Medical_Insurance_Cost_Prediciton.ipynb

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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"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"insurance.csv\")"
      ],
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      },
      "execution_count": 50,
      "outputs": []
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        "df.head()"
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              "   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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          "metadata": {},
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      "source": [
        "df.shape"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A6GKjT-myt5n",
        "outputId": "2a1617b1-3823-4b67-aaff-9948356ed575"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1338, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yXKPEuhxyv8V",
        "outputId": "455bf425-d9b8-4cc6-b95e-aa34af416662"
      },
      "execution_count": 53,
      "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": "markdown",
      "source": [
        "in the dataset 'sex','smoker','region' are categorical features."
      ],
      "metadata": {
        "id": "kje11KCUy6Mn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#checking for missing values\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rxeV5e_2y1e1",
        "outputId": "b87a6729-504a-4586-ca73-66c76b9a1bca"
      },
      "execution_count": 54,
      "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": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#getting the statistical measures of the dataset\n",
        "df.describe()"
      ],
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          "height": 300
        },
        "id": "PVBh2T9ezNEl",
        "outputId": "cf320407-e0a6-449e-e8fb-a89614a3112c"
      },
      "execution_count": 55,
      "outputs": [
        {
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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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              "      <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",
              "      <td>26.296250</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4740.287150</td>\n",
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              "    <tr>\n",
              "      <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",
              "      <td>5.000000</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a5dfacb9-2d06-4414-87e0-8e74ce7ced1c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a5dfacb9-2d06-4414-87e0-8e74ce7ced1c 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-a5dfacb9-2d06-4414-87e0-8e74ce7ced1c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-20c37151-e6f3-469f-a06f-bb21eb61deab\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-20c37151-e6f3-469f-a06f-bb21eb61deab')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <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-20c37151-e6f3-469f-a06f-bb21eb61deab button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#visualizing the distribution of age\n",
        "sns.set()\n",
        "plt.figure(figsize = (4,4))\n",
        "sns.distplot(df['age'])\n",
        "plt.title(\"Age Distribution\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        },
        "id": "AJKn8CTSzxiW",
        "outputId": "a8fd3d90-088f-48ef-e3fb-99a4eee6e9fd"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-56-dc930e4a66d8>:4: 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(df['age'])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['sex'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XjlH6ggl4sQa",
        "outputId": "d7b54464-7958-41df-ad08-e944509a8c8c"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "male      676\n",
              "female    662\n",
              "Name: sex, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#visualizing the gender column\n",
        "plt.figure(figsize = (5,5))\n",
        "sns.countplot(x = 'sex', data = df)\n",
        "plt.title(\"Sex Distribution\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "id": "9ATbUNHf0aP5",
        "outputId": "631b3061-84ad-4d61-82a4-ba02aec2eb90"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#visualizing the BMI  distribution\n",
        "plt.figure(figsize = (6,6))\n",
        "sns.displot(df['bmi'])\n",
        "plt.title('BMI Distribution')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "id": "y9oHvMlJ4pV8",
        "outputId": "a3f8dffc-e286-44c7-ab7a-8427d144ebcd"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['children'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vc-G6R1l5fYX",
        "outputId": "3a01e76b-bd1a-4e71-fa5e-2fe5edfe9281"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    574\n",
              "1    324\n",
              "2    240\n",
              "3    157\n",
              "4     25\n",
              "5     18\n",
              "Name: children, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#visualizing the children column\n",
        "plt.figure(figsize = (5,5))\n",
        "sns.countplot(x = 'children', data = df)\n",
        "plt.title(\"Number Of Children\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "id": "zGoWreZO5EwU",
        "outputId": "1a96a7bc-5f65-4e80-e3d4-b29bffabe656"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['smoker'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ax7vhMms53Am",
        "outputId": "f0313ab6-99c3-474d-8056-4e5f084bccc4"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "no     1064\n",
              "yes     274\n",
              "Name: smoker, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#visualizing the smoker column\n",
        "plt.figure(figsize = (5,5))\n",
        "sns.countplot(x = 'smoker', data = df)\n",
        "plt.title('Smoker')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "id": "nUKx3fhv5cmy",
        "outputId": "985c98fc-7f38-4596-d091-d64ac83e49b6"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['region'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i_NZEbP2503b",
        "outputId": "f3945112-9db5-4703-ea05-eec3264a7e3b"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "southeast    364\n",
              "southwest    325\n",
              "northwest    325\n",
              "northeast    324\n",
              "Name: region, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#visualizing the region column\n",
        "plt.figure(figsize = (5,5))\n",
        "sns.countplot(x = 'region', data = df)\n",
        "plt.title('Region')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "id": "djPb4wG16BHs",
        "outputId": "4b4f6fe9-b1dd-470a-e31d-9f1a4c220b67"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#visualizing the distribution of charge values\n",
        "plt.figure(figsize = (6,6))\n",
        "sns.distplot(df['charges'])\n",
        "plt.title('Charges Distribution')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 764
        },
        "id": "FsiYQJFA6Qjh",
        "outputId": "0c165177-f55e-4a8b-9ebc-887558cf1dd1"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-66-82eed7e71b21>: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(df['charges'])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Encoding the categorical features"
      ],
      "metadata": {
        "id": "uBlGe0FK6m4U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.replace({'sex':{'male': 0, \"female\": 1}}, inplace = True)\n",
        "df.replace({'smoker':{'yes':0,'no':1}}, inplace=True)\n",
        "df.replace({'region':{'southeast':0,'southwest':1,'northeast':2,'northwest':3}}, inplace=True)\n"
      ],
      "metadata": {
        "id": "YlpJ4Y_T6lio"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Splitting the dataset"
      ],
      "metadata": {
        "id": "AYcvk1GW7Ngn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = df.iloc[: ,:-1].values\n",
        "y = df.iloc[: ,-1].values"
      ],
      "metadata": {
        "id": "7D80VKaN7HTv"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bbzAlRQ17YoI",
        "outputId": "674aee1a-0fb0-4e91-c886-29b0030b96cc"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[19.    1.   27.9   0.    0.    1.  ]\n",
            " [18.    0.   33.77  1.    1.    0.  ]\n",
            " [28.    0.   33.    3.    1.    0.  ]\n",
            " ...\n",
            " [18.    1.   36.85  0.    1.    0.  ]\n",
            " [21.    1.   25.8   0.    1.    1.  ]\n",
            " [61.    1.   29.07  0.    0.    3.  ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Iisc3ebU7iZV",
        "outputId": "a0539498-2652-480d-e091-bbdf8a81a8f5"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[16884.924   1725.5523  4449.462  ...  1629.8335  2007.945  29141.3603]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Splitting the dataset into training set and testing set"
      ],
      "metadata": {
        "id": "b65ydsbI7lrE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, random_state = 42)"
      ],
      "metadata": {
        "id": "h14rP09X7jw9"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(x.shape)\n",
        "print(x_train.shape)\n",
        "print(x_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SPIVrUbt77u1",
        "outputId": "8ffac6ef-1558-4883-d7c9-a2ea69660bb5"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1338, 6)\n",
            "(1070, 6)\n",
            "(268, 6)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Linear Regression"
      ],
      "metadata": {
        "id": "ooHFjRqf8GPT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "reg = LinearRegression()\n",
        "reg.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "AiB0I2Nd8DXU",
        "outputId": "9ee2c971-39fd-4ac3-914b-39d323d8e190"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
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            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = reg.predict(x_test)"
      ],
      "metadata": {
        "id": "h8B3cxpf9t0K"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "# R squared value\n",
        "r2_test = metrics.r2_score(y_test, y_pred)\n",
        "print('R squared vale : ', r2_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "64lpFzUZ91OH",
        "outputId": "881bfc63-6e09-434b-8bc4-21e77f475953"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R squared vale :  0.783021587162344\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Making a Predicitve System"
      ],
      "metadata": {
        "id": "tAmYmytoAb8R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "input_data = (31,1,25.74,0,1,0)\n",
        "\n",
        "# changing input_data to a numpy array\n",
        "input_data_array = np.asarray(input_data)\n",
        "\n",
        "# reshaping the array\n",
        "input_data_reshaped = input_data_array.reshape(1,-1)\n",
        "\n",
        "prediction = reg.predict(input_data_reshaped)\n",
        "print(prediction)\n",
        "\n",
        "print('The insurance cost is USD ', prediction[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m95wtXh0AbZ-",
        "outputId": "e7478b68-6f06-4652-c66d-25d514bd82b2"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[4016.99266999]\n",
            "The insurance cost is USD  4016.9926699873304\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_9vHdXTlArA6"
      },
      "execution_count": 76,
      "outputs": []
    }
  ]
}