Projetos_Portfólio/Projeto_Portfólio_1.ipynb

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      "name": "Projeto_Portfólio_1.ipynb",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/bruno353/Data_Science_Projects/blob/main/Projeto_Portf%C3%B3lio_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gH0a5qocx9kb"
      },
      "source": [
        "#Projeto de Portfólio\n",
        "####O desafio e os dados desse projeto foram retirados do site: https://sejaumdatascientist.com/como-usar-data-science-para-fazer-a-empresa-vender-mais/\n",
        "####o dataset: https://www.kaggle.com/anmolkumar/health-insurance-cross-sell-prediction\n",
        "Nesse projeto, fomos contratados por uma empresa de seguros que deseja implementar um novo seguro de carros no seu negócio. O nosso objetivo \n",
        "é, através de um dataset que apresenta um conjunto de clientes que comprariam ou não o seguro, criar um modelo de machine learning que prediz se \n",
        "determinado cliente estaria predisposto a se interessar no produto.\n",
        "\n",
        " Tentaremos responder 2 perguntas: \n",
        "\n",
        " 1-Principais Insights sobre os atributos mais relevantes de clientes interessados em adquirir um seguro de automóvel.\n",
        " 2-Qual a acurácia do modelo criado para predizer se os clientes comprariam o seguro da empresa ?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O0hFq-sQyWdd"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8RqAkBJN0N0z"
      },
      "source": [
        "df = pd.read_csv('train.csv')"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "CGKjH6DW0Re_",
        "outputId": "5dd20ec3-4a2b-495a-bba8-957c009ea415"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\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>id</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Driving_License</th>\n",
              "      <th>Region_Code</th>\n",
              "      <th>Previously_Insured</th>\n",
              "      <th>Vehicle_Age</th>\n",
              "      <th>Vehicle_Damage</th>\n",
              "      <th>Annual_Premium</th>\n",
              "      <th>Policy_Sales_Channel</th>\n",
              "      <th>Vintage</th>\n",
              "      <th>Response</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Male</td>\n",
              "      <td>44</td>\n",
              "      <td>1</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0</td>\n",
              "      <td>&gt; 2 Years</td>\n",
              "      <td>Yes</td>\n",
              "      <td>40454.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>217</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Male</td>\n",
              "      <td>76</td>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1-2 Year</td>\n",
              "      <td>No</td>\n",
              "      <td>33536.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>183</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Male</td>\n",
              "      <td>47</td>\n",
              "      <td>1</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0</td>\n",
              "      <td>&gt; 2 Years</td>\n",
              "      <td>Yes</td>\n",
              "      <td>38294.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>27</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Male</td>\n",
              "      <td>21</td>\n",
              "      <td>1</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1</td>\n",
              "      <td>&lt; 1 Year</td>\n",
              "      <td>No</td>\n",
              "      <td>28619.0</td>\n",
              "      <td>152.0</td>\n",
              "      <td>203</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Female</td>\n",
              "      <td>29</td>\n",
              "      <td>1</td>\n",
              "      <td>41.0</td>\n",
              "      <td>1</td>\n",
              "      <td>&lt; 1 Year</td>\n",
              "      <td>No</td>\n",
              "      <td>27496.0</td>\n",
              "      <td>152.0</td>\n",
              "      <td>39</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id  Gender  Age  ...  Policy_Sales_Channel  Vintage  Response\n",
              "0   1    Male   44  ...                  26.0      217         1\n",
              "1   2    Male   76  ...                  26.0      183         0\n",
              "2   3    Male   47  ...                  26.0       27         1\n",
              "3   4    Male   21  ...                 152.0      203         0\n",
              "4   5  Female   29  ...                 152.0       39         0\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d4dSuElM0ZwZ"
      },
      "source": [
        "df.drop(['id'], axis = 1, inplace = True)"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZBXig0rK0ydw",
        "outputId": "639b0370-37ab-4e45-d7f3-40e463a54b10"
      },
      "source": [
        "for col in df:\n",
        "\tprint(f'{col}: {df[col].unique()}')"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Gender: ['Male' 'Female']\n",
            "Age: [44 76 47 21 29 24 23 56 32 41 71 37 25 42 60 65 49 34 51 26 57 79 48 45\n",
            " 72 30 54 27 38 22 78 20 39 62 58 59 63 50 67 77 28 69 52 31 33 43 36 53\n",
            " 70 46 55 40 61 75 64 35 66 68 74 73 84 83 81 80 82 85]\n",
            "Driving_License: [1 0]\n",
            "Region_Code: [28.  3. 11. 41. 33.  6. 35. 50. 15. 45.  8. 36. 30. 26. 16. 47. 48. 19.\n",
            " 39. 23. 37.  5. 17.  2.  7. 29. 46. 27. 25. 13. 18. 20. 49. 22. 44.  0.\n",
            "  9. 31. 12. 34. 21. 10. 14. 38. 24. 40. 43. 32.  4. 51. 42.  1. 52.]\n",
            "Previously_Insured: [0 1]\n",
            "Vehicle_Age: ['> 2 Years' '1-2 Year' '< 1 Year']\n",
            "Vehicle_Damage: ['Yes' 'No']\n",
            "Annual_Premium: [ 40454.  33536.  38294. ...  20706. 101664.  69845.]\n",
            "Policy_Sales_Channel: [ 26. 152. 160. 124.  14.  13.  30. 156. 163. 157. 122.  19.  22.  15.\n",
            " 154.  16.  52. 155.  11. 151. 125.  25.  61.   1.  86.  31. 150.  23.\n",
            "  60.  21. 121.   3. 139.  12.  29.  55.   7.  47. 127. 153.  78. 158.\n",
            "  89.  32.   8.  10. 120.  65.   4.  42.  83. 136.  24.  18.  56.  48.\n",
            " 106.  54.  93. 116.  91.  45.   9. 145. 147.  44. 109.  37. 140. 107.\n",
            " 128. 131. 114. 118. 159. 119. 105. 135.  62. 138. 129.  88.  92. 111.\n",
            " 113.  73.  36.  28.  35.  59.  53. 148. 133. 108.  64.  39.  94. 132.\n",
            "  46.  81. 103.  90.  51.  27. 146.  63.  96.  40.  66. 100.  95. 123.\n",
            "  98.  75.  69. 130. 134.  49.  97.  38.  17. 110.  80.  71. 117.  58.\n",
            "  20.  76. 104.  87.  84. 137. 126.  68.  67. 101. 115.  57.  82.  79.\n",
            " 112.  99.  70.   2.  34.  33.  74. 102. 149.  43.   6.  50. 144. 143.\n",
            "  41.]\n",
            "Vintage: [217 183  27 203  39 176 249  72  28  80  46 289 221  15  58 147 256 299\n",
            " 158 102 116 177 232  60 180  49  57 223 136 222 149 169  88 253 107 264\n",
            " 233  45 184 251 153 186  71  34  83  12 246 141 216 130 282  73 171 283\n",
            " 295 165  30 218  22  36  79  81 100  63 242 277  61 111 167  74 235 131\n",
            " 243 248 114 281  62 189 139 138 209 254 291  68  92  52  78 156 247 275\n",
            "  77 181 229 166  16  23  31 293 219  50 155  66 260  19 258 117 193 204\n",
            " 212 144 234 206 228 125  29  18  84 230  54 123 101  86  13 237  85  98\n",
            "  67 128  95  89  99 208 134 135 268 284 119 226 105 142 207 272 263  64\n",
            "  40 245 163  24 265 202 259  91 106 190 162  33 194 287 292  69 239 132\n",
            " 255 152 121 150 143 198 103 127 285 214 151 199  56  59 215 104 238 120\n",
            "  21  32 270 211 200 197  11 213  93 113 178  10 290  94 231 296  47 122\n",
            " 271 278 276  96 240 172 257 224 173 220 185  90  51 205  70 160 137 168\n",
            "  87 118 288 126 241  82 227 115 164 236 286 244 108 274 201  97  25 174\n",
            " 182 154  48  20  53  17 261  41 266  35 140 269 146 145  65 298 133 195\n",
            "  55 188  75  38  43 110  37 129 170 109 267 279 112 280  76 191  26 161\n",
            " 179 175 252  42 124 187 148 294  44 157 192 262 159 210 250  14 273 297\n",
            " 225 196]\n",
            "Response: [1 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_IB3-h6a1mdJ",
        "outputId": "1c9f8b6e-6494-4a1b-fb06-2750873863b2"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender                   object\n",
              "Age                       int64\n",
              "Driving_License           int64\n",
              "Region_Code             float64\n",
              "Previously_Insured        int64\n",
              "Vehicle_Age              object\n",
              "Vehicle_Damage           object\n",
              "Annual_Premium          float64\n",
              "Policy_Sales_Channel    float64\n",
              "Vintage                   int64\n",
              "Response                  int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aoFeEnha1M9c"
      },
      "source": [
        "Agora vamos limpar os dados: trocar textos por número, retirar Na, Padronizar uma escala e retirar dados incorretos.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y1x1e-l10zZg",
        "outputId": "1c307cbe-f9e5-44c5-924c-0497fcfe5ae8"
      },
      "source": [
        "#Não tem valores com Na:\n",
        "df.isnull().sum().sort_values(ascending=False)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Response                0\n",
              "Vintage                 0\n",
              "Policy_Sales_Channel    0\n",
              "Annual_Premium          0\n",
              "Vehicle_Damage          0\n",
              "Vehicle_Age             0\n",
              "Previously_Insured      0\n",
              "Region_Code             0\n",
              "Driving_License         0\n",
              "Age                     0\n",
              "Gender                  0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xrWnRVk518w-"
      },
      "source": [
        "#Trocar o Gender para 1 e 0\n",
        "df.Gender = df[\"Gender\"].apply(lambda x: 1 if x == 'Female' else 0)"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FC7p93ae2vRc"
      },
      "source": [
        "#Trocar o Vehicle_Damage:\n",
        "df.Vehicle_Damage = df[\"Vehicle_Damage\"].apply(lambda x: 1 if x == 'Yes' else 0)"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bsuGnjQa2V-w"
      },
      "source": [
        "#Agora vamos usar um Hot Enconding para transformar as colunas com texto de Vehicle_Age em 1 e 0.\n",
        "df = pd.get_dummies(df, drop_first = True)"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "UEuQtToi3VSg",
        "outputId": "51863943-ba08-4985-d6d3-67431345044b"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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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>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Driving_License</th>\n",
              "      <th>Region_Code</th>\n",
              "      <th>Previously_Insured</th>\n",
              "      <th>Vehicle_Damage</th>\n",
              "      <th>Annual_Premium</th>\n",
              "      <th>Policy_Sales_Channel</th>\n",
              "      <th>Vintage</th>\n",
              "      <th>Response</th>\n",
              "      <th>Vehicle_Age_&lt; 1 Year</th>\n",
              "      <th>Vehicle_Age_&gt; 2 Years</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>44</td>\n",
              "      <td>1</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>40454.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>217</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>76</td>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>33536.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>183</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>47</td>\n",
              "      <td>1</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>38294.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>27</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>21</td>\n",
              "      <td>1</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>28619.0</td>\n",
              "      <td>152.0</td>\n",
              "      <td>203</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>29</td>\n",
              "      <td>1</td>\n",
              "      <td>41.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>27496.0</td>\n",
              "      <td>152.0</td>\n",
              "      <td>39</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Gender  Age  ...  Vehicle_Age_< 1 Year  Vehicle_Age_> 2 Years\n",
              "0       0   44  ...                     0                      1\n",
              "1       0   76  ...                     0                      0\n",
              "2       0   47  ...                     0                      1\n",
              "3       0   21  ...                     1                      0\n",
              "4       1   29  ...                     1                      0\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sH-r6QOr3WD7"
      },
      "source": [
        "#Vou tirar os Outliers do Annual_Premium, que podem dar algum problema no modelo.\n",
        "min_thresold, max_thresold = df.Annual_Premium.quantile([0.05, 0.95])\n",
        "df = df[(df.Annual_Premium<max_thresold) & (df.Annual_Premium>min_thresold)]"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WO655HJ-5mzh"
      },
      "source": [
        "#separar entre X e y:\n",
        "\n",
        "X = df.drop(['Response'], axis = 1)\n",
        "y = df['Response']"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sB73xs_S0ZqU"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y)"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Mc-H-3U5LFx"
      },
      "source": [
        "#fazendo padronização escala\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "transfer = StandardScaler()\n",
        "X_train = transfer.fit_transform(X_train)\n",
        "X_test = transfer.transform(X_test)\n"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EZ10oirjB0hY"
      },
      "source": [
        "Agora vamos analisar os dados e suas correlações através do plot de gráficos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fSQQvmDvA3gO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "b6743534-7d0e-4ecc-f563-bf664e7634e4"
      },
      "source": [
        "#porcentagem de quantas pessoas responderam não e quantas repsonderam sim.\n",
        "df.groupby('Response').size().plot(kind='pie', autopct='%.2f')"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d12fdf350>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "V8U4qcOvMJ2H",
        "outputId": "a2dd3ecb-1c37-458e-c3c2-ce993e4912d5"
      },
      "source": [
        "df.groupby('Vehicle_Damage').size().plot(kind='pie', autopct='%.2f')"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d0ddccb50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        },
        "id": "Xr_V3TFTlkme",
        "outputId": "06a4d4bb-36ac-4610-89bb-265a5a621d98"
      },
      "source": [
        "#heatmap de correlação entre variáveis\n",
        "#percebe-se que a maior correlação positiva com a resposta é a feature 'Vehicle_Damage'\n",
        "plt.figure(figsize=(16,10))\n",
        "sns.heatmap(df.corr(method='pearson'), annot=True)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d12ecb190>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 937
        },
        "id": "_x5CWHNYlumF",
        "outputId": "84a61db9-96c5-488e-a58c-833eb6e53f4f"
      },
      "source": [
        "df.hist(figsize = (12,12))"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f9d12cfad90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9d12cb4a90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9d12c6a150>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f9d12c207d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9d12be5190>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9d12b9b850>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f9d12b5f1d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9d12b1b310>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9d12b1b350>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f9d12acfad0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9d12a49710>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9d12a7cd90>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "id": "a3IT9fOMnjXL",
        "outputId": "9bf9e5ff-3b3b-4fb0-a521-b89340f873d3"
      },
      "source": [
        "sns.catplot(x='Previously_Insured', y=\"Annual_Premium\",hue = 'Response', kind=\"box\", data=df)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f9d127c82d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 415.75x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-vSgbotDokMg"
      },
      "source": [
        "Agora vamos criar o algoritmo e ver qual o melhor modelo para essa issue"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TJiwTjG41Ptc",
        "outputId": "a21a20e3-8fe7-453b-a33b-ba1901832de4"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "model = LogisticRegression()\n",
        "model.fit(X_train, y_train)\n",
        "model.score(X_test, y_test)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8816458933426656"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ddVwkr6z2u7K",
        "outputId": "b58fb7a1-2071-4784-bc4b-dc4a83651baf"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "model = RandomForestClassifier()\n",
        "model.fit(X_train, y_train)\n",
        "model.score(X_test, y_test)"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8719277465205804"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R-EAB2Y99DSQ",
        "outputId": "94386490-b5e7-47ea-92bf-e30587d99142"
      },
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "model = GaussianNB()\n",
        "model.fit(X_train, y_train)\n",
        "model.score(X_test, y_test)"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7281880097989071"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hi3XVfgy9Qux",
        "outputId": "2cc85280-47d1-43cd-bf2f-00d8e2c10ef7"
      },
      "source": [
        "from sklearn.neural_network import MLPClassifier\n",
        "model = MLPClassifier()\n",
        "model.fit(X_train, y_train)\n",
        "model.score(X_test, y_test)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8815651331197674"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "biQgfRIFCNEs"
      },
      "source": [
        "#Conclusão:\n",
        "O modelo de LogisticRegression mostrou-se o mais eficiente: 88% de acurácia, com a feature de 'Vehicle_Damage' sendo a maior correlação entre um cliente comprar ou não o seguro('Response').\n",
        "\n",
        "A correlação mais negativa (inversamente proporcional) com a 'Response' é a feature 'Previously_insured', ou seja, se o cliente já tem um seguro de automóvel, sua chance de adquirir um novo se torna menor."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t_4ENvIaCAwu"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}