our project modeling V06.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " > ## Importing libraries and data¶\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#visualization\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "#Linear Regression\n",
    "from scipy.stats import linregress\n",
    "\n",
    "#Train_test_split\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#cross_val_score\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "#scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import precision_score,recall_score, confusion_matrix, classification_report,accuracy_score, f1_score\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import cross_val_score \n",
    "import matplotlib.pyplot as plt\n",
    "#models\n",
    "\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from xgboost import XGBRegressor\n",
    "from catboost import CatBoostRegressor\n",
    "#score\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "#Importing data\n",
    "df = pd.read_csv(\"insurance.csv\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ## Analyze by describing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\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": [
    "# take a look at the data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>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"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# statistical measuers\n",
    "display(df.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1338 entries, 0 to 1337\n",
      "Data columns (total 7 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       1338 non-null   int64  \n",
      " 1   sex       1338 non-null   object \n",
      " 2   bmi       1338 non-null   float64\n",
      " 3   children  1338 non-null   int64  \n",
      " 4   smoker    1338 non-null   object \n",
      " 5   region    1338 non-null   object \n",
      " 6   charges   1338 non-null   float64\n",
      "dtypes: float64(2), int64(2), object(3)\n",
      "memory usage: 73.3+ KB\n"
     ]
    }
   ],
   "source": [
    "# Getting some informations about the dataset\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Findings:\n",
    "\n",
    "There are four numerical variables:\n",
    "    - Continuous:\n",
    "    - Age\n",
    "    - BMI\n",
    "    - Charges\n",
    "    - Discrete:\n",
    "    - children  \n",
    "There are three categorical variables:\n",
    "    - Sex\n",
    "    - Smoker\n",
    "    - Region"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### is there is a relationship between the features and each other?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "sns.heatmap(df.corr(), annot=True, fmt='.1g', cmap=\"viridis\", cbar=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hmmm... not much"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ## It's time to prepare our data 🙌🏻:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### checking the missing data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No missing data, cool!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Does it indicate that the data is really stable? check the outliers..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " first we will need some statistical information..."
   ]
  },
  {
   "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>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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zeado\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='bmi'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bmi\n",
    "sns.boxplot(df['bmi'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### i got them 😈"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26.29625, 34.69375)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q1 = df.bmi.quantile(0.25)\n",
    "Q3 = df.bmi.quantile(0.75)\n",
    "Q1, Q3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.3975"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IQR = Q3 - Q1\n",
    "IQR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13.7, 47.290000000000006)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower_limit = Q1 - 1.5*IQR\n",
    "upper_limit = Q3 + 1.5*IQR\n",
    "lower_limit, upper_limit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's find out which this data is.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>116</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>49.06</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>11381.32540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>46</td>\n",
       "      <td>female</td>\n",
       "      <td>48.07</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>9432.92530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>47</td>\n",
       "      <td>male</td>\n",
       "      <td>47.52</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>8083.91980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>54</td>\n",
       "      <td>female</td>\n",
       "      <td>47.41</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southeast</td>\n",
       "      <td>63770.42801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>847</th>\n",
       "      <td>23</td>\n",
       "      <td>male</td>\n",
       "      <td>50.38</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>2438.05520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>37</td>\n",
       "      <td>female</td>\n",
       "      <td>47.60</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>46113.51100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1047</th>\n",
       "      <td>22</td>\n",
       "      <td>male</td>\n",
       "      <td>52.58</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>southeast</td>\n",
       "      <td>44501.39820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088</th>\n",
       "      <td>52</td>\n",
       "      <td>male</td>\n",
       "      <td>47.74</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>9748.91060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1317</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>53.13</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1163.46270</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      age     sex    bmi  children smoker     region      charges\n",
       "116    58    male  49.06         0     no  southeast  11381.32540\n",
       "286    46  female  48.07         2     no  northeast   9432.92530\n",
       "401    47    male  47.52         1     no  southeast   8083.91980\n",
       "543    54  female  47.41         0    yes  southeast  63770.42801\n",
       "847    23    male  50.38         1     no  southeast   2438.05520\n",
       "860    37  female  47.60         2    yes  southwest  46113.51100\n",
       "1047   22    male  52.58         1    yes  southeast  44501.39820\n",
       "1088   52    male  47.74         1     no  southeast   9748.91060\n",
       "1317   18    male  53.13         0     no  southeast   1163.46270"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['bmi'] > upper_limit]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "only 9 records! we can get red of them safely "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new = df.drop(df.index[[116,401,286,543,847,860,1047,1088,1317]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### The rest of the features are stable, thank God ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> # DEA "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### take a look at the features and its effect in the target \"charges\" ...\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(14,6))\n",
    "ax = f.add_subplot(122)\n",
    "sns.scatterplot(x='bmi',y='charges',data=df,palette='viridis',hue='smoker')\n",
    "ax.set_title('Scatter plot of Charges vs bmi')\n",
    "plt.savefig('sc.png');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### insights:\n",
    "- Most of the people who smoke are obese people\n",
    "- Even though a person is not obese but he/she smokes, his/her charges tends to be higher.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32.300    13\n",
       "28.310     9\n",
       "30.800     8\n",
       "34.100     8\n",
       "28.880     8\n",
       "          ..\n",
       "44.745     1\n",
       "26.070     1\n",
       "27.300     1\n",
       "37.715     1\n",
       "29.200     1\n",
       "Name: bmi, Length: 548, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"bmi\"].value_counts()   # Body mass index, providing an understanding of body, ideally 18.5 to 24.9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### I see that It will be clearer if we classify people into categories according to their \"bmi\" 💡"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['weight_condition'] = np.nan\n",
    "data = [df]\n",
    "\n",
    "for col in data:\n",
    "    col.loc[col.bmi < 18.5, \"weight_condition\"] = \"Underweight\"\n",
    "    col.loc[(col.bmi >= 18.5) & (col.bmi < 24.986), \"weight_condition\"] = \"Normal\"\n",
    "    col.loc[(col.bmi >= 25) & (col.bmi < 29.926), \"weight_condition\"] = \"Overweight\"\n",
    "    col.loc[col.bmi >= 30, \"weight_condition\"] = \"Obese\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "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>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>region</th>\n",
       "      <th>charges</th>\n",
       "      <th>weight_condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>16884.92400</td>\n",
       "      <td>Overweight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1725.55230</td>\n",
       "      <td>Obese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>4449.46200</td>\n",
       "      <td>Obese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>21984.47061</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>3866.85520</td>\n",
       "      <td>Overweight</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age     sex  children smoker     region      charges weight_condition\n",
       "0   19  female         0    yes  southwest  16884.92400       Overweight\n",
       "1   18    male         1     no  southeast   1725.55230            Obese\n",
       "2   28    male         3     no  southeast   4449.46200            Obese\n",
       "3   33    male         0     no  northwest  21984.47061           Normal\n",
       "4   32    male         0     no  northwest   3866.85520       Overweight"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.drop(\"bmi\", axis = 1, inplace = True)\n",
    "display(df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### age VS charges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Scatter plot of Charges vs age')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(14,6))\n",
    "ax = f.add_subplot(121)\n",
    "sns.scatterplot(x='age',y='charges',data=df,palette='magma',hue='smoker',ax=ax)\n",
    "ax.set_title('Scatter plot of Charges vs age')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Insights\n",
    "- Charges increases as the age of the person increases\n",
    "- Smokers and obese people have the highest charges regardless of their age.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### i'll do the same thing with \"age\" so that the data to be follow the same flo 🤙🏻"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['age_cat'] = np.nan\n",
    "lst = [df]\n",
    "\n",
    "for col in lst:\n",
    "    col.loc[(col['age'] >= 18) & (col['age'] <= 35), 'age_cat'] = 'Young Adult'\n",
    "    col.loc[(col['age'] > 35) & (col['age'] <= 55), 'age_cat'] = 'Senior Adult'\n",
    "    col.loc[col['age'] > 55, 'age_cat'] = 'Elder'\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>sex</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>region</th>\n",
       "      <th>charges</th>\n",
       "      <th>weight_condition</th>\n",
       "      <th>age_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>16884.92400</td>\n",
       "      <td>Overweight</td>\n",
       "      <td>Young Adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1725.55230</td>\n",
       "      <td>Obese</td>\n",
       "      <td>Young Adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>4449.46200</td>\n",
       "      <td>Obese</td>\n",
       "      <td>Young Adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>21984.47061</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Young Adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>3866.85520</td>\n",
       "      <td>Overweight</td>\n",
       "      <td>Young Adult</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      sex  children smoker     region      charges weight_condition  \\\n",
       "0  female         0    yes  southwest  16884.92400       Overweight   \n",
       "1    male         1     no  southeast   1725.55230            Obese   \n",
       "2    male         3     no  southeast   4449.46200            Obese   \n",
       "3    male         0     no  northwest  21984.47061           Normal   \n",
       "4    male         0     no  northwest   3866.85520       Overweight   \n",
       "\n",
       "       age_cat  \n",
       "0  Young Adult  \n",
       "1  Young Adult  \n",
       "2  Young Adult  \n",
       "3  Young Adult  \n",
       "4  Young Adult  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(\"age\", axis = 1, inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relation between sex and charges: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use(\"seaborn\")\n",
    "fig, ax = plt.subplots(figsize=(5,5))\n",
    "plt.pie(x=df[\"sex\"].value_counts(), \n",
    "        colors=[\"skyblue\",\"pink\"], \n",
    "        labels=[\"Male\",\"Female\"], \n",
    "        shadow = True, \n",
    "        autopct=\"%1.2f%%\", \n",
    "        explode = (0, 0.1)\n",
    "        )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Almost the same proportion..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zeado\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\zeado\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Cheking the charges distributions for males and females\n",
    "x1 = sns.FacetGrid(df, row='sex', height=4, aspect=3)\n",
    "x1 = x1.map(sns.distplot, 'charges', color='cornflowerblue')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### As we can see the two distributions are almost the same for both women/men, so we can affirm that there is no influence on the medical charges when it comes to the sex variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  let's see how many children our patients have.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zeado\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use(\"seaborn\")\n",
    "fig, ax = plt.subplots(figsize=(6,6))\n",
    "sns.countplot(df[\"children\"], palette=\"hls\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Most patients do not have children"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Creating a violinplot for each category\n",
    "plt.figure(figsize=(12,4))\n",
    "sns.violinplot(x='children', y='charges', data=df, hue='sex', palette='YlGnBu')\n",
    "plt.title('Charges according to number of children', size='23')\n",
    "plt.ylabel('Charges',size=18)\n",
    "plt.xlabel('Number of children',size=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, almost all categories have the same range and mean of costs also the distributions are very similar, except for the people who have 5 children. This might be because of the small size of the sample of this kind of people!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relation between smoking and charges: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PktSoUaMMfzE3btxYLi4uOnXqVLrxvsWLF8/R16z79++XpHRj0lM1adJExYsX161btx5qnGqq1K+S/f39NXPmTO3bt892jvr06aOOHTumCZSpHvX8px5fq1at0oVj6V7QbtasmSTpxx9/TLPOMAyNGjVK//nPf+Tm5qYFCxakCX2SdP36dZ08eVJOTk4Zfl0uSc2bN8+wfUmqWrVqumWp7Xz55ZcaNGiQ1q5da/ujwdvbWz179sxw6MkfeXl5ac6cOXrrrbfSHNOVK1e0e/duhYeHS1KG/348PDxUrly5dMtLliwpSbpz546ke0E+IiLCNkzkj5o0aZLmD5vs8vT0zHA4zB/3f/ToUcXFxal8+fKqVatWhm2l/mGW0fnPaC701H1kNPY8M6nHPnLkSE2ePFm7du2yjZ3u1q2bunTpkuF5yGj/qSE7u9e3YRiqW7duhjchu7i42P6Yyuj4Z82apeDgYFksFi1cuDDdkKnk5GTbv6EmTZpkeOwtW7bMtP2Mru+6devK2dlZR44cUe/evfXxxx/bOksqVqwoPz+/TN9L4FExJhvIhilTpujQoUMKDw/XokWLNGLEiMeyn9RfavdLDb739/L9cV1G7u+RvV+ZMmV0+PBhWw9P6g1HixcvfuB0hZcvX07T81SkSJEst/+ja9euZVlb6rrIyEhdu3Ytw1/82TFt2jQNHz5chw8f1sqVK7Vy5Uq5urqqfv366tSpk5555hk5OaX/+HvU85/d47t/21R37tzRV199JScnJ8XGxurjjz/W0KFD02yT+l4lJSWl6XXMyB9vJJMyfr+8vb0VEBCgWbNm6bvvvtN3330nSapQoYJatWqlnj17ZnuscEpKinbt2qXt27fr5MmTunjxoi2gpZ4rwzDSvS6j8y7J9h6lpKRI+l8PbsmSJTP81sRisahs2bI56hWWpEKFCmVr/4/y/ma2H0dHR0lpz0tgYGCGN3H27t1bvr6+euutt3T58mWFhoZq7dq1Wrt2rZycnFSnTh21b99eL7zwQro/0CT7Xt+S9Omnn8rJyUlJSUlavHixZsyYkWb9rVu3bH9stGnTJtN9SNm/vkuVKqV58+ZpwoQJ+vnnn/Xzzz9LuncNtWzZUt27d5e3t3eW+wIeFiEbyIZixYpp6tSpGjJkiJYvX27rTcmp1F/Wmcko+D2sjHpqpf/9Mv9jgKhbt2664RV/9MfevgcND/mjBx3//du4urrmqO37lSxZUuvWrdORI0e0a9cu/fjjjzp+/Lj27t2rvXv3as2aNQoKCkp3PI96/nNyfBm9P61atdJLL72k/v37a+nSpWrbtm2amzxTX+vu7v7Aa/Dpp59Otyyz98vPz0+dO3dWSEiIvvvuOx04cEDnzp3Txx9/rFWrVmnGjBl65plnstzf3bt3NXDgQIWFhcnJyUlVq1ZV586dValSJXl7e+vQoUN6//33M3ytmTcUP8x7mN39Z/QHwh9ldf1mdz87d+7McFhN48aN5evrKzc3Ny1dulQnT55USEiIvv/+ex05ckQHDhzQgQMH9PHHH2vNmjW2XvJUj3p95+T4M7q+a9WqpcmTJ8vPz09btmxRx44dbd/s3P9aR0dHderUKcv9ZHTjcGbXd9u2bdWkSRPt3r1be/fu1f79+3Xx4kWtX79e69ev16hRo/TKK6888NiAnCJkA9nUqlUrvfDCC9q8ebNGjx6tfv36Zbhd6i/SjMYUZ3d6KzNcvXo1w6+MUx83nRqoU38Rd+nSRX5+fo+1plKlSuns2bM6f/686tatm+E2qUMV7p/95GHVqlXL9lVwbGysdu/erXfeeUfHjh3Tp59+qj59+jzyPu6XOnQmozHyqVLX/XEsar58+TR//nw5OzurT58+Cg4O1rhx47RhwwZbOPLw8JB0L8C99957ptZeuHBhPf/883r++eclSSdPntSyZcu0Y8eObIXslStXKiwsTF5eXlq6dGm64QSpw5IeRWqbV65cUVJSUoahMTtTSD6sR3l/c2LXrl3Z2s5qtcpqtWrIkCFKSEjQvn37NG3aNJ0/f14fffSRxowZ89A1ZCT1s+Jhj3/+/Pl64okn9MYbb+jdd9/VxIkT9fnnn9s+p4oUKSJnZ2clJiZq6tSpGfbGP6wCBQqoU6dOtvB+/vx5BQcH65NPPtH8+fPVq1evTL9RAR4WY7KBHBg/frzKli2r3377zXYD2h+l9o5ev3493bpDhw491vrul9F80OfPn9fx48fl6upqG5+ZOr4zJCQkw3YiIiLUtm1bDRw40DY29WGl7uvzzz/PcP2+ffsUFRUlDw+PDHtis+PChQvq1q1bulDo5uamzp0725ZfunTpodrPSurxhYaGZjjG9tatW7b3pVGjRmnWOTg42G46HTlypMqUKaPw8PA011nZsmVVrlw5RUZG2r72/qOJEyfqueeey/ZDlN5//301b95c27dvT7PcarXabnq9efPmA9/71Gu7Z8+e6QJ2SkqKbRhKdnr7M1OiRAl5eXnp7t272r17d7r1R48ezXCecLPUrFlTBQoU0Pnz53X06NEMt0m9ths2bPhYaoiLi1PPnj3VtGnTNPdiuLi4qGXLlvL395eU8XCKR1W/fn05ODgoLCwswz9mEhIS9PXXX0tKf32n1ihJ/fr1s02ref+QEWdnZ9vNrpn9obFw4UJ17dpVy5Yty1bNq1evVuvWrdNtX758eY0fP14FCxZUYmLiY71u8PdFyAZyIHWGDgcHh0wfYpB6d3tQUFCaB5V8//33Wrdu3Z9SpyQtWLAgTRCIiorSW2+9pZSUFPXo0cM2PrRjx44qU6aMvvvuO82ZMydNzZGRkXr77bd17tw5FS5c+JF7lnr16qWCBQtqz549WrZsWZrAdfr0adtMLv7+/raxqjlVtmxZ3b59W7/++mua+X2le2Fxz549ktLf5GgGX19f+fj46ObNm3r77bcVGxtrW3f79m29+eabiouL0z/+8Y8s/4hwc3PTlClTJN0bK586u4UkDRo0SJI0evRo/d///V+a123cuFEbN27UiRMnsn0zV/ny5XXlyhUtXLgw3TjarVu3Srp3Y+CD3vvUMb2hoaFp5qq+c+eOAgICbDc+ZnSTbk6kHv/UqVMVERFhW37t2jXbw0ceF1dXV/Xt21eS9Pbbb6fp0U1OTtacOXN06NAheXh4ZHpz76MqUKCAXFxcdO3aNb333ntpvjG7c+eO7eFRj+P6LlOmjDp27KjExES98cYbacaN3717VxMmTNDFixdVpUqVTG9clO4NB5k+fbqcnZ21adMm2x9g0v/e32nTpumHH35I87rdu3dr+fLlOnnyZIY3OWakcuXKunDhgj766KM014t0L8hHR0erWLFiWY4zBx4Ww0WAHEp9OuH9D8i434ABA/TVV1/pwIEDateunby9vXXp0iUdO3bMNtzkz2C1WtW7d2/Vq1dP7u7uOnDggG7fvm27cSqVq6urFixYoJdfflnLly/Xp59+qurVq9tmILlz5448PT1tvZqPonjx4nr//fc1bNgwzZ07Vxs3blS1atV069YthYWFKSkpSc8884xefvnlh96HxWJRYGCg/vnPf2rmzJlav369qlSpojt37ujQoUOKi4tT69atH3hj1cOaO3euBgwYoJ07d6pVq1aqW7euUlJSdPDgQUVHR6tq1aqaNWvWA9tp1qyZnnnmGW3btk1jx461DRvx8/NTeHi4Nm3apO7du6tatWoqXbq0IiIiFBERIYvFoilTpmT7m4Bu3brpyy+/1N69e9WmTRvVqVNHhQoV0tmzZ3Xy5Enly5dPkyZNemA7/fr1s7XTtm1bVa9eXXfv3tXhw4cVExMjq9WqkydPPnKP4TPPPKOwsDCtW7dOzz77rBo0aCBnZ2ft379fhQoVUpkyZR7LtxSphg8frpMnT2r37t3q2LGj6tWrp4IFC+rYsWO6dOmSihUrpg8++OCxDj2YPHmyevfuraCgIO3cuVNVq1ZVcnKyjhw5oqioKNWqVUu9evV6LPueMmWKzp07p8OHD6t169aqV6+eXFxcdOjQIUVGRqpcuXKaP3/+A8d/e3p66p///KeWLFmiCRMmaMeOHXJ3d1fz5s01bNgwLViwQAMGDJCXl5cqVKigixcv2v5QGzJkSJqx3Flp2LChevTooY0bN+qZZ56Rj4+PihUrpsuXL+vo0aNycHDQpEmTMr2HBXgU9GQDD+HNN99UlSpVMlxXtWpVrV27Vq1bt1ZcXJx2796t5ORkTZs2zZSgml0LFy7UgAEDdObMGX377bcqUaKERo0apY8++ihdr2SNGjW0bds2DRw4UO7u7vrhhx907NgxVapUSaNHj9batWtzPJNIZv7xj39o69at6tWrl202ilOnTqlRo0ZatGiRZs+eneMbKv+oYcOGWr16tdq3b6/Y2FiFhobqyJEj8vLy0jvvvKMFCxY8tqd3lilTRps3b9awYcNUsmRJ7du3Tz/99JMqVqyof/3rX9qwYUO2x5uPHz9exYoVU3h4eJp5yqdPn66FCxeqUaNGOn/+vEJDQxUfH68OHTpo/fr16Z5mmRVHR0ctXLhQI0eOVOXKlW03i8bExOiFF17Q1q1b0817nZGaNWtq48aNat26tZKTk7Vnzx6dPXtWtWvX1rx587R+/Xq5uLjo8OHDGQ6lyokpU6Zozpw5qlGjhg4fPqxDhw6pefPmWrt2baYzhZjF2dlZS5YsUWBgoLy9vXX06FHt3r1b+fLl08svv6xt27Zler+BWZ5++mmtX7/eNk3nnj17dODAAZUpU0ZvvfWWgoODTR3PfL+CBQtqzZo1GjdunCpVqqSffvpJ+/btU4kSJTRixAht3bpVlSpVylZbr732mipVqqTLly9r5syZtuVDhw5VUFCQ2rRpo+vXrys0NFQ3b95U8+bNtWLFihzP7jRlyhRNmjRJNWrU0C+//KKQkBBduXJFnTp10oYNG9SxY8cctQdkl8XIzu3CAAAAALKNnmwAAADAZIRsAAAAwGSEbAAAAMBkhGwAAADAZH/JKfyuX4+2dwkAAAD4G/DwKJjhcnqyAQAAAJMRsgEAAACTEbIBAAAAkxGyAQAAAJMRsgEAAACTEbIBAAAAkxGyAQAAAJMRsgEAAACTEbIBAAAAkxGyAQAAAJP9JR+rDgDImywRgfYuAbmQUXm8vUsAcoyebJOdO/e7Bg8eqKFDX9EbbwzR9evXtHTpQg0ZMlCvvvqSdu3aqaSkJA0ePFD79/+gmzcj1bdvT129esXepQMAAMAk9GSb7Kef9svT00vDhr2pI0cO69tvd+ny5YtasmSl4uPj9eqrL6levQYKCJim0aNHqHjxEnr99TdUqtQT9i4dAAAAJiFkm6xLl2e1evUnGjVqmNzc3FWlilW//npCQ4e+IklKSkrSlSuXVaWKVd7etXX8+DE1bNjYzlUDAADATAwXMdl3332rWrV89MEHS9SyZWt98cV2+fj4auHC5Zo/f6latWqjsmXL6vjxYzpzJkK1a/to7dpV9i4bAAAAJqIn22ReXtX0zjsT5ejoKAcHB02dOlP/+c+Xeu21f+rOnTg1a9ZSKSmG3n13qgIDZ6tUqSf0yisDVKdOXXl5VbN3+QAAADCBxTAMw95FmO369Wh7lwAAeAjMLoKMMLsIcjMPj4IZLme4CAAAAGAyQjYAAABgMkI2AAAAYDJCNgAAAGAyZhcBAAC5WkRAfXuXgFyo8pQD9i4hS3/bkG32Hezc+QwAAIBUDBf5kyQnJ+vNN4dqyJBBun37tmntPvNMe9PaAgAAgDn+tj3Zf7bIyBu6deuWVq7k6Y4AAAB/dXYN2d26dVPBgvcm8C5XrpwGDx6ssWPHymKxqEqVKgoICJCDg4M2bNigdevWycnJSUOGDFHLli3tWfZDmTVrui5cOK/AwCmKi4vVf//7X0nSiBFvq3Llp9WrVzfVqOGtCxfOq04dX8XGxuiXX8JVocKTmjhxqs6cOa0FC+YpJcVQTEy0Rox4SzVr1rK1HxFxWu+/P1uGYahw4cIaNy5A7u7u9jpcAACAvzW7hez4+HhJUnBwsG3Z4MGDNWLECDVo0ECTJk1SSEiIateureDgYG3evFnx8fHy8/NTkyZN5OLiYq/SH8qoUWMVEDBeRYoUVdWq1fXcc911/vw5BQZO0ZIlK3TlymV98MFSlShRQh07ttLy5R9r5MjR6tnzWUVHR+vs2TMaOnSkKld+Wt9885W++GJ7mpA9c+Y0jRs3SRUrVtKOHZ9p9epP9Oqrr9vxiAEAAP6+7BayT5w4oTt37mjgwIFKSkrSm2++qfDwcNWvf+8O4mbNmmnfvn1ycHCQj4+PXFxc5OLiogoVKujEiRPy9va2V+mP5MyZ0zp06KBCQr6RJEVH33sEfKFChfXEE09IkvLnz6+KFStJktzc3JWQEK8SJUrq44//LVdXV8XFxcnNzS1Nu7//flZz5rwrSUpOTlL58k/+WYcEAACAP7BbyM6XL58GDRqkHj166LffftPLL78swzBksVgkSW5uboqOjlZMTIxtSEnq8piYmCzbLlq0gJycHLPc5kbEox/D/TJ7bn2q+Hg3OTs7ysvLqho1aqhr166KjIzUxo0b5eFRUI6ODrY2HBwstv93cnJQ8eLuGjdurt577z1VrlxZ8+fP18WLF+XhUdC2baVKlTRv3hyVKVNGYWFhun79+gNrAoDcxuzPZgB/Xbk959gtZFesWFFPPvmkLBaLKlasqCJFiig8PNy2PjY2VoUKFZK7u7tiY2PTLL8/dGckKiruwQWYPOXe9evRWa6/eTNWiYnJ6tGjr959d6pWrVqjuLhYDRz4iq5fj1ZKimFr4/7/T0pKUWRkjFq1aqdXXhmsYsWKycOjpP7731tpXvfGG6M1cuQopaSkSJLGjp34wJoAILex2LsAAHlGbsk5mYV9i2EYxp9ciyRpzZo1OnnypCZPnqyrV6+qf//+KleunF5++WXbmOyGDRuqXr16GjhwoDZt2qSEhAT16NFDW7dulaura6Zt55aTDgDIGbOfYYC/htNBn9m7BORCueVhNJmFbLv1ZHfv3l3jxo3Tiy++KIvFosDAQBUtWlQTJ07U3LlzValSJbVv316Ojo7y9/eXn5+fDMPQyJEjswzYAAAAgL3ZrSf7caInGwDyJnqykRF6spGR3N6TzRMfAQAAAJMRsgEAAACTEbIBAAAAk9n1ser2FBFQ39T2csu4IAAAANgfPdm53BdfbNeSJQvsXQYAAABygJANAAAAmOxvO1zEHr74Yrv27duj+Ph4RUbeUI8eL2rv3m919myEXn/9DV27dlXffhuqpKQkubu7a/r02Wlev2nTOv3nP1/LYrGodet26tGjt52OBAAAAFkhZP/J4uLiNG/eIu3c+bXWr1+j5cs/1uHDYVq/frU8Pavq/fcXy8HBQW++OVS//PK/x8yfPXtGISH/0eLF/5bFYtGIEa+pQYOGqlDhKfsdDAAAADJEyP6TVaniKUlydy+op56qKIvFooIFCyoxMUnOzs6aPPlfyp8/v65du6akpCTb686cidDVq1f0xhtDJEnR0dG6cOECIRsAACAXImT/ySwWS4bLk5IStWfPbn344Se6e/euBg3qm2Z9hQpP6qmnKmnOnPmyWCxav361KlV6+s8oGQAAADn0tw3ZuW3KPUdHR+XPn1+DBvnLxcVZxYuX0I0b123rq1Sxyte3nl57bZASEhJVtWp1eXh42LFiAAAAZMZiGIZh7yLMdv16tL1LAAA8BEtEoL1LQC50Ougze5eAXCi3dJh6eBTMcDlT+AEAAAAmI2QDAAAAJiNkAwAAACYjZAMAAAAmI2QDAAAAJiNkAwAAACYjZAMAAAAmI2QDAAAAJiNkAwAAACYjZAMAAAAmI2QDAAAAJiNkAwAAACYjZAMAAAAmI2QDAAAAJiNkAwAAACYjZAMAAAAmI2QDAAAAJiNkAwAAACaza8iOjIxU8+bNFRERod9//10vvvii/Pz8FBAQoJSUFEnShg0b9Pzzz6tnz54KDQ21Z7kAAABAttgtZCcmJmrSpEnKly+fJGnGjBkaMWKE1qxZI8MwFBISouvXrys4OFjr1q3TihUrNHfuXCUkJNirZAAAACBbnOy145kzZ6p3795avny5JCk8PFz169eXJDVr1kz79u2Tg4ODfHx85OLiIhcXF1WoUEEnTpyQt7d3lm0XLVpATk6Oj/0YAADmuhFh7woA5BUeHgXtXUKW7BKyt2zZomLFiqlp06a2kG0YhiwWiyTJzc1N0dHRiomJUcGC/zuBbm5uiomJeWD7UVFxj6dwAMBjZbF3AQDyjOvXo+1dgqTMw75dQvbmzZtlsVj0ww8/6JdfftGYMWN08+ZN2/rY2FgVKlRI7u7uio2NTbP8/tANAAAA5EZ2GZO9evVqrVq1SsHBwapatapmzpypZs2aaf/+/ZKkPXv2yNfXV97e3goLC1N8fLyio6MVEREhq9Vqj5IBAACAbLPbmOw/GjNmjCZOnKi5c+eqUqVKat++vRwdHeXv7y8/Pz8ZhqGRI0fK1dXV3qUCAAAAWbIYhmHYuwiz5ZYxOgCAnLFEBNq7BORCp4M+s3cJyIUqTzlg7xIkZT4mm4fRAAAAACYjZAMAAAAmI2QDAAAAJiNkAwAAACYjZAMAAAAmI2QDAAAAJiNkAwAAACYjZAMAAAAmI2QDAAAAJiNkAwAAACYjZAMAAAAmI2QDAAAAJiNkAwAAACYjZAMAAAAmI2QDAAAAJiNkAwAAACbLUcju16+ffvjhh0zX79q1S507d37kogAAAIC8zCmrlXfu3FFUVJTt5wMHDqht27Z68skn022bkpKiPXv26MKFC+ZXCQAAAOQhDwzZ3bp1U3R0tCTJYrEoMDBQgYGBGW5vGIaaNGlifpUAAABAHpJlyC5WrJhmz56tY8eOyTAMLVq0SG3btpWnp2e6bR0cHFSsWDGGiwAAAOBvL8uQLUnNmzdX8+bNJUmXLl1S7969VatWrcdeGAAAAJBXPTBk32/GjBmPqw4AAADgLyNHIVuS9uzZo+3bt+vGjRtKTk5Ot95iseiTTz4xpTgAAAAgL8pRyF69erWmTZsmSSpevLhcXFweS1EAAABAXpajkB0UFCQvLy99+OGHKlGixOOqCQAAAMjTcvQwmsuXL6tXr14EbAAAACALOQrZFSpU0I0bNx5XLQAAAMBfQo5C9iuvvKLg4GCdOnXqcdUDAAAA5Hk5GpMdFhYmNzc3Pfvss6pYsaKKFSsmi8WSZhtmFwEAAMDfXY5C9t69eyVJTzzxhO7cuaOLFy8+lqIAAACAvCxHIXvXrl2m7Tg5OVkTJkzQ2bNn5ejoqBkzZsgwDI0dO1YWi0VVqlRRQECAHBwctGHDBq1bt05OTk4aMmSIWrZsaVodAAAAgNly/DAas4SGhkqS1q1bp/3799tC9ogRI9SgQQNNmjRJISEhql27toKDg7V582bFx8fLz89PTZo0YY5uAAAA5Fo5Ctn9+vXL1nZBQUEP3KZNmzZq0aKFJOnSpUsqUaKEdu/erfr160uSmjVrpn379snBwUE+Pj5ycXGRi4uLKlSooBMnTsjb2zvTtosWLSAnJ8ds1QoAyD1uRNi7AgB5hYdHQXuXkKUchewLFy6kW5aSkqKoqCjFx8erbNmyqlKlSvZ37uSkMWPG6D//+Y/mz5+v0NBQ242Ubm5uio6OVkxMjAoW/N9JdHNzU0xMTJbtRkXFZbsGAEDuYXnwJgAgSbp+PdreJUjKPOybMiY7OTlZISEhmjBhggYNGpSjwmbOnKm33npLPXv2VHx8vG15bGysChUqJHd3d8XGxqZZfn/oBgAAAHKbHM2TnRlHR0e1a9dOPXr00HvvvZet13z22WdatmyZJCl//vyyWCyqUaOG9u/fL0nas2ePfH195e3trbCwMMXHxys6OloRERGyWq1mlA0AAAA8Fqbe+PjUU09p1apV2dq2Xbt2GjdunPr06aOkpCSNHz9elStX1sSJEzV37lxVqlRJ7du3l6Ojo/z9/eXn5yfDMDRy5Ei5urqaWTYAAABgKothGIYZDSUkJGjQoEG6ePGiqVP9PYzcMkYHAJAzlohAe5eAXOh00Gf2LgG5UOUpB+xdgiSTxmRnNrtIQkKCzp49q9u3b2vYsGE5rw4AAAD4C3nk2UWke2OyK1WqpC5dusjPz8+UwgAAAIC8ym5PfAQAAAD+qh7qxsfk5GQdP35cFy9elIuLi0qXLq3q1aubXRsAAACQJ+U4ZIeGhmrKlCm6evWqUu+ZtFgsKlmypAICAtSqVSvTiwQAAADykhyF7IMHD2rYsGEqXry4Ro4cqcqVK8swDJ05c0Zr1qzR8OHDFRQUpDp16jyuegEAAIBcL0che8GCBSpbtqw2bdqU7qmLfn5+euGFF7RkyRJ9+OGHphYJAAAA5CU5euLj0aNH1aNHjwwfa+7u7q7u3bvryJEjphUHAAAA5EWmPFY9lcViUWJioplNAgAAAHlOjkJ2rVq1tGnTJsXFxaVbFxMTo40bN6pmzZqmFQcAAADkRTkakz106FD169dPXbp0Ud++ffXUU09Jku3Gx6tXr2rKlCmPo04AAAAgz8hRyPb19dWCBQv0zjvvaNasWbJYLLZp/Dw8PDRv3jw1bNjwsRQKAAAA5BU5nie7devWatGihcLDw22PWS9VqpRq1aolJ6eHerYNAAAA8JeSrTHZq1atUteuXZWUlCRJcnR0lLe3tzp16qSQkBANGzZMq1ateqyFAgAAAHlFliHbMAyNHj1a06ZN07Vr13Tp0qV025QrV04ODg6aOXOm3nzzzcdWKAAAAJBXZBmyN27cqG3btsnPz0979uxRhQoV0m0zcuRIhYSE6Nlnn9WXX36pzz777HHVCgAAAOQJDwzZ9erV06RJk+Tq6prpdq6urgoMDJSXl5fWrVtnepEAAABAXpJlyD59+rRat26dvYYcHNS+fXv9+uuvphQGAAAA5FVZhmxHR0e5uLhku7GiRYvKwcHUh0gCAAAAeU6WifjJJ5/U8ePHs93YsWPHVKZMmUcuCgAAAMjLsgzZnTt31vbt23Xq1KkHNnTq1Clt375dzZo1M604AAAAIC/KMmT36tVLZcqUkb+/v7Zt26bk5OR026SkpGjHjh166aWX5Obmpv79+z+2YgEAAIC8wGKkPhc9E2fOnNFrr72m33//XQUKFFD16tXl4eGhlJQURUZGKjw8XHFxcSpdurQWLVqkqlWr/lm1Z+r69Wh7lwAAeAiWiEB7l4Bc6HTQZ/YuAblQ5SkH7F2CJMnDo2CGyx/4HPRKlSpp27ZtWr16tT7//HMdOnTI9uRHZ2dn1a5dW+3atVOvXr1ydJMkAAAA8Ff1wJAtSS4uLnrppZf00ksvSZJu3rwpR0dHFS5c+LEWBwAAAORF2QrZf1SsWDGz6wAAAAD+MpjUGgAAADAZIRsAAAAwGSEbAAAAMNlDjcl+VImJiRo/frwuXryohIQEDRkyRE8//bTGjh0ri8WiKlWqKCAgQA4ODtqwYYPWrVsnJycnDRkyRC1btrRHyQAAAEC22SVkb9u2TUWKFNHs2bMVFRWl5557Tl5eXhoxYoQaNGigSZMmKSQkRLVr11ZwcLA2b96s+Ph4+fn5qUmTJkwVCAAAgFzNLiG7Q4cOat++ve1nR0dHhYeHq379+pKkZs2aad++fXJwcJCPj49cXFzk4uKiChUq6MSJE/L29rZH2QAAAEC22CVku7m5SZJiYmI0fPhwjRgxQjNnzpTFYrGtj46OVkxMjAoWLJjmdTExMQ9sv2jRAnJycnw8xQMAHpsbEfauAEBekdmTFnMLu4RsSbp8+bJef/11+fn5qWvXrpo9e7ZtXWxsrAoVKiR3d3fFxsamWX5/6M5MVFTcY6kZAPB4WexdAIA84/r1aHuXICnzsG+X2UVu3LihgQMH6u2331b37t0lSdWqVdP+/fslSXv27JGvr6+8vb0VFham+Ph4RUdHKyIiQlar1R4lAwAAANlml57spUuX6vbt21q8eLEWL14sSfrXv/6ladOmae7cuapUqZLat28vR0dH+fv7y8/PT4ZhaOTIkXJ1dbVHyQAAAEC2WQzDMOxdhNlyy9cHAICcsUQE2rsE5EKngz6zdwnIhSpPOWDvEiTlsuEiAAAAwF8ZIRsAAAAwGSEbAAAAMBkhGwAAADAZIRsAAAAwGSEbAAAAMBkhGwAAADAZIRsAAAAwGSEbAAAAMBkhGwAAADAZIRsAAAAwmZO9CwD+TiIC6tu7BORClaccsHcJAACT0ZMNAAAAmIyQDQAAAJiMkA0AAACYjJANAAAAmIyQDQAAAJiMkA0AAACYjJANAAAAmIyQDQAAAJiMkA0AAACYjJANAAAAmIyQDQAAAJiMkA0AAACYjJANAAAAmIyQDQAAAJiMkA0AAACYzMneBfyVWSIC7V0CAAAA7ICebAAAAMBkhGwAAADAZHYN2UeOHJG/v78k6ffff9eLL74oPz8/BQQEKCUlRZK0YcMGPf/88+rZs6dCQ0PtWS4AAACQLXYL2R9++KEmTJig+Ph4SdKMGTM0YsQIrVmzRoZhKCQkRNevX1dwcLDWrVunFStWaO7cuUpISLBXyQAAAEC22C1kV6hQQQsWLLD9HB4ervr160uSmjVrpu+//15Hjx6Vj4+PXFxcVLBgQVWoUEEnTpywV8kAAABAtthtdpH27dvrwoULtp8Nw5DFYpEkubm5KTo6WjExMSpYsKBtGzc3N8XExDyw7aJFC8jJydH8onPoRoS9KwCQF3h4FHzwRn8TfG4CyK7c/tmZa6bwc3D4X6d6bGysChUqJHd3d8XGxqZZfn/ozkxUVNxjqTGnLPYuAECecP16tL1LyDX43ASQXbnlszOzsJ9rZhepVq2a9u/fL0nas2ePfH195e3trbCwMMXHxys6OloRERGyWq12rhQAAADIWq7pyR4zZowmTpyouXPnqlKlSmrfvr0cHR3l7+8vPz8/GYahkSNHytXV1d6lAgAAAFmya8guV66cNmzYIEmqWLGiVq1alW6bnj17qmfPnn92aQAAAMBDyzXDRQAAAIC/CkI2AAAAYDJCNgAAAGAyQjYAAABgMkI2AAAAYDJCNgAAAGAyQjYAAABgMkI2AAAAYDJCNgAAAGAyQjYAAABgMkI2AAAAYDJCNgAAAGAyQjYAAABgMkI2AAAAYDJCNgAAAGAyQjYAAABgMkI2AAAAYDJCNgAAAGAyQjYAAABgMkI2AAAAYDJCNgAAAGAyQjYAAABgMkI2AAAAYDJCNgAAAGAyQjYAAABgMkI2AAAAYDJCNgAAAGAyQjYAAABgMkI2AAAAYDJCNgAAAGAyJ3sXkB0pKSmaPHmyfv31V7m4uGjatGl68skn7V0WAAAAkKE80ZO9c+dOJSQkaP369Ro1apTeffdde5cEAAAAZCpPhOywsDA1bdpUklS7dm0dP37czhUBAAAAmcsTw0ViYmLk7u5u+9nR0VFJSUlycsq4fA+Pgn9WaVnzmGHvCpDLlGjINQFkic9NZIDPTuRFeaIn293dXbGxsbafU1JSMg3YAAAAgL3liZBdp04d7dmzR5L0888/y2q12rkiAAAAIHMWwzAMexfxIKmzi5w8eVKGYSgwMFCVK1e2d1kAAABAhvJEyAYAAADykjwxXAQAAADISwjZAAAAgMkI2QAAAIDJCNkAAACAyQjZAAAAgMl4ogtgslGjRqlr165q0aKFIiIiNHPmTJUoUUK///67UlJSNGLECDVo0EDz5s3Tjz/+qJSUFHXu3FkDBgywd+kAYHdbtmzRt99+q7t37+rcuXN6+eWX5eXlpalTp8rR0VGurq6aOnWqypQpY+9SgSwRsgGT9ejRQ2vXrlWLFi20adMm+fj4KCYmRoGBgYqKilLfvn31+eef67PPPtOqVatUqlQpbdmyxd5lA0CuERMToxUrVui3337T4MGDVaBAAU2fPl1Vq1bVzp079e6772r+/Pn2LhPIEiEbMFmDBg00ffp0RUZGat++ffLx8dGhQ4d09OhRSVJSUpKioqI0d+5czZ07Vzdu3FDTpk3tXDUA5B5eXl6SpNKlSyshIUExMTGqWrWqJKlevXqaM2eOPcsDsoWQDZjMYrGoa9eumj59upo0aaLSpUurdOnSGjx4sO7evaslS5bIzc1NX331lebOnSvDMNS5c2d17txZZcuWtXf5AGB3Foslzc8lS5bUiRMn5OXlpZ9++klPPfWUfQoDcoCQDTwGzz//vFq0aKGtW7eqfPnymjBhgvr27auYmBj5+fnJxcVFhQsX1rPPPqvChQurSZMmjC8EgExMmzZNU6dOlWEYcnR0VGBgoL1LAh6Ix6oDj8HVq1c1evRoffLJJ/YuBQAA2AFT+AEm+/rrr/XPf/5To0aNsncpAADATujJBgAAAExGTzYAAABgMkI2AAAAYDJCNgAAAGAypvADgFwqISFBH3/8sXbs2KFz587JwcFBTz31lDp06KD+/fvL1dX1T6vF399fFy9e1K5du/60fQJAXkbIBoBcKCkpSYMGDdLPP/+sbt26qVevXkpOTtbBgwc1d+5c7dq1S0FBQXJxcbF3qQCADBCyASAX+vLLL3XgwAEtWLBA7dq1sy3v16+f/v3vf2v27NnatGmT/Pz87FglACAzjMkGgFzo8OHDkqQmTZqkW9enTx85Ozvr559//pOrAgBkFyEbAHIhNzc3SdL69evTrcufP78OHTqkWbNmSbo3XvrVV1/Vzp079cwzz6hmzZrq3Lmzvv32W8XExGjSpEmqX7++GjVqpEmTJunu3btp2jt48KAGDBggHx8f+fj4qF+/fvrpp5+yrC82NlY9evSQj4+PwsLCbMtPnz6t119/Xb6+vqpVq5Z69+6tvXv3pnmtv7+/Bg0apHnz5snHx0eNGjXSr7/++lDnCQByKx5GAwC50KlTp/Tcc88pMTFRVapUUZs2bdSoUSP5+PikG4ft7++vs2fPKiEhQf369VPBggW1bNky3blzR1WrVlX+/PnVpk0bHTx4UDt27NCwYcM0dOhQSVJISIiGDh2qChUq6IUXXpAkbdy4UZcuXdL8+fPVunVr2z5Sb3xMSEjQq6++qrCwMC1btkyNGjWSJP3666/y8/NTiRIl1L17dzk7O2vHjh0KDw/XnDlz1KlTJ1tbx48fV/HixTVw4EBduHBBo0aNkqOj4591egHgsSNkA0AutXv3bo0fP16RkZG2ZQUKFFCrVq00dOhQVaxYUdK90HrgwAEtXbpULVu2lCStXr1a77zzjmrXrm3rDTcMQy1atFDp0qW1bt06JSUlqXXr1rJYLNqxY4fc3d0lSbdv31aXLl0k3Qvhzs7OtpC9c+dOjRgxQrt27dKiRYvUvHlzW23+/v66cuWKtm7dqgIFCki6dwNn//799dtvvyk0NFQuLi62eoOCgtSgQYPHfyIBwA4YLgIAuVSLFi0UGhqqefPm6dlnn5WHh4fi4uK0Y8cOPfvsszpw4IBtW1dXVzVt2tT2c2oAT+2JliSLxaKyZcvq+vXrkqT/+7//05UrV9SnTx9bwJakQoUKqW/fvrp69aqOHz+epqaAgAB9/fXXmjp1apqAHRUVpQMHDqh58+a6e/eubt68qZs3b+r27dtq27atbty4oWPHjtm2z5cvn+rVq2fSmQKA3IfZRQAgF3N1dVWnTp1sQy3Cw8O1cuVK7dixQwEBAfryyy8lSUWKFJGT0/8+0lOHXhQvXjxNe46Ojkr9AvPChQuS/hfI71epUiVJ0qVLl+Tj4yNJunjxojZu3ChJOnTokJ577jnb9ufPn5ckBQcHKzg4OMNjuXz5su3/ixQpIgcH+nkA/HURsgEgl4mLi9OyZctUvXr1NNP3SVL16tU1Z84c3b59W3v27FFUVJQkpQnY97NYLJnuJ6vRgqnrnJ2d07Q1efJkHTp0SBs3blS3bt1Ut25dSVJycrKkezOftGnTJsM2n376adv/M/4awF8d3QgAkMu4urpqxYoVmfYIS/cCq8ViUb58+R56P2XLlpUknTlzJt26s2fPSpKeeOIJ27IyZcqod+/eGj16tNzd3RUQEKDExMQ0bTk6Oqpx48Zp/itZsqQSEhKUP3/+h64VAPIaQjYA5DKOjo7q1KmTDhw4oK1bt6Zbf+vWLX399ddq3LjxIwXX6tWry8PDQ2vXrlVMTIxteUxMjNasWSMPDw/VqFEj3etKlCih4cOH69SpU1q5cqUkqWTJkqpRo4Y+/fRTXb161bZtYmKixo8fr+HDhyspKemhawWAvIbhIgCQC40dO1ZHjx7V6NGjtW3bNjVt2lTu7u46d+6ctmzZosTERE2aNOmR9uHs7KyJEydqxIgReuGFF9S9e3dJ0qZNm3Tt2jXNnz8/03HTffr00ebNm7V48WJ16tRJ5cuX14QJE9S/f3+98MILevHFF1WkSBF9/vnnOnLkiEaNGqWiRYs+Ur0AkJfQkw0AuVCxYsW0ZcsWvfHGG7p165YWLVqkyZMna8eOHWrXrp22b9+up5566pH30759e61cuVIlS5bUokWLtGzZMpUrV05BQUGZjq2W7vW2BwQEKD4+XlOmTJEk+fj4aO3atapRo4Y++ugjzZ49W3fu3NG7776rV1555ZFrBYC8hHmyAQAAAJPRkw0AAACYjJANAAAAmIyQDQAAAJiMkA0AAACYjJANAAAAmIyQDQAAAJiMkA0AAACYjJANAAAAmIyQDQAAAJjs/wHa+IyjUzqNhwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Countplot to compare the number of smokers and non-smokers\n",
    "plt.figure(figsize=(12,4))\n",
    "sns.countplot(x='smoker', data=df, hue='sex', palette='YlOrBr') \n",
    "plt.title('Number of smokers and non-smokers', size='23')\n",
    "plt.ylabel('Count',size=18)\n",
    "plt.xlabel('Smoker',size=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### notice :\n",
    "- 0 for females\n",
    "- 1 for males\n",
    "------------------\n",
    "- smokers = 1: yes\n",
    "- smokers = 0: no\n",
    "\n",
    " Males tend to smoke more than females."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### As we can see charges for smokers are much higher than charges for non-smokers. Sex doesn't have any effect on charges when you are a smoker"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Relation between regions and charges: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Countplot to compare the number of individuals from diffrent regions\n",
    "plt.figure(figsize=(12,4))\n",
    "sns.countplot(x='region', data=df, palette='husl') \n",
    "plt.title('Number of individuals from diffrent regions', size='23')\n",
    "plt.ylabel('Count',size=18)\n",
    "plt.xlabel('Region',size=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Almsot all the regions have equal number of individuals."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion:\n",
    "##### We have found out that region and gender does not bring significant difference on charges among its groups. Age, BMI,      number of children and smoking are the once that drives the charges. \n",
    "As:\n",
    "-  age has an impact on the charges, when a person is older the health costs are larger.\n",
    "-  if you are a smoker you must expect some huge medical charges compared to non-smokers. Especially for people who have high BMI values (>35) it will result very serious health care charges.\n",
    "- no matter where you live, this won't have any impact on your medical insurance bills.\n",
    "- the number of children doesn't affect the medical costs billed by health insurance.\n",
    "-  it doesn't matter if you are a men or a women your health bills won't change."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### before go  to modeling, we have to prepare the data types of the features:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> # label encoding:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "les = {}\n",
    "for col in ['sex','smoker','region', 'weight_condition', 'age_cat']:\n",
    "    les[col] = LabelEncoder()\n",
    "    df[col]  = les[col].fit_transform(df[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>sex</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>region</th>\n",
       "      <th>charges</th>\n",
       "      <th>weight_condition</th>\n",
       "      <th>age_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16884.92400</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1725.55230</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4449.46200</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>21984.47061</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3866.85520</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sex  children  smoker  region      charges  weight_condition  age_cat\n",
       "0    0         0       1       3  16884.92400                 2        2\n",
       "1    1         1       0       2   1725.55230                 1        2\n",
       "2    1         3       0       2   4449.46200                 1        2\n",
       "3    1         0       0       1  21984.47061                 0        2\n",
       "4    1         0       0       1   3866.85520                 2        2"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>charges</th>\n",
       "      <th>weight_condition</th>\n",
       "      <th>age_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16884.92400</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1725.55230</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4449.46200</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21984.47061</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3866.85520</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   children  smoker      charges  weight_condition  age_cat\n",
       "0         0       1  16884.92400                 2        2\n",
       "1         1       0   1725.55230                 1        2\n",
       "2         3       0   4449.46200                 1        2\n",
       "3         0       0  21984.47061                 0        2\n",
       "4         0       0   3866.85520                 2        2"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df =df.drop(columns=['sex','region'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Great ♥️"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We are almost ready to finally prepare the models! so let's split the data to work on it..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> # Modeling :\n",
    "There are good models, But we looking for the Best!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> # Splitting the data into Training data & Testing Data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "x= df.drop('charges',axis=1).values\n",
    "y =df.charges.values\n",
    "x_tr,x_tst,y_tr,y_tst = train_test_split(x,y,test_size=.2,random_state=25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "x_tr = sc.fit_transform(x_tr)\n",
    "x_tst = sc.transform(x_tst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Experience 8 algorithms\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr  = LinearRegression()\n",
    "knn = KNeighborsRegressor(n_neighbors=10)\n",
    "dt  = DecisionTreeRegressor(max_depth = 3)\n",
    "rf  = RandomForestRegressor(max_depth = 3, n_estimators=500)\n",
    "ada = AdaBoostRegressor( n_estimators=50, learning_rate =.01)\n",
    "gbr = GradientBoostingRegressor(max_depth=2, n_estimators=100, learning_rate =.2)\n",
    "xgb = XGBRegressor(max_depth = 3, n_estimators=50, learning_rate =.2)\n",
    "cb  = CatBoostRegressor(learning_rate =.01, max_depth =5, verbose = 0)\n",
    "\n",
    "regressors = [('Linear Regression', lr), ('K Nearest Neighbours', knn),('Decision Tree', dt), ('Random Forest', rf), \n",
    "              ('AdaBoost', ada),('Gradient Boosting Regressor', gbr),('XGBRegressor', xgb),('CatBoostRegressor', cb)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression : 72 %\n",
      "K Nearest Neighbours : 86 %\n",
      "Decision Tree : 85 %\n",
      "Random Forest : 86 %\n",
      "AdaBoost : 85 %\n",
      "Gradient Boosting Regressor : 88 %\n",
      "XGBRegressor : 88 %\n",
      "CatBoostRegressor : 88 %\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "for regressor_name, regressor in regressors:\n",
    "\n",
    "    regressor.fit(x_tr, y_tr)    \n",
    "  \n",
    "    y_pred = regressor.predict(x_tst)\n",
    "    accuracy = round(r2_score(y_tst,y_pred),3)*100\n",
    "    \n",
    "    print('{:s} : {:.0f} %'.format(regressor_name, accuracy))\n",
    "    plt.rcParams[\"figure.figsize\"] = (20,8)\n",
    "    plt.bar(regressor_name,accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best Models\n",
    "- Gradient Boosting Regressor : 88 %\n",
    "- CatBoostRegressor : 88 %\n",
    "- XGBRegressor : 88 %\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### R square (r2) Score & Root Mean Squared Error(RMSE)\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Gradient Boosting Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R square Score =  0.87451\n",
      "Root Mean Squared Error =  4209.24584\n"
     ]
    }
   ],
   "source": [
    "y_predict = gbr.predict(x_tst)\n",
    "gbr_r2_score = r2_score(y_tst, y_predict)\n",
    "print('R square Score = ', round(gbr_r2_score, 5))\n",
    "\n",
    "mse = mean_squared_error(y_tst, y_predict)\n",
    "rmse_1 = np.sqrt(mse)\n",
    "print('Root Mean Squared Error = ', round(rmse_1,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- XGBRegressor\n",
    "- note : the best algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R square Score =  0.87804\n",
      "Root Mean Squared Error =  4149.5777\n"
     ]
    }
   ],
   "source": [
    "y_predict = xgb.predict(x_tst)\n",
    "xgb_r2_score = r2_score(y_tst, y_predict)\n",
    "print('R square Score = ', round(xgb_r2_score, 5))\n",
    "\n",
    "mse = mean_squared_error(y_tst, y_predict)\n",
    "rmse_2 = np.sqrt(mse)\n",
    "print('Root Mean Squared Error = ', round(rmse_2, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- CatBoostRegressor.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R square Score =  0.87774\n",
      "Root Mean Squared Error =  4154.72088\n"
     ]
    }
   ],
   "source": [
    "y_predict = cb.predict(x_tst)\n",
    "cb_r2_score = r2_score(y_tst, y_predict)\n",
    "print('R square Score = ', round(cb_r2_score, 5))\n",
    "\n",
    "mse = mean_squared_error(y_tst, y_predict)\n",
    "rmse_3 = np.sqrt(mse)\n",
    "print('Root Mean Squared Error = ', round(rmse_3, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross Validation\n",
    "It is a resampling procedure wich is used to evaluate the machine learning models on limited data samples. Its goal is to predict new data that is that is not tested before. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.85890086 0.79149981 0.85040421 0.83031141 0.86551417]\n"
     ]
    }
   ],
   "source": [
    "score = cross_val_score(xgb, x, y, cv=5)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Accuracy Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "r2_scores = [gbr_r2_score*1000,xgb_r2_score*1000,cb_r2_score*1000]\n",
    "model_names = [\"Gradient Boosting R\",\"XGBRegressor\",\"CatBoostRegresso\"]\n",
    "\n",
    "total_bar = np.arange(len(model_names))\n",
    "color = ['#9edd1d', '#3edd1d', '#f7c851']\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "bar = plt.bar(model_names, r2_scores, alpha=1,color=color)\n",
    "plt.xticks(total_bar, model_names)\n",
    "plt.ylabel('Accuracy',fontsize=15, color='black')\n",
    "plt.xlabel('Model Name',fontsize=15, color='black')\n",
    "plt.title('Model Performance Comparison', fontsize=16, color='black', fontweight='bold')\n",
    "\n",
    "def autolabel(bar):\n",
    "    for bar in bar:\n",
    "        height = int(bar.get_height())\n",
    "        ax.text(bar.get_x() + .4, .5*height,\n",
    "                height, va='bottom',\n",
    "                fontsize=14, color='black')\n",
    "        \n",
    "autolabel(bar)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m = [rmse_1,rmse_2,rmse_3]\n",
    "model_names = [\"Gradient Boosting R\",\"XGBRegressor\",\"CatBoostRegresso\"]\n",
    "\n",
    "total_bar = np.arange(len(model_names))\n",
    "color = ['#9edd1d', '#3edd1d', '#f7c851']\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "bar = plt.bar(model_names, m, alpha=1,color=color)\n",
    "plt.xticks(total_bar, model_names)\n",
    "plt.ylabel('Accuracy',fontsize=15, color='black')\n",
    "plt.xlabel('Model Name',fontsize=15, color='black')\n",
    "plt.title('Root Mean Squared Error', fontsize=16, color='black', fontweight='bold')\n",
    "\n",
    "def autolabel(bar):\n",
    "    for bar in bar:\n",
    "        height = int(bar.get_height())\n",
    "        ax.text(bar.get_x() + .4, .5*height,\n",
    "                height, va='bottom',\n",
    "                fontsize=14, color='black')\n",
    "        \n",
    "autolabel(bar)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}