From 96da2f483d9356d6547845fc3b4e5efde54d49eb Mon Sep 17 00:00:00 2001
From: Produpro <136916627+Produpro@users.noreply.github.com>
Date: Wed, 3 Jan 2024 16:39:04 +0000
Subject: [PATCH] new

---
 data/processed/clean_test.csv  |  269 ++++++++
 data/processed/clean_train.csv | 1070 ++++++++++++++++++++++++++++++++
 src/explore.ipynb              |  834 ++++++++++++++++++++++++-
 3 files changed, 2170 insertions(+), 3 deletions(-)
 create mode 100644 data/processed/clean_test.csv
 create mode 100644 data/processed/clean_train.csv

diff --git a/data/processed/clean_test.csv b/data/processed/clean_test.csv
new file mode 100644
index 000000000..9882ac1eb
--- /dev/null
+++ b/data/processed/clean_test.csv
@@ -0,0 +1,269 @@
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diff --git a/data/processed/clean_train.csv b/data/processed/clean_train.csv
new file mode 100644
index 000000000..6786ac8bb
--- /dev/null
+++ b/data/processed/clean_train.csv
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diff --git a/src/explore.ipynb b/src/explore.ipynb
index a323483c5..3defc3ee5 100644
--- a/src/explore.ipynb
+++ b/src/explore.ipynb
@@ -9,11 +9,839 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 18,
             "metadata": {},
             "outputs": [],
             "source": [
-                "# Your code here"
+                "import pandas as pd\n",
+                "from sklearn.preprocessing import MinMaxScaler\n",
+                "from sklearn.feature_selection import SelectKBest, f_regression\n",
+                "from sklearn.model_selection import train_test_split\n",
+                "import matplotlib.pyplot as plt\n",
+                "import seaborn as sns\n",
+                "from sklearn.linear_model import LinearRegression\n",
+                "from sklearn.metrics import mean_squared_error, r2_score"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 2,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "text/html": [
+                            "<div>\n",
+                            "<style scoped>\n",
+                            "    .dataframe tbody tr th:only-of-type {\n",
+                            "        vertical-align: middle;\n",
+                            "    }\n",
+                            "\n",
+                            "    .dataframe tbody tr th {\n",
+                            "        vertical-align: top;\n",
+                            "    }\n",
+                            "\n",
+                            "    .dataframe thead th {\n",
+                            "        text-align: right;\n",
+                            "    }\n",
+                            "</style>\n",
+                            "<table border=\"1\" class=\"dataframe\">\n",
+                            "  <thead>\n",
+                            "    <tr style=\"text-align: right;\">\n",
+                            "      <th></th>\n",
+                            "      <th>age</th>\n",
+                            "      <th>sex</th>\n",
+                            "      <th>bmi</th>\n",
+                            "      <th>children</th>\n",
+                            "      <th>smoker</th>\n",
+                            "      <th>region</th>\n",
+                            "      <th>charges</th>\n",
+                            "    </tr>\n",
+                            "  </thead>\n",
+                            "  <tbody>\n",
+                            "    <tr>\n",
+                            "      <th>0</th>\n",
+                            "      <td>19</td>\n",
+                            "      <td>female</td>\n",
+                            "      <td>27.900</td>\n",
+                            "      <td>0</td>\n",
+                            "      <td>yes</td>\n",
+                            "      <td>southwest</td>\n",
+                            "      <td>16884.92400</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>1</th>\n",
+                            "      <td>18</td>\n",
+                            "      <td>male</td>\n",
+                            "      <td>33.770</td>\n",
+                            "      <td>1</td>\n",
+                            "      <td>no</td>\n",
+                            "      <td>southeast</td>\n",
+                            "      <td>1725.55230</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>2</th>\n",
+                            "      <td>28</td>\n",
+                            "      <td>male</td>\n",
+                            "      <td>33.000</td>\n",
+                            "      <td>3</td>\n",
+                            "      <td>no</td>\n",
+                            "      <td>southeast</td>\n",
+                            "      <td>4449.46200</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>3</th>\n",
+                            "      <td>33</td>\n",
+                            "      <td>male</td>\n",
+                            "      <td>22.705</td>\n",
+                            "      <td>0</td>\n",
+                            "      <td>no</td>\n",
+                            "      <td>northwest</td>\n",
+                            "      <td>21984.47061</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>4</th>\n",
+                            "      <td>32</td>\n",
+                            "      <td>male</td>\n",
+                            "      <td>28.880</td>\n",
+                            "      <td>0</td>\n",
+                            "      <td>no</td>\n",
+                            "      <td>northwest</td>\n",
+                            "      <td>3866.85520</td>\n",
+                            "    </tr>\n",
+                            "  </tbody>\n",
+                            "</table>\n",
+                            "</div>"
+                        ],
+                        "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"
+                        ]
+                    },
+                    "execution_count": 2,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "total_data = pd.read_csv(\"https://raw.githubusercontent.com/4GeeksAcademy/linear-regression-project-tutorial/main/medical_insurance_cost.csv\")\n",
+                "total_data.head()"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 3,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "text/html": [
+                            "<div>\n",
+                            "<style scoped>\n",
+                            "    .dataframe tbody tr th:only-of-type {\n",
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+                            "    }\n",
+                            "\n",
+                            "    .dataframe tbody tr th {\n",
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+                            "    }\n",
+                            "\n",
+                            "    .dataframe thead th {\n",
+                            "        text-align: right;\n",
+                            "    }\n",
+                            "</style>\n",
+                            "<table border=\"1\" class=\"dataframe\">\n",
+                            "  <thead>\n",
+                            "    <tr style=\"text-align: right;\">\n",
+                            "      <th></th>\n",
+                            "      <th>age</th>\n",
+                            "      <th>sex</th>\n",
+                            "      <th>bmi</th>\n",
+                            "      <th>children</th>\n",
+                            "      <th>smoker</th>\n",
+                            "      <th>region</th>\n",
+                            "      <th>charges</th>\n",
+                            "    </tr>\n",
+                            "  </thead>\n",
+                            "  <tbody>\n",
+                            "    <tr>\n",
+                            "      <th>0</th>\n",
+                            "      <td>19</td>\n",
+                            "      <td>female</td>\n",
+                            "      <td>27.900</td>\n",
+                            "      <td>0</td>\n",
+                            "      <td>yes</td>\n",
+                            "      <td>southwest</td>\n",
+                            "      <td>16884.92400</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>1</th>\n",
+                            "      <td>18</td>\n",
+                            "      <td>male</td>\n",
+                            "      <td>33.770</td>\n",
+                            "      <td>1</td>\n",
+                            "      <td>no</td>\n",
+                            "      <td>southeast</td>\n",
+                            "      <td>1725.55230</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>2</th>\n",
+                            "      <td>28</td>\n",
+                            "      <td>male</td>\n",
+                            "      <td>33.000</td>\n",
+                            "      <td>3</td>\n",
+                            "      <td>no</td>\n",
+                            "      <td>southeast</td>\n",
+                            "      <td>4449.46200</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>3</th>\n",
+                            "      <td>33</td>\n",
+                            "      <td>male</td>\n",
+                            "      <td>22.705</td>\n",
+                            "      <td>0</td>\n",
+                            "      <td>no</td>\n",
+                            "      <td>northwest</td>\n",
+                            "      <td>21984.47061</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>4</th>\n",
+                            "      <td>32</td>\n",
+                            "      <td>male</td>\n",
+                            "      <td>28.880</td>\n",
+                            "      <td>0</td>\n",
+                            "      <td>no</td>\n",
+                            "      <td>northwest</td>\n",
+                            "      <td>3866.85520</td>\n",
+                            "    </tr>\n",
+                            "  </tbody>\n",
+                            "</table>\n",
+                            "</div>"
+                        ],
+                        "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"
+                        ]
+                    },
+                    "execution_count": 3,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "total_data = total_data.drop_duplicates().reset_index(drop = True)\n",
+                "total_data.head()"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 5,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "text/html": [
+                            "<div>\n",
+                            "<style scoped>\n",
+                            "    .dataframe tbody tr th:only-of-type {\n",
+                            "        vertical-align: middle;\n",
+                            "    }\n",
+                            "\n",
+                            "    .dataframe tbody tr th {\n",
+                            "        vertical-align: top;\n",
+                            "    }\n",
+                            "\n",
+                            "    .dataframe thead th {\n",
+                            "        text-align: right;\n",
+                            "    }\n",
+                            "</style>\n",
+                            "<table border=\"1\" class=\"dataframe\">\n",
+                            "  <thead>\n",
+                            "    <tr style=\"text-align: right;\">\n",
+                            "      <th></th>\n",
+                            "      <th>age</th>\n",
+                            "      <th>bmi</th>\n",
+                            "      <th>children</th>\n",
+                            "      <th>sex_n</th>\n",
+                            "      <th>smoker_n</th>\n",
+                            "      <th>region_n</th>\n",
+                            "      <th>charges</th>\n",
+                            "    </tr>\n",
+                            "  </thead>\n",
+                            "  <tbody>\n",
+                            "    <tr>\n",
+                            "      <th>0</th>\n",
+                            "      <td>0.021739</td>\n",
+                            "      <td>0.321227</td>\n",
+                            "      <td>0.0</td>\n",
+                            "      <td>0.0</td>\n",
+                            "      <td>0.0</td>\n",
+                            "      <td>0.000000</td>\n",
+                            "      <td>0.251611</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>1</th>\n",
+                            "      <td>0.000000</td>\n",
+                            "      <td>0.479150</td>\n",
+                            "      <td>0.2</td>\n",
+                            "      <td>1.0</td>\n",
+                            "      <td>1.0</td>\n",
+                            "      <td>0.333333</td>\n",
+                            "      <td>0.009636</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>2</th>\n",
+                            "      <td>0.217391</td>\n",
+                            "      <td>0.458434</td>\n",
+                            "      <td>0.6</td>\n",
+                            "      <td>1.0</td>\n",
+                            "      <td>1.0</td>\n",
+                            "      <td>0.333333</td>\n",
+                            "      <td>0.053115</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>3</th>\n",
+                            "      <td>0.326087</td>\n",
+                            "      <td>0.181464</td>\n",
+                            "      <td>0.0</td>\n",
+                            "      <td>1.0</td>\n",
+                            "      <td>1.0</td>\n",
+                            "      <td>0.666667</td>\n",
+                            "      <td>0.333010</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>4</th>\n",
+                            "      <td>0.304348</td>\n",
+                            "      <td>0.347592</td>\n",
+                            "      <td>0.0</td>\n",
+                            "      <td>1.0</td>\n",
+                            "      <td>1.0</td>\n",
+                            "      <td>0.666667</td>\n",
+                            "      <td>0.043816</td>\n",
+                            "    </tr>\n",
+                            "  </tbody>\n",
+                            "</table>\n",
+                            "</div>"
+                        ],
+                        "text/plain": [
+                            "        age       bmi  children  sex_n  smoker_n  region_n   charges\n",
+                            "0  0.021739  0.321227       0.0    0.0       0.0  0.000000  0.251611\n",
+                            "1  0.000000  0.479150       0.2    1.0       1.0  0.333333  0.009636\n",
+                            "2  0.217391  0.458434       0.6    1.0       1.0  0.333333  0.053115\n",
+                            "3  0.326087  0.181464       0.0    1.0       1.0  0.666667  0.333010\n",
+                            "4  0.304348  0.347592       0.0    1.0       1.0  0.666667  0.043816"
+                        ]
+                    },
+                    "execution_count": 5,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "total_data[\"sex_n\"] = pd.factorize(total_data[\"sex\"])[0]\n",
+                "total_data[\"smoker_n\"] = pd.factorize(total_data[\"smoker\"])[0]\n",
+                "total_data[\"region_n\"] = pd.factorize(total_data[\"region\"])[0]\n",
+                "num_variables = [\"age\", \"bmi\", \"children\", \"sex_n\", \"smoker_n\", \"region_n\", \"charges\"]\n",
+                "\n",
+                "scaler = MinMaxScaler()\n",
+                "scal_features = scaler.fit_transform(total_data[num_variables])\n",
+                "total_data_scal = pd.DataFrame(scal_features, index = total_data.index, columns = num_variables)\n",
+                "total_data_scal.head()"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 7,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "text/html": [
+                            "<div>\n",
+                            "<style scoped>\n",
+                            "    .dataframe tbody tr th:only-of-type {\n",
+                            "        vertical-align: middle;\n",
+                            "    }\n",
+                            "\n",
+                            "    .dataframe tbody tr th {\n",
+                            "        vertical-align: top;\n",
+                            "    }\n",
+                            "\n",
+                            "    .dataframe thead th {\n",
+                            "        text-align: right;\n",
+                            "    }\n",
+                            "</style>\n",
+                            "<table border=\"1\" class=\"dataframe\">\n",
+                            "  <thead>\n",
+                            "    <tr style=\"text-align: right;\">\n",
+                            "      <th></th>\n",
+                            "      <th>age</th>\n",
+                            "      <th>bmi</th>\n",
+                            "      <th>children</th>\n",
+                            "      <th>smoker_n</th>\n",
+                            "    </tr>\n",
+                            "  </thead>\n",
+                            "  <tbody>\n",
+                            "    <tr>\n",
+                            "      <th>0</th>\n",
+                            "      <td>0.108696</td>\n",
+                            "      <td>0.230024</td>\n",
+                            "      <td>0.0</td>\n",
+                            "      <td>1.0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>1</th>\n",
+                            "      <td>0.065217</td>\n",
+                            "      <td>0.263250</td>\n",
+                            "      <td>0.4</td>\n",
+                            "      <td>1.0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>2</th>\n",
+                            "      <td>0.739130</td>\n",
+                            "      <td>0.580172</td>\n",
+                            "      <td>0.4</td>\n",
+                            "      <td>1.0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>3</th>\n",
+                            "      <td>0.978261</td>\n",
+                            "      <td>0.686306</td>\n",
+                            "      <td>0.0</td>\n",
+                            "      <td>1.0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>4</th>\n",
+                            "      <td>0.630435</td>\n",
+                            "      <td>0.286252</td>\n",
+                            "      <td>0.4</td>\n",
+                            "      <td>1.0</td>\n",
+                            "    </tr>\n",
+                            "  </tbody>\n",
+                            "</table>\n",
+                            "</div>"
+                        ],
+                        "text/plain": [
+                            "        age       bmi  children  smoker_n\n",
+                            "0  0.108696  0.230024       0.0       1.0\n",
+                            "1  0.065217  0.263250       0.4       1.0\n",
+                            "2  0.739130  0.580172       0.4       1.0\n",
+                            "3  0.978261  0.686306       0.0       1.0\n",
+                            "4  0.630435  0.286252       0.4       1.0"
+                        ]
+                    },
+                    "execution_count": 7,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "X = total_data_scal.drop(\"charges\", axis = 1)\n",
+                "y = total_data_scal[\"charges\"]\n",
+                "\n",
+                "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)\n",
+                "\n",
+                "selection_model = SelectKBest(f_regression, k = 4)\n",
+                "selection_model.fit(X_train, y_train)\n",
+                "\n",
+                "selected_columns = X_train.columns[selection_model.get_support()]\n",
+                "X_train_sel = pd.DataFrame(selection_model.transform(X_train), columns = selected_columns)\n",
+                "X_test_sel = pd.DataFrame(selection_model.transform(X_test), columns = selected_columns)\n",
+                "\n",
+                "X_train_sel.head()"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 8,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "text/html": [
+                            "<div>\n",
+                            "<style scoped>\n",
+                            "    .dataframe tbody tr th:only-of-type {\n",
+                            "        vertical-align: middle;\n",
+                            "    }\n",
+                            "\n",
+                            "    .dataframe tbody tr th {\n",
+                            "        vertical-align: top;\n",
+                            "    }\n",
+                            "\n",
+                            "    .dataframe thead th {\n",
+                            "        text-align: right;\n",
+                            "    }\n",
+                            "</style>\n",
+                            "<table border=\"1\" class=\"dataframe\">\n",
+                            "  <thead>\n",
+                            "    <tr style=\"text-align: right;\">\n",
+                            "      <th></th>\n",
+                            "      <th>age</th>\n",
+                            "      <th>bmi</th>\n",
+                            "      <th>children</th>\n",
+                            "      <th>smoker_n</th>\n",
+                            "    </tr>\n",
+                            "  </thead>\n",
+                            "  <tbody>\n",
+                            "    <tr>\n",
+                            "      <th>0</th>\n",
+                            "      <td>0.673913</td>\n",
+                            "      <td>0.176352</td>\n",
+                            "      <td>0.0</td>\n",
+                            "      <td>1.0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>1</th>\n",
+                            "      <td>0.239130</td>\n",
+                            "      <td>0.259349</td>\n",
+                            "      <td>0.8</td>\n",
+                            "      <td>1.0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>2</th>\n",
+                            "      <td>0.717391</td>\n",
+                            "      <td>0.549502</td>\n",
+                            "      <td>0.6</td>\n",
+                            "      <td>1.0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>3</th>\n",
+                            "      <td>0.282609</td>\n",
+                            "      <td>0.495830</td>\n",
+                            "      <td>0.6</td>\n",
+                            "      <td>0.0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>4</th>\n",
+                            "      <td>0.282609</td>\n",
+                            "      <td>0.603444</td>\n",
+                            "      <td>0.4</td>\n",
+                            "      <td>1.0</td>\n",
+                            "    </tr>\n",
+                            "  </tbody>\n",
+                            "</table>\n",
+                            "</div>"
+                        ],
+                        "text/plain": [
+                            "        age       bmi  children  smoker_n\n",
+                            "0  0.673913  0.176352       0.0       1.0\n",
+                            "1  0.239130  0.259349       0.8       1.0\n",
+                            "2  0.717391  0.549502       0.6       1.0\n",
+                            "3  0.282609  0.495830       0.6       0.0\n",
+                            "4  0.282609  0.603444       0.4       1.0"
+                        ]
+                    },
+                    "execution_count": 8,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "X_test_sel.head()"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 9,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "X_train_sel[\"charges\"] = y_train.values\n",
+                "X_test_sel[\"charges\"] = y_test.values\n",
+                "X_train_sel.to_csv(\"../data/processed/clean_train.csv\", index = False)\n",
+                "X_test_sel.to_csv(\"../data/processed/clean_test.csv\", index = False)"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 10,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "text/html": [
+                            "<div>\n",
+                            "<style scoped>\n",
+                            "    .dataframe tbody tr th:only-of-type {\n",
+                            "        vertical-align: middle;\n",
+                            "    }\n",
+                            "\n",
+                            "    .dataframe tbody tr th {\n",
+                            "        vertical-align: top;\n",
+                            "    }\n",
+                            "\n",
+                            "    .dataframe thead th {\n",
+                            "        text-align: right;\n",
+                            "    }\n",
+                            "</style>\n",
+                            "<table border=\"1\" class=\"dataframe\">\n",
+                            "  <thead>\n",
+                            "    <tr style=\"text-align: right;\">\n",
+                            "      <th></th>\n",
+                            "      <th>age</th>\n",
+                            "      <th>bmi</th>\n",
+                            "      <th>children</th>\n",
+                            "      <th>smoker_n</th>\n",
+                            "      <th>charges</th>\n",
+                            "    </tr>\n",
+                            "  </thead>\n",
+                            "  <tbody>\n",
+                            "    <tr>\n",
+                            "      <th>0</th>\n",
+                            "      <td>0.108696</td>\n",
+                            "      <td>0.230024</td>\n",
+                            "      <td>0.0</td>\n",
+                            "      <td>1.0</td>\n",
+                            "      <td>0.020339</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>1</th>\n",
+                            "      <td>0.065217</td>\n",
+                            "      <td>0.263250</td>\n",
+                            "      <td>0.4</td>\n",
+                            "      <td>1.0</td>\n",
+                            "      <td>0.034446</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>2</th>\n",
+                            "      <td>0.739130</td>\n",
+                            "      <td>0.580172</td>\n",
+                            "      <td>0.4</td>\n",
+                            "      <td>1.0</td>\n",
+                            "      <td>0.516374</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>3</th>\n",
+                            "      <td>0.978261</td>\n",
+                            "      <td>0.686306</td>\n",
+                            "      <td>0.0</td>\n",
+                            "      <td>1.0</td>\n",
+                            "      <td>0.196070</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>4</th>\n",
+                            "      <td>0.630435</td>\n",
+                            "      <td>0.286252</td>\n",
+                            "      <td>0.4</td>\n",
+                            "      <td>1.0</td>\n",
+                            "      <td>0.137177</td>\n",
+                            "    </tr>\n",
+                            "  </tbody>\n",
+                            "</table>\n",
+                            "</div>"
+                        ],
+                        "text/plain": [
+                            "        age       bmi  children  smoker_n   charges\n",
+                            "0  0.108696  0.230024       0.0       1.0  0.020339\n",
+                            "1  0.065217  0.263250       0.4       1.0  0.034446\n",
+                            "2  0.739130  0.580172       0.4       1.0  0.516374\n",
+                            "3  0.978261  0.686306       0.0       1.0  0.196070\n",
+                            "4  0.630435  0.286252       0.4       1.0  0.137177"
+                        ]
+                    },
+                    "execution_count": 10,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "train_data = pd.read_csv(\"../data/processed/clean_train.csv\")\n",
+                "test_data = pd.read_csv(\"../data/processed/clean_test.csv\")\n",
+                "\n",
+                "train_data.head()"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 12,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 1000x1400 with 8 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "fig, axis = plt.subplots(4, 2, figsize = (10, 14))\n",
+                "total_data = pd.concat([train_data, test_data])\n",
+                "\n",
+                "sns.regplot(data = total_data, x = \"age\", y = \"charges\", ax = axis[0, 0])\n",
+                "sns.heatmap(total_data[[\"charges\", \"age\"]].corr(), annot = True, fmt = \".2f\", ax = axis[1, 0], cbar = False)\n",
+                "\n",
+                "sns.regplot(data = total_data, x = \"bmi\", y = \"charges\", ax = axis[0, 1])\n",
+                "sns.heatmap(total_data[[\"charges\", \"bmi\"]].corr(), annot = True, fmt = \".2f\", ax = axis[1, 1], cbar = False)\n",
+                "\n",
+                "sns.regplot(data = total_data, x = \"children\", y = \"charges\", ax = axis[2, 0])\n",
+                "sns.heatmap(total_data[[\"charges\", \"children\"]].corr(), annot = True, fmt = \".2f\", ax = axis[3, 0], cbar = False)\n",
+                "\n",
+                "sns.regplot(data = total_data, x = \"smoker_n\", y = \"charges\", ax = axis[2, 1])\n",
+                "sns.heatmap(total_data[[\"charges\", \"smoker_n\"]].corr(), annot = True, fmt = \".2f\", ax = axis[3, 1], cbar = False)\n",
+                "\n",
+                "plt.tight_layout()\n",
+                "plt.show()"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 13,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "X_train = train_data.drop([\"charges\"], axis = 1)\n",
+                "y_train = train_data[\"charges\"]\n",
+                "X_test = test_data.drop([\"charges\"], axis = 1)\n",
+                "y_test = test_data[\"charges\"]"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 15,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "text/html": [
+                            "<style>#sk-container-id-1 {color: black;}#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>"
+                        ],
+                        "text/plain": [
+                            "LinearRegression()"
+                        ]
+                    },
+                    "execution_count": 15,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "model = LinearRegression()\n",
+                "model.fit(X_train, y_train)"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 16,
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Intercep (a): 0.3195827183308946\n",
+                        "Coefficients (b1, b2): [ 0.1829699   0.18111814  0.04293572 -0.36780589]\n"
+                    ]
+                }
+            ],
+            "source": [
+                "print(f\"Intercep (a): {model.intercept_}\")\n",
+                "print(f\"Coefficients (b1, b2): {model.coef_}\")"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 17,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "text/plain": [
+                            "array([ 0.10702315,  0.07685187,  0.2083241 ,  0.48685684,  0.12995459,\n",
+                            "        0.19726353,  0.45755859,  0.00832554,  0.1556988 ,  0.16287349,\n",
+                            "        0.14781084,  0.51037008,  0.4699569 ,  0.25944432,  0.14442757,\n",
+                            "        0.13842465,  0.05134994,  0.49351746,  0.03848664,  0.0705325 ,\n",
+                            "        0.04276279,  0.45855586,  0.22245537,  0.47182868,  0.47460549,\n",
+                            "        0.06698449,  0.55116209,  0.5686984 ,  0.15588314,  0.20127518,\n",
+                            "        0.0770878 ,  0.19051001, -0.00377298,  0.17967676,  0.61602286,\n",
+                            "        0.18050297,  0.06028104,  0.0447116 ,  0.48287007,  0.13064786,\n",
+                            "        0.08387671,  0.45610213,  0.54228697,  0.17124485,  0.09976616,\n",
+                            "        0.03950023,  0.07028081,  0.12772953,  0.04874519,  0.13229562,\n",
+                            "        0.09175793,  0.1653832 ,  0.47297483,  0.04437078,  0.16108797,\n",
+                            "        0.14414371,  0.14902535,  0.02014774,  0.48426463,  0.13172362,\n",
+                            "        0.2337337 ,  0.11691   ,  0.18103216,  0.00130779,  0.25281818,\n",
+                            "        0.14612565,  0.14268127,  0.47671561,  0.38076578,  0.23721802,\n",
+                            "        0.0985881 ,  0.02074985,  0.09073304, -0.02353705,  0.16236641,\n",
+                            "       -0.03138165,  0.2565207 ,  0.12504588,  0.10968997,  0.18040282,\n",
+                            "        0.06744998,  0.2799815 ,  0.22113857,  0.16267796,  0.55597538,\n",
+                            "        0.19898383,  0.04579872,  0.02570684,  0.07916184,  0.05255163,\n",
+                            "        0.22816448,  0.43057083,  0.07512691,  0.03748228,  0.01880713,\n",
+                            "        0.54721403,  0.21529555,  0.12903987,  0.05446998,  0.11569352,\n",
+                            "        0.20795263,  0.22682342,  0.50110515,  0.21732695,  0.50137854,\n",
+                            "       -0.01600829,  0.46957981,  0.41332418,  0.2017602 ,  0.08226512,\n",
+                            "        0.11683646,  0.16550402,  0.1082546 ,  0.13626428,  0.08239053,\n",
+                            "        0.07260913,  0.01202092,  0.03641557,  0.09811134,  0.13316956,\n",
+                            "        0.56050484,  0.10541088,  0.17146834,  0.13462028,  0.12469681,\n",
+                            "        0.13956866,  0.06124501,  0.18245571,  0.46131889,  0.60450743,\n",
+                            "        0.55450085,  0.16795516,  0.02727505,  0.16407366,  0.14898202,\n",
+                            "        0.06111621,  0.53502378,  0.45241841,  0.38651557,  0.06349719,\n",
+                            "        0.09207573,  0.06604174,  0.56637323,  0.14984842,  0.07447133,\n",
+                            "        0.11736278,  0.14915453,  0.12001266,  0.54423909,  0.20548556,\n",
+                            "        0.14030485,  0.08597912,  0.09883229,  0.25042454,  0.10036146,\n",
+                            "        0.40712707,  0.56650365,  0.23678639,  0.14502383,  0.15821719,\n",
+                            "        0.17644215,  0.0423529 ,  0.18420881,  0.19605582,  0.54877436,\n",
+                            "        0.50657843,  0.19343802,  0.38914002,  0.01618966,  0.01030186,\n",
+                            "        0.06067801,  0.06482339,  0.05783428,  0.14406415,  0.14014174,\n",
+                            "        0.40123872,  0.02316775,  0.15574376,  0.09330499,  0.16907982,\n",
+                            "        0.21960806,  0.0603543 ,  0.16364783,  0.14208435,  0.50619874,\n",
+                            "        0.15588573,  0.1004351 ,  0.07899524, -0.01230504,  0.48757654,\n",
+                            "        0.10321602,  0.50321841,  0.17524198,  0.08246532,  0.0022336 ,\n",
+                            "        0.02087167,  0.4976116 ,  0.0649581 ,  0.19845904,  0.25182294,\n",
+                            "        0.40132856,  0.39821218,  0.09139659,  0.18197902,  0.44013825,\n",
+                            "        0.11044732,  0.14375918,  0.14947295,  0.03091685,  0.53419087,\n",
+                            "        0.48852993,  0.13659058,  0.08001097,  0.10293763,  0.16059531,\n",
+                            "        0.06053542,  0.05999413,  0.16999533,  0.12997991,  0.02167566,\n",
+                            "        0.0411866 ,  0.5853076 ,  0.57626919,  0.19889381,  0.16018383,\n",
+                            "        0.58987134,  0.52417146,  0.09322178,  0.40522036,  0.04790518,\n",
+                            "        0.23823008,  0.45553524,  0.07344824,  0.1356301 ,  0.11847613,\n",
+                            "        0.13415772,  0.13040895,  0.05356746,  0.06988678,  0.06020767,\n",
+                            "        0.08525029,  0.05719976,  0.07077613,  0.12603303,  0.00065633,\n",
+                            "        0.11607321,  0.0460198 ,  0.15928356,  0.01612079,  0.14595144,\n",
+                            "        0.04164691,  0.1644924 ,  0.18188899,  0.20536071,  0.57084948,\n",
+                            "        0.34689418,  0.13745529,  0.10007179,  0.12707503,  0.17492857,\n",
+                            "        0.44912095,  0.5917897 ,  0.22352118,  0.25422525,  0.22987977,\n",
+                            "        0.07559116,  0.59831759,  0.10786316])"
+                        ]
+                    },
+                    "execution_count": 17,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "y_pred = model.predict(X_test)\n",
+                "y_pred"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 19,
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "MSE: 0.009150574110332004\n",
+                        "R2 Score: 0.8045531086669286\n"
+                    ]
+                }
+            ],
+            "source": [
+                "print(f\"MSE: {mean_squared_error(y_test, y_pred)}\")\n",
+                "print(f\"R2 Score: {r2_score(y_test, y_pred)}\")"
             ]
         }
     ],
@@ -33,7 +861,7 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.8.13"
+            "version": "3.11.4"
         },
         "orig_nbformat": 4,
         "vscode": {