Customer_Segmentation.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Customer_Segmentation.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8hV3whu65UDH"
      },
      "source": [
        "**Importing the libraries and Data Set**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2l0DVkx532W0"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "dataset = pd.read_csv(\"/content/Mall_Customers.csv\")"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0v90I_kk57Ay"
      },
      "source": [
        "**Exploratory Data Analysis**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7BsQ9zdX6Bhr",
        "outputId": "2e8ea0a3-9dba-4f50-9538-9237eb113b7e"
      },
      "source": [
        "dataset.info()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 200 entries, 0 to 199\n",
            "Data columns (total 5 columns):\n",
            " #   Column                  Non-Null Count  Dtype \n",
            "---  ------                  --------------  ----- \n",
            " 0   CustomerID              200 non-null    int64 \n",
            " 1   Gender                  200 non-null    object\n",
            " 2   Age                     200 non-null    int64 \n",
            " 3   Annual Income (k$)      200 non-null    int64 \n",
            " 4   Spending Score (1-100)  200 non-null    int64 \n",
            "dtypes: int64(4), object(1)\n",
            "memory usage: 7.9+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AOIneKjn6GvA",
        "outputId": "cba88fb3-f597-4aac-c2de-ccf543941e67"
      },
      "source": [
        "dataset.columns"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['CustomerID', 'Gender', 'Age', 'Annual Income (k$)',\n",
              "       'Spending Score (1-100)'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "eywoJost6KcD",
        "outputId": "8a12fa45-781a-40ec-dd7a-7a3af17da1d8"
      },
      "source": [
        "dataset.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
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              "    }\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$)</th>\n",
              "      <th>Spending Score (1-100)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Male</td>\n",
              "      <td>19</td>\n",
              "      <td>15</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Male</td>\n",
              "      <td>21</td>\n",
              "      <td>15</td>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Female</td>\n",
              "      <td>20</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Female</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Female</td>\n",
              "      <td>31</td>\n",
              "      <td>17</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CustomerID  Gender  Age  Annual Income (k$)  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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ook6YRZ36UGk"
      },
      "source": [
        "dataset.drop([\"CustomerID\"] , axis=1, inplace=True)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "zIOyE9OM6obr",
        "outputId": "855762b9-9949-49fa-c989-498de602a2b5"
      },
      "source": [
        "dataset.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Annual Income (k$)</th>\n",
              "      <th>Spending Score (1-100)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Male</td>\n",
              "      <td>19</td>\n",
              "      <td>15</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Male</td>\n",
              "      <td>21</td>\n",
              "      <td>15</td>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Female</td>\n",
              "      <td>20</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Female</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Female</td>\n",
              "      <td>31</td>\n",
              "      <td>17</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Gender  Age  Annual Income (k$)  Spending Score (1-100)\n",
              "0    Male   19                  15                      39\n",
              "1    Male   21                  15                      81\n",
              "2  Female   20                  16                       6\n",
              "3  Female   23                  16                      77\n",
              "4  Female   31                  17                      40"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JWu2n3qY63py"
      },
      "source": [
        "**Data Visualization**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "id": "gShfUYqQ67jB",
        "outputId": "60eb27ed-b3d8-4f0b-fb57-807405e308f7"
      },
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "plt.title(\"Ages Frequency\")\n",
        "sns.axes_style(\"dark\")\n",
        "sns.violinplot(y=dataset[\"Age\"])\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "kd1PMF-T9GL6",
        "outputId": "7dd3e52b-6fc1-4a05-c7f5-ac7d4fd7c83c"
      },
      "source": [
        "plt.figure(figsize=(15,6))\n",
        "plt.subplot(1,2,1)\n",
        "sns.boxplot(y=dataset[\"Spending Score (1-100)\"], color=\"red\")\n",
        "plt.subplot(1,2,2)\n",
        "sns.boxplot(y=dataset[\"Annual Income (k$)\"], color=\"green\")\n",
        "plt.show()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "osFg_qqV9Uof",
        "outputId": "c72fb10b-13be-4935-e393-3753bd0e6ce6"
      },
      "source": [
        "genders = dataset.Gender.value_counts()\n",
        "sns.set_style(\"darkgrid\")\n",
        "plt.figure(figsize=(10,4))\n",
        "sns.barplot(x=genders.index, y=genders.values)\n",
        "plt.show()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "Q16hSFQI9lzX",
        "outputId": "9cd043d3-796b-4d81-f395-ee731d39ffa8"
      },
      "source": [
        "age18_25 = dataset.Age[(dataset.Age <= 25) & (dataset.Age >= 18)]\n",
        "age26_35 = dataset.Age[(dataset.Age <= 35) & (dataset.Age >= 26)]\n",
        "age36_45 = dataset.Age[(dataset.Age <= 45) & (dataset.Age >= 36)]\n",
        "age46_55 = dataset.Age[(dataset.Age <= 55) & (dataset.Age >= 46)]\n",
        "age55above = dataset.Age[dataset.Age >= 56]\n",
        "\n",
        "x = [\"18-25\",\"26-35\",\"36-45\",\"46-55\",\"55+\"]\n",
        "y = [len(age18_25.values),len(age26_35.values),len(age36_45.values),len(age46_55.values),len(age55above.values)]\n",
        "\n",
        "plt.figure(figsize=(15,6))\n",
        "sns.barplot(x=x, y=y, palette=\"rocket\")\n",
        "plt.title(\"Number of Customer and Ages\")\n",
        "plt.xlabel(\"Age\")\n",
        "plt.ylabel(\"Number of Customer\")\n",
        "plt.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "0lD4nIah-Ap3",
        "outputId": "59236a1c-2d66-4e17-cac2-a7d35fd7acdb"
      },
      "source": [
        "ss1_20 = dataset[\"Spending Score (1-100)\"][(dataset[\"Spending Score (1-100)\"] >= 1) & (dataset[\"Spending Score (1-100)\"] <= 20)]\n",
        "ss21_40 = dataset[\"Spending Score (1-100)\"][(dataset[\"Spending Score (1-100)\"] >= 21) & (dataset[\"Spending Score (1-100)\"] <= 40)]\n",
        "ss41_60 = dataset[\"Spending Score (1-100)\"][(dataset[\"Spending Score (1-100)\"] >= 41) & (dataset[\"Spending Score (1-100)\"] <= 60)]\n",
        "ss61_80 = dataset[\"Spending Score (1-100)\"][(dataset[\"Spending Score (1-100)\"] >= 61) & (dataset[\"Spending Score (1-100)\"] <= 80)]\n",
        "ss81_100 = dataset[\"Spending Score (1-100)\"][(dataset[\"Spending Score (1-100)\"] >= 81) & (dataset[\"Spending Score (1-100)\"] <= 100)]\n",
        "\n",
        "ssx = [\"1-20\", \"21-40\", \"41-60\", \"61-80\", \"81-100\"]\n",
        "ssy = [len(ss1_20.values), len(ss21_40.values), len(ss41_60.values), len(ss61_80.values), len(ss81_100.values)]\n",
        "\n",
        "plt.figure(figsize=(15,6))\n",
        "sns.barplot(x=ssx, y=ssy, palette=\"nipy_spectral_r\")\n",
        "plt.title(\"Spending Scores\")\n",
        "plt.xlabel(\"Score\")\n",
        "plt.ylabel(\"Number of Customer Having the Score\")\n",
        "plt.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "hts6p-gy-Xmi",
        "outputId": "53e81055-dc91-4145-c8de-4b4606bd3965"
      },
      "source": [
        "ai0_30 = dataset[\"Annual Income (k$)\"][(dataset[\"Annual Income (k$)\"] >= 0) & (dataset[\"Annual Income (k$)\"] <= 30)]\n",
        "ai31_60 = dataset[\"Annual Income (k$)\"][(dataset[\"Annual Income (k$)\"] >= 31) & (dataset[\"Annual Income (k$)\"] <= 60)]\n",
        "ai61_90 = dataset[\"Annual Income (k$)\"][(dataset[\"Annual Income (k$)\"] >= 61) & (dataset[\"Annual Income (k$)\"] <= 90)]\n",
        "ai91_120 = dataset[\"Annual Income (k$)\"][(dataset[\"Annual Income (k$)\"] >= 91) & (dataset[\"Annual Income (k$)\"] <= 120)]\n",
        "ai121_150 = dataset[\"Annual Income (k$)\"][(dataset[\"Annual Income (k$)\"] >= 121) & (dataset[\"Annual Income (k$)\"] <= 150)]\n",
        "\n",
        "aix = [\"$ 0 - 30,000\", \"$ 30,001 - 60,000\", \"$ 60,001 - 90,000\", \"$ 90,001 - 120,000\", \"$ 120,001 - 150,000\"]\n",
        "aiy = [len(ai0_30.values), len(ai31_60.values), len(ai61_90.values), len(ai91_120.values), len(ai121_150.values)]\n",
        "\n",
        "plt.figure(figsize=(15,6))\n",
        "sns.barplot(x=aix, y=aiy, palette=\"Set2\")\n",
        "plt.title(\"Annual Incomes\")\n",
        "plt.xlabel(\"Income\")\n",
        "plt.ylabel(\"Number of Customer\")\n",
        "plt.show()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wHTp0SJK0c+dORUREKDo6Wunp6ZKk9PR0xcTEeKsEAAAAAGjwvHakTpJmzpyp6dOnq6KiQuHh4Zo/f77OnDmjKVOmaNWqVWrfvr0WL17szRIAAAAAoEHzaqjr2rWrVq9efd70FStWeHO1AAAAANBoePV76gAAAAAA3kWoAwAAAAALI9QBAAAAgIUR6gAAAADAwgh1AAAAAGBhhDoAAAAAsDBCHQAAAABYGKEOAAAAACyMUAcAAAAAFkaoAwAAAAALI9QBAAAAgIUR6gAAAADAwgh1AAAAAGBh1YY6Y4yOHj3qq1oAAAAAADVUbaiz2WxKSUnxVS0AAAAAgBpye/rlddddp6ysLF/UAgAAAACoIX93T9i/f7/Wrl2r9u3bq3nz5s7pa9eu9WphAAAAAAD33Ia6v/3tb76oAwAAAABQC25Pv+zQoYOOHj2qjz76SB06dFDz5s115swZX9QGAAAAAHDDbah7/vnntWzZMqWlpUmSKioq9OCDD3q9MAAAAACAe25D3aZNm/Tiiy86r6cLDQ1VSUmJ1wsDAAAAALjnNtQ1bdpUNptNNptNklRaWur1ogAAAAAAnnF7o5T4+HjNmjVLxcXF+uc//6k333xTt9xyiy9qAwAAAAC44TbUTZo0SR9++KFatmypb775RqmpqerXr58vagMAAAAAuOE21ElSv3791KNHDzkcDklSUVGR2rZt69XCAAAAAADuuQ11//jHP/Tcc8+pWbNmstlsMsbIZrMpMzPTF/UBAAAAAKrhNtT9/e9/19q1axUYGOiLegAAAAAANeD27pfh4eHOrzMAAAAAANQvbo/UPfDAA7r11lvVo0cP2e125/Q//elPXi0MAAAAAOCe21A3a9Ys9e3bV507d1aTJm4P7AEAAAAAfMhtqHM4HHr44YdrtfDo6Gi1bNlSTZo0kZ+fn1avXq2ioiJNnTpVOTk56tChgxYvXqw2bdrUavkAAAAA0Ni5PfQ2cOBAvf7668rPz1dRUZHzn6dWrFihjIwMrV69WpKUlpamqKgobdy4UVFRUUpLS6t99QAAAADQyLk9Uvf2229LkpYuXeqcdjFfaZCZman/+7//kyQlJSXprrvu0oMPPlirZQEAAABAY+c21G3ZsuWiVjBp0iTZbDaNGzdO48aNU0FBgUJCQiRJwcHBKigouKjlAwAAAEBj5jbUVVRU6LXXXtPu3bslSb1799a4cePUtGlTtwt/7bXXFBoaqoKCAk2cOFGdOnWqMt9ms8lms7ldjp+fTW3btnD7PABoSNjuob6iNwGgfnEb6mbPni2Hw6HbbrtNkvTWW29p9uzZmjt3rtuFh4aGSpKCgoIUGxurrKwsBQUFKT8/XyEhIcrPz/foS80rK42KikrdPg+wouDgVnVdAuqput7u0Ztwpa57EwAao+r2y25D3b///W+99dZbzp+joqI0YsQItystLS3VmTNnFBAQoNLSUn344Yf6/e9/r+joaKWnpyslJUXp6emKiYnxcBgAAAAAgJ9zG+r8/Px0+PBhXXnllZKk7Oxs+fn5uV1wQUGBJk+eLEmqrKzU8OHDNXDgQN1www2aMmWKVq1apfbt22vx4sUXOQQAAAAAaLzchroZM2Zo/PjxCg8PlzFG33//vUenXoaHh1c5wndWu3bttGLFitpVCwAAAACowm2ou+mmm7Rx40YdOnRIks672QkAAAAAoO64/fLxcePGyW63q0uXLurSpYvsdrvGjRvni9oAAAAAAG64PFL3ww8/KC8vT6dPn9aBAwdkjJEknTx5UqdOnfJZgQAAAAAA11yGuu3bt2v16tXKzc3Vk08+6Qx1LVu21LRp03xWIAAAAADANZehLjk5WcnJydqwYYOGDBniy5oAAAAAAB5ye01dbm6uTp48KWOMHn30USUnJ2v79u2+qA0AAAAA4IbbUPfmm28qICBA27dvV1FRkRYsWKCFCxf6ojYAAAAAgBtuQ93Za+nef/99JSUl6Ze//KVzGgAAAACgbrkNdddff73uuecebdu2Tf3799fJkyfVpInblwEAAAAAfMDtl4/PnTtXBw8eVHh4uJo3b67CwkLNmzfPF7UBAAAAANxwG+r27NkjSfrPf/7j9WIAAAAAADXjNtT97W9/cz4uKytTVlaWunXrppUrV3q1MAAAAACAe25D3ZIlS6r8fPToUU6/BAAAAIB6osZ3PAkLC9PXX3/tjVoAAAAAADXk9kjd448/LpvNJkk6c+aMDh48qOuuu87rhQEAAAAA3HMb6q6//nrnYz8/PyUkJOimm27yalEAAAAAAM+4DHXHjh3TsWPHlJycXGX6V199pWPHjikwMNDrxQEAAAAAqufymrrHH39chYWF500vKirS3LlzvVoUAAAAAMAzLkPdd999p169ep03PTIyku+sAwAAAIB6wmWoKykpcfmiiooKrxQDAAAAAKgZl6GuY8eOev/998+b/v777ys8PNyrRQEAAAAAPOPyRimPPPKI7rvvPq1fv17dunWTJH366af65JNPzvtCcgAAAABA3XB5pO6qq67S2rVr1atXL+Xk5CgnJ0e9evXSW2+9pauvvtqXNQIAAAAAXKj2e+rsdrtGjx7tq1oAAAAAADXk8kgdAAAAAKD+I9QBAAAAgIW5DHUTJkyQJD399NM+KwYAAAAAUDMur6n74YcftHfvXm3ZskUJCQkyxlSZf/aOmAAAAACAuuMy1KWmpuqFF15Qbm6u5s+fX2WezWbTypUrvV4cAAAAAKB6LkPd0KFDNXToUP3v//6vJk+e7MuaAAAAAAAeqvYrDSRp8uTJyszM1O7duyVJvXv31uDBgz1eQWVlpUaPHq3Q0FAtXbpU2dnZmjZtmoqKitStWzctWLBAdru99iMAAAAAgEbM7d0vFy5cqJUrVyoiIkIRERFauXKlnn32WY9XcPa1Zz3zzDO6++67tWnTJrVu3VqrVq2qXeUAAAAAAPehbuvWrVq+fLnGjBmjMWPGaNmyZXrvvfc8Wnhubq62bt2qMWPGSJKMMfroo480ZMgQSVJycrIyMzMvonwAAAAAaNzcnn4pScXFxWrbtq0k6cSJEx4vfN68eXrwwQdVUlIiSSosLFTr1q3l7//TasPCwpSXl+d2OX5+NrVt28Lj9QJAQ8B2D/UVvQkA9YvbUHffffcpOTlZffr0kTFGu3bt0vTp090u+L333lNgYKCuv/56ffzxxxdVZGWlUVFR6UUtA6ivgoNb1XUJqKfqertHb8KVuu5NAGiMqtsvuw11w4cPV+/evfXvf/9bkjR9+nQFBwe7XenZ77jbtm2bysrKdPLkSc2dO1fFxcVyOBzy9/dXbm6uQkNDazAUAAAAAMC53F5TJ0khISGKiYlRTEyMR4FOkh544AFt27ZNW7Zs0bPPPqu+fftq4cKF6tOnjzZs2CBJWrNmjaKjo2tfPQAAAAA0ch6FukvpwQcf1PLlyxUbG6uioiKNHTvW1yUAAAAAQIPh0Y1SLlafPn3Up08fSVJ4eDhfYwAAAAAAl0i1R+oqKys1dOhQX9UCAAAAAKihakOdn5+frr76an3//fe+qgcAAAAAUANuT78sLi5WQkKCunfvrubNmzunL1myxKuFAQAAAADccxvq7r//fl/UAQAAAACoBbehrnfv3srJydF3332nX/3qVzp16pQqKyt9URsAAAAAwA23X2nwz3/+U6mpqZo1a5YkKS8vT5MnT/Z6YQAAAAAA99yGuldeeUWvvfaaAgICJElXXXWVjh075vXCAAAAAADuuQ11drtddrvd+bPD4fBqQQAAAAAAz7m9pq5Xr15asmSJTp8+rQ8//FCvvvqqoqOjfVEbAAAAAMANt0fqpk+frsDAQHXu3Fmvv/66Bg0apClTpviiNgAAAACAG26P1DVp0kRJSUnq3r27bDabrr76atlsNl/UBgAAAABww22o27p1q/7nf/5HV155pYwxOnLkiObMmaNBgwb5oj4AAAAAQDXchronn3xSK1euVMeOHSVJhw8fVkpKCqEOAAAAAOoBt9fUtWzZ0hnoJCk8PFwtW7b0alEAAAAAAM+4PFK3ceNGSdL111+ve++9V/Hx8bLZbHr33Xd1ww03+KxAAAAAAIBrLkPde++953z8i1/8Qrt27ZIkBQYGqqyszPuVAQAAAADcchnq5s+f78s6AAAAAAC14PZGKdnZ2Xr55ZeVk5Mjh8PhnL5kyRKvFgYAAAAAcM9tqJs8ebLGjBmjwYMHq0kTt/dVAQAAAAD4kNtQ16xZM40fP94XtQAAAAAAashtqBs/fryef/559evXT3a73Tm9W7duXi0MAAAAAOCe21D3xRdfKCMjQx999JFsNpskyWazaeXKlV4vDgAAAABQPbeh7t1339XmzZurHKUDAAAAANQPbu988stf/lInTpzwRS0AAAAAgBpye6TuxIkTio+P1w033KCmTZs6p/OVBgAAAABQ99yGuj/+8Y++qAMAAAAAUAtuQ13v3r19UYfXtW57mZqdc6QRKKuoUHHR6bouAwAAALgobkNdz549nXe9rKiokMPhUPPmzbV3716vF3cpNWvaVA+s546d+H8Wxo+XRKgDAACAtbkNdfv27XM+NsYoMzNTn3zyiVeLAgAAAAB4xu3dL89ls9l08803a/v27d6qBwAAAABQA26P1G3cuNH5+MyZM/r000/VrFkztwsuKyvTHXfcofLyclVWVmrIkCFKTU1Vdna2pk2bpqKiInXr1k0LFizgO/AAAAAAoJbchrr33nvP+djPz08dOnTQCy+84HbBdrtdK1asUMuWLVVRUaHbb79dAwcO1PLly3X33XcrISFBs2bN0qpVq3T77bdf3CgAAAAAoJFyG+rmz59fqwXbbDa1bNlSkuRwOORwOGSz2fTRRx9p4cKFkqTk5GQ9//zzhDoAAAAAqCWXoe755593+SKbzabJkye7XXhlZaVGjRqlw4cP6/bbb1d4eLhat24tf/+fVhsWFqa8vDy3y/Hzs6lt2xZunwfUFH2F+oz+RH1FbwJA/eIy1LVocf4Gu7S0VG+++aaKioo8CnV+fn7KyMhQcXGxJk+erEOHDtWqyMpKo6Ki0lq99qzg4FYX9Xo0TBfbV5cCvQlX6ro/6U24Ute9CQCNUXX7ZZeh7p577nE+PnnypFauXKnVq1dr2LBhVeZ5onXr1urTp48++eQTFRcXy+FwyN/fX7m5uQoNDa3RsgAAAAAA/0+1X2lQVFSkRYsWacSIEaqsrNSaNWv04IMPKigoyO2Cjx07puLiYknS6dOntWPHDkVERKhPnz7asGGDJGnNmjWKjo6+BMMAAAAAgMbJ5ZG6p556Sps2bdItt9yitWvXOm964qn8/Hw99NBDqqyslDFGQ4cO1eDBg3XNNddo6tSpWrx4sbp27aqxY8de9CAAAAAAoLFyGeqWL18uu92uF198UUuWLHFON8bIZrNp79691S64S5cuSk9PP296eHi4Vq1adRElAwAAAADOchnqPv/8c1/WAQAAAACohWqvqQMAAAAA1G+EOgAAAACwMEIdAAAAAFgYoQ4AAAAALIxQBwAAAAAWRqgDAAAAAAsj1AEAAACAhRHqAAAAAMDCCHUAAAAAYGGEOgAAAACwMEIdAAAAAFgYoQ4AAAAALIxQBwAAAAAWRqgDAAAAAAsj1AEAAACAhRHqAAAAAMDCCHUAAAAAYGGEOgAAAACwMEIdAAAAAFgYoQ4AAAAALIxQBwAAAAAWRqgDAAAAAAsj1AEAAACAhRHqAAAAAMDCCHUAAAAAYGGEOgAAAACwMEIdAAAAAFgYoQ4AAAAALIxQBwAAAAAW5u+tBR89elQzZsxQQUGBbDabbrnlFk2YMEFFRUWaOnWqcnJy1KFDBy1evFht2rTxVhkAAAAA0KB57Uidn5+fHnroIb3zzjt6/fXX9eqrr+qrr75SWlqaoqKitHHjRkVFRSktLc1bJQAAAABAg+e1UBcSEqJu3bpJkgICAtSpUyfl5eUpMzNTSUlJkqSkpCRt3rzZWyUAAAAAQIPntdMvz3XkyBEdPHhQPXr0UEFBgUJCQiRJwcHBKigocPt6Pz+b2rZt4e0y0QjRV6jP6E/UV/QmANQvXg91JSUlSk1N1SOPPKKAgGht86oAABatSURBVIAq82w2m2w2m9tlVFYaFRWVXlQdwcGtLur1aJgutq8uBXoTrtR1f9KbcKWuexMAGqPq9stevftlRUWFUlNTlZiYqLi4OElSUFCQ8vPzJUn5+fkKDAz0ZgkAAAAA0KB5LdQZY/Too4+qU6dOmjhxonN6dHS00tPTJUnp6emKiYnxVgkAAAAA0OB57fTLPXv2KCMjQ507d9bIkSMlSdOmTVNKSoqmTJmiVatWqX379lq8eLG3SgAAAACABs9roS4yMlL/+c9/LjhvxYoV3lotAAAAADQqXr2mDgAAAADgXYQ6AAAAALAwn3xPHQAAwKXUrk1z+dv5GIOqHOUOFR4/VddlAD7H1hAAAFiOv91fX734fl2XgXrmmt8NqusSgDrB6ZcAAAAAYGGEOgAAAACwMEIdAAAAAFgYoQ4AAAAALIxQBwAAAAAWRqgDAAAAAAsj1AEAAACAhRHqAAAAAMDCCHUAAAAAYGGEOgAAAACwMEIdAAAAAFgYoQ4AAAAALIxQBwAAAAAWRqgDAAAAAAsj1AEAAACAhRHqAAAAAMDCCHUAAAAAYGGEOgAAAACwMEIdAAAAAFgYoQ4AAAAALIxQBwAAAAAWRqgDAAAAAAsj1AEAAACAhRHqAAAAAMDCCHUAAAAAYGGEOgAAAACwMK+FuocfflhRUVEaPny4c1pRUZEmTpyouLg4TZw4UcePH/fW6gEAAACgUfBaqBs1apSWLVtWZVpaWpqioqK0ceNGRUVFKS0tzVurBwAAAIBGwWuhrlevXmrTpk2VaZmZmUpKSpIkJSUlafPmzd5aPQAAAAA0Cv6+XFlBQYFCQkIkScHBwSooKPDodX5+NrVt28KbpaGRoq9Qn9GfqK/oTdRn9CcaI5+GunPZbDbZbDaPnltZaVRUVHpR6wsObnVRr0fDdLF9dSnQm3ClrvuT3oQrdd2bEv0J1+pDfwLeUN12z6d3vwwKClJ+fr4kKT8/X4GBgb5cPQAAAAA0OD4NddHR0UpPT5ckpaenKyYmxperBwAAAIAGx2uhbtq0abr11lv1zTffaODAgXrjjTeUkpKiDz/8UHFxcdqxY4dSUlK8tXoAAAAAaBS8dk3ds88+e8HpK1as8NYqAQAAAKDR8enplwAAAACAS4tQBwAAAAAWRqgDAAAAAAsj1AEAAACAhRHqAAAAAMDCCHUAAAAAYGGEOgAAAACwMEIdAAAAAFgYoQ4AAAAALIxQBwAAAAAWRqgDAAAAAAsj1AEAAACAhRHqAAAAAMDCCHUAAAAAYGGEOgAAAACwMEIdAAAAAFgYoQ4AAAAALMy/rgsAAAAAGoq27exq6t+srstAPVPhKFNRYbnXlk+oAwAAAC6Rpv7N9H8fp9R1Gahn7uqTJsl7oY7TLwEAAADAwgh1AAAAAGBhhDoAAAAAsDBCHQAAAABYGKEOAAAAACyMUAcAAAAAFkaoAwAAAAALI9QBAAAAgIUR6gAAAADAwgh1AAAAAGBhdRLqtm3bpiFDhig2NlZpaWl1UQIAAAAANAg+D3WVlZV67LHHtGzZMq1bt05vv/22vvrqK1+XAQAAAAANgs9DXVZWljp27Kjw8HDZ7XYlJCQoMzPT12UAAAAAQIPg81CXl5ensLAw58+hoaHKy8vzdRkAAAAA0CDYjDHGlyt899139cEHH2ju3LmSpPT0dGVlZWnWrFm+LAMAAAAAGgSfH6kLDQ1Vbm6u8+e8vDyFhob6ugwAAAAAaBB8HupuuOEGffvtt8rOzlZ5ebnWrVun6OhoX5cBAAAAAA2Cv89X6O+vWbNm6Te/+Y0qKys1evRo/fKXv/R1GQAAAADQIPj8mjoAAAAAwKVTJ18+DgAAAAC4NAh1AAAAAGBhhDoAAAAAsDBCXR07cuSIVq9e7XK+MUZPPPGEYmNjlZiYqM8++6xGy8/KytLIkSM1cuRIjRgxQps2bXLO27Ztm4YMGaLY2FilpaVd8PVFRUWaOHGi4uLiNHHiRB0/ftxtXWvWrFFcXJzi4uK0Zs2aGtUL73DXZ5s3b1ZiYqJGjhypUaNGaffu3c55nvw+s7OzNXbsWMXGxmrKlCkqLy+XJJWXl2vKlCmKjY3V2LFjdeTIEUlSYWGh7rrrLvXs2VOPPfZYrcZUXFys1NRUDR06VPHx8dq3b58k1z37c0uXLlVsbKyGDBmiDz74wDnd1fvC1Rhxcdz1piR9/PHHGjlypBISEnTnnXc6p3trGzZp0iRFRkbqvvvuq9WY3nnnHSUmJiohIUFPP/20c7qr98PPuXrPffrpp0pMTFRsbKyeeOIJnb0k3tOeh3vu+vH48eOaPHmyEhMTNWbMGH3xxRfOefW1HxctWqRBgwapZ8+eVaYvX75cw4YNU2JioiZMmKCcnBznPG9s9yXX211cmLt+/PrrrzVu3Dhdf/31+tvf/uacfvToUd11110aNmyYEhIStGLFCuc8b+0jX375ZcXGxuraa6/VsWPHajzWXbt2KTk5Wdddd53efffdKvO6du3q/Cz729/+1jndk/1ybT6vutrW1nsGdeaVV14xQ4cONQMHDjR33nmnyc/PP+85W7duNZMmTTJnzpwx+/btM2PGjKnROkpLS01FRYUxxpi8vDzTt29fU1FRYRwOh4mJiTGHDx82ZWVlJjEx0Xz55Zfnvf6pp54yS5cuNcYYs3TpUrNgwYJq6yosLDTR0dGmsLDQFBUVmejoaFNUVFSjmnFpedJnJ0+eNGfOnDHGGHPw4EEzZMgQY4znv8/U1FTz9ttvG2OMmTlzpnnllVeMMca8/PLLZubMmcYYY95++21z//33G2OMKSkpMbt27TKvvvqqmTNnTq3GNWPGDPPPf/7TGGNMWVmZOX78uDHGdc+e68svvzSJiYmmrKzMHD582MTExBiHw1Ht+8LVGFF7nvTm8ePHTXx8vMnJyTHGGPPjjz8aY4zXtmHGGLNjxw6TmZlpUlJSajymY8eOmUGDBpmCggJjzE99umPHDmOM6/fDuap7z40ePdrs27fPnDlzxkyaNMls3bq12jGiZjzpxyeffNI899xzxhhjvvrqKzN+/HhjTP3tR2OM2bdvn8nLyzM33nhjlek7d+40paWlzrGf7UdvbfddbXdxYZ70448//mj2799vnn32WbNs2TLn9Ly8PPPpp58aY4w5ceKEiYuLc/ajt/aRn332mcnOzjaDBw92bv9qIjs72xw8eNA8+OCDZv369VXm/bx3z/Jkv1ybz6uutrX1HUfq6sjJkyf13HPP6ZlnntH999+v+fPnq3nz5uc9LzMzU0lJSbLZbLrxxhtVXFys/Px8j9fTvHlz+fv/9M0VZWVlstlskn46gtexY0eFh4fLbrcrISFBmZmZLtcvSUlJSdq8eXO1dW3fvl39+vVT27Zt1aZNG/Xr14+/xtUhT/usZcuWzt44deqU87Env09jjD766CMNGTJEkpScnOzspS1btig5OVmSNGTIEO3cuVPGGLVo0UKRkZFq1qxZrcZ14sQJ7dq1S2PGjJEk2e12tW7dWpLrnj1XZmamEhISZLfbFR4ero4dOyorK8vl+6K6MaJ2PO3NtWvXKjY2Vu3bt5ckBQUFSfLeNkySoqKi1LJly1qNKzs7Wx07dlRgYKBzWRs2bJDk+v1wLlfvufz8fJ08eVI33nijbDabkpKSnOP1pOdRPU/78euvv1bfvn0lSREREcrJydGPP/5Yb/tRkm688UaFhIScN71v377OMd54443Kzc2V5L3tvqvtLs7naT8GBQWpe/fuzs95Z4WEhKhbt26SpICAAHXq1El5eXmSvLOPlKTrrrtOV1xxRa3HfMUVV6hLly5q0sSzaOLpfrmmn1er29bWd4S6OtKkSRPZbDbnYe8rrrhCAQEB5z0vLy9PYWFhzp/DwsKcb0xP7d+/XwkJCRoxYoTmzJkjf3//85YbGhp6weUWFBQ4dwbBwcEqKCioti5Plwvf8LTPJGnTpk0aOnSo7rvvPs2bN0/S+b/nC/0+CwsL1bp1a+dO5dwezcvL0+WXXy7pp++obNWqlQoLCy96XEeOHFFgYKAefvhhJSUl6dFHH1Vpaakk1z17LlfjcjW9ujGidjztzW+//VbFxcW66667NGrUKKWnp0vyrDelmm/DLlbHjh31zTff6MiRI3I4HMrMzHR+WPbk/eBpb55bryc9j+p52o9dunTRxo0bJf30h4Xvv/9eubm59bYfPbVq1SoNHDjwgrVcqu0+nw88V5N9tztHjhzRwYMH1aNHD0ne2Ud6W1lZmUaNGqVbbrnFGUI93S/X9PNqXb8XL4bPv3wcP2nRooUef/xxLVy4UD/++KO+/PJLpaamXvAvMRerR48eWrdunb7++mv993//t3PDXVM2m815BAfWUJM+i42NVWxsrHbt2qU///nPeumll3xfsIccDocOHDigmTNnqkePHnriiSeUlpamKVOmVHkePVt/edqblZWV+uyzz/TSSy/p9OnTuvXWW50fTmrKF/3Qpk0bzZ49W1OnTlWTJk3Us2dPHT582KvrPBc9Xzue9mNKSormzp2rkSNHqnPnzuratav8/Pxqtc768rvKyMjQp59+qpdffrmuS8H/71J9RiwpKVFqaqoeeeSRC4bC+tKD7rz33nsKDQ1Vdna2JkyYoM6dO9c65DZkHKmrQzExMfrzn/+s3/zmNzp27Jj+/ve/n/ec0NBQ5195JSk3N1ehoaFVnvPKK684LyCt7q8JERERatGihb744ovzlpuXl3fecqWfDu2fPQUkPz/feUqRq7o8XS58x5M+O1evXr2UnZ2tY8eOefT7bNeunYqLi+VwOCRV7dHQ0FAdPXpU0k9B7MSJE2rXrp1Hde/fv9/Z1z8/9SEsLExhYWHOD/dDhw7VgQMHJLnu2XO5Gper6dWNEbXnSW+GhYWpf//+atGihQIDAxUZGanPP//ca9swTxw9etTZm6+99tp586Ojo/XGG2/o9ddf19VXX62rrrrKuU537wdPe/Pcej3pebjnST8GBARo/vz5ysjI0IIFC1RYWKjw8PB63Y/V2bFjh5YsWaIXX3xRdrv9grVcqu0+nw9qpqb77p+rqKhQamqqEhMTFRcX55zujX2kpx5++GGNHDlS9957b43GcnYd4eHh6t27tw4cOODxfrmmn1cv5r1Y1wh1daSkpMR5p6mWLVsqIiJCJSUl5z0vOjpa6enpMsbok08+UatWrc47N/6OO+5QRkaGMjIyzmu87OxsZ8Pn5OTo0KFD6tChg2644QZ9++23ys7OVnl5udatW6fo6GhJ0sKFC513yTy7fklKT09XTExMtXX1799f27dv1/Hjx3X8+HFt375d/fv3v4T/c6gJT/vsu+++c17b89lnn6m8vFzt2rWr9vc5Y8YMZWVlyWazqU+fPs7rhtasWePspejoaOcdpTZs2KC+fft6/FfBHj16OPv6bN+dFRwcrLCwMB06dEiStHPnTkVERDjXeaGezcrK0owZM5zPWbduncrLy5Wdna1vv/1W3bt3d/m+qG6MqB1PezMmJkZ79uyRw+HQqVOnlJWVpYiICK9twzxx+eWXO3vztttuO2/+2dOZjh8/rldffVVjx451rvNC74e8vDxNmDBBkly+50JCQhQQEKBPPvlExpgLjuXnY4TnPO3H4uJi5x323njjDUVGRiogIKBe96MrBw4c0KxZs/Tiiy86r1WVXPegdHHbfVfbXZzP0350xRijRx99VJ06ddLEiROrzPPGPtJTZ/8g8te//tXj1xw/ftz5njt27Jj27t2ra665ptoe3LRpkxYuXFhlvJ5+Xq1uW1vv+f7eLDDGmKKiInPPPfeYUaNGmcGDB5s77rjD5Obmnve8M2fOmNmzZ5uYmBgzfPhwk5WVVaP1rFmzxgwbNsyMGDHCJCUlmU2bNjnnbd261cTFxZmYmBjzwgsvOKenpKSYvXv3GmN+upPb+PHjTWxsrJkwYYIpLCx0W9cbb7xhbr75ZnPzzTebVatW1aheXFqe9tnSpUudfXLLLbeYXbt2Oee5+n2OGDHCHD161BhjzOHDh83o0aPNzTffbP74xz+asrIyY4wxp0+fNn/84x/NzTffbEaPHm0OHz7sfP3gwYNNr169zI033mgGDBhwwTvFVefAgQMmOTnZDB8+3Pzud79z3rXKVc+uX7/eeUc2Y4x54YUXTExMjImLi6tyZytX7wtXY0TteNqbxhjz17/+1cTHx5uEhASzfPly53RvbcNuu+0206dPH3PDDTeYAQMGmG3bttVobFOnTjXx8fEmPj7eeWc2Y1y/H7Kyssw999zjfJ6r91xWVpZJSEgwMTExZs6cOc471roaIzznaT/u3bvXxMXFmbi4ODN58uQqd4Wsr/341FNPmQEDBphrr73WDBgwwPzlL38xxhgzYcIEExUVZUaMGGFGjBhh7rvvPudrvLXdd7XdRVWe9mN+fr4ZMGCA6dmzp7npppvMgAEDzIkTJ8yuXbtM586dzfDhw52/37P/397aR65YscIMGDDAdO3a1fTr18888sgjNRrz/v37zYABA0yPHj1M7969zbBhw4wxxuzZs8cMHz7cJCYmmuHDhzvvem2M6x5ctmyZWbJkiTGmdp9XXW1r6zubMVb58oWG6ciRI/rXv/6lUaNG1XUpTpMmTaryfSewvkvdZydPntQjjzyiv/zlL5dkeb7w1FNPaeTIkerSpUtdl4JzeGMbaLVt2Msvv6zLL7/cOn8NbsDoR9esuN23Ol9+RmxI+8jp06frkUceaXSnohPq6lhxcbFycnLUtWvXui4FDRh9hvqK3kR9Qj+iPqEfUROEOgAAAACwMG6UAgAAAAAWRqgDAAAAAAsj1AEAGrSePXvWdQkAAHgVoQ4AAAAALMy/rgsAAMAXPv74Yz3//PNq166dvvjiC3Xr1k3PPPOMbDabsrKyNG/ePJWWlsput+ull15S06ZNNXv2bH366afy8/PTQw89pL59+2r16tXavHmzTp06pe+++0733HOPKioqlJGRIbvdrrS0NLVt21aHDx/WnDlzVFhYqMsuu0yPP/64IiIi6vq/AQDQABHqAACNxoEDB7Ru3TqFhITotttu0549e9S9e3dNnTpVixYtUvfu3XXy5ElddtllWrlypSRp7dq1+vrrrzVp0iRt2LBBkvTll19qzZo1Ki8vV2xsrKZPn6709HTNmzdP6enpuvvuuzVz5kzNmTNHV111lfbv3685c+Y4lwkAwKVEqAMANBrdu3dXWFiYJKlLly7KyclRq1atFBwcrO7du0uSAgICJEl79uzRnXfeKUmKiIhQ+/bt9c0330iS+vTp43xeq1atFB0dLUnq3Lmz/vOf/6ikpET79u3T/fff71x3eXm5bwYJAGh0CHUAgEbDbrc7H/v5+amysvKil9OkSRM1bdrU+biyslLGGLVu3VoZGRkXVzAAAB7gRikAgEbt6quv1g8//KCsrCxJ0smTJ+VwOBQZGam1a9dKkr755hsdPXpUnTp18miZAQEBuuKKK7R+/XpJkjFGn3/+uXcGAABo9DhSBwBo1Ox2uxYtWqQnnnhCp0+f1mWXXably5fr9ttv1+zZs5WYmCg/Pz/Nnz+/yhE6d55++mnNnj1bL774ohwOh4YNG6YuXbp4cSQAgMbKZowxdV0EAAAAAKB2OP0SAAAAACyMUAcAAAAAFkaoAwAAAAALI9QBAAAAgIUR6gAAAADAwgh1AAAAAGBhhDoAAAAAsDBCHQAAAABY2P8HG12Q7TWl7u0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z-C95Uue_EIg"
      },
      "source": [
        "X = dataset.iloc[:, [3, 4]].values\n",
        "y = dataset.iloc[:, 3].values"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C7Tx_C9J_HHw"
      },
      "source": [
        "**Splitting Dataset into train and test dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "waq2LybV_NIX"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KuNUEUWJ_wVF"
      },
      "source": [
        "**Feature Scaling**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cPInAKdp_0u4"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc_X = StandardScaler()\n",
        "X_train = sc_X.fit_transform(X_train)\n",
        "X_test = sc_X.transform(X_test)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N47qBwPYBSrc"
      },
      "source": [
        "**Elbow method to find the optimal number of clusters**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "vqx30gmsBXaZ",
        "outputId": "4091e093-0bcd-42ab-d2ed-397b5d6f4b5b"
      },
      "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(X)\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')\n",
        "plt.show()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qxXevGaDBdlH"
      },
      "source": [
        "**Fitting K-Means to dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z6fnKWnrBjWh"
      },
      "source": [
        "kmeans = KMeans(n_clusters = 5, init = 'k-means++', random_state = 42)\n",
        "y_kmeans = kmeans.fit_predict(X)"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W__O9JZKBnog"
      },
      "source": [
        "**Visualizing the Clusters**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "NHgPGoirBsRv",
        "outputId": "fa52d95e-9f94-440b-c938-54be605a4f63"
      },
      "source": [
        "plt.scatter(X[y_kmeans == 0, 0], X[y_kmeans == 0, 1], s = 100, c = 'red', label = 'Cluster 1')\n",
        "plt.scatter(X[y_kmeans == 1, 0], X[y_kmeans == 1, 1], s = 100, c = 'blue', label = 'Cluster 2')\n",
        "plt.scatter(X[y_kmeans == 2, 0], X[y_kmeans == 2, 1], s = 100, c = 'green', label = 'Cluster 3')\n",
        "plt.scatter(X[y_kmeans == 3, 0], X[y_kmeans == 3, 1], s = 100, c = 'cyan', label = 'Cluster 4')\n",
        "plt.scatter(X[y_kmeans == 4, 0], X[y_kmeans == 4, 1], s = 100, c = 'magenta', label = 'Cluster 5')\n",
        "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 300, c = 'yellow', label = 'Centroids')\n",
        "plt.title('Clusters of customers')\n",
        "plt.xlabel('Annual Income (k$)')\n",
        "plt.ylabel('Spending Score (1-100)')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}