From 90598bdec7d01f65e900df45a20fa17705e408a4 Mon Sep 17 00:00:00 2001
From: Chesta Gupta <76441931+chesta123@users.noreply.github.com>
Date: Fri, 22 Apr 2022 12:19:10 +0530
Subject: [PATCH 1/2] Created using Colaboratory

---
 Customer_Segmentation_correct.ipynb | 1387 +++++++++++++++++++++++++++
 1 file changed, 1387 insertions(+)
 create mode 100644 Customer_Segmentation_correct.ipynb

diff --git a/Customer_Segmentation_correct.ipynb b/Customer_Segmentation_correct.ipynb
new file mode 100644
index 0000000..6c47b83
--- /dev/null
+++ b/Customer_Segmentation_correct.ipynb
@@ -0,0 +1,1387 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Customer Segmentation_correct.ipynb",
+      "provenance": [],
+      "collapsed_sections": [],
+      "authorship_tag": "ABX9TyPyqN8wsgw/h/XHAWBnlKzA",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/chesta123/Customer-Segmentation/blob/main/Customer_Segmentation_correct.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9knzLcwMICw6"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "%matplotlib inline\n",
+        "from sklearn.cluster import KMeans"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df=pd.read_csv('/content/Customer_Segmentation.csv')"
+      ],
+      "metadata": {
+        "id": "ADhKXZasKhTZ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.head()"
+      ],
+      "metadata": {
+        "id": "1egfQdiEKzaX",
+        "outputId": "efa4ae30-79bc-4b3d-d4bc-f598981d78cb",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)\n",
+              "0           1    Male   19                  15                      39\n",
+              "1           2    Male   21                  15                      81\n",
+              "2           3  Female   20                  16                       6\n",
+              "3           4  Female   23                  16                      77\n",
+              "4           5  Female   31                  17                      40"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-afcb558d-abc8-47b5-b4b7-0a8e062a98ca\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>CustomerID</th>\n",
+              "      <th>Gender</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Annual Income (k$)</th>\n",
+              "      <th>Spending Score (1-100)</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>19</td>\n",
+              "      <td>15</td>\n",
+              "      <td>39</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>21</td>\n",
+              "      <td>15</td>\n",
+              "      <td>81</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>Female</td>\n",
+              "      <td>20</td>\n",
+              "      <td>16</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4</td>\n",
+              "      <td>Female</td>\n",
+              "      <td>23</td>\n",
+              "      <td>16</td>\n",
+              "      <td>77</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5</td>\n",
+              "      <td>Female</td>\n",
+              "      <td>31</td>\n",
+              "      <td>17</td>\n",
+              "      <td>40</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-afcb558d-abc8-47b5-b4b7-0a8e062a98ca')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-afcb558d-abc8-47b5-b4b7-0a8e062a98ca button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-afcb558d-abc8-47b5-b4b7-0a8e062a98ca');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# df['Gender'].replace('Male',1,inplace=True)\n",
+        "# df['Gender'].replace('Female',0,inplace=True)"
+      ],
+      "metadata": {
+        "id": "t4DoiuT0oIcT"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "replace_dict = {'Male':1,'Female':0}\n",
+        "replace_dict"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "K2rNtmJ6q2hv",
+        "outputId": "e5871421-3b39-4c9a-9b58-313914def6c2"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'Female': 0, 'Male': 1}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df['Gender']=df['Gender'].map(replace_dict)"
+      ],
+      "metadata": {
+        "id": "ykTKGOtxrQeb"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "hB3twCSsrexh",
+        "outputId": "f034464f-a30a-48db-9459-969008668466"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)\n",
+              "0           1       1   19                  15                      39\n",
+              "1           2       1   21                  15                      81\n",
+              "2           3       0   20                  16                       6\n",
+              "3           4       0   23                  16                      77\n",
+              "4           5       0   31                  17                      40"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-dd5f0498-75d1-48e3-b65b-8dc9df394506\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>CustomerID</th>\n",
+              "      <th>Gender</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Annual Income (k$)</th>\n",
+              "      <th>Spending Score (1-100)</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>19</td>\n",
+              "      <td>15</td>\n",
+              "      <td>39</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>21</td>\n",
+              "      <td>15</td>\n",
+              "      <td>81</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>20</td>\n",
+              "      <td>16</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4</td>\n",
+              "      <td>0</td>\n",
+              "      <td>23</td>\n",
+              "      <td>16</td>\n",
+              "      <td>77</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5</td>\n",
+              "      <td>0</td>\n",
+              "      <td>31</td>\n",
+              "      <td>17</td>\n",
+              "      <td>40</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dd5f0498-75d1-48e3-b65b-8dc9df394506')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
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+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-dd5f0498-75d1-48e3-b65b-8dc9df394506 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-dd5f0498-75d1-48e3-b65b-8dc9df394506');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "2 factors are represnted on 2 axis ..3rd factor is shown by dot sizes and 4th factor by color"
+      ],
+      "metadata": {
+        "id": "BmmFY3wSyAg_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.scatter(df['Annual Income (k$)'],df['Spending Score (1-100)'],df['Age'],df['Gender'])"
+      ],
+      "metadata": {
+        "id": "T7Lh5z3HHUoF",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 282
+        },
+        "outputId": "76be6afd-13ac-4cae-d58f-3cbef2168a0e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.collections.PathCollection at 0x7f51f52fecd0>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Plotting 3-D graph**"
+      ],
+      "metadata": {
+        "id": "z06QPzkF0o0K"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# from mpl_toolkits.mplot3d import Axes3D"
+      ],
+      "metadata": {
+        "id": "RZkIx61Gz-gM"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# # Creating figure\n",
+        "# fig = plt.figure(figsize=(10,6))\n",
+        "# ax=plt.axes(projection=\"3d\")"
+      ],
+      "metadata": {
+        "id": "J7VUIevIz_zG"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# x = df['Gender']\n",
+        "# y = df['Age']\n",
+        "# z = df['Annual Income (k$)']"
+      ],
+      "metadata": {
+        "id": "cfcr54of2FJL"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# # Creating plot\n",
+        "# ax.scatter3D(x,y,z,color='green')"
+      ],
+      "metadata": {
+        "id": "1BNRCKat1Nkj"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# plt.show()"
+      ],
+      "metadata": {
+        "id": "30bqdMt12Tlt"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "km = KMeans(n_clusters = 5)"
+      ],
+      "metadata": {
+        "id": "V1Etytqw5cp0"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_predicted = km.fit_predict(df[['Annual Income (k$)','Spending Score (1-100)','Age','Gender']])"
+      ],
+      "metadata": {
+        "id": "nGuCXkYR85p7"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_predicted"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "tfYkMpMiRnpc",
+        "outputId": "6dcaaf03-94c8-4561-c75f-25ac5da805ec"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3,\n",
+              "       2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3,\n",
+              "       2, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3,\n",
+              "       1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 4, 0, 1, 0, 4, 0, 4, 0,\n",
+              "       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, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0,\n",
+              "       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], dtype=int32)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 16
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Apppending a column in df named cluster"
+      ],
+      "metadata": {
+        "id": "29sCAvgNTjrn"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df['cluster']=y_predicted"
+      ],
+      "metadata": {
+        "id": "rZFoRAsjSeMp"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.head"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "tCc0SAbsS4lJ",
+        "outputId": "11090cf5-7f39-4274-bc72-7b187ab834e4"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<bound method NDFrame.head of      CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)  \\\n",
+              "0             1       1   19                  15                      39   \n",
+              "1             2       1   21                  15                      81   \n",
+              "2             3       0   20                  16                       6   \n",
+              "3             4       0   23                  16                      77   \n",
+              "4             5       0   31                  17                      40   \n",
+              "..          ...     ...  ...                 ...                     ...   \n",
+              "195         196       0   35                 120                      79   \n",
+              "196         197       0   45                 126                      28   \n",
+              "197         198       1   32                 126                      74   \n",
+              "198         199       1   32                 137                      18   \n",
+              "199         200       1   30                 137                      83   \n",
+              "\n",
+              "     cluster  \n",
+              "0          2  \n",
+              "1          3  \n",
+              "2          2  \n",
+              "3          3  \n",
+              "4          2  \n",
+              "..       ...  \n",
+              "195        0  \n",
+              "196        4  \n",
+              "197        0  \n",
+              "198        4  \n",
+              "199        0  \n",
+              "\n",
+              "[200 rows x 6 columns]>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 18
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df1 = df[df.cluster==0]\n",
+        "df2 = df[df.cluster==1]\n",
+        "df3 = df[df.cluster==2]\n",
+        "df4 = df[df.cluster==3]\n",
+        "df5 = df[df.cluster==4]"
+      ],
+      "metadata": {
+        "id": "LCnMYZX9S57M"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df1"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "VlExT_QpWcqt",
+        "outputId": "e2f520e2-72c1-440f-f158-32f315ecc39b"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)  \\\n",
+              "123         124       1   39                  69                      91   \n",
+              "125         126       0   31                  70                      77   \n",
+              "127         128       1   40                  71                      95   \n",
+              "129         130       1   38                  71                      75   \n",
+              "131         132       1   39                  71                      75   \n",
+              "133         134       0   31                  72                      71   \n",
+              "135         136       0   29                  73                      88   \n",
+              "137         138       1   32                  73                      73   \n",
+              "139         140       0   35                  74                      72   \n",
+              "141         142       1   32                  75                      93   \n",
+              "143         144       0   32                  76                      87   \n",
+              "145         146       1   28                  77                      97   \n",
+              "147         148       0   32                  77                      74   \n",
+              "149         150       1   34                  78                      90   \n",
+              "151         152       1   39                  78                      88   \n",
+              "153         154       0   38                  78                      76   \n",
+              "155         156       0   27                  78                      89   \n",
+              "157         158       0   30                  78                      78   \n",
+              "159         160       0   30                  78                      73   \n",
+              "161         162       0   29                  79                      83   \n",
+              "163         164       0   31                  81                      93   \n",
+              "165         166       0   36                  85                      75   \n",
+              "167         168       0   33                  86                      95   \n",
+              "169         170       1   32                  87                      63   \n",
+              "171         172       1   28                  87                      75   \n",
+              "173         174       1   36                  87                      92   \n",
+              "175         176       0   30                  88                      86   \n",
+              "177         178       1   27                  88                      69   \n",
+              "179         180       1   35                  93                      90   \n",
+              "181         182       0   32                  97                      86   \n",
+              "183         184       0   29                  98                      88   \n",
+              "185         186       1   30                  99                      97   \n",
+              "187         188       1   28                 101                      68   \n",
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+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1f67a07e-ece8-4ae4-a7dd-906cf6b7d480')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
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+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-1f67a07e-ece8-4ae4-a7dd-906cf6b7d480 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-1f67a07e-ece8-4ae4-a7dd-906cf6b7d480');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 20
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.scatter(df1['Annual Income (k$)'],df1['Spending Score (1-100)'],df1['Age'],df1['Gender'])\n",
+        "plt.scatter(df2['Annual Income (k$)'],df2['Spending Score (1-100)'],df2['Age'],df2['Gender'])\n",
+        "plt.scatter(df3['Annual Income (k$)'],df3['Spending Score (1-100)'],df3['Age'],df3['Gender'])\n",
+        "plt.scatter(df4['Annual Income (k$)'],df4['Spending Score (1-100)'],df4['Age'],df4['Gender'])\n",
+        "plt.scatter(df5['Annual Income (k$)'],df5['Spending Score (1-100)'],df5['Age'],df5['Gender'])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 282
+        },
+        "id": "1YL_a2qDV4X9",
+        "outputId": "c83c0e41-4eac-4c52-9b79-d75fb1eb4479"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.collections.PathCollection at 0x7f51f45bb790>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 21
+        },
+        {
+          "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": [
+        "km.predict([[23,25,87,1]])"
+      ],
+      "metadata": {
+        "id": "f12zD40oWP3G",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "495b5b03-d7cf-4e10-fb42-db659daefb65"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:451: UserWarning: X does not have valid feature names, but KMeans was fitted with feature names\n",
+            "  \"X does not have valid feature names, but\"\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([2], dtype=int32)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 22
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "This is an unsupervised ML model. Therefore accuracy can't be calculated"
+      ],
+      "metadata": {
+        "id": "6FviBRLOziPn"
+      }
+    }
+  ]
+}
\ No newline at end of file

From 7a006815d1bb2a848b0682f062109b08d9a9ce4a Mon Sep 17 00:00:00 2001
From: Chesta Gupta <76441931+chesta123@users.noreply.github.com>
Date: Fri, 22 Apr 2022 12:37:10 +0530
Subject: [PATCH 2/2] Add files via upload

---
 Customer Segmentation_correct.ipynb | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 Customer Segmentation_correct.ipynb

diff --git a/Customer Segmentation_correct.ipynb b/Customer Segmentation_correct.ipynb
new file mode 100644
index 0000000..440acbd
--- /dev/null
+++ b/Customer Segmentation_correct.ipynb	
@@ -0,0 +1 @@
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{'Male':1,'Female':0}\n","replace_dict"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"K2rNtmJ6q2hv","executionInfo":{"status":"ok","timestamp":1649938626647,"user_tz":-330,"elapsed":38,"user":{"displayName":"Chesta Gupta","userId":"17654074163390916896"}},"outputId":"e5871421-3b39-4c9a-9b58-313914def6c2"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'Female': 0, 'Male': 1}"]},"metadata":{},"execution_count":5}]},{"cell_type":"code","source":["df['Gender']=df['Gender'].map(replace_dict)"],"metadata":{"id":"ykTKGOtxrQeb"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"hB3twCSsrexh","executionInfo":{"status":"ok","timestamp":1649938626650,"user_tz":-330,"elapsed":36,"user":{"displayName":"Chesta 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(1-100)'],df['Age'],df['Gender'])"],"metadata":{"id":"T7Lh5z3HHUoF","colab":{"base_uri":"https://localhost:8080/","height":282},"executionInfo":{"status":"ok","timestamp":1649938626651,"user_tz":-330,"elapsed":36,"user":{"displayName":"Chesta Gupta","userId":"17654074163390916896"}},"outputId":"76be6afd-13ac-4cae-d58f-3cbef2168a0e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.collections.PathCollection at 0x7f51f52fecd0>"]},"metadata":{},"execution_count":8},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["**Plotting 3-D graph**"],"metadata":{"id":"z06QPzkF0o0K"}},{"cell_type":"code","source":["# from mpl_toolkits.mplot3d import Axes3D"],"metadata":{"id":"RZkIx61Gz-gM"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# # Creating figure\n","# fig = plt.figure(figsize=(10,6))\n","# ax=plt.axes(projection=\"3d\")"],"metadata":{"id":"J7VUIevIz_zG"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# x = df['Gender']\n","# y = df['Age']\n","# z = df['Annual Income (k$)']"],"metadata":{"id":"cfcr54of2FJL"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# # Creating plot\n","# ax.scatter3D(x,y,z,color='green')"],"metadata":{"id":"1BNRCKat1Nkj"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# plt.show()"],"metadata":{"id":"30bqdMt12Tlt"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["km = KMeans(n_clusters = 5)"],"metadata":{"id":"V1Etytqw5cp0"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["y_predicted = km.fit_predict(df[['Annual Income (k$)','Spending Score (1-100)','Age','Gender']])"],"metadata":{"id":"nGuCXkYR85p7"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["y_predicted"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tfYkMpMiRnpc","executionInfo":{"status":"ok","timestamp":1649938627569,"user_tz":-330,"elapsed":37,"user":{"displayName":"Chesta Gupta","userId":"17654074163390916896"}},"outputId":"6dcaaf03-94c8-4561-c75f-25ac5da805ec"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3,\n","       2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3,\n","       2, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3,\n","       1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 4, 0, 1, 0, 4, 0, 4, 0,\n","       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, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0,\n","       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], dtype=int32)"]},"metadata":{},"execution_count":16}]},{"cell_type":"markdown","source":["Apppending a column in df named cluster"],"metadata":{"id":"29sCAvgNTjrn"}},{"cell_type":"code","source":["df['cluster']=y_predicted"],"metadata":{"id":"rZFoRAsjSeMp"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df.head"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tCc0SAbsS4lJ","executionInfo":{"status":"ok","timestamp":1649938627579,"user_tz":-330,"elapsed":44,"user":{"displayName":"Chesta Gupta","userId":"17654074163390916896"}},"outputId":"11090cf5-7f39-4274-bc72-7b187ab834e4"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<bound method NDFrame.head of      CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)  \\\n","0             1       1   19                  15                      39   \n","1             2       1   21                  15                      81   \n","2             3       0   20                  16                       6   \n","3             4       0   23                  16                      77   \n","4             5       0   31                  17                      40   \n","..          ...     ...  ...                 ...                     ...   \n","195         196       0   35                 120                      79   \n","196         197       0   45                 126                      28   \n","197         198       1   32                 126                      74   \n","198         199       1   32                 137                      18   \n","199         200       1   30                 137                      83   \n","\n","     cluster  \n","0          2  \n","1          3  \n","2          2  \n","3          3  \n","4          2  \n","..       ...  \n","195        0  \n","196        4  \n","197        0  \n","198        4  \n","199        0  \n","\n","[200 rows x 6 columns]>"]},"metadata":{},"execution_count":18}]},{"cell_type":"code","source":["df1 = df[df.cluster==0]\n","df2 = df[df.cluster==1]\n","df3 = df[df.cluster==2]\n","df4 = df[df.cluster==3]\n","df5 = df[df.cluster==4]"],"metadata":{"id":"LCnMYZX9S57M"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df1"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"VlExT_QpWcqt","executionInfo":{"status":"ok","timestamp":1649938627584,"user_tz":-330,"elapsed":41,"user":{"displayName":"Chesta Gupta","userId":"17654074163390916896"}},"outputId":"e2f520e2-72c1-440f-f158-32f315ecc39b"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["     CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)  \\\n","123         124       1   39                  69                      91   \n","125         126       0   31                  70                      77   \n","127         128       1   40                  71                      95   \n","129         130       1   38                  71                      75   \n","131         132       1   39                  71                      75   \n","133         134       0   31                  72                      71   \n","135         136       0   29                  73                      88   \n","137         138       1   32                  73                      73   \n","139         140       0   35                  74                      72   \n","141         142       1   32                  75                      93   \n","143         144       0   32                  76                      87   \n","145         146       1   28                  77                      97   \n","147         148       0   32                  77                      74   \n","149         150       1   34                  78                      90   \n","151         152       1   39                  78                      88   \n","153         154       0   38                  78                      76   \n","155         156       0   27                  78                      89   \n","157         158       0   30                  78                      78   \n","159         160       0   30                  78                      73   \n","161         162       0   29                  79                      83   \n","163         164       0   31                  81                      93   \n","165         166       0   36                  85                      75   \n","167         168       0   33                  86                      95   \n","169         170       1   32                  87                      63   \n","171         172       1   28                  87                      75   \n","173         174       1   36                  87                      92   \n","175         176       0   30                  88                      86   \n","177         178       1   27                  88                      69   \n","179         180       1   35                  93                      90   \n","181         182       0   32                  97                      86   \n","183         184       0   29                  98                      88   \n","185         186       1   30                  99                      97   \n","187         188       1   28                 101                      68   \n","189         190       0   36                 103                      85   \n","191         192       0   32                 103                      69   \n","193         194       0   38                 113                      91   \n","195         196       0   35                 120                      79   \n","197         198       1   32                 126                      74   \n","199         200       1   30                 137                      83   \n","\n","     cluster  \n","123        0  \n","125        0  \n","127        0  \n","129        0  \n","131        0  \n","133        0  \n","135        0  \n","137        0  \n","139        0  \n","141        0  \n","143        0  \n","145        0  \n","147        0  \n","149        0  \n","151        0  \n","153        0  \n","155        0  \n","157        0  \n","159        0  \n","161        0  \n","163        0  \n","165        0  \n","167        0  \n","169        0  \n","171        0  \n","173        0  \n","175        0  \n","177        0  \n","179        0  \n","181        0  \n","183        0  \n","185        0  \n","187        0  \n","189        0  \n","191        0  \n","193        0  \n","195        0  \n","197        0  \n","199        0  "],"text/html":["\n","  <div id=\"df-1f67a07e-ece8-4ae4-a7dd-906cf6b7d480\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>CustomerID</th>\n","      <th>Gender</th>\n","      <th>Age</th>\n","      <th>Annual Income (k$)</th>\n","      <th>Spending Score (1-100)</th>\n","      <th>cluster</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>123</th>\n","      <td>124</td>\n","      <td>1</td>\n","      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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":20}]},{"cell_type":"code","source":["plt.scatter(df1['Annual Income (k$)'],df1['Spending Score (1-100)'],df1['Age'],df1['Gender'])\n","plt.scatter(df2['Annual Income (k$)'],df2['Spending Score (1-100)'],df2['Age'],df2['Gender'])\n","plt.scatter(df3['Annual Income (k$)'],df3['Spending Score (1-100)'],df3['Age'],df3['Gender'])\n","plt.scatter(df4['Annual Income (k$)'],df4['Spending Score (1-100)'],df4['Age'],df4['Gender'])\n","plt.scatter(df5['Annual Income (k$)'],df5['Spending Score (1-100)'],df5['Age'],df5['Gender'])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":282},"id":"1YL_a2qDV4X9","executionInfo":{"status":"ok","timestamp":1649938627585,"user_tz":-330,"elapsed":40,"user":{"displayName":"Chesta Gupta","userId":"17654074163390916896"}},"outputId":"c83c0e41-4eac-4c52-9b79-d75fb1eb4479"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.collections.PathCollection at 0x7f51f45bb790>"]},"metadata":{},"execution_count":21},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXgAAAD4CAYAAADmWv3KAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOydd3hUxdeA37nbUwkJvTcBQVBAUEFEiooiKip2AUX0Z0GxoX5gQUSsgFjACoqIIqCCiKiAAmKhSu+9prfte+f744aEzW4KJCQkzPs8eZKdO+VsNjl37plThJQShUKhUFQ+tPIWQKFQKBSnB6XgFQqFopKiFLxCoVBUUpSCVygUikqKUvAKhUJRSTGXtwAACQkJsmHDhuUthkKhUFQoVq1alSSlrFbQ9TNCwTds2JCVK1eWtxgKhUJRoRBC7C3sujLRKBQKRSVFKXiFQqGopCgFr1AoFJWUIhW8EOJTIcQxIcSGE9qqCiF+EUJsz/kel9MuhBDvCCF2CCH+E0K0O53CKxQKhaJgirODnwJcla/tGeA3KWUz4Lec1wC9gWY5X0OAD0pHzJJxbH8S21btxOv2lrcoCoVCUWYU6UUjpfxDCNEwX/N1QLecn6cCS4DhOe2fSyOD2V9CiCpCiFpSysOlJfDJkJmaxaib3mLTiq2YrWakLvnfuIH0vrdHeYijUCgUZcqp2uBrnKC0jwA1cn6uA+w/od+BnLYQhBBDhBArhRArExMTT1GMwnm5/9tsWL4Fr9uHM8OFK8vNe49+yoZlm8P2TzqYzO71e/F5fadFHoVCoShLSuwHL6WUQoiTzjkspfwQ+BCgQ4cOpZ6zOPlwKhuWbcHv9Qe1e11eZk+YT+suLXPbMlIyefnmt9n451bMVhOapvHwu/fS846upS2WQqFQlBmnuoM/KoSoBZDz/VhO+0Gg3gn96ua0lTlZqVmYLaaQdikN5X8iL9/8NhuWbcbn8eHKdJOd7mT8kMls+Wd7WYmrUCgUpc6pKvgfgAE5Pw8Avj+h/e4cb5qLgPTysr/XaVYLkzlUwVsdVi6+tkPu68QDyWxasRW/LxDUz+v2Meed+addTsWZjZQ+9Kz30I9djp7YAz3rU6TUy1usUkEGktBTh6IfaYN+tCN65jtIGSh6oKLCUBw3ya+AFUBzIcQBIcS9wFiglxBiO9Az5zXAfGAXsAP4CHjwtEhdDMwWM49+cB82hxWhCQBsDisJdapy7f+uyO2XnpiB2RJqqZJSknwoNaRdcXYh05+DrMmgH4TAfsiagMx8pbzFKjFSBpApt4LnV8ANMg2yP0Zmji1yrKLiUBwvmtsKuBTiipLjPfNQSYUqLbrd0pnaTWsyZ+JPJB9IpuPV7bj6vp5ERDty+9Q/t27YsVaHlU7XKDf+sxmpp4L7J+BE91oXOL9BRj+JEI6Chp75eP8CPRk48YzKDc4ZyOinEMJaXpIpSpEzItnY6eSc9k0YPuXhAq9bbRYenDCIiQ9/gtflQUpDuVerU5VrhvQqQ0kVpxOpZ4N3hfHCejFCiyx6kJ4Cwgwyf/yEBD0LTCVX8Lqu43F6sEfaEUKUeL7iL3wMCOfboIPMBqXgKwWVXsEXhysHXk69FnWYPeFHUg6lctG1Hehzf6+gnb6i4qI7v4eM50HkWCSljowZhRZxXeEDTQ0AK+AMbtcSjK8SIKVk1rh5TBv9Le4sN7HVYnlw/CAuu/niEs1bbKwdIZy93VQTRJWykUFx2hGGVaV86dChg1TpghWnA+nbjky+EXDnu2JHxM9CWJoVPt6zHJn6IJCzuxYg4j5FWEtmvps3eSGTnvgcj9OT22aLsPLKj8/R9rJWJZq7uOhZ70LWhxgmKAsIEyLuY4S1Q1FDFWcIQohVUsoCP7BKu4NfuXAdKxeuIyY+it739iCuemx5i6QoB6RzOhAucM2HdE5HxL5Q6Hhh6wzVl4B7sfEEYOuB0KJLLNf0MXOClDuAx+llxtg5ZabgtaiHkbbu4FkMIgrs1yBMJXsyUZxZVEoF/+24uUwZ+TUepwezxcTs8T8yee2bxNeKK2/RFGWNfhgI5/oXAP1QsaYQWhxE9CtVsTKSMsK2H92bVKrrFIWwnAuWc8t0TUXZUenSBXs9Pj555svc3ZHfFyArNZvpr8wqZ8kU5YL1IiDcWYoDrMH2bqmno6c9hX7kPMM3PP1ZpJ5VrGWyM5yMu38yN1QdyI3V7uHDp7/A6yk45UWLTqGmIbPFRPtebYq1nkJRHCqdgs9Oy0aYgt9WwB8gcX9yOUmkKE+E4ybQooATg95MoEUhHDfmtkgpkSkDwD0f8ABucM1Fpg4ucg0pJc9cOZpfPl9CVlo2GcmZfP/uT7x6+4QCxzw4fhCOKHtuDIbVbqZ+cwu3P9vtlN6nQhGOSqfgY6vFEBkTEdRmi7DRtlvZ2DWLQvp3Ir1rkdJVdF8pka556OkvoWdNRurhH+sVBSO0KET8bLD3BuzGl703In52sC3d9x8E9hBsr/eCbzPSt7XQNbav3sWeDfvwefJ8yr1uH//8tJqkg+E3Fo3bNOCj9W9z3cNX0ff+Ony5ei/vL1xDLNeiJ9+CDJRLhg9FJaPS2eA1TePVn/6Pp3uNIuAP4Pf6uahPO64f2rtc5ZJSIjOeBdd8w7daOKDq1whz+EArAJkxElxzARdgQTq/hIS5CE0dGBeGlAHwbwEkmFsiTDUQVd4ufJB+iFxPmRMRJggcAkvzAoce25eEZgrdK5mtZhIPpJBQJz7suBoNqnH/632QSeMN3/Pj+NYhk2+HaosQIjTdRmXF5/Wxf8shoqpEUL1+tfIWp1JQ6RQ8QNMLGjF93yQObDtEZGwENRtWL9sgknB4l4N7AUZYOCCdyIwXEFU/Cdtd+neB6wfy3Pt8oCcjs6ciooeWkdAVD+n5G5k+DI4/IQkbxL6NsF1S+EBLG5D+0HbpBct5hQ5tfmFTfN7QsQG/TsNWBd/AAaRrdph1dZAZ4F0Ktm6Fy11J+HnqYj54bAq6rhPwBWjctiEvfPtEgTdHRfGodCaa49gjbDQ9vxG1GtUof+UOEDhopLLMRTdymxTYPxGEJV+jz8iJogiLDBxBpg0BPcnYEcts0FOQqf8r0uQhTHUg4laCDmSFAyIHh7gO5k82Vq1uPDc+dg32SJsxTBPYIqwMef1OHFFFBMsF9mHY/EPeDASOFD42SCY3MiTitmKw7veNTHzoY7LTnbgy3XjdPrat3MlTPUdxJsTpVGQq5Q7+jMRyPsGh4bYQL44gzI1B5vfCcID5gtMgXOVAOmeGj87Ej3R+jYh+vNDxIvo5sHVGOmcBGiLiZoStS+513fktZI0DPRGp1Ybo4WgOw/R375g7uKBHG36b9gcWm5krB3WnZRhPmZA1rR2Q7rkgnfmvGE8VRSB92w3Tn28jIJC2HojY0RXKjDdj7Bw8zuCbkx7QST6YwsblW4JqNyhODqXgywhhaY6MfQ0yXjD+mW3dETHPFtzfVA0ZOxbSn8nZyfvBeikion/ZCV3RCOwjODHYcXwQ2FvkcCEE2LohwphFdOdcyBhFrslMPwTpw5FaJMJmFIZp1+M82vUo3JwTgv0qyHofAgfIO+C1g62j4aNeCFJPQ6bcBjKT3M2DZxEyZRDEzzoznlyLwZHdx8JfEMb5huLUUQq+DNEcvcFR/MNezXE10toO/NtAiwfzuRXmn7ZcsLQD90KMQ+kTEA7jWknInkBaso/vPqrJ1rUOWnfK5rp7koiyvJOr4E8FIWwQPxOZ9YHhoiks4LgFETmwyLHS9V1OIrQTnwx9ENgJ/o1gaX3KcpUl517cnEM7j6IHgk1fAb9O03aNy0mqyoFS8Gc4wlTTSAClKBLh6IvMfg90L3nRqxqICISjZJGoGYmJPNC9OVnpJnxejQ1/R/HzjKp8uOQgESU8BxRaDCJmOMQMP7mB/j2E5tgBMBlPBBVEwd/2XD+WzvoLV1bee7E6rHS4oi31W4Qt6awoJpX2kFVx9iG0SET8t2DrCVgAs5E7Jr/PO4bb6q/T/uCxS0cwrOtIFk1fWuiB3rwvm5KdYSh3AK9HIz3ZzKLvirazny6E9QIQEaEXpA/MBZt3dq/fy+jbxvFAu6f4aPg0MpIzT6OURVO3WS0mLB9Nu55tsNotxFaLof+TfRnx9bBylasyoHbwikqFMNVExE0sst/kJz/nxw9/wZ1teLDsWLObXev3MfjVO8L237XRFpJ6wO00sXdrOeZNt18FWe8Zfvq5Zw92sPdCmOuHHbJ99S6GdX0er9uL1CX7Nh1gyTfL+XjDOByR9jITPT+NzmvAawtHltv6lRW1gy8F3E4Pv325lB/e/5ndG/aVtziKIshMzeKH93/OVe4A7mwPcyb8SHa6EXC0YfkWhl32PDfVuJf/6zOCtpfsxeYIthHbIwK06rAFGc5/vgwQwmY8sUTcCVptMDWG6CcRsa8XOOazkTPwOD1I3Xha8Xn9ZCRlsmTG8rISW1GGqB18CclOz+bBC58h5XAquq4jEDz12UNc1r+IwBpFuXF0byIWmxlfvh25yWLm2P5kXJsO8MyVL+e67h3YnMgTr6Xw07QqHNxtw52tYY/UaXaei85XpYHMKrciGYb9/hmIeaZY/fduCo29cGd7wrYrKj5KwZeQaaNncWxfEv4TIhlfH/QeXfp1wmQ+e8LMKxJ1mtYk4Av1l5e6Tu0mNRhx7dggv+ykwxZsdsk7P25nxc+x7Nlip1kbFxf2yMBkjgURU2qy7dm4ny9fmcXejfs579KW3PZcPxJqVy3xvNnp2Xz+0kzSj4XmM7JH2mnZ6ZwSr6E481AKvoTs33ooSLmDcYCXkZxJXA1V+uxMxBHl4N6xd/DJs1/idRm7eKvDwn2v34XNYWP/luA88V63xuwPE7jpgSQu7ZPOpX3SjQvCAVGPIETpWDp3rtvDY11G4HHl2Me3HOT3b1bwyaZxxCac+k0kEAjwaJeRHNpxOCghGoA90kaDc+vQ+YaORc6j6zpCCOWqW4FQCr6EnNOuEWt+W4/Xlbfjs1jNxCSUvOqP4vRxwyNXc077JiycugQhBFcOujw38rRZ+8akHE4Jyiwx7e3a1DmnKd36bMgpSh1rKPeI20tNpinPG0Vqjq8b8AVwZbmYO2khd4646ZTnXblgLcf2JYYod6EJBo+9k96De+SmLQ7H7vV7mfDgx2xasRWLzUKvu7py/1sDyvVQVlE8lIIvIbc+cwMrF65jz4b9CE2gByQvzHoKk0mZZ850Wl3SnFaXhGaJvGf0raxdvAGvy4se0NFMGvZIG80vexVRvaaRyEw4Sn0nu2f9PvJ7anrdPrav2lWiefdtOYTPHVp8xBFl57xLW2K15c95lEfSoRQeu3QkzgwjeMzr8rJw6u8c2nmE138pvNyhovxRCr6EWO1Wxv3xMhuWbSE73ck5FzYpFZupovxodF4D3vtnLNPHzGbnmt0079iU25/rR+0mOQFn4XzPS4FmHRpzdF9irocLgM1hDXsTOhmatG2AxWbBn+/cIeALUKtx9ULHzn3/55DDaJ/Hx6Y/t7Fn434atqpXItkUpxel4EsBk9l0xhQUUZQO9VvU4ZnPHynTNQe9fCurfl6Hx+0l4AtgsVuISYjm6vt6lmje87u3pkGreuxatwdvzk7eHmmj/1PXFZntcufaPSGmHQCTxcT+rYeUgj/DUQpeoThJpJT889MaFny6CLPFRJ/7ryiVG3y95nWYvO5Nvn17LrvX7+P8y1tz3cNXEVUlskTzaprGG7+9wOwJP7JkxnIc0Q76Db2arjcXks00h2YdGrNm0frcG8NxAr4ADc4tPNe9omCknol0fg6epWBuiIgcjDA3LfV1xJmQb7lDhw5y5cqV5S2GQlEsPnt+BrPHzcsNlLJF2LjvtTu47qHyrRp2Okg9msaglo/iTHflpnKw2i20vbw1Y358rpylq5hI6UIm9c3J9+/BiDe1IeK/QBQjRfSJCCFWSSk7FHRdRbKeIlL6kM5Z6KkPome8jPQXnY5WUfHJSMlk5hs/BEXBepwePn52Ol53xSy4URhxNaowccUY2vVqg9liJjI2gmsfvJIXZz1Z3qJVXFzzIHCMvEIvOuBCZhQcgXyqKBPNKSCljky5xyjUjAswI12zoOoXiCLKuykqNge2HQ4bBSuAxAPJ1Glaq3wEO43Ua16HsQtGlLcYlQaZqzfy4S+8uPupUKF38NJ/AOn7D6lnle3C3hXgX0/eh+TPqbH6SqHDpJQc2HaIbat24naGKdOmOOOp07Rm2ENHKSUJdZT3lKIYmFsRVBoyt730bfAVcgcvpURmjgbnNznVjgTEfYqwti0bAfxbcgot5G/fVuAQXdd55bbx/DVvFWaLCavdyltLXlL5risYsQkxXP/IVcz9YGGumcYeYePuF/tjc9jKWTpFRcCoWzAZ9ESMLKAaYEVEP13qa5VIwQshhgGDMUrKrAcGAbWAGUA8sAq4S5Z2NWDPEnB+C3hAGv9kMvV+qL6ibMKozU1AWCF/FkFTowKH/PzZYv7+cTVelxevC1yZLkbd9CYfbxh3moVVlDb3vXYXLTudw0+f/IbJYqLv/67kwqtUrVxF8RBaBCTMQWZ/muNF0wAReT/CUvq1Z09ZwQsh6gBDgXOllC4hxDfArcDVwDgp5QwhxCTgXuCDUpH2OP5thNTelBk5IeRRpbpUWKyXGsrcvxOjok7OHbiQjH7bVu3Ec4JZRko4sP3waRdVUfoIIbj0xou49MaLylsURQVFaFWMIvBFFIIvKSW1wZsBhxDCDEQAh4HuwLc516cC15dwjTCr1jd20CcibCAK9xeWehbSuwbp31Fo9Z6iEMKEiJ8OUY8atT7tVyPiZyCsFxY4pl6LOtgcwTIrm61CUXpIKfl5ymIGnzeMW2rfx9i7J3J0b2J5i1WunPIOXkp5UAjxJrAP47RxIYZJJk3mVUA4AJS+kdl2JVjngXcZYAIZQFR5p1DzjPStR6YMBKRhWrFdBlXGI8Sp5YwRwoGIuhei7i1W/2sfuIKl3/7FzrV7EJpAShgxQ5UkUyhKiykjZzB7/I+5DgyLv1rGvz+t5uON44mrHlvO0pUPJTHRxAHXAY2ANGAmcNVJjB8CDAGoXz98ebGCx2pQ5V3wrQE9BSytjeLUBSClNGz08oTak54/wDUTIm49qbVPFYvVwpuLX2TDsi04M1y06NhUpROuYGQkZ/Ljh7+wffVuWl1yDlfd24PImNOTl0ZxcmRnOPn27XlBsQh6QMeV5eH7ifMZ+PJt5Shd+VGSQ9aewG4pZSKAEGI20BmoIoQw5+zi6wIHww2WUn4IfAhGJOvJLi6EAGu74nWW2aCn5mt0IX3rEJSNggcwmUy0vUzlrKmIJB9O5YELnsKZ4cTr9vHPT6uZPWE+k9a8QXRcGZz7KArl4PbDmK0mvO7gdp/Hx4blpe9fXlEoiQ1+H3CRECJCGLaRHsAmYDFwPHn1AOD7kolYNFlp2fz3xyZ2rN0d3rYuIg0b/QlIrGBqeLpFU5Qj6UkZfDFqJiP6vMoXo2aSlph+ynN99epsMlOzcnOyeJxe0o6l8/17C0pLXEUJqFYvIXxSNLPprM6Zc8oKXkr5N8Zh6moMF0kNY0c+HHhcCLEDw1Xyk1KQs0A2/72dOxs9yPPXvcawS0fy7FWj8fvyFTYQgh27HsTt0nA7NVzZGof3OvCbym73rihbkg+nMrjVMGaMncPf81czY+wcBrd6nKSDyac0339LNoWU+fO6faxdtKE0xFWUkLjqsXTp1wlrPkcGi83MDY9eU05SlT8l8qKRUr4gpWwhpWwtpbxLSumRUu6SUnaUUjaVUt4spTxtIZu6rjOiz6tkpzvJTnfizvawfulmJj0+NShS1O308OSVS3n4ymZ88Hxt3nysHg9d1ZRpo388XaIpypmvXp1NZlp27o7b6/aRlZbN9DGzT2m++ufWCTnE10waDVurdLlnCk9++iBXD+6BzWFFaIJG59Vn7M8jqdus8qWPKC4VMpL1OBnJmbiygo1uXrePuZMW8tv0pYz9eSTNOzTh2L4kAPbvsLN/x/EyY342/VVw5KmiYrNuycaQHXfAH2Ddko2nNF+9FnVCzH96QKeeikQ+Y7DaLDw04R7+N24gfl+g0EpVZwsVOheNkSc71OauB3SyUrN57upXCAQCxNWIxZevMLbJrFGrYeHVbMoTv8+PK9tddEdFWOqeUzt3x12vqYtPlm5mwcF1fPjrfPTEXui+HcWeKxAI8N3En8Je++aN708qpsKZ6eLT/5vOXU0eYlCLR5n51g/4fX6kfx8y+xNk9hfIQFKx51OEommaUu45VGgFb7aYeeTdwSEBRMdxZ7lJO5ZBdFwUd468CVuEcdBqspiIrBLJgFG3lKW4xWbayzPpE3knN8QNYOglz5GRkln0IEUQd/zfjVgdVqJi/UxatI06jb0IAUIAgb2QfC26nlasuVKPpIWtaQqQtD85JLNkQfi8PoZe8n98+/Y8juw+xoFth5j6/NfMeX0IMqkPMvNtZOYbyKSeSO+q4r5VhaJAKrSJBqD3vT2o37IuX706m5UL1wU9lkspia5quLDd8X830qxdY9Yu3kB01Siuuqf7GRn8sGLuSma89j0Bv/E+tq/ezesD32P0DwWnQVCE0vSCRry2cCQZux/BZMpR7EEEIOM1qPJqofNIKdm2cmeBStwWacNSzN3istn/cGxvYr65XFxz2yqMnOAAPiMWL304JPxSNrmVFABI31akczroiQj7FWC/BiEq9pNAhVfwAK0uac7Ibx7n4U7PcmT3Mfy+ACazxgNvDwx6VOvY+wI69j6zk0KtX7o5KGeN3+tn059nrx9vSWh1SXP0ZhICBXTwFV1FbPKTU/nxw1/R9VAzjC3CyvWP9C62Et6wfHPImVHD5h4C4eQLHATpLDL9hqJ0kO7fkGnDMHJc6UjPcnDOgqpTjcDKCkqlUPAANoeN9/4Zy+/frCA9KYNWnVvQslOzU5pL6pngW4WUIidaNglh6wa27kV+2FJKMlOziIh2YLaE//Xqus6/C9ayZtEGajRIoMcdlxJTNRqA6vUTsDqseF15EXlVa6qI11NGq2mYZMLiQE8bDgKEvR/C1ino6tG9ifzwwcKw5hmTWaPH7Zcy4MXim/lqNKgW8tkmHrZgsYaL3bAbX4rTjpQ6MmMkRuLA47iMmg+e38F+eXmJVmIqjYIHsNqt9Lr7shLNobt+gfQnMGr0uHK+S6RrHtgugiofFLhjO7TzCMOveJmkA4av9X1v3EW/ocE+uLqu8/z1r7NuyUbcWW5sEVY+f/EbJq4YQ91zanP14B4snLKYA9sOH1+apz57qETv6awm+mlIuTH8tcBuCGwBBNK9ABl5H1rUw7mXN63YhtliCqvgNbOJTX9tw+vx4TAXL59Rr7u7MW3Ut0FtqYlW/v6tOl2uTkfkKhgHRD18ynmSFCeJngR6mHMu6UR6/0VUYAVfcZ89iklGSibfv7eAT56bzppF6wv1eJC6EzKexLiTH6/WdLy/E7x/gffPAsc/d/UrHN2TiN8XwO8L8Olz0/nvj01BfVb+vC5XuYMREZmd5uT9Rz8DjJvUOyvG8Nz0x3hs0v18unk8zS8s/UovZwua9TyIehrjbnkiJo7XxNy/w8rbw+J58JIFvD14PAd3GGmcq9WtGs5JCwCf28ehnUeZN2lhsWWJqx7L678+T+0mNbA6rFhsFpq1a8Q53WYYqWPNzcHSFhE7Bi2yeEnsFKWAFlPABQfCXLHdYCvVDj4/ezcf4LHOI/B5fHhcXr6bOJ+L+3bg2WmPht+F+zdh/OMXwPE7uq1zyCVXtpvDu44F3UB8Hj8bl2+lTddzc9vWLt6Qq9xzp5WSDcu35L42W8xc1Kd98d+oolC0qMHoEXeBe15Oix0yR4LMYts6B0/d2ASvR6AHNHZv/pMl36xi3NKXadW5BTUaVuPA1kP4faGGcq/Lyz/z13DzE32LLUuLjs2Ysm0iifuTMFnMxNeKy7kyECIHlvStKk4BIezIiP7gnEmemUYY1eLs15anaCWmUu/gJz70Mdnp2XhybJ7ubA8rfljJ+qWbww8w1QJZmMubo8CslTaHFZMl+OZgsVmIqxHsqVOjQbVcd80TUZklTy+aZkOLuBGf6MPfC47i9Ri79w9G1sHtNKEHjH8FPSBxZbmZ9MRUhBC8tfglOt/QKeyGQDNp1GpS46RlEUJQvX61E5S7orwR0c9C5CAQ0YAGlg6I+K8RBe7uKwaVWsFvWrGV/BYZt9NTYDSjMNUBe0/CFsRFGEVG7H3CjtU0jUffvw+bw4rZasYeaaNe89p0v+PSoH7db++C1W4JUhi2nJqeitNLRkomg1s/zpgBv3FghwmfF7atC/dZw+a/tgMQEx/NiBnDmPDnK9gi8uc5sXDjsPB/D4qKhRBmtOhhaDVWodXcghb/JcLcpLzFKjGV2kQTkxBD8sGUoDZ7hI342gVXUhKxb4J1DtI13ygDGNhnuKtZL0TEvIDQCk4Ne+XAy2lwbl02Lt9KTEI03W65BIs12I82Oi6KiX+N4YNhU1j/x2biasRy94v96X77pQXMqigtZoz9juSDKfi8fp6+uQlDxx4gqkqAtMTQfU5sQnTQ65admjF67rNMfvJzDm4/TKPz6nP/WwNo0PLszVSoOPMRJSldV1p06NBBrlxZtE/yyTJv8kImPfF5rl+50ATRVaP4Yud7RESH37kpKi+DWw9j76YD+VolQmhBZyf2CBuDX7uD6x7qXbYCKhQniRBilZSyQ0HXK/UO/pohvRCaYPors8lIzqTNZefy0IR7lHI/S6lWLz6MghfUaFiN1CNpaCYNPaDTb9g19H2w2MXJSh3p2wKBPWBujTCX/xOClJK1izfw5/f/El01il53X0atRid/9qAoeyr1Dl6hOJF1v2/k/64Zg8eZF2hki7Dy6k8jaNquEckHU0ioG489zCF4WSClH5n2MHj+BGEyDvwjBqDFPFUu8hgySd66931+n7kCd7YHs9WEyWTi+W+fPOOjws8GitrBV+pDVoXiRNpe1opnvhhKzYbVQEDNRtV5dtqjnHdpSxyRduqeU7vclDsArjngWQG4jTKTeMH5BdK7rkzFkHIzwxoAACAASURBVHoK0jUP6fmdLf9sYck3hnIH8HsDeFxe3hj0HoGwORYUZxKV2kSjUOSnyw2d6HJDJ6SUZ1wiL+leQF6A3XG84F0G1rZlIoPu+gnSnwZhqIY1c6vj84S68Lqz3RzZfYw6Tc/eYhoVAbWDV5yVnGnKHQBTTUL/JW2gxZfJ8lLPgvThgMd4gpDZxMZlYLXpIX0Dfl0VG68AKAWvUJwhiMhBwIkmIs1IOGYvo5qivjW5O/fjdL02FZM52BRjtVvo2PsCYuKDXUkVZx5KwSsUZwjC3BQR/wVYLwatOtiuQsTPQmhlpEi1quTPrRwZo/PG7KM0btMAk8WE2WqmS79ODP/ikbKRSVEizkobvPQsR2aMAD0DrJcgYl8tNIBJoSgrhKUNourU8lncfC6YGoN/G3A8ZYeDJh0HMnntfWSnZ2OxWbDaw1dQU5x5nHU7eOnbjkz9X05BhUzwLEamP1HeYhULV5aLtMT0k6oBqlAUFyGEcXNx9AURC1ptiH4SETkYgMjYSKXcKxhn3w7e+yd55dEAvOBZVl7SFItAIMC4IZP59Ys/0EwaNRokMPbnkdRoUK28RVNUMoQWg4h9FWILL2WoqBicdTt4RBQhKYHP8Mo5X42ZzZKv/yTgD+Dz+Di04wjPXDVa7eQVCkWhnH0K3nF1jjuaDaMIhB2iny1noQrnz+9XBtVp1XXJkd3HyEzJKkepFArFmc5ZZ6IRwgHxs8H1LVJPQdg6I6wdy1usQnFEhz5hSF3H6lD20MqC9O8Dz+KclNRXIrSCM54qFMXl7NvBA0KLREQOQIsedsYrd4C7X+iP7QRlbouwceWg7uUbVq8oNfTsL5BJ1yAz30BmvIo81g3pWVreYikqASrZWAVh3ZKNTHv5W5yZLrr068QtT1+Hpp2V9+dKhQwcQSb24nh92FxEDKL6CoSwhB2nUMBZni64MtG2WyvadmtV3mIoShvPMhBamOLeAfBtBOv55SGVopKgFPwpImUAPEuQnj/BVB3huAFhqn7a1vN6fKxcsJbsdCfterVR9TwrC1okYS2lUgcRWebiKCoXSsGfAlJ6kSkDwL/ZKOeHFZn9PsR9jLBeWOrr7d18gCe6vYDP7UNKScAf4P63BtD3f1eGyhZIBs8SwAOWdghLi1KXR1GK2LoRquA1MNcFc9NyEEhRmVAK/hSQzlng20RealcvSJBpj0O1P0o9U+Ert44jIykjqID45Cem0unqdtRoUA0pJdK7GrLGgW81CAvHO0tLK0TcBwgtNOVrWRHwB/h7/mp2rt1DnaY16dKvk4qIzEEIB1T9HJn6EOipgA7mpsZndiZmvFRUKEqk4IUQVYCPgdYYVsR7gK3A10BDYA/QX0qZWiIpzzTccwnN242R2yawG8yNS22ptMR09m89RMhZuBD8NW8VfR/siUx7CDxLAb9xTfrz+vn+Q6YMRiR8GyyqrrNz7R58Hh+N2zY8bR45zkwXw7qO5PDOo7iy3Dii7Hw0fBoT/xpDQp3gNLjSvxvp/Ar8e8BUBxHRH2FpGdTH7fQgpcQReWYHp50MwtIKqi02yvQJK8JUp7xFUlQSSrqDnwAskFLeJISwAhHAc8BvUsqxQohngGeA4SVc58xCRBRwQS/1qFiLNfxHpGkCW4QNXD/kVAHyh+0HPghsR3rXIXKKRiyb8zfvPPgR7mwPQhM5dUj7MODF/qXumfPVq7PZv+UQPo+RvMqV5cbj8jLx4U94ac7Tuf30zPGQ/QlGNkM/oCFds5COaxExo9m+ehfvDf2Urf/uAKBx24Y8OG4grbu0DF20AiKEAHOj8hZDUck45f9mIUQs0BX4BEBK6ZVSpgHXAcfT4U0Fri+pkGcaIuI2IH/hbs14tDbVLtW1ImMjadfzPMz5FL0Qgi43dES6vgPchU8ifeD9G4DVv/7H2DvfIfVoOq4sN35PFi9+upGb7nyRtC2d0f17Sk12v8/Pwqm/5yr34+gBnX8XrMkTz/0TZH+G4Sp4/EalA25wzWPXvxN5otsLbFqxjYBfJ+DX2b5qF89cNZpNK7aWmrwKRWWjJNu1RkAi8JkQYo0Q4mMhRCRQQ0p5OKfPESBs+XUhxBAhxEohxMrExMQSiFEO2LpDxB2A1fB0EBFgqo2Ie/e0LPfMF0Np3aUFFpsFe6SNqrXiGDP/OaKqRILIf6MJh2bY5YGPhn+Bx5VXdHrSom2c3yWbyBid2LhkZNLV6HrJUyAs+GwRN9cYTOrRtLDXzZa8G5bMnEhYkxcALj4dsTAoVcNxPE4vk5/8PKgt4A/w25dLGX7lyzx9xSh++eJ3/L6Cnm4UispNSUw0ZqAd8IiU8m8hxAQMc0wuUkophAgbSSWl/BD4EIxApxLIUeYIIRAxTyMj7zaq4GgJYGmPEKcn8Cg6Loo3fn2B5MOpuDJd1G5aM9eUIiJvQ3pzCjUXLDHYuuPz+ti1bm9uq8ksqd3Qy/GzPCFASj+4f4SIW05Z3sVfL+fdRz7B4/SGvW6xWeh5Z1cApPRBYGeh861dZgs9g8hh81/byc50omkaVruFEde+yoZlW3KLRG9esY2FU5Yw9ucRmMym8JMoFJWUkij4A8ABKeXfOa+/xVDwR4UQtaSUh4UQtYBjJRXyTEWYaoKpd5mtF18rDvL5vwtbN2TkYMj+gPzVeAzsYO+JMDdA+PyGFs+JqpGhpTZzJi3OU0F4pJR8/PS0ApU7QItOTbnvjbuOL5bzVfA9XitEL0spuTF+EADV6ieQcjgN7wlPKO5sD1v+2c6yOf9w2c0Xn8Q7USgqPqe85ZRSHgH2CyGa5zT1ADYBPwADctoGAN+XSEJFkWjRQxHVl0PE/SCq5JiNogA7RNyCiH0NAJPYS5vOecpb1wX//RmZuzuWEmOc9So2LN/Cgk8X8fePqwo0cWSmZvHK7eO5u+nDDL/iZQ7vOkryoRTSEtMLlNURaePuF/rnesEIYQZL4dGana/2oZkK/lM9bpc/sutYkHI/jjvbw6LpKreL4uyjpF40jwBf5njQ7AIGYdw0vhFC3AvsBfqXcA1FMRBaVUTME8joYTkBWG4wN88tRahnvgPZH3Hf/5l54t9GuF3Gtvjpm5sweMRh2nXLIiK2OX7HaEZ2GUbqsQyQEs2kITTB01Me5pK+eUFcuq7zVI+X2LvpAH6vn6N7jvHwRc/y9u+jCs1TL0xaiKlERA1Dpg4mJB8LADYGvnwH/yxagDPdScAf+tgRXcUPAjJTC/5zLsgbSaGozJTIaCylXCul7CClbCOlvF5KmSqlTJZS9pBSNpNS9pRSppSWsIo8fF5fWEUqhIawtEJY2+cqd+n+OccF0UPT87J5c85Ozr0wG5NZYrZK5k6tz4Fj44mqcTOPdRnBkd3HcGe5cWd7cGa4yE5zMua28Wz6a1vuOod2HOHAtsP4vcbuXtclPo+PfZsOULNR2HN1APy+APM//pWxd73Dr9P+wOv2ImydwNI2/ABTXWo068/kNW9yxcDLia4aRWRsBAhDsY/9eifT12ziq9WbGPLCQcKZeuyRNq4YeHnxf7kKRSVBZZOsQEgpmf/xr0wb9S3Jh1Ox2iz0vOsy7nvtDiJjC85boif1ySmkHIwzS8PvFUTHaQhTFb6eaGHaW/F43eHv++2vaMvYBSMAOLzrKPed93iQR44j2sbwqUOxR9p44frXg64BIMBiteS6TTqi7FjsFsYvHkKd+PsIv4N3IKpORZyQdEtKSW/brbw4ZQcXdMnEkhMU6/PCqHsb8s9vsbkHx7YIG51v6MTwqQ+ryFBFpaOobJIq32wFYsrzM5g0bCpJB1OQusTj8vLzlMU82nkE3ny+5seR0gv+HWGvRUTpxFQNIIQf9CSW/RhZoHIHI2XxcWrUlzRrm4XVbphMTGZJZFQW53fVaN+rLS/OeZq4GrHG+Sk5nkdCBPnEu7LcZCRl8tOkFws5YnUjPYuCWoQQXNSnFe0uzVPuABYrjPxoL+deVJtr7r+Ca4b0YvTcZ5VyV5y1KMNkBSEjOZOZb84NCRrye/0c3ZvIHzNX0PPOrkgp2bl2D85MFy06NsVSrAwEhnotwKM1lyAlmfkKY77czpSx1di0KpI6jTzc9/xhHIFnkXIhWalZODNduRYTKWWBjjI+r44eCGAq0FsmVDnf88odwJfkn1Ro8Oj799D4/AJMPgrFWYRS8BWENb+tx2w1hSh4yPES+WoZbS47l2eueJnEA8m5Xif/99UwOlzYAvybilyj67Vp7Nlix+MK1bRCQLue5+U1eBZjc/i5/6XDwR0DRyFwgDkTfyrUVfJEls93MOT5YBfPHesdzP4wgYBfo++jzTmvW/CY+i2bkLXvEvCtwGwxniL8Pg0/bZVyVyhyUCaaCoQIs5M9kVE3v8XBHUdyD0edGS5G3fQmLnkfUFCOnLw/gatuS8HukAgtdKttdVgZ8FJxgp90wu24CyPxoJV9O6rlvt650c4T1zfht1lxLPm+Cs9e8yXrft8YMi6y7kTMUd0x9ilmzJFdiKg7+aTWVigqM0rBVxAu6HEePm94f3R7pI2LrmnPrnV70APBboTCpLF0XgREPcqJH/f2/xx8/kZNFsxoij9gKP+oWJ1xc7fT4Bw3NkcAR5TxVSXBzEtznqZZu7wsmUnH6uIPY/bPSDWBqQ43PnYNtojipQS2R9rZd2Aoxx8o536WgNulcfxG4XF5+fr10HAKoUWhxb2PqLEaUWMVWtWPyzUtskJxpqFMNBWEmPhobnn6Oma+NTcoL4vFZiYmPprv31uAzxPmBiCl4c8edS+6/UqcB/+P529PZdtaK16PAClpe7Gkel0wmaBOIy+TF21j5wY7B3fbqJJgovU1szFb85KoBfwBht8czdivLETFBHBE6bidgoBf8Pxd9Xj+h1S63nQxfl+Ar16djc/jIyvNSWZKVsgNCMDqsHDR9X0R5rrItGePnwgEd/KuRuopCK1qyHhRyhk8FYrKgtrBVyDufrE/D70ziOr1ExDC2Lk3v7Ap6YkZ7Nt8IOwYqUsu7mt4UWnmuoy+vxlbVkficQmkbuj/T16pQXaGCY87T6k2aS3o2tdH26vHByl3gK0rd5JyxM49XVrw6as1+f2HGGa+X50BF7dk15YY/p63CoDut3Xho//eZsrWiUxa/To1G1bDEW0o45r1PYz7YSczN27ky9VHsFt3IWyXgLkRfe7OwObIs8nb7Do3DtmLTBlYaBCVQqEIRu3gKxBCCHrf04Pe9/Qg4A/gcXnpX3NwqL95Dla7hee+eozYhBgA9m05yPqlm3N3+o1buXjxs91EVwkghA5SoOsamqU5RNyAcPRDaDEh80pdEhnr54EX9tKxRyY+r+CiKzJpd1kmE4Y3RtdDlXBCnXg+3TKBlQvWsnLBH9z96BQcEW40TQI7kCm3I6u8C77VNGvj5o1ZO5n5QXUCPrju3iTO75wNAR94/wFbp9L7pSoUlRil4CsoJrOJtYs3YLKYCsy0e3HfDkHpBeZ/9Ctet3EzqFrDx5uzduCI0smr8SGNr8BuhLlpWOUOcE77urw6fSM16rmx2sBqNxR6i3ZO3pq9GW9EvfAym0x0uqY9HXskIdM1glJESj84p3E8YVrz812MmLw3eALpQnoWGZGvCoWiSJSCr8CEs2efyD/z13Cl5RYiYyPodVdX5n6wMNdt/PrBiVhsktRjZuZOSWDTyghqNvBy3T1JNGnlRma+gbB1CTuvWV9Czfp6UJARGDZ8e6Qkuso8oJBKSzLP/KLr5NxgdJA6ElmID44kfMZMhUIRDqXgKzBtLju3QM8aMCJFATJTsvj+vZ8JBPKUY9c+6ezfbuPJfk3x+QQ+j8b6v3WWfBfHw2MO0Lbzbg6v+Ye4mjVo2Lp+UJCTdM/DYi0g17tFB9ePEP1UwYJbO/HfnxGMf7IuB3fbia/p5X8vJ3Hp7dfjzViKrYAz050bHfw2F6T4jCsHdadxmwaF/HYUCoVS8BWYmKrR3PxkX2a/PQ93mIpHJxLwB+98NU3y+tD6OLPy3BH1gIbHBW8/Xg+zVWK1v0vArxNfK47hXwylZadmxmBZVIWkwnfZiQd1Rt5VD7fT6Jd8xMobQ+vis0VSr7qNhi2cIU8HuzbbeLxvE9yudcA65n/0G28ueYnmHZoUIYtCcfaivGgqOANfuoX73x5AQp2qaJrAbDEVq3LRku+rcHC3jXBBSVKCz6ORne7Cne3h4I4jPN3zJfZu2m90sPXEqK8eDjPYuhW69q+fTSBwghN9wxYu7n9hL+t+HsvIuxqzf4cdl1PkFiRxZQtmTKiR4xtv4HZ6+GrM7CLfp0JxNqMUfAVHCEGfIb2Yvm8Sc7Om8dA792C2FqzgTWYNW4SVBdOrFVzRKYzS97i8TH3xG+Oq41rQIgn75yOsiMjBBa4v9XQ8GcsJBIw1Gp/rYvzcHfS+MwW304szC44dsGAySQIB8PuMRGaZ6aYQuY4fGCsUivAoBV9JEEJgtVtp17MNMoybIhh+8/e/NZBJa97k8c9ewe8v/scvdcnfP6421tIioMp7hP75CIgegTAXYhv3/MFl1zoxWwwZb37wGDaHzjfvVmf5j7E8/8keLuiahdUGZovxZbVB/wePYbHm3ZGsdgvXPXRVseVXKM5GlIKvZNRuUpPO13fE5gg2YpvMGlFxkVw1qBt1m9Vi5ps/nPTc+ol2/KzxQH5bvISscYUHI0kvDVu6uemBY2gmic2hk5ZkZtq4GtRp7KV1x2xs9tDx9Zt5MFulkeddGDe0ei3qnPR7UCjOJpSCr2Ds23KQxy97nqsdt3FHw/+xaMaykD5PT32YG4b2pNctWQwYnsQN96XQ9aZzmPjXqziijJqsqxb+V1id67C0yDlk1fV08P4ZvpOeiPT+UfAktotYNCuSWZOqo+swf1o8S+fFommSTj0zcnf2+flhSjxel2a4zkvwenx884Yq96tQFIbyoqlAuLJcPNZlBFmpWUgJx/Yl8fbgScTGR9O+V16KXJO+jEGPTURKHaTLsIszGyyH0BP3QuAAeqA1J5P10RZxQjZJ17zCO2d/A7bLcl/+PX8108fM4sDWw8TVqsKRnXXwGB6crFwcQ8pRM4GAwGyVaAUcH3icJk7w8kTqMtcNVKFQhEft4CsQK35Yid/rDwoA9Tg9QeYW6duATB0KMguBEyEkAg/gA99SCOwDdBqdW0D46wmYLCYcUXZsDiuPvDeY8y9vbVxwzy98oPef3B+/GjuHl/u/zaY/t5GRnMneDfvx5Ft616YI/F6NTf9G4nWHv+n0uCkVmyPvjdscVq4e3LPI96BQnM2oHXwFwpnpRtdDXV+y053G9wwnyZufo3Y99wnpB8Jzx7CjvHxfQ6QeXqF2uOp82nU/jyrVY+l8Q0cioh15F/Wibg6GC2TKkVS+GDUTnzt8OcH8rF0WVeAOvlkbF2Om72L6xFbo4hz6P3Udbbu1Kta8CsXZilLwFYhO17Tjg2GfBbXZIqxcMaAb2RlOHrpwOB/+uqVI5Q7QuXcGF16ewT+/xZDfVBNbLYaXZj+F1V5APndzPQhsKHhyLQGA5d/9i6YV3wwUmyALLRvYulM2YzqtRSS8ijCHz3ejUCjyUCaaCkS1uvE88emD2CNtRMQ4sNotXNrvIq4e0pNZ4+ZxbH9SgTvgcLz42R5adczGZJYgQDNrVKkey6Q1bxSs3AGsFxZ8DcDSBgCvy0vAX3i+nNwpHVba92yB2VpUEVnNOFdQKBRFonbwFYzut3bhkr4XsmfDPhLqxpNQ2yiA8fNni/G5/ezbZqNhi8LTFhzHZIa35uxn04ab2Lq+PTUbVeeiPu0xWwr/sxD2ngTSx6BpoSkL/D4zlrh+gJErx2TW8IeJRzJbTVisFrxuH1aHhWsfuIKBo/oj0heDHl5+r1uwbUNNiPHQrL0bR6Qq9KFQFIZS8BUQe4SNFh2bBbUdzwn/1Ts1eOyN/Tgii+cDKTQrrbs/yHm9qhd7fZ1qLPmhKpdckYg9Im8dj1uwb7uV6tZWxNWAZu0ac+7Fzdm4fAveE+zwZouJ6vWr8fGGt/H7AtgcVrQcu5Ie+T/IfAWjtquBlDB9fHVmvl8TYXIAbxDw61wxsBsWq4ml3/6NlJIuN3Si/9PXUa1ufLHfi0JRmVEmmkrC+Ze3RtMES76rwi8zq+LKFpx4HiuxY+SPsYKIBuEAU0NE1S8RpuIrd4DNf23n3WcbMmtyNbIzNTwugcct+P37Koy4swXLZud50Yz6fjjdbu2M1W4hItqBxWah/RVtmbB8NBarBUekPVe5A+D5lfxnAh+PrsXX71bHlS1wZrhxZrjwOD3Mff9n5rzzE4kHkkk6mMLcyQsZ0vYJDu44fPK/QIWiEiLOhBJoHTp0kCtXrixvMSoMUkqcmS7sEbbcxGJ7Nu7nkU7P5mSVlLTumE2/+xNp0NyLI6YmCU0eAntv0JPAvwu0eDC3yE0DHAgE+PP7lfz82SI8Ti+db+jIlQO75QZGnci/C9Yw+tZxODNcmMyS2Kp+MtNN+DwaJrOJe165jf5PXRc0JjvDSeL+ZKrWrEJMfLSxpj9AWmIisVFL0Lw/gJ4Bgd0c98IBSEs2cUe7c/H7ircXEZqgfc82vLpgxCn8ZhWKioUQYpWUskOB15WCr1j8+NGvvDf0E6PsnoCLr+3AS3OeRgjBhmWbefOe90k6lIJm0tA0jbteuJl+j14TlM89PwF/gBHXvsqGZVtwZxv2b1uEjdiEaN79Zyxx1WOD+mekZHJLrfvw+0LTApssJsYvfTnEhJSfnz79jakjpzD6i/+o1dCDI0LPjVIVObrcla3x0JXNOLgrfNbLgjBZTHyXOhV7RFEHtgpFxaYoBa9MNBWILf9sZ/z9k3NrqiKN4Kc3Br0HQKvOzfn0vxv5erODr9b7+XZvD/oNvbxQ5Q7wy+e/s35pnnIHI4Aq+VAqkx6fEtLfYrMQE+/DatfztetEx3qwF3H4ueqXdbw39DOuunUPdRq5c5X76j+icpU7wKQXanN4n5WTUe5g5KlRmSYVCqXgKxSfPDs9bPui6cuQUkemDYX0x3BY/sBhXYXmfAuZ1BsZSCx03rmTfsYTpmBIwB/gj2//CgmuWr9oPp/8sZG7nzxCdBU/ZouOzRHg6juS+eiPzWxe+mmBa0npZubrn+Fxeuh9R0pudOqh3VZ+/75KTgESo5Tfollx6CeR8fI4cTViiY6LOulxCkVlo9J50Ugp0QN6sYpeVDTSEjPCtgcCASN9gHdpPh9xF+heZMYoRNzEAufNTi/YrzzgDxDwB9CseYq2dq1Z2Ow6Nz+YyI0PJOLK0rBH6Jhy/pou7PwT8ELIXFLPQibfSNpRK2DHHpF348hINbHk+yoMeeEQAH6fwO87uZ07GKalQaNvK/KpRaE4GyjxDl4IYRJCrBFCzMt53UgI8bcQYocQ4mshRCERM6WHrutMffEbrqtyN71tt3H/BU+ydeXOsli6zLj81s5h22s0qI50Ti8gACgAnkVIWbDJol3P8zBZwt8Q655TC4vVEtRWq866XGWuaRAZk6fcAWKrZiIDh0Inc30NgUN06JaOEJJN/0bkevpkpZvwuDRG3tXY2MVLaNC84GRi9ig7VoclpL3bLRfT667Lwow4Obb8s51hXUfSL2EQT/V8iR1rd5d4ToWirCkNE82jwOYTXr8GjJNSNgVSgXtLYY0imf7KLGa++QOuTDdSSnat28tT3V8k5UhqWSxfJtwy/DpqNgp2adRMGi999xTI8Lt7AwmFKPibn+yL1R6qLG0RVoa8fndIuxCF55bRTFaQzlApAimAh33bbUgJn79ZMze5mM+jYXfobFoZyeCuLZj9YTVuefgYNrselHpB0zRaXtSMAS/1R4SxzS+Z8WeJ3ST3bz3IU91fYsOyLWSmZLF20QYe7/o8x/YVbuqqiBzYfpi5H/zML5//TlZadnmLoyhlSqTghRB1gWuAj3NeC6A78G1Ol6nA9SVZoyCk1JGeJUjnLKR/F7PG/RhiR/b7Aiz8/PfTsXy5YDKZ+HzHuzz52UNcct2F3PxkX+akTqFJm4ZgvQQIVdIAaNVBRBY4b61GNXj791E0vaARFpsZm8NKfO2qPD3lYS7q0z6MIIVUbAIEOphCi3EIe3fcTgcrFxv5b3asj+D5AY04vNdK/XPcuenpk49Y+GxsLcY92Yzh056i682XULd5bTpffyHvrHiFd/4cg8/jC5t4LeAPsODTxYXKVxRzJszH6wm+ifm8PuZO+qVE855JSCkZ98Bk7m/7BJOe/JyJD3/MrXWGsHTWX+UtmqIUKakNfjzwNBCd8zoeSJNSHo9hPwCELbsjhBgCDAGoX7/+SS1qHCg+kJeWVupkZ7QI6efz+Eg+VHl28GB4iFw5oBtXDugW3B45COmaCTL/7toO0U8VaZNuen4jPlj1OqlH0/B5fFSrl1DgGBF5PzJzVAEmIRs4rkeIUP95YW2P2zwK+CK3bd3yaAZe3IKa9b3E1zSRfNSa4y4pefyT/3Fpv4u4tN9FIXOlHU3P8yY6Ab8vQGoJn9oSDySjB4JvHn5vgMT9SSWa90zi1y/+YNGXS4MijAFeu3siLS9qRkIdFQ1cGTjlHbwQog9wTEq56lTGSyk/lFJ2kFJ2qFat2skN9q4wlLt05pgC3LRs7ya/PrJH2el41fmnIl6FQ5hqIapOB3NLwAo4QFSFmBfRHNcUe564GlWoXr9a4TcERz+w9TLWCL4A5nMQMc8UPH+9vsRWy688BEf22UhJtPPN4Y+Yuu0dvkubSvdbuxQ4zwU92uCICnXHdETZaX9FyT7zzjd0wh4Z7ENvj7TR+fqOJZr3TGL2hHlBbrHH0XXJr9MKqcilqFCUxETTGegrhNgDzMAwzUwAqgghjj8Z1AUOlkjCcOgp5PeNfvT1g0TGRmKLMM507ZF22vdqQ/sr2oaZoHIiLC3REr5HVFuESJiLqL4cLaJf6a8jBCL2RjqTRgAAIABJREFUDUTcO2DtDKa6YGmLiB2FiP8q7O79xLEPjh+U+zkdxxZhY8BLtxARHUFCnfiQg938XNj7fBq2ro/1hNqzVruF2k1r0qVfyRRxr7u6ct6l52KPtGGPtGGLsNKxdzs631B5FHz6/7d33uFRVVsffteZlpkkEELoRRBRQRQQFcEKV6xcUa+9l3vRe+0iKGKvnyJ2sSPCtYINFfRaUAEFlCIgKF06hJI6mXr298cZQsLMpJFkMpP9Pk+ezJwzZ5+158ys2WfttX9re2HM7UF/kLxt+fVsjaauqJWVrCJyInC7UmqwiEwCPlRKvSciLwOLlFJjKzq+uitZVWgDavsZwO4QgROc/SiWp/nunZls37ST3gMPpffAHjpdroEy/5tFjL/nPdb9sZGWHXO49J5zOf7cftVqI+AL8PFzU/nqzemYpmLQ5cdzzi2Da0VlUinFn7+sZO2S9XTp1Ymuh++/z202JB6/4nm+e2dmVCjKnZHGnRNvov+QSiShNQ2CepEq2MvB7481os8GFgCXKqUq1K+tiVSB8s9B5d8JKg+cfa0RpZFZ+YFJiFIKgvNQJR+DWYi4TgT36Yhoudy6RqkQ+L9F+b4CDCTtDHCdgEhyrxHcsGIz/zliBL4iX2kJSIfLToeD2jH218dTch1JKqK1aJKc4rwiQrtGkpH2AyKWkBjiAclCmk9GbDmJNjFlUaYXtfMyCK/ak/YpHrAfimS/QT0t8agz1v6+nleHT2Th9CU40xwMuuwErnrkovLlGTUNGu3g4+AtLMFb4CW7TbPycrVYWTqY20AyECMxS94LdxXxwo1vECr+ktvGrMHt2Tsl0A6u4zCavZIQ+5INFd4C/u+tJ64TEVvrSo8xC5+A4olA+RvQ9SszeXdsf375phhDhGPO7stFI8+m1X7VTBbQaPYR7eD3oqTYx9NDX2HmR7MRETKbZXDLK9eW5nsr//eo/LvBzAcUpA1CmjyMGPHzyOvCxv/0GcGWtbk8+s4f9OwfbwGKA2k5J2E/QsmCWfQCFL3Cnol5BRnXYmTcUPFxW/uAKj8ZufRXDyMv3J+A38AMW+3Z7DZcHifPznqETofoWrGa+kOrSe7F45c9z6yP5xD0hwj4guzYvIuHL3yKlQvXoIJ/oHbdZI3e8QMB8H2Dyr+9Xm386s3p5G7YQSgQolmL6FzvPdgrWcGqUf6ZUPQa1vX0Rf78UPSata/Cg8v/sCoF/3d9R3xeW6lzB2txVUlhSamqp0bTUGhUDn7XtnzmfrkganFH0Bfkw6c/R3nfBPZe0u8H/4xKFRlrk89f/h9+r2XH0nkeQvGUAcQOhg4LVIQqfpM92VbgLTKY9FIOD1zTkklPvIy3sIIC3rbO5Z6u/j2N/B2x1wYqBWuXrCN3w47aMFujqRUal4PfkocjRkFp01RsWbMNQhsoWwu0FHGCWX8OvqwmyOSxLWOqKpqmC9KHIlJxvnijJ7yl9GHAJ9x4alcmjG7DT9OyeOsxHzf0HRklS7AbybwF2JOplL/Tjs0eP6Rpd9op2BE7v1yjSQSNysG369oaM8acgzPNQe+/HQqufkCsKkBhsHeOsb1u6Nitfenj9SvTuO/KzuzKteMtMigqMPD7hLDjYiR9aL3ZlLQ4j2C3IsfMqU3ZvsVBwGd97AM+IXfDDmZ9PDfmoZJ2CmTeEclayqDjgTYC/vhfmVAgFCUGp9Ekkkbl4F1uF1c/chGuMqXc7E47Gc0yOOuG0xDPpWA0pbxolxsybo1anamUYsmsP5g05jO+Gj+d4oJo9cSact7tZ5ZbKr9wZiYX9erOnRfsz6PX7c8L91+Jq8VIvYirCkj6NdYdGLD5Lyf+kvIf+YDXz6ZVW2IdCoCRfgnScjaS9RItuo+l18De2J3Rd4HONAcDLjqW9Cae2u1ALaKUYsvabezampdoUzT1RKPLogGYO20Bk56cws6teRx5ai8uGHFWad1RZe5EFb1mpdTZWiDpV1sLi8rgLSzhjpMfYu2SdYQCIewuO8qEe96/lb5nxFBfrCZKKcbePI4v35xeTi/E5XHSvE0znp/zGE2yU3NRV12ggotR+aNYPHMzoy7pWM7Jp6W7eGzaKHoc261KbRXuKuL2gfezedVWSop8iAiudBdde3fm0WmjGmwd2AXfLWb0lS9SsKMQ0zQ58IgujHr3Vlq016JiyYxOk6wDHrv0OWZ8+HOUmqHDaef135+ibZc2+3wOpRS/frWQD0ZPYd2yDWRkpXPGtYM47ZqBuDP0QpSaYIZ38tz1b/P1xNnY7TZCwRCnXDWAG1/4Z7XuhkzTZP43i5nzxTwMm8GxZ/elx7EHN9g7qg0rNnNd7+Hl5LQNm0HLDjmMX/EcNptetZqsaAdfy5QU+/hHztUE40zM2Rw2rn7kYs6//cx6tkxTVdb9sZHVv62lS69OdDgoppp1SvHizeP47KX/EQ6Fy213Z6Zx76TbOaIRCfKlGpU5+JSryVrXFO4oxLDFn7oIB8NMuP8D2uzfiuPO6VuPlmmqSseD29Hx4NR37LvZsHxzlHMHUGHFtr9Sr0qVZg+NapK1NmjWOgtbBQ4ewO/18+6jH9aTRRpNxRx6fLeYJRkV0LVPaqlkasqjHTxWvFsFfsXMvwNz17WYRW+gzNia2A6ng3/cNrhcJk4stq3XC140DYPBQwfhznCXu/N0up30OOaglJNB1pSn0YdolFKogrugZCrWMnYF/p9RxS9B9n8RR3QpwEvvORdfsZ+Pn5tKKBBbSqBLz051aremcpRS/PLlQjau2EzbA1pz1Gm9G+xEaF3SpHkmY+c9zut3/pe5UxfgdDs5/ZqBXHz3uYk2TVPHNPpJVlUyFZU/krLL2Usx2iItpsd1CkX5xdzY9y62rNlKKLgnxunyOHni63vp3u+gWrNzw4rNfP/+LHxFPnoO6EGfQYdFqWBq9qCU4omrXmTmh7MJh0xsDhv9zzySOyfe2CidvKZy/CV+5k5bSHG+lx7HHkz7rvueDVfX6EnWSlDeccR07mAVEwnOB2fs3PaMpum8MOdRnrvhDWZM+hnTNGmzfytueP6aWnXub9z1Nh89M5VwKEw4FGbK2K9o06UVY6Y/QEZW/alcJhOLZyxj5oezS9cRBP1Bfvp0Lot+WErPEw9JsHWahsacqfN55MKnQQRlmphhk76D+3DX2zdjjyFvkizoIWA4/ipGEAhvrvDw9KbpjJx4E1MKJ/LxzvG8+cdz9BlUe2lnc76YxyfPTyPgC5RmQpQU+Vi3bCNPDX251s6Tamz9KzfmSH3L2m0JsKZqhMNhTDOGFpKmTtm2LpeHzh9DSZGPksISfMV+Ar4gc7+Yz/h730+0efuEdvAVasyYYK/aJJTdYa+TBUiTxnxWbjXrbkKBELM/m1dOmEyzh/26t49ylkpZ2xsaG1duZsSgBzk97WLOcF/CIxc9TcFOLVpWX3zx6jeYoegfVn9JgCljvyQcjk4xTRYavYOX9GuBWI7ZAFtHxNG9vk0qRSnF5jVb4+63O23s3KJ1RWJxYJ8unD98CC63E08TNy63k3NvG8zBR3VNqF1WxtYvmIVPYhY+S9H2BdzUbxQLpy/BDJuEgiFmfjSH2wfcT0OYH2sMrPtjI8E4yRIBXzDmACtZSN7gUi0hrmNRGddB0VgsqeAgSDpIUyQB5fD8JX5eHT6Rr8Z/T8AXqFC8KhwMk9Muux6tSy4uv+98jj+3H5tWbqFNl1Z07tExofYoVYLaeQ2ElkZqvBp8Pe5D/N7WKHOPMw8Fw2xZs43fvv+dXgN6JM7gRsIBvTsxd+r8qDoRAO6MNNwZyVvcvtE7eAAj498o95moks/BzEecvcE1AJH6f3vuP2c0i35YWvphixeCcaY5GHjxcbpAciV0OqRDgymjpwoeh+Bi9tR4NVmzzI6/JHqkboZN1v+5STv4euD0f57E+49/CpR38C6Pi/OHD0nqbLXktbyWEVs7jIxrMZqMQNIGJcS5r/9zI4t/XBY1kjAMwbAZpHlcOFx2XB4nhx7XjRuev7rebdTUDKUCUPIRexfw7nqYlzRPdIxXDKFzj4bxw5TqNGuVxf99dTfNWmfhzkzD08SDM83BadcM5IIRQxJt3j6hR/ANiPV/bMLmsEVlbZqmoueA7gy86Dh8xT4OO6E7B/SqvwIkmlpgdxH3vRh4Th4Tx7QuV8Tb4bKzX/cOHHJM9CK7uqak2Mf0d2fxy5cLcGemcdKlJ9B7YI+UXzvQvd9BvLfhFZb+vJzifC8HH3UATXOaJNqsfUY7+AZEx27tyi2Y2o3D5aB7vwM5/Z9/S4BVmlrBaEqsG2Z3usnzU1fw8v1dmPttJna7jb9dchz/fPzSeneqm1dv5eZjRlFS5MdX7ANgxuTZHHpcNx789I6kzgevCoZh0CMBP6p1SWpfsSSj/YFt6TWgBwu/W1waphGx4u1Drj8twdZp9gURJ8pzLng/YO8wTYu2du5592KM9AsTY1yEe896nLzcgnITvr5iP4t+WMoHT07h4pHnJNA6TU3QMfgGxn2Th/H3f5+Cp4kbm91Gr4GH8uysh2neplmiTdPsI5I5ApyHY6XlCmAD0sB9OuK5IKG2rfptLVvWbCvn3HfjLwnw9kOTuaDtv7jq4Jv4dOyXekFWktDotWg0mvpEKQXB31D+GdZEftogxH5Aos1i5sdzGH3Vi3gL4sh2lMHlcXHKVSdy4/P/rAfLNBVRmRaNHsFrNFVkyaw/GP63B7iw/VBGnfEoK+avrnYbIoI4e2Fk3ohk/LtBOHeAVvu1wAxXbVTu9/qZ9sZ35OXGltTWNBx0DF6jqQILpy/h7sGP4S8JALBj0y5++2EpY75/gIOO6JJg6/adA3p3Jqddczau2ERVbuqdLgfr/9hEVoumdW+cpsboEbxGUwVeHvZWqXPfjd/rZ9yodxJkUe0iIjzwyQgymmXg8jhLt9uddsSIzuYJ+IK06dKqPk3U1AA9gtdoqsD6PzbG3L76t7X71K4yi8DcCbZWiFRcJayu6XhwOyasfIEvx33HL18uIC09jRPO78/Ym8dRuLMIMzIB63I7Oebso8hpq2UyGjo1dvAi0gGYALTCWsHxqlLqWRHJBt4HOgFrgfOVUrv23VSNJnHktGvOplXR0tKtO7esUXtK+VD594FvKogNUCjPFUjGLYgk7sY6Iyudc2/7O+fe9vfSbd37HchLt45n3te/keZxMfi6k7n0Hl0NKhnYlxF8CBimlJovIpnAPBH5GrgS+FYp9X8icidwJ3DHvpuq0SQOR4yi1QB2Z+ztlaHybgb/T4B/zwLX4rdQSiFNhtXMyDqidaeWPPDxiESboakBNR4qKKU2K6XmRx4XAsuAdsAQ4K3Iy94CztpXIzWaRPLX0vVsWR1btvnPuSvZvrF6BdZVaN0e516OEvBOQClfzQzVaPaiVu4FRaQT0BuYA7RSSu0ug7QFK4Sj0SQtaxavw7DH/qo40xysWxY7Ph+X0AoQZ+x9UnkVMY2mquzzJKuIZAAfArcopQrK6mcopZSIxEy6EpGhwFCAjh0Tq9Ot0VREy445sXTCAKuyVosOzavXoK0NVoQzBioERk712tMkHcosRHnfAf9MsHdC0q9E7LWfbrtPI3gRcWA597eVUh9FNm8VkTaR/W2AmEUwlVKvKqWOUEod0aJFi30xIykxTZO83HwCvkDlL9YklG5HH0h2m6yodEGb3aDzoR3pcFC7arUnju5g2w9LqqAsTkg7GTEy983gOsJf4icUjPPDpKkyyixC7RgCRS9AcA6UTEZtPwcVmFfr56qxgxdrqP4GsEwp9VSZXVOAKyKPrwA+rbl5qclXb03nwnZDubjjvzmr2ZX832XPUVzgTbRZmjiICI9OvYOW7ey408O43CZp6SZt93dy30e31azNZq+B/QAQN0gG4ALnUUiTh2vX+Fpg8YxlDO05jDObXM7fMy/jwfPHkL+9INFmJS3K+z6Et7NnDiYMlKAKHqz1c9VYi0ZEjgVmAIuxat0B3IUVh/8A6Aj8hZUmubOithqTFs03b//IM9e+gt+7Z+TucNnp0qszz/30SMrrbicr5q6bMEu+57uP0lj1u5vuR3g59nQ/4jkbo+kDNWpTKWWV7wtvBHtXpMIC8IlhxfzV3Hr8vfi9eyaEbQ4brfZrwRu/P93gJISVUqxe9BdFu4rZv+d+ZDbLSLRJUZg7/wWBH2LssWG0XlattirToqnx1VFKzcSSxIuFFi6PgVKKcXe9U865AwT9IdYuWcey2cvp3u+gBFmniYcKb2LXhh95eGg7lv/mweFUfD4+hy/7FzFy7CdkZt6KGFnVbldEwHGI9ddAeev+DwiUlM/2CQfD7Nqax8+fzeO4c/omyLJoVsxfzYPnjSEvNx+bzUbQH+SMoYO4dszl2Gx7h8MSiL0zBH5i7xKBdTH30mikCryFJUwZ+xWPXPQ0z/7nVZbOXl7vVesDvgA7NsVe82WGTVbMX1Ov9miqhgosY8R5+7FsnoeAz6C4wEbAb7BwVgb3XN4BQtUXHYt7rtAazMInMPNuxiwaiwrn1lrb5c4T3oIKLkGZhRW+bvkvK2Nq05QU+lj+y8o6sa0m7NqWz+0D72fLmm34ivwU53sJ+IJMff1bxt/9XqLNK4d4LgfZe/2EGzJuqvVzNaz7qzpi+bxV3DHoIULBEL5iP2II30z8kSNP7c2o926pt193h8uBM82Br3jv/GewO+w0b6s13xsiC2d4yd1oJxwqPx4KBQxWLXGxalGAA+LeJFcds+gFKHoFKyYbAr5DFb2Eajoaw33qvp8AUOGtqLxbreLf4gAVRLn/gTQZhUQ5HWjeNptdW6NVI10eJzntq5k9VId88cr/CAWiJ4D9Xj+fvDCNS+89F5c7sVIQuxF7e8h+B1XwsHUdjOaQcSOG5x+1fq6UHsErcye+3Ge4c9AIivKKSx2rMhW+Yj9zp83ng9E1mwM2TZP53yzivcc/4YtXv6ZgR8UjIbBCNEcP7oPdGf27anfa6XvG4TWyRVO3rFqsCAZiRyPFMFi1pHIN9cpQ/h+h6DWsibfdjspv/eWPQIXWxz5OlaC8H1pzBHnDUf4fUCq27K9SIdTOiyC4wGpXFVn/Sz6KO8F3wYghpKVHO0YxDAZefGx1u1lnLJn1Z1Sx+t2IIWz9a3s9W1Qx4uiO0fwdjNaLMVp+XyfOHVJ4BK/CG1E7zmXmZCEUbB3zNX5vgMljPueCEWdhGFX/rdu2fjsjTnqQnZt3EfAFsTvtjL3lTYaOvixuab353yzi0UueJVASQEWq4djsNpxuBy63k8e+vBtHDZe9a+qW7NbNcLjSCAWj77wMI41mraoff98bVfQyUdXWSwmjvBORJneVPya0BrXjIsAHysrCUv6vwdYFsicgRnr5ZvzfgbkL6w6hLD4o+QSVOSxqLuGE8/uzZsk6Jo/5rHRgYrPbeODjEQ1qArNlxxwMQ0oF0coSCoRp0rzh2FqfpOwIXuXdCuYu1iyzU1IcPwTjLfBSUlj1EZhSijtOfojNq7dSUuQjHArj9/oJ+IK8dsfbLPhucdQxfy3bwH1nPUF+bkHkGMvBiwF9BvXkvQ2vckCvhpdBobHof9aRxMsnsLsc9Bl02L6fJFRRPDto3cqXQSkTtfNqULtKnbu1wwuhP1EF90a1ooKLQRXHPoU4Y9ogIlz10EW8/ddL3D7ueu6dNIwPNr/Gocd1q0qv6o2/X3cyDlf0AMlmNzjs+G6NVrc+JR28Cq2F4B+ASZPsMA6nSXarINc/soHxPy/juanLOfGsXYACEVyeqsfmFv24lB0bd8asfuP3+nn7kQ+jtk96cgoBf/TtYygQ5pdpC2Lu0zQc3Olp3P/RcNLSXTjdlsSAy+PCk+nmoSl3YrPv2xyOCm0AFX13ULpfEZ1hEZhtOfeYS2wD4PsfyiwfOxejBZAW5yRBKxYch6wWTTnunL70GdSzwaVGAnQ9fH8uu+88nG5n6fVwZ6SR0645w8ffkGDrEkfDu1K1QWgViB0UnDgkj0/HNeeFaSvIaBrG7oA2+8Eto9fT5RAfK5afU60P7KqFaytczbf6t7+itv0+68+45dBsDhubVm6hS89OVbZBU/8cftJhTFz9Iv976wc2LN9E5x4dGHT5iWRkpVd+cAWo8HbUjnPwFvmx2QSXO1bRaxuurPPKbwwtt2QN4iEOCK0FZ88929yDofDJGC82rOXyDTAPvzpcMOIsjjnrKL6e8AN5uQX0GtCDY885qlGHPlPTwRtN2D2yadE2yF0v/YWnieXcd+NOV5z1z1wKjcHVarpJ80zsDjtBf+wvV6wvfFbLJmxYvinm60OBEE2aN8yl6RoLpcIQXEzTLIPzhg2uVb125Z0Aysun43Lod3I+bToFcKXtcfIlXmHhzCZIVhr9zyxzoNEE6+sbR+pChWCveLoY2aimoyH/9sgWP4gHxI1kvVBrfUok7Q9sy1UPX5RoMxoMKRmiwdGbsr9d3Y8owRlDvM/u9JDTonpKgP2HHBl3NO5yOxl83aCo7WfdcFqcTAShS69OtGhA6WapiFImyv8jZv59mPn3ofw/xs00iTo29BcqdwBq11WonZejtp+MCkcX/qgx/p+AAL/NyuDmwV2Z/FIL8nbYCIVgy3oHbzzchgev7hC9RsJ1EtGTpWWwtUfs+0VtNtynIC2+s3Ku3ZciTe5DWkxH7FrwLxVJyRG8iB2VeTcU3APE19Y2xARb9YTOPJlubnn1Wp659hUrIyYy2HK5nXTs1o4h10fnKx937tHM/GQusz/7tTRV0+VxkuZxcefE2l/coNmDMnehdl4G4Q17Mk18n4KtPWRPRIyK1x6o/GFgbqNUjSO8AZV/F5I9rnYMtLWEEDRrEcJfYjBhdBsmjG5T7iVOt5Mm2eXv8sRogsocAYWjif6Mu5Gmj8Q9pdhaIBn/qh37NQ2aGmvR1CZ1pUVjlnwGhY+CWUz0l8AAW2ckZ2qN9F+Wzl7Ou499zPJfVpKe5WHwtSdzxtCT4i6mUEox7+tFTHv9G4ryijny1N6cevXAfY7hairG3HkFBH4lalk4DnAegZH9VqzDgEje+NZDiJ7IdGK0XlIr9in/HNSuoSyebXD3pZ3xeaMnbJ1pDv67ZmzMdEzl+xZV9IwVk8cA5zFI5m2WYqUm5alMiyalHTzsjp8uQZV8AiUfRiZfw2DfD2n2CmJrU3kjmqREhdahtp9BdOWk3biQnC/ihieUUqhtfSILgspgtMJoOaPW7DQLX0UVPc0zw9vy/SdNS528iDV6/9fjl8ZdX7HH1hBgJLSeq6b+qTOxsWRBxAbOnoizJypzOISWgdEUsR+QaNM0dU1oWWQ5fhwHLw4ILUMhqPzbILTGUnXMGoPY2iIiqIyboOhpULvXSrgh49ZaM1GpMAS+R8TBLaPX0/ekfD56tRXbtzal82G9OH/4EA7pX7kAnUjKf5U1NaBRfSrE8ICzT6LN0NQXUvnqRUUa7LxkT5w9uAC14xJo8TUidoz0K1G2dlb1HWxI+hWI67jas9E/3ZIMxocI9D+1gP6nFmDdXTyS9KmLmsTSqBy8ppHhPAoqypZRJthagSpgT0kDE8wdVl1UewcAJG0QkhadHVUbKP+M8itRSxEIzLGkZTWaGqIdvCZlEXGgbB0g/GfsF9jaI0ZOJH5dlnAkz7weMJoDDqImgcUGlWT4pBJKmRCYgfJ9D0ZzxHMOYmubaLOSHj0jo0ltzArWOZgbEVsOeK4A3FjL+N2Qfi1i1I92ibjPIbo2K4AdXAPqxYZEo5SJyrselXczlLwNxS+hck9D+X9KtGlJj3bwmtRGPDE3b1zt5N7L23Oq60LOaDGP8U+dSEHgP0j26xiZ9bc2QeztkaxnQTKtOQPxgNEKyX4LkRir81IR/w8Q+LlMqCoIlKDyR1R5QZomNjpEo0lt3OdB8euUTZXcttHBjacfiLfQZmWxAO89tZnP3sjntcXnk1PPkQFJGwCunyNFOFxg796o0h2Vf3rseQizwFqgplfZ1piU/BSFgiG+e3cmj136LC/dNp6/lm1ItEmaBCHp/wJ7FxB36bYPXmyL32uUK0VnFYHxMenJKQmwEkSciLMP4ujRqJw7EJlriDXWNMHQOk37QtKP4AO+AC/c+AY/TfmV9CYerh1zBR888QmrfluLr9iPzW7wxStfM+KtGzj+3H7Vbt80TULBMM4YWtOaho8YHmj+AfimoUqs6l2//qAIhYqiXhsKhPnp01/Yr1t7EOGIU3rSskPtF0LWlEfc/0AVv8meSlZgrTTuX6mUhKZikn4l6+NXvsCPH/xUWq7L7rBh2G0ESsqr7KU39TBp6+tVlg4t2FHIy8Pe4vv3ZxEKhml7QGuGPnEZ/c88skZ2ahoO1/Uezqrf1sbcJyKlmu/KNDnhgv4Me+3f+6z5rqkY5f8RlT8CzBIgDM6jkaynET2Cr5DKVrIm/b3gT5/MLVeLMRQMRzl3sG7BVy1cW6U2A74AN/a7i+nvziToD6FMxcblm3n04mf4YVL8mX2lQijfN6iiF1DFE1DhbXvtN1HBRSj/7KhiDJr6Y/B1J8dU9wRLnsDv9ZdW6fpx0s+8cvuEKrVret/HzD0Nc/vfUb7/1abJKY+4jkdazEJyPkFa/oiR/bp27rVA0jt4l7t8poFhSMzqauGwSXrT2BkVe/PDBz+zc0seoWB5OVa/N8DLt71FrLseFVyOyj0elT8cVfQ8qnA0KncgZuEzlqaJfwYq91hLcjbvetS2Yy3pWqWrOdU3p10zkB79m5Hm2Z2hoYhdGcm65l+89g3eSso6mt5PoeARCK+ySubl3Y7y/1y7hqc4IjbE3hkxshNtSsqQ9A7+2ievwOWxnLzdaSczOwNX2h6n3/UwL/9+aCOjXtlIu/bfoszCStv8ecov+IpiywwX7Cxi+8ad5bYpsxi18xIwt0dqXiqsrI0AeN9EFT2H2nV9ZL8XVKG1v+SAdg6VAAAJN0lEQVRjVMF9Ney5pqbY7DYe+m8ud7+2lkHn7+Dw4wtxueOn49mddlYviq7UVY6SyZRXLPWhfImZsNVodpP0k6x/u+Q4ctpnM+eL+WRmZ3DG0JP47p2ZjBs1gZEvrqZn/3ycLhPDBhSOQRWNgayxiOuYuG16mnoQgVjTE2bYjLprUCVTrJqWsVAlkTS9WJV3fFAyBZVxK1JNXXrNvmHYMjlyQCFHDihk8Zx07r0sviSACps40yqZu4kKJxhWbrtGk0CS3sED9DzhEHqecEjp87NuOI3TLvwVw78Ym63syKwEFKhd/4EWX8aVCj75ihP54YOfSotz7EZEOLDP/tEl9gIzgFh6IruJX1AZcUJwAdhOruB4TW0jmTejds4F5aPb4V5rABAHl8fFAb0r1oSRjJtRgZ8iypUGiAdJv6p2jdZoqknSh2hioVQJjtBkbLY49SoJo7z/jXv8ocd148QLjik3EedMc5Ce5WHYG/+JPqBMjnWNkDiV7jV1hjh6INmTwXMN9qx/ct1TF5aG+sri8ji54flrMIyKvyriOAhp/ilk3Ixk3orkfK5rDWgSTtKnScZCBRejdl4RXaihLPZuGDmfxm9DKeZOW8BnL31F/vZC+pzckyH/OSVOVZ3pqPxb46gCApIDKo/yeb6793mQlnMQiZ3Voak/Znw4m9fu+C/bN+4AoFWnllz35OX0PUNLTGsaJo204IedPfKvcahE50NE6Hv64fQ9/fDKT+c6Hmz7R8qm7X3XkAZNH4H8kaDyKV8oOQ0yR2nn3kA47h9Hc+w5fcnblg8iNGtZXnDMDG2BoschtALsPSBzOIZNF0zXNFxSMkSD/UAsdcB4uCHtzFo7nYgNyZ4AaScDrsjkmhts+yHNXsNIG4DkfALuM639CNi7Ic2ew/CcV2t2aPYdEaFZq6xo5+6fA9tPAN8X1g+57yPIPQYzsDRBlmo0lZOSIRoA0/shFD5YptTabgwwspGc/yFG5RV/qosy86zSb0Ym2LrELOitlKpRoW9N4jC3Hhm5A9sLoy1Gy+/r3R6NBhK0klVEThWRP0VkpYjcWRfnqAzD8w/IGGZNgEp6RDY2DewHIs0n1YlzBxAjC3H2RuwHxHXi2rknF8osju3cAcxNMRe+aTQNgVqPwYuIDXgRGARsAH4RkSlKqXq/lzXSL0d5zgP/LGvC1X4g4uhe32Zokh2pWIdG/2BrGip1Mcl6FLBSKbUaQETeA4YACQlWirgh7aREnFqTIoikoYx2satD2fSAQdNwqYsQTTtgfZnnGyLbyiEiQ0XkVxH5NTc3tw7M0GhqkewJWBPkZZAMyH4jIeZoNFUhYWmSSqlXgVfBmmRNlB0aTVUw7B1QrRagvO9DcBE4j0LcZze+4hyapKIuHPxGoEOZ5+0j2zSapEbEjqRfAlySaFM0mipRF8OPX4CuItJZrKrBFwJaVk+j0WjqmVofwSulQiJyA/AVYAPGKaV+r+3zaDQajaZi6iQGr5SaCkyti7Y1Go1GUzX0DJFGo9GkKNrBazQaTYrSILRoRCQXqKQmWq2SA2yvx/PVFbofDYdU6APofjQ0KuvHfkqpuOXgGoSDr29E5NeKBHqSBd2PhkMq9AF0Pxoa+9oPHaLRaDSaFEU7eI1Go0lRGquDfzXRBtQSuh8Nh1ToA+h+NDT2qR+NMgav0Wg0jYHGOoLXaDSalEc7eI1Go0lRUtrBi0gHEZkuIktF5HcRuTmyPVtEvhaRFZH/zRJta1UQEZuILBCRzyPPO4vInEhpxPcj4m4NGhHJEpHJIvKHiCwTkX7JeD1E5NbIZ2qJiLwrImnJcD1EZJyIbBORJWW2xXz/xeK5SH8WicjhibO8PHH6MTryuVokIh+LSFaZfSMj/fhTRE5JjNXRxOpHmX3DRESJSE7kebWvR0o7eCAEDFNKdQeOBq4Xke7AncC3SqmuwLeR58nAzcCyMs8fB55WSh0A7AKuSYhV1eNZ4Eul1MFAT6z+JNX1EJF2wE3AEUqpHliieheSHNdjPHDqXtvivf+nAV0jf0OBl+rJxqownuh+fA30UEodBiwHRgJEvvMXAodEjhkbKS3aEBhPdD8QkQ7AycC6Mpurfz2UUo3mD/gUq1bsn0CbyLY2wJ+Jtq0KtrfH+vINBD4HBGuFmz2yvx/wVaLtrKQPTYE1RCb3y2xPquvBnqpl2ViCfZ8DpyTL9QA6AUsqe/+BV4CLYr2uIfzt3Y+99p0NvB15PBIYWWbfV0C/RNtfUT+AyVgDoLVATk2vR6qP4EsRkU5Ab2AO0EoptTmyawvQKkFmVYdngBGAGXneHMhTSoUiz2OWRmxgdAZygTcjoabXRSSdJLseSqmNwJNYo6vNQD4wj+S7HruJ9/5XqfxmA+VqYFrkcVL1Q0SGABuVUr/ttava/WgUDl5EMoAPgVuUUgVl9ynrp7BB54qKyGBgm1JqXqJt2UfswOHAS0qp3kAxe4VjkuR6NMMqJN8ZaAukE+M2OxlJhve/MkRkFFZ49u1E21JdRMQD3AXcWxvtpbyDFxEHlnN/Wyn1UWTzVhFpE9nfBtiWKPuqyDHAmSKyFngPK0zzLJAlIrs1/ZOhNOIGYINSak7k+WQsh59s1+MkYI1SKlcpFQQ+wrpGyXY9dhPv/U+68psiciUwGLgk8mMFydWPLlgDh98i3/f2wHwRaU0N+pHSDl5EBHgDWKaUeqrMrinAFZHHV2DF5hssSqmRSqn2SqlOWJNF3ymlLgGmA+dGXpYM/dgCrBeRgyKb/gYsJcmuB1Zo5mgR8UQ+Y7v7kVTXowzx3v8pwOWR7I2jgfwyoZwGh4icihXGPFMp5S2zawpwoYi4RKQz1iTl3ETYWBlKqcVKqZZKqU6R7/sG4PDId6f61yPREwx1PHlxLNbt5iJgYeTvdKz49bfACuAbIDvRtlajTycCn0ce74/1QV0JTAJcibavCvb3An6NXJNPgGbJeD2AB4A/gCXARMCVDNcDeBdr3iAYcR7XxHv/sSbyXwRWAYuxsoYS3ocK+rESK0a9+7v+cpnXj4r040/gtETbX1E/9tq/lj2TrNW+HlqqQKPRaFKUlA7RaDQaTWNGO3iNRqNJUbSD12g0mhRFO3iNRqNJUbSD12g0mhRFO3iNRqNJUbSD12g0mhTl/wGrs+dA/4zStgAAAABJRU5ErkJggg==\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["km.predict([[23,25,87,1]])"],"metadata":{"id":"f12zD40oWP3G","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1649938627588,"user_tz":-330,"elapsed":38,"user":{"displayName":"Chesta Gupta","userId":"17654074163390916896"}},"outputId":"495b5b03-d7cf-4e10-fb42-db659daefb65"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/sklearn/base.py:451: UserWarning: X does not have valid feature names, but KMeans was fitted with feature names\n","  \"X does not have valid feature names, but\"\n"]},{"output_type":"execute_result","data":{"text/plain":["array([2], dtype=int32)"]},"metadata":{},"execution_count":22}]},{"cell_type":"markdown","source":["This is an unsupervised ML model. Therefore accuracy can't be calculated"],"metadata":{"id":"6FviBRLOziPn"}}]}
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