Insurance.ipynb

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        "<a href=\"https://colab.research.google.com/github/arssite/MachineLearning/blob/main/Insurance.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "id": "bomtYFUSiU8S"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import accuracy_score"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "id=pd.read_csv('insurance.csv')"
      ],
      "metadata": {
        "id": "Ci8MTNbVij6d"
      },
      "execution_count": 4,
      "outputs": []
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      "execution_count": 5,
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          "data": {
            "text/plain": [
              "   age     sex     bmi  children smoker     region      charges\n",
              "0   19  female  27.900         0    yes  southwest  16884.92400\n",
              "1   18    male  33.770         1     no  southeast   1725.55230\n",
              "2   28    male  33.000         3     no  southeast   4449.46200\n",
              "3   33    male  22.705         0     no  northwest  21984.47061\n",
              "4   32    male  28.880         0     no  northwest   3866.85520"
            ],
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              "      <td>16884.92400</td>\n",
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              "summary": "{\n  \"name\": \"id\",\n  \"rows\": 1338,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14,\n        \"min\": 18,\n        \"max\": 64,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          21,\n          45,\n          36\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"male\",\n          \"female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.098186911679014,\n        \"min\": 15.96,\n        \"max\": 53.13,\n        \"num_unique_values\": 548,\n        \"samples\": [\n          23.18,\n          26.885\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"children\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"smoker\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"region\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"southeast\",\n          \"northeast\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"charges\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12110.011236694001,\n        \"min\": 1121.8739,\n        \"max\": 63770.42801,\n        \"num_unique_values\": 1337,\n        \"samples\": [\n          8688.85885,\n          5708.867\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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            "text/plain": [
              "(1338, 7)"
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          "execution_count": 6
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          "data": {
            "text/plain": [
              "               age          bmi     children       charges\n",
              "count  1338.000000  1338.000000  1338.000000   1338.000000\n",
              "mean     39.207025    30.663397     1.094918  13270.422265\n",
              "std      14.049960     6.098187     1.205493  12110.011237\n",
              "min      18.000000    15.960000     0.000000   1121.873900\n",
              "25%      27.000000    26.296250     0.000000   4740.287150\n",
              "50%      39.000000    30.400000     1.000000   9382.033000\n",
              "75%      51.000000    34.693750     2.000000  16639.912515\n",
              "max      64.000000    53.130000     5.000000  63770.428010"
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              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>bmi</th>\n",
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              "      <th>count</th>\n",
              "      <td>1338.000000</td>\n",
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              "      <td>1338.000000</td>\n",
              "      <td>1338.000000</td>\n",
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              "      <th>mean</th>\n",
              "      <td>39.207025</td>\n",
              "      <td>30.663397</td>\n",
              "      <td>1.094918</td>\n",
              "      <td>13270.422265</td>\n",
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              "      <th>std</th>\n",
              "      <td>14.049960</td>\n",
              "      <td>6.098187</td>\n",
              "      <td>1.205493</td>\n",
              "      <td>12110.011237</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>15.960000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1121.873900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>27.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>4740.287150</td>\n",
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              "      <th>50%</th>\n",
              "      <td>39.000000</td>\n",
              "      <td>30.400000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>9382.033000</td>\n",
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              "      <th>75%</th>\n",
              "      <td>51.000000</td>\n",
              "      <td>34.693750</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>16639.912515</td>\n",
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              "      <th>max</th>\n",
              "      <td>64.000000</td>\n",
              "      <td>53.130000</td>\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-23b292fb-91e2-4c43-8768-fa10eefb7775 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-23b292fb-91e2-4c43-8768-fa10eefb7775');\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",
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              "        element.appendChild(docLink);\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    40% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-171a3d1a-7a27-4166-b416-a10a898c57df button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"id\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 460.6106090399993,\n        \"min\": 14.049960379216154,\n        \"max\": 1338.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          39.20702541106129,\n          39.0,\n          1338.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 463.29524977918294,\n        \"min\": 6.098186911679014,\n        \"max\": 1338.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          30.66339686098655,\n          30.4,\n          1338.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"children\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 472.5368318870757,\n        \"min\": 0.0,\n        \"max\": 1338.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1338.0,\n          1.0949177877429,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"charges\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20381.922846226596,\n        \"min\": 1121.8739,\n        \"max\": 63770.42801,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          13270.422265141257,\n          9382.033,\n          1338.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "id.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1aHgUKlAi8tM",
        "outputId": "ae0d6c57-04fa-4d17-e9ad-659ac3bc05fe"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1338 entries, 0 to 1337\n",
            "Data columns (total 7 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       1338 non-null   int64  \n",
            " 1   sex       1338 non-null   object \n",
            " 2   bmi       1338 non-null   float64\n",
            " 3   children  1338 non-null   int64  \n",
            " 4   smoker    1338 non-null   object \n",
            " 5   region    1338 non-null   object \n",
            " 6   charges   1338 non-null   float64\n",
            "dtypes: float64(2), int64(2), object(3)\n",
            "memory usage: 73.3+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "id.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jOgGuzoFi9i8",
        "outputId": "03f17716-bd77-4a04-cc38-b7b1fdd6b649"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age         0\n",
              "sex         0\n",
              "bmi         0\n",
              "children    0\n",
              "smoker      0\n",
              "region      0\n",
              "charges     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "id['sex'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WYMmpjUsjTjc",
        "outputId": "2f2d6604-30c9-41bc-8951-b1801396f9cc"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sex\n",
              "male      676\n",
              "female    662\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "id['smoker'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IPmpfIQ3jfb9",
        "outputId": "0f3f540e-6eb5-4117-924b-9bcf182f25b5"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "smoker\n",
              "no     1064\n",
              "yes     274\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: generate who is smoker and female in sex\n",
        "\n",
        "smoker_female = id[(id['sex'] == 'female') & (id['smoker'] == 'yes')]\n",
        "print(smoker_female)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1EzWB2MEjmZ8",
        "outputId": "dea08b90-9474-43d3-9d3b-8f3db46e2d38"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      age     sex     bmi  children smoker     region      charges\n",
            "0      19  female  27.900         0    yes  southwest  16884.92400\n",
            "11     62  female  26.290         0    yes  southeast  27808.72510\n",
            "23     34  female  31.920         1    yes  northeast  37701.87680\n",
            "58     53  female  22.880         1    yes  southeast  23244.79020\n",
            "64     20  female  22.420         0    yes  northwest  14711.74380\n",
            "...   ...     ...     ...       ...    ...        ...          ...\n",
            "1308   25  female  30.200         0    yes  southwest  33900.65300\n",
            "1313   19  female  34.700         2    yes  southwest  36397.57600\n",
            "1314   30  female  23.655         3    yes  northwest  18765.87545\n",
            "1323   42  female  40.370         2    yes  southeast  43896.37630\n",
            "1337   61  female  29.070         0    yes  northwest  29141.36030\n",
            "\n",
            "[115 rows x 7 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: generate count who is smoker male and female\n",
        "\n",
        "smoker_male = id[(id['sex'] == 'male') & (id['smoker'] == 'yes')]\n",
        "smoker_female = id[(id['sex'] == 'female') & (id['smoker'] == 'yes')]\n",
        "\n",
        "print(\"Number of male smokers:\", smoker_male.shape[0])\n",
        "print(\"Number of female smokers:\", smoker_female.shape[0])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ASDkye57kOb3",
        "outputId": "4bdf4c73-58af-42f7-eddd-fd2c9741cfa9"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of male smokers: 159\n",
            "Number of female smokers: 115\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "smoker_male = id[(id['sex'] == 'male') & (id['smoker'] == 'yes') & (id['age'] >=50)]\n",
        "smoker_female = id[(id['sex'] == 'female') & (id['smoker'] == 'yes') & (id['age'] >=50) ]\n",
        "\n",
        "print(\"Number of male smokers:\", smoker_male.shape[0])\n",
        "print(\"Number of female smokers:\", smoker_female.shape[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vV-9Sgeuk-k-",
        "outputId": "3097adf7-6a53-4d8a-e7c0-f6c74727e3c7"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of male smokers: 40\n",
            "Number of female smokers: 28\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set()\n",
        "plt.figure(figsize=(6,6))\n",
        "sns.distplot(id['age'])\n",
        "plt.title('Age Distribution')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 764
        },
        "id": "iPaeW0AqjszN",
        "outputId": "6c9d8400-d5c4-4c1e-c7cf-85c2ff9e39f6"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-18-ddc20c780d7b>:3: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(id['age'])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(6,6))\n",
        "sns.countplot(x='sex', data=id)\n",
        "plt.title('Sex Distribution')\n",
        "plt.show"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 742
        },
        "id": "VL1N9hSykLmt",
        "outputId": "284cfa51-90a3-460b-a7e1-23583abd8843"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show(close=None, block=None)>"
            ],
            "text/html": [
              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
              "      pre.function-repr-contents {\n",
              "        overflow-x: auto;\n",
              "        padding: 8px 12px;\n",
              "        max-height: 500px;\n",
              "      }\n",
              "\n",
              "      pre.function-repr-contents.function-repr-contents-collapsed {\n",
              "        cursor: pointer;\n",
              "        max-height: 100px;\n",
              "      }\n",
              "    </style>\n",
              "    <pre style=\"white-space: initial; background:\n",
              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>matplotlib.pyplot.show</b><br/>def show(*args, **kwargs)</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.10/dist-packages/matplotlib/pyplot.py</a>Display all open figures.\n",
              "\n",
              "Parameters\n",
              "----------\n",
              "block : bool, optional\n",
              "    Whether to wait for all figures to be closed before returning.\n",
              "\n",
              "    If `True` block and run the GUI main loop until all figure windows\n",
              "    are closed.\n",
              "\n",
              "    If `False` ensure that all figure windows are displayed and return\n",
              "    immediately.  In this case, you are responsible for ensuring\n",
              "    that the event loop is running to have responsive figures.\n",
              "\n",
              "    Defaults to True in non-interactive mode and to False in interactive\n",
              "    mode (see `.pyplot.isinteractive`).\n",
              "\n",
              "See Also\n",
              "--------\n",
              "ion : Enable interactive mode, which shows / updates the figure after\n",
              "      every plotting command, so that calling ``show()`` is not necessary.\n",
              "ioff : Disable interactive mode.\n",
              "savefig : Save the figure to an image file instead of showing it on screen.\n",
              "\n",
              "Notes\n",
              "-----\n",
              "**Saving figures to file and showing a window at the same time**\n",
              "\n",
              "If you want an image file as well as a user interface window, use\n",
              "`.pyplot.savefig` before `.pyplot.show`. At the end of (a blocking)\n",
              "``show()`` the figure is closed and thus unregistered from pyplot. Calling\n",
              "`.pyplot.savefig` afterwards would save a new and thus empty figure. This\n",
              "limitation of command order does not apply if the show is non-blocking or\n",
              "if you keep a reference to the figure and use `.Figure.savefig`.\n",
              "\n",
              "**Auto-show in jupyter notebooks**\n",
              "\n",
              "The jupyter backends (activated via ``%matplotlib inline``,\n",
              "``%matplotlib notebook``, or ``%matplotlib widget``), call ``show()`` at\n",
              "the end of every cell by default. Thus, you usually don&#x27;t have to call it\n",
              "explicitly there.</pre>\n",
              "      <script>\n",
              "      if (google.colab.kernel.accessAllowed && google.colab.files && google.colab.files.view) {\n",
              "        for (const element of document.querySelectorAll('.filepath')) {\n",
              "          element.style.display = 'block'\n",
              "          element.onclick = (event) => {\n",
              "            event.preventDefault();\n",
              "            event.stopPropagation();\n",
              "            google.colab.files.view(element.textContent, 401);\n",
              "          };\n",
              "        }\n",
              "      }\n",
              "      for (const element of document.querySelectorAll('.function-repr-contents')) {\n",
              "        element.onclick = (event) => {\n",
              "          event.preventDefault();\n",
              "          event.stopPropagation();\n",
              "          element.classList.toggle('function-repr-contents-collapsed');\n",
              "        };\n",
              "      }\n",
              "      </script>\n",
              "      </div>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(6,6))\n",
        "sns.distplot(id['bmi'])\n",
        "plt.title('BMI Distribution')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 764
        },
        "id": "UScg3cWAknot",
        "outputId": "23152d83-0dc2-4b26-9399-6e0cb07a3062"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-21-f73183b6f756>:2: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(id['bmi'])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "id.replace({'sex':{'male':0,'female':1}}, inplace=True)\n",
        "id.replace({'smoker':{'yes':0,'no':1}}, inplace=True)\n",
        "id.replace({'region':{'southeast':0,'southwest':1,'northeast':2,'northwest':4}},inplace=True)"
      ],
      "metadata": {
        "id": "1BDk3swykscM"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "id.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "knCQfveymMHt",
        "outputId": "566e87e7-2dbe-47cf-909a-053e1d0fbeb2"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age  sex     bmi  children  smoker  region      charges\n",
              "0   19    1  27.900         0       0       1  16884.92400\n",
              "1   18    0  33.770         1       1       0   1725.55230\n",
              "2   28    0  33.000         3       1       0   4449.46200\n",
              "3   33    0  22.705         0       1       4  21984.47061\n",
              "4   32    0  28.880         0       1       4   3866.85520"
            ],
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              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-85c06806-d75c-4ce3-b893-e8899d5693cc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "id",
              "summary": "{\n  \"name\": \"id\",\n  \"rows\": 1338,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14,\n        \"min\": 18,\n        \"max\": 64,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          21,\n          45,\n          36\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.098186911679014,\n        \"min\": 15.96,\n        \"max\": 53.13,\n        \"num_unique_values\": 548,\n        \"samples\": [\n          23.18,\n          26.885\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"children\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"smoker\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"region\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"charges\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12110.011236694001,\n        \"min\": 1121.8739,\n        \"max\": 63770.42801,\n        \"num_unique_values\": 1337,\n        \"samples\": [\n          8688.85885,\n          5708.867\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "source": [
        "# @title age\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "id['age'].plot(kind='hist', bins=20, title='age')\n",
        "plt.gca().spines[['top', 'right',]].set_visible(False)"
      ],
      "cell_type": "code",
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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g3XDDDcrMzFRsbKzdtsrKyjRv3jx99NFHqqqqUp8+fTR9+nRdd9117n5ZAADAB3jVkaL9+/crLy9Pv/jFLxQTE1Nn/Xfffae33npLvXv31sKFC/XUU0+prKxMo0aN0sGDB+3aTp48WZ9++qlmzZql5557TocOHdK4ceNUXV3trpcDAAB8iFcdKerfv78GDhwoScrKytLu3bvt1kdEROgf//iHmjZtaluWlJSk/v3764033tCMGTMkSV9++aU++eQTrVixQikpKZKkqKgopaena/PmzUpPT3fTKwIAAL7Cq44UGY2XLqdZs2Z2gUiSmjdvrnbt2unEiRO2Zdu2bZPJZFLv3r1ty6Kjo9W5c2dt27bNuUUDAAC/4FWh6FqYzWbb/KNaBQUFioqKksFgsGsbHR2tgoICd5cIAAB8gFedPrsWf/7zn2UwGHTnnXfalpnNZgUHB9dpGxISUueU3LUIDHRulgwIcH02dUcfsFe7z9n3nsU4eAfGwTswDpfm06Fo/fr1WrNmjebPn6/rr7/eLX0ajQa1bNncLX05k8nU9PKN4BLse+/AOHgHxsE7MA7189lQlJeXp5kzZ+qRRx7RrbfearfOZDKpuLi4znNKS0sVEhLiUL8Wi1Vmc4VD2/ipgACjy9+gZnOlamosLu0D9mrHlX3vWYyDd2AcvENDHocrOaDhk6Fox44deuyxxzRixAg99thjddZHR0crPz9fVqvVbl7RoUOH1LFjR4f7r672vTdSTY3FJ+v2B+x778A4eAfGwTswDvXzuZOKBw4c0EMPPaSkpCTNnj273japqakqLS1Vfn6+bdmhQ4e0Z88epaamuqtUAADgQ7zqSFFlZaXy8vIkSUVFRSovL9emTZskSb169ZLVatWDDz6oxo0b67777rObNN2iRQt16NBBktS9e3elpKRo2rRpmjp1qho3bqwXXnhBsbGxuvnmm93/wgAAgNfzqlB08uTJOqfDah+vWrVKkmxzhX7729/atevVq5dee+012+OFCxdq3rx5mjlzpqqrq5WSkqLp06crMNCrXjIAAPASXpUQIiIi9O23316yzeXW1woODtbcuXM1d+5cZ5QGAAD8nM/NKQIAAHAFQhEAAIAIRQAAAJIIRQAAAJIIRQAAAJIIRQAAAJIIRQAAAJIIRQAAAJIIRQAAAJIIRQAAAJIIRQAAAJIIRQAAAJIIRQAAAJIIRQAAAJIIRQAAAJIIRQAAAJIIRQAAAJIIRQAAAJIIRQAAAJIIRQAAAJIIRQAAAJIIRQAAAJIIRQAAAJIcDEUnTpxwVh0AAAAe5VAo6tu3rx544AG98847qqiocFZNAAAAbudQKJo0aZJOnDihrKws9e7dW1OmTNG2bdtksVicVR8AAIBbBDry5AkTJmjChAnas2eP3n//fW3cuFEffPCBWrVqpcGDB2vo0KGKj493Vq0AAAAu41AoqhUXF6e4uDg98cQT+ve//633339fGzZs0GuvvaaoqCgNGzZMw4YNU5s2bZzRHQAAgNM59ddnBoNBN954o37961+rW7duslqtOnLkiBYtWqSBAwfaTrcBAAB4G6ccKZJkO0K0efNmlZeXq2PHjpo6daqGDh2qgIAAbdiwQcuWLdMTTzyhv/zlL87qFgAAwCkcCkXffPON3nvvPW3cuFEnTpzQz372M91xxx0aMWKEYmNj7do++OCDaty4sZ555pmLbu/IkSNasWKFdu7cqf379ys6OloffPBBnXZr167V8uXL9d133ykqKkqZmZnq16+fXZuysjLNmzdPH330kaqqqtSnTx9Nnz5d1113nSMvGQAA+CmHQtGIESPUpEkTDRgwQCNGjFDv3r1lNF78jFyHDh2UkJBw0fX79+9XXl6eunXrJovFIqvVWqfNxo0bNWPGDE2YMEFJSUnKzc1VRkaGXn/9dbttT548WQcOHNCsWbPUuHFjLVy4UOPGjdP69esVGOi0A2QAAMBPOJQO5s6dq0GDBql58+ZX1D4pKUlJSUkXXd+/f38NHDhQkpSVlaXdu3fXafPSSy9p8ODBmjx5sm2b+/bt0+LFi5WTkyNJ+vLLL/XJJ59oxYoVSklJkSRFRUUpPT1dmzdvVnp6+tW8TAAA0AA4NNH6tttuu+JAdCUudZRJkgoLC3X48GGlpaXZLU9PT1d+fr7Onz8vSdq2bZtMJpN69+5taxMdHa3OnTtr27ZtTqsXAAD4D4dC0apVq/Tggw9edP3YsWP1xhtvONKFnYKCAkkXjvr8WExMjKqqqlRYWGhrFxUVJYPBYNcuOjratg0AAIAfc+j02bp16y55OqxDhw5as2aN7rrrLke6sSktLZUkmUwmu+W1j2vXm81mBQcH13l+SEhIvafkrlZgoHPvoxsQ4Pr78rqjD9ir3efse89iHLwD4+AdGIdLcygUFRYW6u67777o+ujoaK1Zs8aRLryO0WhQy5bOO2XoLiZTU0+X0GCx770D4+AdGAfvwDjUz6FQ1KhRI33//fcXXX/ixInLzhO6GiEhIZIu/Ny+devWtuVms9luvclkUnFxcZ3nl5aW2tpcK4vFKrPZuTe/DQgwuvwNajZXqqaGe9K5U+24su89i3HwDoyDd2jI43AlBzQcCkXdunXT22+/rd/+9rdq0aKF3bqysjJt2LBB3bp1c6QLO9HR0ZIuzBmq/XPt40aNGikyMtLWLj8/X1ar1W5e0aFDh9SxY0eH66iu9r03Uk2NxSfr9gfse+/AOHgHxsE7MA71c+gwTkZGhk6cOKERI0botddeU35+vvLz87Vq1SqNGDFC33//vTIyMpxVqyIjI9W+fXtt2rTJbnlubq6Sk5MVFBQkSUpNTVVpaany8/NtbQ4dOqQ9e/YoNTXVafUAAAD/4fCRoldeeUUzZ87U008/bTsqY7VaFRERoaVLl6p79+5XvL3Kykrl5eVJkoqKilReXm4LQL169VJYWJgmTpyoKVOmqF27dkpMTFRubq527dql1atX27bTvXt3paSkaNq0aZo6daoaN26sF154QbGxsbr55psdeckAAMBPOXxp5969e+sf//iH9uzZo6NHj0qS2rVrpy5dutT5SfzlnDx5Uo899pjdstrHq1atUmJiooYMGaLKykrl5OQoOztbUVFRWrRoUZ3wtXDhQs2bN08zZ85UdXW1UlJSNH36dK5mDQAA6mWw1ncvDVxUTY1Fp06dceo2AwONatmyuSY//08dLCp16rZj2oZo4eN9dfr0Gc4fu1ntuLLvPYtx8A6Mg3doyOPQunXdS/X8lFMOmxw4cECFhYW26wT91IgRI5zRDQAAgMs4FIqOHj2q3//+99q1a1e9N2+VJIPBQCgCAABez6FQNHPmTO3bt0/Tpk1Tz54961xpGgAAwFc4FIr+85//6KGHHtK9997rrHoAAAA8wqHrFLVs2bLee4wBAAD4GodC0ejRo/Xee++ppqbGWfUAAAB4hEOnz9q3by+LxaLhw4fr9ttv1/XXX6+AgIA67bhgIgAA8HYOhaLMzEzbn5955pl62xgMBu3du9eRbgAAAFzOoVC0atUqZ9UBAADgUQ6Fol69ejmrDgAAAI9yyhWtz58/r6+//lonT55Ujx49FBYW5ozNAgAAuI1Dvz6TLpxCS0lJ0V133aWJEyfq22+/lSSdOnVKiYmJWrduncNFAgAAuJpDoWj9+vWaO3eu+vTpo6efftruVh9hYWFKSkpSbm6uw0UCAAC4mkOhaOXKlRowYIAWLFigfv361VnfpUsX7d+/35EuAAAA3MKhUHTkyBGlpqZedH1oaKhKSkoc6QIAAMAtHApFJpNJp0+fvuj6AwcOqHXr1o50AQAA4BYOhaLU1FStWbNGZrO5zrr9+/dr7dq16t+/vyNdAAAAuIVDP8mfPHmyfvOb32jIkCHq16+fDAaD3nnnHa1fv16bN29W69at9cgjjzirVgAAAJdx6EhReHi4NmzYoD59+ujvf/+7rFar3n33XW3dulWDBw/WmjVruGYRAADwCQ5fvLFVq1Z6+umn9fTTT+vUqVOyWCwKCwuT0ejwJZAAAADcxilXtK7FUSEAAOCrHApFixYtumwbg8GgRx991JFuAABuZjQaZDQanLa9gACj7f8Wi1UWi/UyzwDcz2WhyGAwyGq1EooAwMcYjQaFhjazBRlnMpmaqqbGopKSCoIRvI5Doeibb76ps8xisaioqEhvvPGG/u///k85OTmOdAEAcDOj0aCAAKOee/0LHTte5tRtR4QHa8rdN8poNBCK4HWcOqdIkoxGoyIjIzV16lT97ne/05w5c7RgwQJndwMAcLFjx8t0sKjU02UAbuPSn4jddNNNysvLc2UXAAAATuH0I0U/tnv3bn6aj2vi7EmetZjgCQC4GIdC0TvvvFPvcrPZrM8//1ybN2/WyJEjHekCDZArJ3kywRMAcDEOhaKsrKyLrmvZsqXGjx/PL89w1Vw1yZMJngCAS3EoFG3ZsqXOMoPBIJPJpBYtWjiy6cv2+8orr+jAgQNq3ry5brzxRk2ZMkWRkZF27dauXavly5fru+++U1RUlDIzM9WvXz+X1QXnYpInAMCdHDo/0bZt2zr/tWnTxqWBaPv27crIyFCHDh20ePFiTZs2Td98840eeOABnT171tZu48aNmjFjhtLS0pSTk6OEhARlZGRox44dLqsNAAD4LpdOtHaFjRs3qk2bNpo7d64MhgsTccPCwnTfffdp9+7d6tmzpyTppZde0uDBgzV58mRJUlJSkvbt26fFixdz7SQAAFCHQ6GoU6dOtmBypQwGg/bs2XPNfVZXV6t58+Z2/QYHB0uSrNYL80QKCwt1+PBh/f73v7d7bnp6up599lmdP39eQUFB11wDAP/Erx6Bhs2hUPToo4/qo48+0oEDB5SSkqKoqChJUkFBgT799FP98pe/1MCBA51SaK3bbrtN7777rl5//XUNGzZMJSUlev755xUXF6cePXrY+pdkq6dWTEyMqqqqVFhYqJiYGKfWBcC38atHAA6Fouuuu04nT57U+++/r+joaLt1Bw8e1H333afrrrtOv/nNbxwq8sd69uypRYsW6Xe/+53+9Kc/SZI6d+6s5cuXKyAgQJJUWnphcq7JZLJ7bu3j2vXXKjDQuV+arvgS9kQfzuLqWt21L358A0x4zpWOQ0CA0aW/emzUKEA1NRanbdeV+E7yX3wvXZpDoWjFihW655576gQi6cJRmbvvvlvLly93aij6z3/+oyeeeEK/+c1v1LdvX5WUlGjJkiUaP3683njjDTVp0sRpfdXHaDSoZcvmLu3DFUympp4uwWu4e1+w773DlY6Dq371yPvAHvvDs9j/9XMoFBUXFysw8OKbCAwMVHFxsSNd1DFnzhwlJSXZXSMpISFBffv21bvvvqtRo0YpJCREklRWVqbWrVvb2pnNZkmyrb8WFotVZnPFNT+/PgEBRpe/Qc3mSp/6V6or94e79kXt6/Clfe+PrnQc/OV95wx8J/mvhvy9dCUHNBwKRb/85S/1xhtvaOjQoQoPD7dbV1xcrDfffFMdO3Z0pIs6Dh48qAEDBtgtu/7669WyZUsdPXpUkmxHrgoKCuyOYhUUFKhRo0Z1rmd0taqrfe+NVFNj8cm6XcHd+4J97x08PQ6e7t/bsD88i/1fP4dC0R/+8AeNHTtWgwYN0sCBA/WLX/xCknT48GFt2bJFVqtVzz77rFMKrdWmTZs6v14rKirS6dOn1bZtW0lSZGSk2rdvr02bNtlN9M7NzVVycjK/PIPP4VdRgGe56jMo8Tn0Jg6Fop49e2rNmjV68cUX9dFHH9kuntikSROlpKRo4sSJio2NdUqhtUaPHq25c+dqzpw56t+/v0pKSrR06VK1atVKaWlptnYTJ07UlClT1K5dOyUmJio3N1e7du3S6tWrnVoP4Gr8KgrwLFd+BiU+h97E4Ys3duzYUYsXL5bFYtGpU6ckXbiYotHomjfPmDFjFBQUpDfffFPr169X8+bNlZCQoIULF6ply5a2dkOGDFFlZaVycnKUnZ2tqKgoLVq0SN27d3dJXYCrcC84wLNc9RmU+Bx6G6dd0dpoNKpx48Zq1qyZywKRdOHij3feeafuvPPOy7YdOXKkRo4c6bJaAHfiXnCAZ/EZ9H8Op5evvvpKDz74oLp166bExET97//+ryTp1KlTevjhh7V9+3aHiwQAAHA1h0LRf/7zH9111106cuSIhg0bJovlvzPZw8LCVF5errfeesvhIgEAAFzNoVD0wgsvKCYmRrm5ucrMzKyzPjExUTt37nSkCwAAALdwKBR99dVXuu222xQUFFTvjWHDw8P1ww8/ONIFAACAWzgUigIDA+1Omf3U8ePH1axZM0e6AAAAcAuHQlG3bt304Ycf1ruuoqJCGzZs0E033eRIFwAAAG7hUCiaNGmSdu/erfHjx2vbtm2SpG+//VZr167VbbfdplOnTumRRx5xSqEAAACu5PCRouzsbB05ckRTp06VJM2fP18zZsyQxWJRdna2OnXq5JRCAQAAXOmaL95otVp15swZ9ejRQx9++KH27t2rw4cPy2q1KjIyUl27dq138jUAAIA3uuZQVFVVpV69eikzM1Pjxo1T586d1blzZ2fWBgAA4DbXfPosKChIP/vZz7jjPAAA8AsOzSm69dZb9e677+r8+fPOqgcAAMAjHLohbGxsrLZs2aIhQ4bo1ltvVdu2bdWkSZM67W6++WZHugEAAHA5h0LR448/bvvziy++WG8bg8GgvXv3OtINAACAy111KHr++eeVnp6uTp06adWqVa6oCQAAwO2uOhRlZ2frl7/8pTp16qRevXrp9OnT+tWvfqVXX31VycnJrqgRAADA5RyaaF3LarU6YzMAAAAe45RQBAAA4OscmmgNAAAaHqPRIKPR+XetsFisslg8d/bpmkJRUVGRvv76a0lSWVmZJOnIkSMymUz1tu/Spcs1lgcAALyJ0WhQaGgzBQQ4/2RTTY1FJSUVHgtG1xSKXnzxxTo/wZ89e3addlarlZ/kAwDgR4xGgwICjHru9S907HiZ07YbER6sKXffKKPR4DuhaN68ea6oAwAA+JBjx8t0sKjU02U41VWHoltvvdUVdQAAAHgUvz4DAAAQoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQgAAECSD4eit99+WyNGjFB8fLwSExM1duxYnT171rb+448/1rBhwxQfH69BgwZp/fr1HqwWAAB4O5+8IezSpUuVk5OjCRMmKCEhQadPn1Z+fr5qamokSZ9//rkyMjJ0xx13aNq0afr3v/+tP/7xj2revLluueUWD1cPAAC8kc+FooKCAi1atEhLlizRr3/9a9vyQYMG2f68dOlS3XDDDfrTn/4kSUpKSlJhYaFeeuklQhEAAKiXz50+27BhgyIiIuwC0Y+dP39e27dvrxN+0tPTdfDgQR07dswdZQIAAB/jc6Fo586d6tixo5YsWaLk5GR17dpVo0eP1s6dOyVJR48eVVVVlaKjo+2eFxMTI+nCkSYAAICf8rnTZ99//712796tffv26cknn1TTpk31yiuv6IEHHtDmzZtVWnrhjr0mk8nuebWPa9c7IjDQuVkyIMD12dQdfTiLq2t1176o7cfR/vxlf3jKlY4D+/m/+E6y50/7wxnfS/78WfG5UGS1WlVRUaEXX3xRnTp1kiR169ZN/fv31+rVq5WSkuLS/o1Gg1q2bO7SPlzBZGrq6RK8hrv3hbfve2+vz1k8/To93b+3YX/Y43vpvzxZm8+FIpPJpNDQUFsgkqTQ0FDFxcXpwIEDGjx4sCSprKzM7nlms1mSFBIS4lD/FotVZnOFQ9v4qYAAo8vfBGZzpWpqLC7tw1lcvT/ctS9qX4ej/fnL/vCUKx0H9vN/8Z1kz5/2hzO+l3z1s3IlBzR8LhR16NBBR48erXfduXPn1K5dOzVq1EgFBQXq06ePbV3tXKKfzjW6FtXVvvFB/rGaGotP1u0K7t4X3r7vvb0+Z/H06/R0/96G/WGP76X/8mRtvnNS9//r16+fSkpKtHfvXtuy06dP6+uvv1aXLl0UFBSkxMREffjhh3bPy83NVUxMjCIiItxdMgC4jNFoUGCg0an/uWsOjbPrNhoNLq8b/s3njhQNHDhQ8fHxmjRpkjIzM9W4cWNlZ2crKChId911lyTp4Ycf1pgxYzRr1iylpaVp+/bt+uCDD/TCCy94uHoAcB6j0aDQ0GY+NWk5NLixLBarS06/1NRYVFJSIYvF6vRto2HwuVBkNBqVnZ2tefPmaebMmaqqqlLPnj31+uuvq3Xr1pKknj176uWXX9bChQu1bt06tWnTRnPmzFFaWpqHqwcA5zEaDQoIMOq517/QseNll3/CFerR6TqNSY9z2vZ+rEXTRjIaDU6vOSI8WFPuvlFGo4FQhGvmc6FIksLCwvTnP//5km0GDBigAQMGuKkiAPCcY8fLdLDI8cuN1Iq4roXTtnUxzq4ZcAafDEW4er50eN2XagWulCve13xW6mI/wxGEIj/n0vP3FqsCmNgIXJIrP4P4L/YznIFQ5Odcdf6+ds6Bs7f74227iqv+1WexWJnLgDpc9RmUXP9Z8SXsZzgDoaiBcNWcA1fMC3DVfAZX/0uSX77gUnzps+LL2M9wBKEIDYYr/yVZ+8uXRo0CbFdi9ZV7nwEALiAUocFxxb8kL3UUijkOAOAbCEWAEzCfAQB8H6EIcCLmMwCA72KyAgAAgDhSBMDHGI2Gq7rx55VOeGdCOwBCEQCf4cgNUJnwDuByCEUAfIarboAqMaEdAKEIgA9iQjsAVyAUAQ2cL82l8aVagavhivc2tx66eoQioIFy+W1PuGEwcFkuvWk3tx66aoQioIFyxwUnXXUjYsBfuOpzWHvrIaPRQCi6CoQioIFz5fwcV92IGPA3rvgc4upxgh4AAECEIgAAAEmEIgAAAEmEIgAAAElMtAYAwG/99PpHV3ovwKvZpj8hFAEA4Gcud/0j7gVYP0IRAAB+xh3XIfNHhCIAAPwU9wm8Ov57YhAAAOAqEIoAAABEKAIAAJBEKAIAAJDkB6HozJkzSk1NVWxsrL766iu7dWvXrtWgQYMUHx+vYcOGaevWrR6qEgAAeDufD0VLlixRTU1NneUbN27UjBkzlJaWppycHCUkJCgjI0M7duxwf5EAAMDr+XQoOnjwoN544w1NnDixzrqXXnpJgwcP1uTJk5WUlKQ//elPio+P1+LFiz1QKQAA8HY+HYrmzJmj0aNHKyoqym55YWGhDh8+rLS0NLvl6enpys/P1/nz591ZJgAA8AE+G4o2bdqkffv26dFHH62zrqCgQJLqhKWYmBhVVVWpsLDQLTUCAADf4ZNXtK6srNT8+fOVmZmpFi3qXlmztPTC1TtNJpPd8trHteuvVWCgc7OkP99cDwCAq+HJvxN9MhQtXbpUrVq10u233+72vo1Gg1q2bO72fgEAaAg8ebNanwtFRUVFevXVV7V48WKVlV24yV1FRYXt/2fOnFFISIgkqaysTK1bt7Y912w2S5Jt/bWwWKwymyuu+fn1CQgwcsdiAAAkmc2VqqmxOH27V3JAw+dC0bFjx1RVVaXx48fXWTdmzBh169ZNCxYskHRhblF0dLRtfUFBgRo1aqTIyEiHaqiudv5gAQAAqabG4rG/Z30uFHXu3FmrVq2yW7Z3717NmzdPs2fPVnx8vCIjI9W+fXtt2rRJAwcOtLXLzc1VcnKygoKC3F02AADwcj4XikwmkxITE+td16VLF3Xp0kWSNHHiRE2ZMkXt2rVTYmKicnNztWvXLq1evdqd5QIAAB/hc6HoSg0ZMkSVlZXKyclRdna2oqKitGjRInXv3t3TpQEAAC/kF6EoMTFR3377bZ3lI0eO1MiRIz1QEQAA8DVcIAcAAECEIgAAAEmEIgAAAEmEIgAAAEmEIgAAAEmEIgAAAEmEIgAAAEmEIgAAAEmEIgAAAEmEIgAAAEmEIgAAAEmEIgAAAEmEIgAAAEmEIgAAAEmEIgAAAEmEIgAAAEmEIgAAAEmEIgAAAEmEIgAAAEmEIgAAAEmEIgAAAEmEIgAAAEmEIgAAAEmEIgAAAEmEIgAAAEmEIgAAAEmEIgAAAEmEIgAAAEmEIgAAAEmEIgAAAEk+GIr+/ve/6+GHH1ZqaqoSEhI0fPhwrVu3Tlar1a7d2rVrNWjQIMXHx2vYsGHaunWrhyoGAAC+wOdC0V/+8hc1bdpUWVlZWrp0qVJTUzVjxgwtXrzY1mbjxo2aMWOG0tLSlJOTo4SEBGVkZGjHjh2eKxwAAHi1QE8XcLWWLl2qsLAw2+Pk5GSVlJRo5cqVeuSRR2Q0GvXSSy9p8ODBmjx5siQpKSlJ+/bt0+LFi5WTk+OhygEAgDfzuSNFPw5EtTp37qzy8nJVVFSosLBQhw8fVlpaml2b9PR05efn6/z58+4qFQAA+BCfO1JUny+++ELh4eFq0aKFvvjiC0lSVFSUXZuYmBhVVVWpsLBQMTExDvUXGOjcLBkQ4HPZFAAAl/Dk34k+H4o+//xz5ebmaurUqZKk0tJSSZLJZLJrV/u4dv21MhoNatmyuUPbAAAA9TOZmnqsb58ORcXFxcrMzFRiYqLGjBnjlj4tFqvM5gqnbjMgwOjRNwEAAN7CbK5UTY3F6du9kgMaPhuKzGazxo0bp9DQUL388ssyGi8cbgsJCZEklZWVqXXr1nbtf7zeEdXVzh8sAAAg1dRYPPb3rE9OZjl79qweeughlZWVafny5QoODrati46OliQVFBTYPaegoECNGjVSZGSkW2sFAAC+wedCUXV1tSZPnqyCggItX75c4eHhdusjIyPVvn17bdq0yW55bm6ukpOTFRQU5M5yAQCAj/C502ezZ8/W1q1blZWVpfLycrsLMsbFxSkoKEgTJ07UlClT1K5dOyUmJio3N1e7du3S6tWrPVc4AADwaj4Xij799FNJ0vz58+us27JliyIiIjRkyBBVVlYqJydH2dnZioqK0qJFi9S9e3d3lwsAAHyEz4Wijz/++IrajRw5UiNHjnRxNQAAwF/43JwiAAAAVyAUAQAAiFAEAAAgiVAEAAAgiVAEAAAgiVAEAAAgiVAEAAAgiVAEAAAgiVAEAAAgiVAEAAAgiVAEAAAgiVAEAAAgiVAEAAAgiVAEAAAgiVAEAAAgiVAEAAAgiVAEAAAgiVAEAAAgiVAEAAAgiVAEAAAgiVAEAAAgiVAEAAAgiVAEAAAgiVAEAAAgiVAEAAAgiVAEAAAgiVAEAAAgiVAEAAAgiVAEAAAgyc9D0cGDB3X//fcrISFBvXv31rPPPqvz5897uiwAAOCFAj1dgKuUlpbqvvvuU/v27fXyyy/r+PHjmj9/vs6ePauZM2d6ujwAAOBl/DYU/e1vf9OZM2e0aNEihYaGSpJqamo0e/ZsPfTQQwoPD/dsgQAAwKv47emzbdu2KTk52RaIJCktLU0Wi0Wffvqp5woDAABeyW9DUUFBgaKjo+2WmUwmtW7dWgUFBR6qCgAAeCuD1Wq1eroIV+jSpYsee+wxjR8/3m75kCFD1L17dz311FPXtF2r1SqLxbm7zGCQjEajSsrOqbrG4tRtNw4KUHCzIKdv21XbdeW2fbFmV26bmt2zbWp2z7Z9sWZXbtsXaw4MMCo0uLEsFotckUwCAi5/HMhv5xS5isFgUECAwSXbDg1u7JLtunLb1Oz726Zm92ybmt2zbV+s2ZXb9sWajUbPncTy29NnJpNJZWVldZaXlpYqJCTEAxUBAABv5rehKDo6us7cobKyMn3//fd15hoBAAD4bShKTU3VZ599JrPZbFu2adMmGY1G9e7d24OVAQAAb+S3E61LS0s1ePBgRUVF6aGHHrJdvHHo0KFcvBEAANTht6FIunCbj6eeekpffvmlmjdvruHDhyszM1NBQUGeLg0AAHgZvw5FAAAAV8pv5xQBAABcDUIRAACACEUAAACSCEUAAACSCEUAAACSCEUAAACSCEU+6e9//7sefvhhpaamKiEhQcOHD9e6dev006srrF27VoMGDVJ8fLyGDRumrVu3eqhi/5SXl6d77rlHSUlJ6tq1qwYMGKB58+bVuefexx9/rGHDhik+Pl6DBg3S+vXrPVRxw3DmzBmlpqYqNjZWX331ld06PhOus2HDBsXGxtb577nnnrNrxxi4x9tvv60RI0YoPj5eiYmJGjt2rM6ePWtbz/dS/QI9XQCu3l/+8he1bdtWWVlZatmypT777DPNmDFDxcXFysjIkCRt3LhRM2bM0IQJE5SUlKTc3FxlZGTo9ddfV0JCgmdfgJ8oKSnRDTfcoHvvvVehoaHav3+/Xn75Ze3fv1+vvvqqJOnzzz9XRkaG7rjjDk2bNk3//ve/9cc//lHNmzfXLbfc4uFX4J+WLFmimpqaOsv5TLjH8uXLFRwcbHscHh5u+zNj4B5Lly5VTk6OJkyYoISEBJ0+fVr5+fm2zwXfS5dghc85efJknWXTp0+39ujRw1pTU2O1Wq3Wm2++2fr444/btRk1apR17NixbqmxoXrrrbesHTt2tBYXF1utVqv1gQcesI4aNcquzeOPP25NS0vzRHl+78CBA9aEhATrm2++ae3YsaN1165dtnV8Jlxr/fr11o4dO9b7/VSLMXC9gwcPWuPi4qz//Oc/L9qG76WL4/SZDwoLC6uzrHPnziovL1dFRYUKCwt1+PBhpaWl2bVJT09Xfn6+zp8/765SG5zQ0FBJUlVVlc6fP6/t27fX+ZdXenq6Dh48qGPHjnmgQv82Z84cjR49WlFRUXbL+Ux4HmPgHhs2bFBERIR+/etf17ue76VLIxT5iS+++ELh4eFq0aKFCgoKJKnOXwwxMTGqqqpSYWGhJ0r0WzU1NTp37py+/vprLV68WP3791dERISOHj2qqqoqRUdH27WPiYmRJNs4wTk2bdqkffv26dFHH62zjs+E+wwZMkSdO3fWgAEDtGzZMtspG8bAPXbu3KmOHTtqyZIlSk5OVteuXTV69Gjt3LlTkvheugzmFPmBzz//XLm5uZo6daokqbS0VJJkMpns2tU+rl0P5+jXr5+OHz8uSerTp48WLFggiXFwp8rKSs2fP1+ZmZlq0aJFnfWMheu1bt1aEydOVLdu3WQwGPTxxx9r4cKFOn78uGbOnMkYuMn333+v3bt3a9++fXryySfVtGlTvfLKK3rggQe0efNmxuEyCEU+rri4WJmZmUpMTNSYMWM8XU6DlJ2drcrKSh04cEBLly7VhAkTtHLlSk+X1aAsXbpUrVq10u233+7pUhqsPn36qE+fPrbHKSkpaty4sf76179qwoQJHqysYbFaraqoqNCLL76oTp06SZK6deum/v37a/Xq1UpJSfFwhd6N02c+zGw2a9y4cQoNDdXLL78so/HCcIaEhEhSnZ+Gm81mu/Vwjk6dOql79+4aOXKklixZou3bt+sf//gH4+AmRUVFevXVVzVp0iSVlZXJbDaroqJCklRRUaEzZ84wFh6Slpammpoa7d27lzFwE5PJpNDQUFsgki7MdYyLi9OBAwcYh8sgFPmos2fP6qGHHlJZWVmdn8DWniv+6bnhgoICNWrUSJGRkW6ttSGJjY1Vo0aNdPToUbVr106NGjWqdxwk1Tmnj2tz7NgxVVVVafz48brpppt000032Y5MjBkzRvfffz+fCS/AGLhHhw4dLrru3LlzfC9dBqHIB1VXV2vy5MkqKCjQ8uXL7a4DIkmRkZFq3769Nm3aZLc8NzdXycnJCgoKcme5DcrOnTtVVVWliIgIBQUFKTExUR9++KFdm9zcXMXExCgiIsJDVfqXzp07a9WqVXb//eEPf5AkzZ49W08++SSfCQ/Jzc1VQECA4uLiGAM36devn0pKSrR3717bstOnT+vrr79Wly5d+F66DOYU+aDZs2dr69atysrKUnl5uXbs2GFbFxcXp6CgIE2cOFFTpkxRu3btlJiYqNzcXO3atUurV6/2XOF+JiMjQ127dlVsbKyaNGmib775RitWrFBsbKwGDhwoSXr44Yc1ZswYzZo1S2lpadq+fbs++OADvfDCCx6u3n+YTCYlJibWu65Lly7q0qWLJPGZcLEHH3xQiYmJio2NlSRt2bJFa9as0ZgxY9S6dWtJjIE7DBw4UPHx8Zo0aZIyMzPVuHFjZWdnKygoSHfddZckvpcuxWC1/uTeEPB6/fv3V1FRUb3rtmzZYkv6a9euVU5Ojr777jtFRUXp8ccfV79+/dxZql/Lzs5Wbm6ujh49KqvVqrZt2+p//ud/9OCDD9r9AmrLli1auHChDh06pDZt2mj8+PG64447PFi5/9u+fbvGjBmjdevWKT4+3racz4TrzJkzR//6179UXFwsi8Wi9u3ba+TIkbr33ntlMBhs7RgD1zt16pTmzZunrVu3qqqqSj179tQf/vAHu1NrfC/Vj1AEAAAg5hQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIkv4fxGPFt4lU6JwAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "qutlVEkRmSnV",
        "outputId": "4700674d-919f-446e-b200-b8870e53d2dc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x=id.drop(columns='charges', axis=1)\n",
        "y=id['charges']"
      ],
      "metadata": {
        "id": "GUIyJQgLmP49"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xtrain,xtest,ytrain,ytest=train_test_split(x,y,test_size=0.2,random_state=2)"
      ],
      "metadata": {
        "id": "lTDgePnwmOPM"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(id.shape,\"\\n\",xtrain.shape,\"\\n\",xtest.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1aLDfUDsnSFu",
        "outputId": "4dae8b8c-6aa9-4972-c726-193ea02de070"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1338, 7) \n",
            " (1070, 6) \n",
            " (268, 6)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model=LinearRegression()"
      ],
      "metadata": {
        "id": "m4AMbjdImgjD"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(xtrain,ytrain)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "-aCfhWHtmk6N",
        "outputId": "f169ff84-668e-4a33-a5f3-8b64fa7185d2"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(xtrain,ytrain)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jswSuNYmmmn9",
        "outputId": "ce693f7e-66d5-4aa9-8d70-37c13e5a8a57"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7514085919979442"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainacc=model.predict(xtrain)\n",
        "trainaccuracy=metrics.r2_score(ytrain,trainacc)\n",
        "trainaccuracy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z2IAOT0knFNd",
        "outputId": "8d689bf7-74a9-43ac-a9f4-91ba1718a995"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7514085919979442"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "testaccu=model.predict(xtest)\n",
        "testaccuracy=metrics.r2_score(ytest,testaccu)\n",
        "testaccuracy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qi9XbgPRoJYu",
        "outputId": "830f78c8-7001-4e1a-ae31-9ebdc9c3e179"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7443018651235678"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Predictions"
      ],
      "metadata": {
        "id": "eEEkpfKJpO4O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#input format (age\tsex\tbmi\tchildren\tsmoker\tregion\tcharges)\n",
        "#inp=np.array([31,1,25.7,0,1,0])\n",
        "x=list(map(int,input().split()))\n",
        "inp=np.asarray(x)\n",
        "inp=inp.reshape(1,-1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u1Ii3iEkn-gM",
        "outputId": "fd5c9893-12e1-492a-b5d9-2affb71c88e0"
      },
      "execution_count": 66,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "25 1 25 1 0 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predic=model.predict(inp)\n",
        "predic"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4GptLR2ApaiN",
        "outputId": "2697132c-0da1-4982-9b20-9629ed7024ce"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([26734.32673655])"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Insurance Cost\",predic[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8n8_HoWppep8",
        "outputId": "11399a71-07c0-4053-84a0-f46178f227ed"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Insurance Cost 26734.326736547773\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=list(map(int,input().split()))\n",
        "inp=np.asarray(x)\n",
        "inp=inp.reshape(1,-1)\n",
        "predic=model.predict(inp)\n",
        "print(\"Insurance Cost\",predic[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wgaJhi7lpjzf",
        "outputId": "07b1c4f4-7136-4b2b-ee07-db83e74aaa62"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "21 1 75 0 0 1\n",
            "Insurance Cost 41559.907632079965\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-EWADKq1pqFu"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}