY  ...              1668              3494\n",
            "\n",
            "[8609 rows x 33 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SIdevup47XNG",
        "outputId": "4faea40e-1a4d-4939-bb4a-65e2783e5ef5"
      },
      "source": [
        "total_vals = (test_dataset == 'ZZ TOTAL').any(axis=1)\n",
        "for i in reversed(range(len(total_vals))):\n",
        "  if total_vals[i]:\n",
        "    test_dataset.drop(test_dataset.index[i],inplace=True)\n",
        "test_dataset.reset_index(drop=True,inplace=True)\n",
        "print(test_dataset)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "           STATE/UT     DISTRICT  ...  OTHER IPC CRIMES  TOTAL IPC CRIMES\n",
            "0    Andhra Pradesh     ADILABAD  ...              1390              6381\n",
            "1    Andhra Pradesh    ANANTAPUR  ...              1634              6913\n",
            "2    Andhra Pradesh     CHITTOOR  ...              2239              5610\n",
            "3    Andhra Pradesh     CUDDAPAH  ...              1741              7048\n",
            "4    Andhra Pradesh    CYBERABAD  ...              3139             19992\n",
            "..              ...          ...  ...               ...               ...\n",
            "783        Delhi UT          STF  ...                 4                15\n",
            "784        Delhi UT         WEST  ...              2393              8813\n",
            "785     Lakshadweep  LAKSHADWEEP  ...                18                40\n",
            "786      Puducherry     KARAIKAL  ...               329               643\n",
            "787      Puducherry   PUDUCHERRY  ...              1464              3113\n",
            "\n",
            "[788 rows x 33 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T4LeCI5S9b9B"
      },
      "source": [
        "##Extracting useful data such as districts, year and crime features. While removing total crimes per district column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fpnogFqz17ot"
      },
      "source": [
        "x_train_vals = train_dataset.iloc[:,1:-1].values\n",
        "x_test_vals = test_dataset.iloc[:,1:-1].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sTIJuaBB089R",
        "outputId": "138eb1f4-e45a-406d-ff13-232c3d8efbee"
      },
      "source": [
        "print(x_train_vals)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[['ADILABAD' 2001 101 ... 0 181 1518]\n",
            " ['ANANTAPUR' 2001 151 ... 0 270 754]\n",
            " ['CHITTOOR' 2001 101 ... 0 404 1262]\n",
            " ...\n",
            " ['LAKSHADWEEP' 2012 0 ... 0 0 32]\n",
            " ['KARAIKAL' 2012 5 ... 0 44 392]\n",
            " ['PUDUCHERRY' 2012 24 ... 0 219 1668]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fIeFzddQTlMt",
        "outputId": "bd9442f9-0a91-474b-ec01-bfbf73e8c476"
      },
      "source": [
        "print(x_test_vals)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[['ADILABAD' 2013 96 ... 0 376 1390]\n",
            " ['ANANTAPUR' 2013 156 ... 0 573 1634]\n",
            " ['CHITTOOR' 2013 72 ... 0 546 2239]\n",
            " ...\n",
            " ['LAKSHADWEEP' 2013 0 ... 0 0 18]\n",
            " ['KARAIKAL' 2013 6 ... 0 32 329]\n",
            " ['PUDUCHERRY' 2013 25 ... 0 206 1464]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VSzvGibDigTn"
      },
      "source": [
        "##Keeping copy of this data for future alterations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "70fF1f68qi0L"
      },
      "source": [
        "X_train = x_train_vals\n",
        "X_test = x_test_vals"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Atup1JRimjj"
      },
      "source": [
        "#Applying Kmeans Algorithm "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A8uZp1qqpURY",
        "outputId": "d1c957c0-8e6d-4b03-a2dc-5cf895fe5bac"
      },
      "source": [
        "print(X_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[['ADILABAD' 2001 1.15834478099478 ... -0.07243098488497635\n",
            "  0.406186562461889 0.21914665054412608]\n",
            " ['ANANTAPUR' 2001 2.240488258804757 ... -0.07243098488497635\n",
            "  0.9732153426377105 -0.2568454995502764]\n",
            " ['CHITTOOR' 2001 1.15834478099478 ... -0.07243098488497635\n",
            "  1.826944067846251 0.05965189868003309]\n",
            " ...\n",
            " ['LAKSHADWEEP' 2012 -1.0275850441813743 ... -0.07243098488497635\n",
            "  -0.7469843275586022 -0.7066705419169762]\n",
            " ['KARAIKAL' 2012 -0.9193706964003766 ... -0.07243098488497635\n",
            "  -0.46665549241549936 -0.48238104710809543]\n",
            " ['PUDUCHERRY' 2012 -0.5081561748325852 ... -0.07243098488497635\n",
            "  0.6482887382672959 0.3126006067144931]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vIraeXNfpxAe",
        "outputId": "bd249597-b004-47a6-a1b2-f5d270ace829"
      },
      "source": [
        "print(X_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[['ADILABAD' 2013 1.0501304332137822 ... -0.07243098488497635\n",
            "  1.6485529909370038 0.13939927461207957]\n",
            " ['ANANTAPUR' 2013 2.348702606585755 ... -0.07243098488497635\n",
            "  2.9036616391913506 0.2914177099825432]\n",
            " ['CHITTOOR' 2013 0.530701563864993 ... -0.07243098488497635\n",
            "  2.7316416721717194 0.6683486665363567]\n",
            " ...\n",
            " ['LAKSHADWEEP' 2013 -1.0275850441813743 ... -0.07243098488497635\n",
            "  -0.7469843275586022 -0.7153929111595437]\n",
            " ['KARAIKAL' 2013 -0.8977278268441771 ... -0.07243098488497635\n",
            "  -0.543108811090891 -0.5216317086996496]\n",
            " ['PUDUCHERRY' 2013 -0.4865133052763857 ... -0.07243098488497635\n",
            "  0.5654643097022883 0.18550322632279395]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "21aeDAvuitwm"
      },
      "source": [
        "###Applying Feature Scaling to the 29 features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W4pA6u0Lp86L"
      },
      "source": [
        "sc = StandardScaler()\n",
        "X_train[:,2:] = sc.fit_transform(X_train[:,2:])\n",
        "X_test[:,2:] = sc.transform(X_test[:,2:])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e0iFDpVgwqxq",
        "outputId": "2a2183b7-97d8-4f74-f5e6-e2275789d54b"
      },
      "source": [
        "print(X_train)\n",
        "print(X_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[['ADILABAD' 2001 1.15834478099478 ... -0.07243098488497636\n",
            "  0.406186562461889 0.21914665054412608]\n",
            " ['ANANTAPUR' 2001 2.240488258804757 ... -0.07243098488497636\n",
            "  0.9732153426377105 -0.2568454995502764]\n",
            " ['CHITTOOR' 2001 1.15834478099478 ... -0.07243098488497636\n",
            "  1.826944067846251 0.05965189868003309]\n",
            " ...\n",
            " ['LAKSHADWEEP' 2012 -1.0275850441813743 ... -0.07243098488497636\n",
            "  -0.7469843275586022 -0.7066705419169762]\n",
            " ['KARAIKAL' 2012 -0.9193706964003767 ... -0.07243098488497636\n",
            "  -0.46665549241549936 -0.48238104710809543]\n",
            " ['PUDUCHERRY' 2012 -0.5081561748325854 ... -0.07243098488497636\n",
            "  0.6482887382672959 0.3126006067144931]]\n",
            "[['ADILABAD' 2013 1.0501304332137822 ... -0.07243098488497636\n",
            "  1.6485529909370038 0.13939927461207957]\n",
            " ['ANANTAPUR' 2013 2.348702606585755 ... -0.07243098488497636\n",
            "  2.9036616391913506 0.2914177099825432]\n",
            " ['CHITTOOR' 2013 0.5307015638649929 ... -0.07243098488497636\n",
            "  2.7316416721717194 0.6683486665363567]\n",
            " ...\n",
            " ['LAKSHADWEEP' 2013 -1.0275850441813743 ... -0.07243098488497636\n",
            "  -0.7469843275586022 -0.7153929111595437]\n",
            " ['KARAIKAL' 2013 -0.8977278268441772 ... -0.07243098488497636\n",
            "  -0.543108811090891 -0.5216317086996496]\n",
            " ['PUDUCHERRY' 2013 -0.48651330527638575 ... -0.07243098488497636\n",
            "  0.5654643097022883 0.18550322632279395]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3r_KHyAbi0FI"
      },
      "source": [
        "###Keeping the necessary fields, i.e. the features (29 types of crimes)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LBxIBGT4rq6S"
      },
      "source": [
        "train = np.delete(X_train,0,axis=1)\n",
        "test = np.delete(X_test,0,axis=1)\n",
        "train = np.delete(train,0,axis=1)\n",
        "test = np.delete(test,0,axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QTc8DbgJsK2x",
        "outputId": "c8630f37-b63b-453b-d2ff-c0c730bae97e"
      },
      "source": [
        "print(train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1.15834478099478 0.3507242547900932 1.1408812243500706 ...\n",
            "  -0.07243098488497636 0.406186562461889 0.21914665054412608]\n",
            " [2.240488258804757 1.5828478068707175 -0.41727638022354047 ...\n",
            "  -0.07243098488497636 0.9732153426377105 -0.2568454995502764]\n",
            " [1.15834478099478 0.29385701392483365 -0.31989152993768977 ...\n",
            "  -0.07243098488497636 1.826944067846251 0.05965189868003309]\n",
            " ...\n",
            " [-1.0275850441813743 -0.7866205625150985 -0.5146612305093913 ...\n",
            "  -0.07243098488497636 -0.7469843275586022 -0.7066705419169762]\n",
            " [-0.9193706964003767 -0.6728860807845793 -0.31989152993768977 ...\n",
            "  -0.07243098488497636 -0.46665549241549936 -0.48238104710809543]\n",
            " [-0.5081561748325854 -0.3885498764582813 0.4591872723491157 ...\n",
            "  -0.07243098488497636 0.6482887382672959 0.3126006067144931]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h8KAVz4XsMGC",
        "outputId": "b851ada4-5c1e-4883-9e86-12f45db9f79d"
      },
      "source": [
        "print(test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1.0501304332137822 0.5781932182511316 0.7513418232066678 ...\n",
            "  -0.07243098488497636 1.6485529909370038 0.13939927461207957]\n",
            " [2.348702606585755 2.037785733792794 -0.22250667965183912 ...\n",
            "  -0.07243098488497636 2.9036616391913506 0.2914177099825432]\n",
            " [0.5307015638649929 0.3696800017451798 -0.31989152993768977 ...\n",
            "  -0.07243098488497636 2.7316416721717194 0.6683486665363567]\n",
            " ...\n",
            " [-1.0275850441813743 -0.7866205625150985 -0.5146612305093913 ...\n",
            "  -0.07243098488497636 -0.7469843275586022 -0.7153929111595437]\n",
            " [-0.8977278268441772 -0.7297533216498389 -0.5146612305093913 ...\n",
            "  -0.07243098488497636 -0.543108811090891 -0.5216317086996496]\n",
            " [-0.48651330527638575 -0.6349745868744062 -0.31989152993768977 ...\n",
            "  -0.07243098488497636 0.5654643097022883 0.18550322632279395]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0GY44Bxxi-uD"
      },
      "source": [
        "###Finding the number of cluster using the Elbow Method"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "Ku1SUcDprSEu",
        "outputId": "790efae8-7c41-4503-dc9a-b8191c010089"
      },
      "source": [
        "from sklearn.cluster import KMeans\n",
        "wcss = []\n",
        "for i in range(1,11):\n",
        "  Kmeans = KMeans(n_clusters = i, init='k-means++', random_state=42)\n",
        "  Kmeans.fit(train)\n",
        "  wcss.append(Kmeans.inertia_)\n",
        "plt.plot(range(1,11),wcss)\n",
        "plt.title('The Elbow Method')\n",
        "plt.xlabel('Number of Clusters')\n",
        "plt.ylabel('WCSS')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'WCSS')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YzqhEft-jHnr"
      },
      "source": [
        "###Finding the number of clusters using Silhouette Coefficient"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y9NMuhvYtXD4",
        "outputId": "555621cf-e5ac-4015-b40f-d455435d8e1b"
      },
      "source": [
        "from sklearn.metrics import silhouette_score\n",
        "  \n",
        "for n_cluster in range(2, 11):\n",
        "    Kmeans1 = KMeans(n_clusters=n_cluster).fit(train)\n",
        "    label = Kmeans1.labels_\n",
        "    sil_coeff = silhouette_score(train, label, metric='euclidean')\n",
        "    print(\"For n_clusters={}, The Silhouette Coefficient is {}\".format(n_cluster, sil_coeff))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For n_clusters=2, The Silhouette Coefficient is 0.8744789604109744\n",
            "For n_clusters=3, The Silhouette Coefficient is 0.44366754781514894\n",
            "For n_clusters=4, The Silhouette Coefficient is 0.3690598781147069\n",
            "For n_clusters=5, The Silhouette Coefficient is 0.3131045231709387\n",
            "For n_clusters=6, The Silhouette Coefficient is 0.31862331438749003\n",
            "For n_clusters=7, The Silhouette Coefficient is 0.30951065665330196\n",
            "For n_clusters=8, The Silhouette Coefficient is 0.18303696098695285\n",
            "For n_clusters=9, The Silhouette Coefficient is 0.1769354660713655\n",
            "For n_clusters=10, The Silhouette Coefficient is 0.18864688782716985\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kbUhKqI0jOvl"
      },
      "source": [
        "###Applying the Kmeans Algorithm for 5 clusters on train data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9NrVl-VXtiCD"
      },
      "source": [
        "Kmeans = KMeans(n_clusters = 5, init='k-means++', random_state=42)\n",
        "Y_kmeans = Kmeans.fit_predict(train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VLZcSl5ptpKy",
        "outputId": "caceea27-b693-424c-8d09-4ac93fbad0a7"
      },
      "source": [
        "print(Y_kmeans)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 0 0 ... 1 1 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xogg2qxFjVAB"
      },
      "source": [
        "###Joining the Districts and Clusters together for reference"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0YFfq6AItzZF"
      },
      "source": [
        "a = pd.DataFrame(X_train)\n",
        "b = pd.DataFrame(Y_kmeans)\n",
        "trainset = pd.concat([a.iloc[:,0], b], ignore_index=True, axis = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GtJCx31Ju0iN",
        "outputId": "518d7c73-6f88-40c4-842c-3f48a80596b7"
      },
      "source": [
        "print(trainset)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                  0  1\n",
            "0          ADILABAD  0\n",
            "1         ANANTAPUR  0\n",
            "2          CHITTOOR  0\n",
            "3          CUDDAPAH  0\n",
            "4     EAST GODAVARI  0\n",
            "...             ... ..\n",
            "8604            STF  1\n",
            "8605           WEST  0\n",
            "8606    LAKSHADWEEP  1\n",
            "8607       KARAIKAL  1\n",
            "8608     PUDUCHERRY  1\n",
            "\n",
            "[8609 rows x 2 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B7EX_Lwjje1z"
      },
      "source": [
        "###Plotting the results for train data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "yGiZFWIXxhNu",
        "outputId": "4899b320-f790-4fcd-ca48-0c1efe94dcc3"
      },
      "source": [
        "plt.bar(trainset[0], trainset[1])\n",
        "plt.title('District Vs Cluster')\n",
        "plt.xlabel('District')\n",
        "plt.ylabel('Cluster')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SlEaOu9PjoIw"
      },
      "source": [
        "###Applying Kmeans onto test data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ZLcI5eHypSZ"
      },
      "source": [
        "Kmeans_test = KMeans(n_clusters = 5, init='k-means', random_state=42)\n",
        "Y_kmeans_test = Kmeans.fit_predict(test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yuQNPK21jwLl"
      },
      "source": [
        "###Joining the districts and clusters for test data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2KYSmmu3yxAH"
      },
      "source": [
        "a = pd.DataFrame(X_test)\n",
        "b = pd.DataFrame(Y_kmeans_test)\n",
        "testset = pd.concat([a.iloc[:,0], b], ignore_index=True, axis = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MdgRgSSKy4oc",
        "outputId": "0a9505b6-b052-4e32-e019-c796749f27a1"
      },
      "source": [
        "print(testset)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "               0  1\n",
            "0       ADILABAD  3\n",
            "1      ANANTAPUR  3\n",
            "2       CHITTOOR  3\n",
            "3       CUDDAPAH  3\n",
            "4      CYBERABAD  2\n",
            "..           ... ..\n",
            "783          STF  0\n",
            "784         WEST  2\n",
            "785  LAKSHADWEEP  0\n",
            "786     KARAIKAL  0\n",
            "787   PUDUCHERRY  0\n",
            "\n",
            "[788 rows x 2 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NgydrQXAj3c8"
      },
      "source": [
        "###Visualizing results for test data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "0iLaM4Qqy8cl",
        "outputId": "f35f2656-2078-4a4e-c91a-14f7c0939c02"
      },
      "source": [
        "plt.bar(testset[0], testset[1])\n",
        "plt.title('District Vs Cluster')\n",
        "plt.xlabel('District')\n",
        "plt.ylabel('Cluster')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yeRgzA8Ukbs1"
      },
      "source": [
        "##These results are not adequate. The initial intuition to compare the cluster centroids would not be very helpful. This is because the data  is not forming optimal clusters. We will now try to preprocess the data better."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ftC56Phj9--1"
      },
      "source": [
        "##Applying Feature Scaling to original data again"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W-UCD7ezAJG2"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "x_train_vals[:,2:] = sc.fit_transform(x_train_vals[:,2:])\n",
        "x_test_vals[:,2:] = sc.transform(x_test_vals[:,2:])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 263
        },
        "id": "6QSiFCJ6s91F",
        "outputId": "09166bb9-f450-4fc1-b125-dc6e1aa9ef5a"
      },
      "source": [
        "display(x_train_vals)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([['ADILABAD', 2001, 1.15834478099478, ..., -0.07243098488497635,\n",
              "        0.406186562461889, 0.21914665054412608],\n",
              "       ['ANANTAPUR', 2001, 2.240488258804757, ..., -0.07243098488497635,\n",
              "        0.9732153426377105, -0.2568454995502764],\n",
              "       ['CHITTOOR', 2001, 1.15834478099478, ..., -0.07243098488497635,\n",
              "        1.826944067846251, 0.05965189868003309],\n",
              "       ...,\n",
              "       ['LAKSHADWEEP', 2012, -1.0275850441813743, ...,\n",
              "        -0.07243098488497635, -0.7469843275586022, -0.7066705419169762],\n",
              "       ['KARAIKAL', 2012, -0.9193706964003766, ..., -0.07243098488497635,\n",
              "        -0.46665549241549936, -0.48238104710809543],\n",
              "       ['PUDUCHERRY', 2012, -0.5081561748325852, ...,\n",
              "        -0.07243098488497635, 0.6482887382672959, 0.3126006067144931]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gMLbCDXktA_b",
        "outputId": "aaf9ab4a-9e05-46f4-c917-fedb5ea3389d"
      },
      "source": [
        "print(x_test_vals)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[['ADILABAD' 2013 1.0501304332137822 ... -0.07243098488497635\n",
            "  1.6485529909370038 0.13939927461207957]\n",
            " ['ANANTAPUR' 2013 2.348702606585755 ... -0.07243098488497635\n",
            "  2.9036616391913506 0.2914177099825432]\n",
            " ['CHITTOOR' 2013 0.530701563864993 ... -0.07243098488497635\n",
            "  2.7316416721717194 0.6683486665363567]\n",
            " ...\n",
            " ['LAKSHADWEEP' 2013 -1.0275850441813743 ... -0.07243098488497635\n",
            "  -0.7469843275586022 -0.7153929111595437]\n",
            " ['KARAIKAL' 2013 -0.8977278268441771 ... -0.07243098488497635\n",
            "  -0.543108811090891 -0.5216317086996496]\n",
            " ['PUDUCHERRY' 2013 -0.4865133052763857 ... -0.07243098488497635\n",
            "  0.5654643097022883 0.18550322632279395]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SntW9ceZknoG"
      },
      "source": [
        "#We now try to reduce the dimensions of the features. We apply Principal Component analysis to reduce the number of features to 2. These will be the 2 best projections of the 29 features on a principal dimension. This will also help us to visualize the results better."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ye_EgtQAlTJL"
      },
      "source": [
        "#Applying Kmeans after PCA "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8XmE-_bOlXts"
      },
      "source": [
        "##Applying PCA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rwW3DXIRtCnI"
      },
      "source": [
        "from sklearn.decomposition import PCA\n",
        "pca = PCA(n_components = 2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4kgn7ftKlbm-"
      },
      "source": [
        "##Merging the data by dropping the original features, while concatenating the 2 new principal features."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9WyM3kJ0kWxu"
      },
      "source": [
        "pca_train_result = pca.fit_transform(x_train_vals[:,2:])\n",
        "x_train = pd.DataFrame(x_train_vals)\n",
        "pca_train = pd.DataFrame(pca_train_result)\n",
        "x_train.drop(x_train.iloc[:,2:], inplace = True, axis = 1) \n",
        "x_trainset = pd.concat([x_train, pca_train], ignore_index=True, axis = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i8kSD9tjuGNt",
        "outputId": "883e45a6-13f4-45ea-d527-a69b5b1723ea"
      },
      "source": [
        "print(x_trainset)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                  0     1         2         3\n",
            "0          ADILABAD  2001  1.428720 -0.785335\n",
            "1         ANANTAPUR  2001  1.513024 -2.004361\n",
            "2          CHITTOOR  2001  2.193210 -2.650716\n",
            "3          CUDDAPAH  2001 -0.024711 -1.138970\n",
            "4     EAST GODAVARI  2001  2.279901 -2.273365\n",
            "...             ...   ...       ...       ...\n",
            "8604            STF  2012 -2.857952  0.496852\n",
            "8605           WEST  2012  5.198004  4.855982\n",
            "8606    LAKSHADWEEP  2012 -2.872274  0.489674\n",
            "8607       KARAIKAL  2012 -2.437590  0.327408\n",
            "8608     PUDUCHERRY  2012 -0.729543 -0.375557\n",
            "\n",
            "[8609 rows x 4 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B0tki0UKkV0O"
      },
      "source": [
        "pca_test_result = pca.transform(x_test_vals[:,2:])\n",
        "x_test = pd.DataFrame(x_test_vals)\n",
        "pca_test = pd.DataFrame(pca_test_result)\n",
        "x_test.drop(x_test.iloc[:,2:], inplace = True, axis = 1)\n",
        "x_testset = pd.concat([x_test, pca_test], ignore_index=True, axis = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BjHY_0XpuclS",
        "outputId": "6b09beec-2a69-4f3b-90fb-4ed478581bb7"
      },
      "source": [
        "print(x_testset)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "               0     1          2         3\n",
            "0       ADILABAD  2013   3.422947 -2.747885\n",
            "1      ANANTAPUR  2013   3.688475 -2.866613\n",
            "2       CHITTOOR  2013   1.783107 -1.790103\n",
            "3       CUDDAPAH  2013   3.601937 -3.637887\n",
            "4      CYBERABAD  2013  16.820744 -7.324309\n",
            "..           ...   ...        ...       ...\n",
            "783          STF  2013  -2.785471  0.433639\n",
            "784         WEST  2013  10.421459  6.033382\n",
            "785  LAKSHADWEEP  2013  -2.859908  0.528613\n",
            "786     KARAIKAL  2013  -2.523605  0.365329\n",
            "787   PUDUCHERRY  2013  -0.900950 -0.175523\n",
            "\n",
            "[788 rows x 4 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tHs8xlrqlwJy"
      },
      "source": [
        "##Extracting the features to apply Kmeans"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mboVIuVptNIk"
      },
      "source": [
        "k_train = x_trainset.iloc[:,2:].values\n",
        "k_test = x_testset.iloc[:,2:].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pn-RRs0Vu6JE",
        "outputId": "8cf48214-fd0c-4175-e03e-d1f24d9d8fdc"
      },
      "source": [
        "print(k_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 1.42871999 -0.78533508]\n",
            " [ 1.51302397 -2.00436132]\n",
            " [ 2.19321035 -2.6507163 ]\n",
            " ...\n",
            " [-2.87227417  0.48967384]\n",
            " [-2.43758956  0.32740834]\n",
            " [-0.72954313 -0.37555689]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Gogl5hGu54U",
        "outputId": "22a35424-6d86-46f9-feac-37ca0ba396b3"
      },
      "source": [
        "print(k_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 3.42294738 -2.74788518]\n",
            " [ 3.68847469 -2.86661272]\n",
            " [ 1.78310673 -1.79010326]\n",
            " ...\n",
            " [-2.85990758  0.52861282]\n",
            " [-2.52360548  0.36532886]\n",
            " [-0.90095024 -0.175523  ]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n08X7DwDl1iz"
      },
      "source": [
        "##Applying the Kmeans"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "svXhKDZtmBJR"
      },
      "source": [
        "###Using Silhouette Coefficient to find number of optimal Clusters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yjMXo1ysDA_Y",
        "outputId": "9b1475fa-c787-4700-a76a-becc335e8922"
      },
      "source": [
        "from sklearn.metrics import silhouette_score\n",
        "  \n",
        "for n_cluster in range(2, 11):\n",
        "    kmeans1 = KMeans(n_clusters=n_cluster).fit(k_train)\n",
        "    label = kmeans1.labels_\n",
        "    sil_coeff = silhouette_score(k_train, label, metric='euclidean')\n",
        "    print(\"For n_clusters={}, The Silhouette Coefficient is {}\".format(n_cluster, sil_coeff))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For n_clusters=2, The Silhouette Coefficient is 0.8400897080419875\n",
            "For n_clusters=3, The Silhouette Coefficient is 0.5967175127312884\n",
            "For n_clusters=4, The Silhouette Coefficient is 0.53189118653319\n",
            "For n_clusters=5, The Silhouette Coefficient is 0.443216850901647\n",
            "For n_clusters=6, The Silhouette Coefficient is 0.43148804372418736\n",
            "For n_clusters=7, The Silhouette Coefficient is 0.45490812654495116\n",
            "For n_clusters=8, The Silhouette Coefficient is 0.4189107804954833\n",
            "For n_clusters=9, The Silhouette Coefficient is 0.42272323685262275\n",
            "For n_clusters=10, The Silhouette Coefficient is 0.43379173660819537\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k71svbR7l8B5"
      },
      "source": [
        "###Using the Elbow method to find number of optimal clusters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "RNy-EKRU8--w",
        "outputId": "df08e731-daee-43c5-b919-429c76ad7200"
      },
      "source": [
        "from sklearn.cluster import KMeans\n",
        "wcss = []\n",
        "for i in range(1,11):\n",
        "  kmeans = KMeans(n_clusters = i, init='k-means++', random_state=42)\n",
        "  kmeans.fit(k_train)\n",
        "  wcss.append(kmeans.inertia_)\n",
        "plt.plot(range(1,11),wcss)\n",
        "plt.title('The Elbow Method')\n",
        "plt.xlabel('Number of Clusters')\n",
        "plt.ylabel('WCSS')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'WCSS')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bQIybK3LmH3A"
      },
      "source": [
        "###Applying 2 Cluster Kmeans as suggested by Silhouette Coeff Method"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "17h1oob8mObn"
      },
      "source": [
        "kmeans3 = KMeans(n_clusters = 2, init='k-means++', random_state=42)\n",
        "y_kmeans1 = kmeans3.fit_predict(k_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gXCuZeEdmR2m",
        "outputId": "351dd23d-613b-4e0e-ef9c-c4d4e370dd28"
      },
      "source": [
        "print(y_kmeans1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 0 0 ... 0 0 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "jde-6fSymS3G",
        "outputId": "25eff6d8-db5f-4e12-9147-c9989a37e8de"
      },
      "source": [
        "plt.scatter(k_train[y_kmeans1 == 0, 0],k_train[y_kmeans1 == 0, 1], s = 10, c = 'red', label =  'Cluster 1')\n",
        "plt.scatter(k_train[y_kmeans1 == 1, 0],k_train[y_kmeans1 == 1, 1], s = 10, c = 'blue', label =  'Cluster 2')\n",
        "plt.scatter(kmeans3.cluster_centers_[:, 0], kmeans3.cluster_centers_[:, 1], s = 300, c = 'yellow', label = 'Centroids')\n",
        "plt.title('Clusters of Customers')\n",
        "plt.xlabel('Feature 1')\n",
        "plt.ylabel('Feature 2')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FnSbYifdmTnM"
      },
      "source": [
        "###This gives us some outliers. For this Outlier Detection, we increase the number of clusters to 4 so that the algorithm can identify the outliers as 1 cluster."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j1NG8dpoDfVo"
      },
      "source": [
        "kmeans2 = KMeans(n_clusters = 4, init='k-means++', random_state=42)\n",
        "y_kmeans = kmeans2.fit_predict(k_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n2mnee4RDzVp",
        "outputId": "cb7892c1-ce65-42af-e6a3-62676a107971"
      },
      "source": [
        "print(y_kmeans)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 0 0 ... 3 3 3]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "pYpzD2ZZEGMi",
        "outputId": "ace03d2f-aa4e-497c-91fe-2f72a0e1e92b"
      },
      "source": [
        "plt.scatter(k_train[y_kmeans == 0, 0],k_train[y_kmeans == 0, 1], s = 10, c = 'red', label =  'Cluster 1')\n",
        "plt.scatter(k_train[y_kmeans == 1, 0],k_train[y_kmeans == 1, 1], s = 10, c = 'blue', label =  'Cluster 2')\n",
        "plt.scatter(k_train[y_kmeans == 2, 0],k_train[y_kmeans == 2, 1], s = 10, c = 'green', label =  'Cluster 3')\n",
        "plt.scatter(k_train[y_kmeans == 3, 0],k_train[y_kmeans == 3, 1], s = 10, c = 'cyan', label =  'Cluster 4')\n",
        "plt.scatter(kmeans2.cluster_centers_[:, 0], kmeans2.cluster_centers_[:, 1], s = 300, c = 'yellow', label = 'Centroids')\n",
        "plt.title('Clusters of Customers')\n",
        "plt.xlabel('Feature 1')\n",
        "plt.ylabel('Feature 2')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xcDzCpGumiRj"
      },
      "source": [
        "###Now that the outliers are identified, we remove them by first adding the clusters to the original data. We do this so the respective districts and data gets removed"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "COmiXXW38UCv"
      },
      "source": [
        "y = pd.DataFrame(y_kmeans)\n",
        "x_result = pd.concat([x_trainset, y], ignore_index=True, axis = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fJKq2ZJ_82hg",
        "outputId": "57fbc442-0830-4229-f59a-1bd3548cdfd3"
      },
      "source": [
        "print(x_result)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                  0     1         2         3  4\n",
            "0          ADILABAD  2001  1.428720 -0.785335  0\n",
            "1         ANANTAPUR  2001  1.513024 -2.004361  0\n",
            "2          CHITTOOR  2001  2.193210 -2.650716  0\n",
            "3          CUDDAPAH  2001 -0.024711 -1.138970  3\n",
            "4     EAST GODAVARI  2001  2.279901 -2.273365  0\n",
            "...             ...   ...       ...       ... ..\n",
            "8604            STF  2012 -2.857952  0.496852  3\n",
            "8605           WEST  2012  5.198004  4.855982  0\n",
            "8606    LAKSHADWEEP  2012 -2.872274  0.489674  3\n",
            "8607       KARAIKAL  2012 -2.437590  0.327408  3\n",
            "8608     PUDUCHERRY  2012 -0.729543 -0.375557  3\n",
            "\n",
            "[8609 rows x 5 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k7pwTUm69A_T",
        "outputId": "0cde600a-0dbf-46c1-ac0f-00c9d67ac458"
      },
      "source": [
        "outliers = (x_result == 2).any(axis=1)\n",
        "for i in reversed(range(len(y_kmeans))):\n",
        "  if outliers[i]:\n",
        "    x_result.drop(x_result.index[i],inplace=True)\n",
        "x_result.reset_index(drop=True,inplace=True)\n",
        "print(x_result)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                  0     1         2         3  4\n",
            "0          ADILABAD  2001  1.428720 -0.785335  0\n",
            "1         ANANTAPUR  2001  1.513024 -2.004361  0\n",
            "2          CHITTOOR  2001  2.193210 -2.650716  0\n",
            "3          CUDDAPAH  2001 -0.024711 -1.138970  3\n",
            "4     EAST GODAVARI  2001  2.279901 -2.273365  0\n",
            "...             ...   ...       ...       ... ..\n",
            "8592            STF  2012 -2.857952  0.496852  3\n",
            "8593           WEST  2012  5.198004  4.855982  0\n",
            "8594    LAKSHADWEEP  2012 -2.872274  0.489674  3\n",
            "8595       KARAIKAL  2012 -2.437590  0.327408  3\n",
            "8596     PUDUCHERRY  2012 -0.729543 -0.375557  3\n",
            "\n",
            "[8597 rows x 5 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jb7LAKoBm2Lv"
      },
      "source": [
        "###We again extract the features to apply Kmeans"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LuH4FJXT9fv1",
        "outputId": "18d8f166-b5e9-469f-a204-ebcd34b99a5c"
      },
      "source": [
        "new_train = x_result.iloc[:,2:4]\n",
        "print(new_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "             2         3\n",
            "0     1.428720 -0.785335\n",
            "1     1.513024 -2.004361\n",
            "2     2.193210 -2.650716\n",
            "3    -0.024711 -1.138970\n",
            "4     2.279901 -2.273365\n",
            "...        ...       ...\n",
            "8592 -2.857952  0.496852\n",
            "8593  5.198004  4.855982\n",
            "8594 -2.872274  0.489674\n",
            "8595 -2.437590  0.327408\n",
            "8596 -0.729543 -0.375557\n",
            "\n",
            "[8597 rows x 2 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TwEQhurom9ah"
      },
      "source": [
        "###Again using the Elbow Method"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "XXaiRSpQ_zdz",
        "outputId": "82297d18-18f8-4c2b-9ef1-e1aa65fae6d9"
      },
      "source": [
        "wcss = []\n",
        "for i in range(1,11):\n",
        "  new_kmeans = KMeans(n_clusters = i, init='k-means++', random_state=42)\n",
        "  new_kmeans.fit(new_train)\n",
        "  wcss.append(new_kmeans.inertia_)\n",
        "plt.plot(range(1,11),wcss)\n",
        "plt.title('The Elbow Method')\n",
        "plt.xlabel('Number of Clusters')\n",
        "plt.ylabel('WCSS')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'WCSS')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YEKOk0XwnAw2"
      },
      "source": [
        "###Applying Kmeans with 4 Clusters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uI-Sm8qVAJmv"
      },
      "source": [
        "new_kmeans1 = KMeans(n_clusters = 4, init='k-means++', random_state=42)\n",
        "new_kmeans_result = new_kmeans1.fit_predict(new_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "MRhgmD-XAabU",
        "outputId": "c7fd8aec-ac1c-4f79-8b1e-bb70e241f2be"
      },
      "source": [
        "new_train = new_train.values\n",
        "plt.scatter(new_train[new_kmeans_result == 0, 0],new_train[new_kmeans_result == 0, 1], s = 10, c = 'red', label =  'Cluster 1')\n",
        "plt.scatter(new_train[new_kmeans_result == 1, 0],new_train[new_kmeans_result == 1, 1], s = 10, c = 'blue', label =  'Cluster 2')\n",
        "plt.scatter(new_train[new_kmeans_result == 2, 0],new_train[new_kmeans_result == 2, 1], s = 10, c = 'green', label =  'Cluster 3')\n",
        "plt.scatter(new_train[new_kmeans_result == 3, 0],new_train[new_kmeans_result == 3, 1], s = 10, c = 'pink', label =  'Cluster 4')\n",
        "plt.scatter(new_kmeans1.cluster_centers_[:, 0], new_kmeans1.cluster_centers_[:, 1], s = 50, c = 'yellow', label = 'Centroids')\n",
        "plt.title('Clusters of Customers')\n",
        "plt.xlabel('Feature 1')\n",
        "plt.ylabel('Feature 2')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}