ml-project-1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a8d30539",
   "metadata": {},
   "source": [
    "# Importing the required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4ded4c8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "import seaborn as sns\n",
    "import joblib as jb\n",
    "from sklearn.linear_model import LinearRegression as lr\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split as tts\n",
    "from sklearn.preprocessing import MinMaxScaler,StandardScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cef01a8",
   "metadata": {},
   "source": [
    "# Load dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0e7d3038",
   "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": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(r\"E:\\AI&ML\\ml datasets\\insurance.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe608d6f",
   "metadata": {},
   "source": [
    "# Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c03a9509",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age         1338\n",
      "sex         1338\n",
      "bmi         1338\n",
      "children    1338\n",
      "smoker      1338\n",
      "region      1338\n",
      "charges     1338\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "age         0\n",
       "sex         0\n",
       "bmi         0\n",
       "children    0\n",
       "smoker      0\n",
       "region      0\n",
       "charges     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df.count())\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fc4524ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>charges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1338.000000</td>\n",
       "      <td>1338.000000</td>\n",
       "      <td>1338.000000</td>\n",
       "      <td>1338.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>39.207025</td>\n",
       "      <td>30.663397</td>\n",
       "      <td>1.094918</td>\n",
       "      <td>13270.422265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.049960</td>\n",
       "      <td>6.098187</td>\n",
       "      <td>1.205493</td>\n",
       "      <td>12110.011237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>15.960000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1121.873900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>26.296250</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4740.287150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>30.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9382.033000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>51.000000</td>\n",
       "      <td>34.693750</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>16639.912515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>64.000000</td>\n",
       "      <td>53.130000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>63770.428010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age          bmi     children       charges\n",
       "count  1338.000000  1338.000000  1338.000000   1338.000000\n",
       "mean     39.207025    30.663397     1.094918  13270.422265\n",
       "std      14.049960     6.098187     1.205493  12110.011237\n",
       "min      18.000000    15.960000     0.000000   1121.873900\n",
       "25%      27.000000    26.296250     0.000000   4740.287150\n",
       "50%      39.000000    30.400000     1.000000   9382.033000\n",
       "75%      51.000000    34.693750     2.000000  16639.912515\n",
       "max      64.000000    53.130000     5.000000  63770.428010"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe() # we found to do scaling in the target!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ef46a5b1",
   "metadata": {},
   "outputs": [
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mbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,0.49],\"title\":{\"text\":\"bmi\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,0.7326],\"title\":{\"text\":\"count\"}},\"xaxis2\":{\"anchor\":\"y2\",\"domain\":[0.51,1.0],\"matches\":\"x\",\"title\":{\"text\":\"bmi\"}},\"yaxis2\":{\"anchor\":\"x2\",\"domain\":[0.0,0.7326],\"matches\":\"y\",\"showticklabels\":false},\"xaxis3\":{\"anchor\":\"y3\",\"domain\":[0.0,0.49],\"matches\":\"x\",\"showticklabels\":false,\"showgrid\":true},\"yaxis3\":{\"anchor\":\"x3\",\"domain\":[0.7426,1.0],\"matches\":\"y3\",\"showticklabels\":false,\"showline\":false,\"ticks\":\"\",\"showgrid\":false},\"xaxis4\":{\"anchor\":\"y4\",\"domain\":[0.51,1.0],\"matches\":\"x\",\"showticklabels\":false,\"showgrid\":true},\"yaxis4\":{\"anchor\":\"x4\",\"domain\":[0.7426,1.0],\"matches\":\"y3\",\"showticklabels\":false,\"showline\":false,\"ticks\":\"\",\"showgrid\":false},\"annotations\":[{\"font\":{},\"showarrow\":false,\"text\":\"sex=female\",\"x\":0.245,\"xanchor\":\"center\",\"xref\":\"paper\",\"y\":1.0,\"yanchor\":\"bottom\",\"yref\":\"paper\"},{\"font\":{},\"showarrow\":false,\"text\":\"sex=male\",\"x\":0.755,\"xanchor\":\"center\",\"xref\":\"paper\",\"y\":1.0,\"yanchor\":\"bottom\",\"yref\":\"paper\"}],\"legend\":{\"title\":{\"text\":\"smoker\"},\"tracegroupgap\":0},\"title\":{\"text\":\"distirbution of age\"},\"barmode\":\"relative\",\"bargap\":0.1},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('518804ce-1a94-464e-a77e-b75df2eded06');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=px.histogram(df, #this graphs abbove and this are useful if they ask they data analysis stuff!!!!!\n",
    "             x=\"bmi\",\n",
    "             marginal=\"box\",\n",
    "             color=\"smoker\",\n",
    "             facet_col='sex',\n",
    "             title=\"distirbution of age\")\n",
    "fig.update_layout(bargap=0.1)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2fa4f3c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "# Plotting Age vs Charges\n",
    "plt.subplot(2, 2, 1)\n",
    "plt.scatter(df.age, df.charges)\n",
    "plt.title('Age vs Charges')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Charges')\n",
    "\n",
    "# Plotting BMI vs Charges\n",
    "plt.subplot(2, 2, 2)\n",
    "plt.scatter(df.bmi, df.charges)\n",
    "plt.title('BMI vs Charges')\n",
    "plt.xlabel('BMI')\n",
    "plt.ylabel('Charges')\n",
    "\n",
    "\n",
    "# Display the plots\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "# from this "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8eb72ff6",
   "metadata": {},
   "source": [
    "### KFold technique used to overcome the varience in the data and helps in coming up  with the average model score and prediction of the model developed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0fc930fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KFold(n_splits=3, random_state=None, shuffle=False)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "kf=KFold(n_splits=3)\n",
    "kf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "549a7a04",
   "metadata": {},
   "source": [
    "### Labelencoder method used to convert the other data type into numeric type "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2649d29b",
   "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>0</td>\n",
       "      <td>27.900</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16884.92400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>33.770</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1725.55230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>33.000</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4449.46200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>22.705</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>21984.47061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>28.880</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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    0  27.900         0       1       3  16884.92400\n",
       "1   18    1  33.770         1       0       2   1725.55230\n",
       "2   28    1  33.000         3       0       2   4449.46200\n",
       "3   33    1  22.705         0       0       1  21984.47061\n",
       "4   32    1  28.880         0       0       1   3866.85520"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le=LabelEncoder()\n",
    "df[\"region\"]=le.fit_transform(df[\"region\"])\n",
    "df[\"sex\"]=le.fit_transform(df[\"sex\"])\n",
    "df[\"smoker\"]=le.fit_transform(df[\"smoker\"])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "42f1422b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#feature engineering techniques to overcome the outliers and make the model more efficient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f10b4a19",
   "metadata": {},
   "outputs": [],
   "source": [
    "fe=df.bmi.quantile(0.95)\n",
    "df=df[df['bmi']<=fe]\n",
    "fee=df.bmi.quantile(0.05)\n",
    "df=df[df['bmi']>=fee]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee8692c5",
   "metadata": {},
   "source": [
    "#### Standard scalar helps in scaling the data from 0-1 so that data can be used efficiently"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6672c497",
   "metadata": {},
   "outputs": [],
   "source": [
    "mms = StandardScaler()\n",
    "mms.fit(df[['charges']])\n",
    "df['charges'] = mms.transform(df[['charges']])\n",
    "mms.fit(df[['age']])\n",
    "df['age'] = mms.transform(df[['age']])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fd60cb6",
   "metadata": {},
   "source": [
    "## data split for Training and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5865ab77",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df.drop(['sex','children','region','charges'],axis='columns')\n",
    "x_train, x_test, y_train, y_test=tts(x,df.charges,test_size=0.25)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18e93349",
   "metadata": {},
   "source": [
    "## Model implimentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "2555a1b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" 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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" 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": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=lr()\n",
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87e51838",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "#### model performance in the training dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "62d755c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7529881771970024"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b16a441",
   "metadata": {},
   "source": [
    "#### model prediction in test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "5b779320",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7217113633501864"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "8dfa8fa3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#save the model using joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a805f11",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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