Medical_Insurance_Price_Prediction.ipynb

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      "source": [
        "This is a prodject for Medical Insurance Price Prediction. This is a filtered and modified version of the Medical Insurance Price Prediction dataset, available under the CC0 1.0 Universal License on the Kaggle website. This is a project done for IBM Data Science Professional course via Coursera.\n",
        "\n",
        "In this project, I will be:\n",
        "\n",
        "- Load the data as a pandas dataframe\n",
        "- Clean the data, taking care of the blank entries\n",
        "- Run exploratory data analysis (EDA) and identify the attributes that most affect the charges\n",
        "- Develop single variable and multi variable Linear Regression models for predicting the charges\n",
        "- Use Ridge regression to refine the performance of Linear regression models."
      ],
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as pyplot\n",
        "import seaborn as sb\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n",
        "from sklearn.linear_model import LinearRegression, Ridge\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "from sklearn.model_selection import cross_val_score, train_test_split"
      ],
      "metadata": {
        "id": "Rv1DvVv12QWg"
      },
      "execution_count": 295,
      "outputs": []
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      "source": [
        "Load the data as a pandas dataframe\n",
        "----\n"
      ],
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        "headers=[\"age\", \"gender\", \"bmi\", \"no_of_children\", \"smoker\", \"region\", \"charges\"]\n",
        "mi=pd.read_csv(\"https://raw.githubusercontent.com/MaliniVibin1502/pythonprojects/main/data/medical_insurance_dataset.csv\")\n",
        "mi.columns=headers"
      ],
      "metadata": {
        "id": "WUSowv9o2qOA"
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      "execution_count": 296,
      "outputs": []
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        "mi.head(10)"
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              "  age  gender     bmi  no_of_children smoker  region      charges\n",
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              "6  37       1  27.740               3      0       1   7281.50560\n",
              "7  37       2  29.830               2      0       2   6406.41070\n",
              "8  60       1  25.840               0      0       1  28923.13692\n",
              "9  25       2  26.220               0      0       2   2721.32080"
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              "  age  gender     bmi  no_of_children smoker  region      charges\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "mi",
              "summary": "{\n  \"name\": \"mi\",\n  \"rows\": 2771,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"21\",\n          \"45\",\n          \"36\"\n        ],\n        \"num_unique_values\": 47,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 2,\n        \"samples\": [\n          1,\n          2\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.130323845974241,\n        \"min\": 15.96,\n        \"max\": 53.13,\n        \"samples\": [\n          25.6,\n          38.285\n        ],\n        \"num_unique_values\": 548,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"no_of_children\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 5,\n        \"samples\": [\n          1,\n          3\n        ],\n        \"num_unique_values\": 6,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"smoker\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"1\",\n          \"0\"\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"region\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 4,\n        \"samples\": [\n          1,\n          3\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"charges\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12153.767132152783,\n        \"min\": 1121.8739,\n        \"max\": 63770.42801,\n        \"samples\": [\n          48673.5588,\n          7045.499\n        ],\n        \"num_unique_values\": 1337,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 299
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(mi.info())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bIaGNpmm33Pr",
        "outputId": "ee439c05-67de-4ca0-c220-ad45fc6cc1dc"
      },
      "execution_count": 300,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2771 entries, 0 to 2770\n",
            "Data columns (total 7 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   age             2767 non-null   object \n",
            " 1   gender          2771 non-null   int64  \n",
            " 2   bmi             2771 non-null   float64\n",
            " 3   no_of_children  2771 non-null   int64  \n",
            " 4   smoker          2764 non-null   object \n",
            " 5   region          2771 non-null   int64  \n",
            " 6   charges         2771 non-null   float64\n",
            "dtypes: float64(2), int64(3), object(2)\n",
            "memory usage: 151.7+ KB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- For continuous attributes (e.g., age), replace missing values with the mean.\n",
        "- For categorical attributes (e.g., smoker), replace missing values with the most frequent value.\n",
        "- Update the data types of the respective columns.\n",
        "- Verify the update using df.info()."
      ],
      "metadata": {
        "id": "5WL896_74yQX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mi['age']=mi['age'].astype(float)\n",
        "mi['age']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bJVlOlDl4q_B",
        "outputId": "9cd9e119-e611-41a4-ea1c-3b0fbc5d16e1"
      },
      "execution_count": 301,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       18.0\n",
              "1       28.0\n",
              "2       33.0\n",
              "3       32.0\n",
              "4       31.0\n",
              "        ... \n",
              "2766    47.0\n",
              "2767    21.0\n",
              "2768    19.0\n",
              "2769    23.0\n",
              "2770    54.0\n",
              "Name: age, Length: 2771, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 301
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_age=mi['age'].mean(axis=0)\n",
        "mean_age\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-eNSa8HV8dKY",
        "outputId": "f5c01882-eba9-41f4-81d5-2921e3feaa12"
      },
      "execution_count": 302,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "39.11709432598482"
            ]
          },
          "metadata": {},
          "execution_count": 302
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mi.replace(np.nan,mean_age,inplace=True)"
      ],
      "metadata": {
        "id": "NHvRuh_u8tFQ"
      },
      "execution_count": 303,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mi['age']=mi['age'].astype('int')"
      ],
      "metadata": {
        "id": "Ucrzj35w6whi"
      },
      "execution_count": 304,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "is_smoker = mi['smoker'].value_counts().idxmax()\n",
        "\n"
      ],
      "metadata": {
        "id": "ZseiXqpE6ncg"
      },
      "execution_count": 305,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mi[\"smoker\"].replace(np.nan,is_smoker,inplace=True)"
      ],
      "metadata": {
        "id": "bJEpASx3-FYM"
      },
      "execution_count": 306,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mi['smoker']=mi['smoker'].astype('int')"
      ],
      "metadata": {
        "id": "5JOqwUtn97k7"
      },
      "execution_count": 307,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mi.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CDtW44Ue_ZgV",
        "outputId": "6412a1ef-13e8-48af-bc18-462583f19a21"
      },
      "execution_count": 308,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2771 entries, 0 to 2770\n",
            "Data columns (total 7 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   age             2771 non-null   int64  \n",
            " 1   gender          2771 non-null   int64  \n",
            " 2   bmi             2771 non-null   float64\n",
            " 3   no_of_children  2771 non-null   int64  \n",
            " 4   smoker          2771 non-null   int64  \n",
            " 5   region          2771 non-null   int64  \n",
            " 6   charges         2771 non-null   float64\n",
            "dtypes: float64(2), int64(5)\n",
            "memory usage: 151.7 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Exploratory Data Analysis (EDA)\n",
        "----------\n"
      ],
      "metadata": {
        "id": "kFVl8WxoPXVY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sb.regplot(x='charges',y='bmi',data=mi,line_kws={\"color\":\"red\"})\n",
        "pyplot.ylim(0,)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "COUoItlbPb3g",
        "outputId": "0755652e-e58d-4802-dd88-ce7234ff8bb6"
      },
      "execution_count": 309,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 54.9885)"
            ]
          },
          "metadata": {},
          "execution_count": 309
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sb.boxplot(x='smoker',y='charges',data=mi)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "pe2ddTSXQt93",
        "outputId": "d7c06567-a35c-4d45-e291-6ca4808560b6"
      },
      "execution_count": 310,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='smoker', ylabel='charges'>"
            ]
          },
          "metadata": {},
          "execution_count": 310
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mi.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "0qpbdPGVRIPX",
        "outputId": "f188654d-ed7e-4ca2-9251-25957363ebf6"
      },
      "execution_count": 311,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     age    gender       bmi  no_of_children    smoker  \\\n",
              "age             1.000000 -0.026584  0.112859        0.037126 -0.016989   \n",
              "gender         -0.026584  1.000000  0.042766        0.015693  0.009008   \n",
              "bmi             0.112859  0.042766  1.000000       -0.001642 -0.035577   \n",
              "no_of_children  0.037126  0.015693 -0.001642        1.000000 -0.002746   \n",
              "smoker         -0.016989  0.009008 -0.035577       -0.002746  1.000000   \n",
              "region         -0.006969  0.022360  0.271200       -0.025594 -0.001112   \n",
              "charges         0.298892  0.062959  0.199906        0.066551  0.143476   \n",
              "\n",
              "                  region   charges  \n",
              "age            -0.006969  0.298892  \n",
              "gender          0.022360  0.062959  \n",
              "bmi             0.271200  0.199906  \n",
              "no_of_children -0.025594  0.066551  \n",
              "smoker         -0.001112  0.143476  \n",
              "region          1.000000  0.054018  \n",
              "charges         0.054018  1.000000  "
            ],
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              "      <td>1.000000</td>\n",
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              "      <td>-0.025594</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>0.054018</td>\n",
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              "      <td>0.298892</td>\n",
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              "      <td>0.066551</td>\n",
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              "\n",
              "\n",
              "<div id=\"df-c5ebbe0c-c8f7-4f87-b16d-9b21ebce2711\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c5ebbe0c-c8f7-4f87-b16d-9b21ebce2711')\"\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-c5ebbe0c-c8f7-4f87-b16d-9b21ebce2711 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\": \"mi\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3708461676277164,\n        \"min\": -0.026583718914251507,\n        \"max\": 1.0,\n        \"samples\": [\n          1.0,\n          -0.026583718914251507,\n          -0.006968695750469211\n        ],\n        \"num_unique_values\": 7,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3710653045739216,\n        \"min\": -0.026583718914251507,\n        \"max\": 1.0,\n        \"samples\": [\n          -0.026583718914251507,\n          1.0,\n          0.02235979480498564\n        ],\n        \"num_unique_values\": 7,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.35786658450834474,\n        \"min\": -0.035576840026459014,\n        \"max\": 1.0,\n        \"samples\": [\n          0.11285863336987123,\n          0.04276613849085359,\n          0.27120039019648207\n        ],\n        \"num_unique_values\": 7,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"no_of_children\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.37353735033404095,\n        \"min\": -0.025593614946885262,\n        \"max\": 1.0,\n        \"samples\": [\n          0.03712590419790424,\n          0.015693039127058536,\n          -0.025593614946885262\n        ],\n        \"num_unique_values\": 7,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"smoker\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.37652107090893044,\n        \"min\": -0.035576840026459014,\n        \"max\": 1.0,\n        \"samples\": [\n          -0.016988911489100974,\n          0.009007748216919746,\n          -0.001112344037318963\n        ],\n        \"num_unique_values\": 7,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"region\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.37216753441749556,\n        \"min\": -0.025593614946885262,\n        \"max\": 1.0,\n        \"samples\": [\n          -0.006968695750469211,\n          0.02235979480498564,\n          1.0\n        ],\n        \"num_unique_values\": 7,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"charges\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3378790207860965,\n        \"min\": 0.05401797096065052,\n        \"max\": 1.0,\n        \"samples\": [\n          0.29889224848275603,\n          0.06295872808334804,\n          0.05401797096065052\n        ],\n        \"num_unique_values\": 7,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 311
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model Development\n",
        "------\n",
        "- Fit a linear regression model that may be used to predict the charges value, just by using the smoker attribute of the dataset. Print the $ R^2 $\n",
        " score of this model.\n",
        "\n",
        "- Fit a linear regression model that may be used to predict the charges value, just by using all other attributes of the dataset. Print the $ R^2 $\n",
        " score of this model. You should see an improvement in the performance.\n",
        "\n",
        "- Create a training pipeline that uses StandardScaler(), PolynomialFeatures() and LinearRegression() to create a model that can predict the charges value using all the other attributes of the dataset. There should be even further improvement in the performance."
      ],
      "metadata": {
        "id": "1GUOUKGWRSuU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lr=LinearRegression()\n",
        "x=mi[['smoker']]\n",
        "y=mi['charges']\n",
        "lr.fit(x,y)\n",
        "y_predict=lr.predict(x)\n"
      ],
      "metadata": {
        "id": "G2qpRpsuRkxZ"
      },
      "execution_count": 312,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "r_square=lr.score(x,y)\n"
      ],
      "metadata": {
        "id": "OtCC461vRdPE"
      },
      "execution_count": 313,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"R sqaure value using linear regression on attributes Smoker and Charges is:\", r_square)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "emVi4-inSliW",
        "outputId": "320e426d-dca6-423a-e34f-1026122c4f1f"
      },
      "execution_count": 314,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R sqaure value using linear regression on attributes Smoker and Charges is: 0.020585299824132375\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr1=LinearRegression()\n",
        "z=mi[[\"age\", \"gender\", \"bmi\", \"no_of_children\", \"smoker\", \"region\"]]\n",
        "y=mi['charges']\n"
      ],
      "metadata": {
        "id": "QxhLQXC7S3Mp"
      },
      "execution_count": 315,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr1.fit(z,y)\n",
        "y_mpredict=lr1.predict(z)"
      ],
      "metadata": {
        "id": "YAno-AMGTK3L"
      },
      "execution_count": 316,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "r_msquare=lr1.score(z,y)"
      ],
      "metadata": {
        "id": "Q4NDfHZYTWyc"
      },
      "execution_count": 317,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"R sqaure value using linear regression on all attributes in dataset with Charges is:\", r_msquare)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y3zmpwXdTQHV",
        "outputId": "30dd037b-8825-4621-e6af-2ff4523231c7"
      },
      "execution_count": 318,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R sqaure value using linear regression on all attributes in dataset with Charges is: 0.14815183114624353\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Input=[('scale', StandardScaler()), ('polynomial',PolynomialFeatures(include_bias=False)), ('model', LinearRegression())]"
      ],
      "metadata": {
        "id": "TXMUp594UCMn"
      },
      "execution_count": 319,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pipe=Pipeline(Input)"
      ],
      "metadata": {
        "id": "WuxVnet1UaD5"
      },
      "execution_count": 320,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "z=mi[[\"age\", \"gender\", \"bmi\", \"no_of_children\", \"smoker\", \"region\"]]"
      ],
      "metadata": {
        "id": "SO7erI1hV2b-"
      },
      "execution_count": 325,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pipe.fit(z,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 159
        },
        "id": "mm-WYOPpUfrN",
        "outputId": "353c9beb-a007-4d8b-df23-a9ad96862185"
      },
      "execution_count": 326,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('scale', StandardScaler()),\n",
              "                ('polynomial', PolynomialFeatures(include_bias=False)),\n",
              "                ('model', LinearRegression())])"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 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-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 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-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 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-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scale&#x27;, StandardScaler()),\n",
              "                (&#x27;polynomial&#x27;, PolynomialFeatures(include_bias=False)),\n",
              "                (&#x27;model&#x27;, 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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scale&#x27;, StandardScaler()),\n",
              "                (&#x27;polynomial&#x27;, PolynomialFeatures(include_bias=False)),\n",
              "                (&#x27;model&#x27;, LinearRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PolynomialFeatures</label><div class=\"sk-toggleable__content\"><pre>PolynomialFeatures(include_bias=False)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 326
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr2=LinearRegression()\n",
        "lr2.fit(z,y)\n",
        "y_pipe=pipe.predict(z)"
      ],
      "metadata": {
        "id": "fQCYDeSbUjzs"
      },
      "execution_count": 327,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(r2_score(y,y_pipe))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EjhYzocBUtoK",
        "outputId": "89d354b6-132f-4a9d-92e2-a6a030d5a432"
      },
      "execution_count": 329,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7676416519365656\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model Refinement\n",
        "-----\n",
        "\n",
        "- Split the data into training and testing subsets, assuming that 20% of the data will be reserved for testing.\n",
        "\n",
        "- Initialize a Ridge regressor that used hyperparameter $ \\alpha = 0.1 $. Fit the model using training data subset. Print the $ R^2 $ score for the testing data.\n",
        "\n",
        "- Apply polynomial transformation to the training parameters with degree=2. Use this transformed feature set to fit the same regression model, as above, using the training subset. Print the $ R^2 $ score for the testing subset."
      ],
      "metadata": {
        "id": "U7szkzb5Wc6F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(z, y, test_size=0.2, random_state=1)"
      ],
      "metadata": {
        "id": "HOFxDD66W8wb"
      },
      "execution_count": 331,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "RidgeModel=Ridge(alpha=0.1)"
      ],
      "metadata": {
        "id": "NeluHuULXn5I"
      },
      "execution_count": 334,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "RidgeModel.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "iEhqIETkYC_k",
        "outputId": "b664f07c-0b02-41a2-c66b-54e27f38b391"
      },
      "execution_count": 335,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ridge(alpha=0.1)"
            ],
            "text/html": [
              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 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-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 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-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 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-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 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-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Ridge(alpha=0.1)</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-21\" type=\"checkbox\" checked><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge(alpha=0.1)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 335
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predict=RidgeModel.predict(x_test)"
      ],
      "metadata": {
        "id": "Pff3F8VPYTPZ"
      },
      "execution_count": 336,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(r2_score(y_test,y_predict))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WIA3TIL4YaTV",
        "outputId": "b46f6db9-3818-49d8-aaae-1251bb5463df"
      },
      "execution_count": 337,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.1455982847601478\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pr=PolynomialFeatures(degree=2,include_bias=False)"
      ],
      "metadata": {
        "id": "j6DPRSHBYl3w"
      },
      "execution_count": 338,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train_pr = pr.fit_transform(x_train)\n",
        "x_test_pr = pr.fit_transform(x_test)\n",
        "RidgeModel.fit(x_train_pr, y_train)\n",
        "y_hat = RidgeModel.predict(x_test_pr)\n",
        "print(r2_score(y_test,y_hat))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BabXYREvYvGd",
        "outputId": "80b5e6ac-c163-463b-ec4a-3919961e4577"
      },
      "execution_count": 339,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.5968802162875089\n"
          ]
        }
      ]
    }
  ]
}