Impute missing data with XGBoost.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import missingno as msno\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data.csv')\n",
    "df.dropna(subset=['price'], inplace=True)\n",
    "df.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>cost</th>\n",
       "      <th>price</th>\n",
       "      <th>weight</th>\n",
       "      <th>purchase_date</th>\n",
       "      <th>product_type</th>\n",
       "      <th>product_level</th>\n",
       "      <th>maker</th>\n",
       "      <th>ingredient</th>\n",
       "      <th>height</th>\n",
       "      <th>width</th>\n",
       "      <th>depth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>$333k</td>\n",
       "      <td>$300,492</td>\n",
       "      <td>3 Ton 90 Kg</td>\n",
       "      <td>Dec 19 2008</td>\n",
       "      <td>Q,B</td>\n",
       "      <td>advanced</td>\n",
       "      <td>M14122</td>\n",
       "      <td>IN732052,IN732053</td>\n",
       "      <td>2.76 meters</td>\n",
       "      <td>97 cm</td>\n",
       "      <td>26 cm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>$430,570</td>\n",
       "      <td>3 Ton 30 Kg</td>\n",
       "      <td>Sep 10 1997</td>\n",
       "      <td>J,D</td>\n",
       "      <td>basic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IN732054,IN732055,IN732056,IN732057,IN732058</td>\n",
       "      <td>2.67 meters</td>\n",
       "      <td>98 cm</td>\n",
       "      <td>26 cm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>$270k</td>\n",
       "      <td>$213,070</td>\n",
       "      <td>3 Ton 40 Kg</td>\n",
       "      <td>Sep 05 2001</td>\n",
       "      <td>J,D</td>\n",
       "      <td>basic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IN732054,IN732059,IN732060</td>\n",
       "      <td>3.0 meters</td>\n",
       "      <td>93 cm</td>\n",
       "      <td>24 cm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>$229,174</td>\n",
       "      <td>3 Ton 50 Kg</td>\n",
       "      <td>Dec 23 2016</td>\n",
       "      <td>U</td>\n",
       "      <td>advanced</td>\n",
       "      <td>M14123</td>\n",
       "      <td>IN732061,IN732062,IN732063</td>\n",
       "      <td>2.5 meters</td>\n",
       "      <td>102 cm</td>\n",
       "      <td>27 cm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>$97k</td>\n",
       "      <td>$122,659</td>\n",
       "      <td>2 Ton 970 Kg</td>\n",
       "      <td>Jan 12 2000</td>\n",
       "      <td>D,R</td>\n",
       "      <td>advanced</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IN732064,IN732065,IN732066</td>\n",
       "      <td>2.47 meters</td>\n",
       "      <td>101 cm</td>\n",
       "      <td>26 cm</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    cost     price        weight purchase_date product_type product_level  \\\n",
       "0  $333k  $300,492   3 Ton 90 Kg   Dec 19 2008          Q,B      advanced   \n",
       "1    NaN  $430,570   3 Ton 30 Kg   Sep 10 1997          J,D         basic   \n",
       "2  $270k  $213,070   3 Ton 40 Kg   Sep 05 2001          J,D         basic   \n",
       "3    NaN  $229,174   3 Ton 50 Kg   Dec 23 2016            U      advanced   \n",
       "4   $97k  $122,659  2 Ton 970 Kg   Jan 12 2000          D,R      advanced   \n",
       "\n",
       "    maker                                    ingredient       height   width  \\\n",
       "0  M14122                             IN732052,IN732053  2.76 meters   97 cm   \n",
       "1     NaN  IN732054,IN732055,IN732056,IN732057,IN732058  2.67 meters   98 cm   \n",
       "2     NaN                    IN732054,IN732059,IN732060   3.0 meters   93 cm   \n",
       "3  M14123                    IN732061,IN732062,IN732063   2.5 meters  102 cm   \n",
       "4     NaN                    IN732064,IN732065,IN732066  2.47 meters  101 cm   \n",
       "\n",
       "   depth  \n",
       "0  26 cm  \n",
       "1  26 cm  \n",
       "2  24 cm  \n",
       "3  27 cm  \n",
       "4  26 cm  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2400x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.bar(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = list(df.columns)\n",
    "target = 'price'\n",
    "features.remove(target)\n",
    "\n",
    "X = df[features]\n",
    "y = df[target].str.strip(\"$\").str.replace(\",\",\"\").astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "class Mean_Data_Transformer(object):\n",
    "    \n",
    "    def fit(self, X, y=None):\n",
    "        self.mean_cost = 1000*X[\"cost\"].str.strip(\"$\").str.strip(\"k\").astype(float).mean() # obtain mean cost\n",
    "        self.mean_weight = X[\"weight\"].map(self.weight2num).mean() # obtain mean weight\n",
    "        self.majority_month = pd.to_datetime(X[\"purchase_date\"]).dt.month.mode()[0] # obtain majority purchase month\n",
    "        self.majority_weekday = pd.to_datetime(X[\"purchase_date\"]).dt.weekday.mode()[0] # obtain majority purchase weekday\n",
    "        self.mean_ingredient_number = X[\"ingredient\"].map(self.get_numbers).mean() # obtain mean ingredient number\n",
    "        self.pl_le = LabelEncoder() # create label-encoder\n",
    "        self.majority_product_level = pd.Series(self.pl_le.fit_transform(X[\"product_level\"])).mode()[0] # obtain majority product level code\n",
    "        self.pt_le = LabelEncoder() # create label-encoder\n",
    "        self.majority_product_type = pd.Series(self.pt_le.fit_transform(X[\"product_type\"])).mode()[0] # obtain majority product type code\n",
    "        \n",
    "    def transform(self, X, y=None):\n",
    "        new_df = pd.DataFrame()\n",
    "        new_df[\"Cost\"] = 1000*X[\"cost\"].str.strip(\"$\").str.strip(\"k\").astype(float) # convert cost to numerical value\n",
    "        new_df[\"Cost\"].fillna(self.mean_cost,inplace=True) # fill in missing cost with mean cost\n",
    "        new_df[\"Weight\"] = X[\"weight\"].map(self.weight2num) # convert weight to numerical value\n",
    "        new_df[\"Weight\"].fillna(self.mean_weight,inplace=True) # fill in missing weight with mean weight\n",
    "        new_df[\"Month\"] = pd.to_datetime(X[\"purchase_date\"]).dt.month # convert purchase date to purchase month\n",
    "        new_df[\"Month\"].fillna(self.majority_month,inplace=True) # fill in missing purchase month with majority purchase month\n",
    "        new_df[\"Weekday\"] = pd.to_datetime(X[\"purchase_date\"]).dt.weekday # convert purchase date to purchase weekday\n",
    "        new_df[\"Weekday\"].fillna(self.majority_weekday,inplace=True) # fill in missing purchase weekday with majority purchase weekday\n",
    "        new_df['Ingredient Number'] = X[\"ingredient\"].map(self.get_numbers) # obtain number of ingredients in recipe\n",
    "        new_df['Ingredient Number'].fillna(self.mean_ingredient_number,inplace=True) # fill in missing ingredient number with mean ingredient number\n",
    "        new_df[\"Product Level\"] = self.pl_le.transform(X[\"product_level\"]) # transform product level with label-encoder\n",
    "        new_df[\"Product Level\"].fillna(self.majority_product_level,inplace=True) # fill in missing product level with majority product level code\n",
    "        new_df[\"Product Type\"] = self.pt_le.transform(X[\"product_type\"]) # transform product type with label-encoder\n",
    "        new_df[\"Product Type\"].fillna(self.majority_product_type,inplace=True) # fill in missing product type with majority product type code\n",
    "        return new_df # return new_df\n",
    "    \n",
    "    def fit_transform(self, X, y=None):\n",
    "        self.fit(X)\n",
    "        return self.transform(X)\n",
    "    \n",
    "    def weight2num(self, x): # function to convert weight to number\n",
    "        if type(x) == str:\n",
    "            x = x.strip('Kg').split(' Ton ')\n",
    "            return float(x[0])*1000+float(x[1])\n",
    "        else:\n",
    "            return x\n",
    "        \n",
    "    def get_numbers(self, x): # function to get number of ingredients in recipe\n",
    "        if type(x) == str:\n",
    "            return len(x.split(','))\n",
    "        else:\n",
    "            return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "class Median_Data_Transformer(object):\n",
    "    \n",
    "    def fit(self, X, y=None):\n",
    "        self.median_cost = 1000*X[\"cost\"].str.strip(\"$\").str.strip(\"k\").astype(float).median() # obtain median cost\n",
    "        self.median_weight = X[\"weight\"].map(self.weight2num).median() # obtain median weight\n",
    "        self.majority_month = pd.to_datetime(X[\"purchase_date\"]).dt.month.mode()[0] # obtain majority purchase month\n",
    "        self.majority_weekday = pd.to_datetime(X[\"purchase_date\"]).dt.weekday.mode()[0] # obtain majority purchase weekday\n",
    "        self.median_ingredient_number = X[\"ingredient\"].map(self.get_numbers).median() # obtain median ingredient number\n",
    "        self.pl_le = LabelEncoder() # create label-encoder\n",
    "        self.majority_product_level = pd.Series(self.pl_le.fit_transform(X[\"product_level\"])).mode()[0] # obtain majority product level code\n",
    "        self.pt_le = LabelEncoder() # create label-encoder\n",
    "        self.majority_product_type = pd.Series(self.pt_le.fit_transform(X[\"product_type\"])).mode()[0] # obtain majority product type code\n",
    "        \n",
    "    def transform(self, X, y=None):\n",
    "        new_df = pd.DataFrame()\n",
    "        new_df[\"Cost\"] = 1000*X[\"cost\"].str.strip(\"$\").str.strip(\"k\").astype(float) # convert cost to numerical value\n",
    "        new_df[\"Cost\"].fillna(self.median_cost,inplace=True) # fill in missing cost with median cost\n",
    "        new_df[\"Weight\"] = X[\"weight\"].map(self.weight2num) # convert weight to numerical value\n",
    "        new_df[\"Weight\"].fillna(self.median_weight,inplace=True) # fill in missing weight with median weight\n",
    "        new_df[\"Month\"] = pd.to_datetime(X[\"purchase_date\"]).dt.month # convert purchase date to purchase month\n",
    "        new_df[\"Month\"].fillna(self.majority_month,inplace=True) # fill in missing purchase month with majority purchase month\n",
    "        new_df[\"Weekday\"] = pd.to_datetime(X[\"purchase_date\"]).dt.weekday # convert purchase date to purchase weekday\n",
    "        new_df[\"Weekday\"].fillna(self.majority_weekday,inplace=True) # fill in missing purchase weekday with majority purchase weekday\n",
    "        new_df['Ingredient Number'] = X[\"ingredient\"].map(self.get_numbers) # obtain number of ingredients in recipe\n",
    "        new_df['Ingredient Number'].fillna(self.median_ingredient_number,inplace=True) # fill in missing ingredient number with median ingredient number\n",
    "        new_df[\"Product Level\"] = self.pl_le.transform(X[\"product_level\"]) # transform product level with label-encoder\n",
    "        new_df[\"Product Level\"].fillna(self.majority_product_level,inplace=True) # fill in missing product level with majority product level code\n",
    "        new_df[\"Product Type\"] = self.pt_le.transform(X[\"product_type\"]) # transform product type with label-encoder\n",
    "        new_df[\"Product Type\"].fillna(self.majority_product_type,inplace=True) # fill in missing product type with majority product type code\n",
    "        return new_df # return new_df\n",
    "    \n",
    "    def fit_transform(self, X, y=None):\n",
    "        self.fit(X)\n",
    "        return self.transform(X)\n",
    "    \n",
    "    def weight2num(self, x): # function to convert weight to number\n",
    "        if type(x) == str:\n",
    "            x = x.strip('Kg').split(' Ton ')\n",
    "            return float(x[0])*1000+float(x[1])\n",
    "        else:\n",
    "            return x\n",
    "        \n",
    "    def get_numbers(self, x): # function to get number of ingredients in recipe\n",
    "        if type(x) == str:\n",
    "            return len(x.split(','))\n",
    "        else:\n",
    "            return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "from xgboost import XGBRegressor\n",
    "class XGBoost_Data_Transformer(object):\n",
    "    \n",
    "    def fit(self, X, y=None):\n",
    "        new_df = pd.DataFrame()\n",
    "        new_df[\"Weight\"] = X[\"weight\"].map(self.weight2num) # convert weight to numerical value\n",
    "        self.mean_weight = new_df[\"Weight\"].mean() # obtain mean weight\n",
    "        new_df[\"Weight\"].fillna(self.mean_weight,inplace=True) # fill in missing weight with mean weight\n",
    "        new_df[\"Month\"] = pd.to_datetime(X[\"purchase_date\"]).dt.month # convert purchase date to purchase weekday\n",
    "        self.majority_month = new_df[\"Month\"].mode()[0] # obtain majority purchase month\n",
    "        new_df[\"Month\"].fillna(self.majority_month,inplace=True) # fill in missing purchase month with majority purchase month\n",
    "        new_df[\"Weekday\"] = pd.to_datetime(X[\"purchase_date\"]).dt.weekday # convert purchase date to purchase weekday\n",
    "        self.majority_weekday = new_df[\"Weekday\"].mode()[0] # obtain majority purchase weekday\n",
    "        new_df[\"Weekday\"].fillna(self.majority_weekday,inplace=True) # fill in missing purchase weekday with majority purchase weekday\n",
    "        new_df[\"Ingredient Number\"] = X[\"ingredient\"].map(self.get_numbers) # obtain number of ingredients in recipe\n",
    "        self.mean_ingredient_number = new_df[\"Ingredient Number\"].mean() # obtain mean ingredient number\n",
    "        new_df['Ingredient Number'].fillna(self.mean_ingredient_number,inplace=True) # fill in missing ingredient number with median ingredient number\n",
    "        self.pl_le = LabelEncoder() # create label-encoder\n",
    "        new_df[\"Product Level\"] = pd.Series(self.pl_le.fit_transform(X[\"product_level\"])) # fit and transform product level with label-encoder\n",
    "        self.majority_product_level = new_df[\"Product Level\"].mode()[0] # obtain majority product level code\n",
    "        new_df[\"Product Level\"].fillna(self.majority_product_level,inplace=True) # fill in missing product level with majority product level code\n",
    "        self.pt_le = LabelEncoder() # create label-encoder\n",
    "        new_df[\"Product Type\"] = pd.Series(self.pt_le.fit_transform(X[\"product_type\"])) # fit and transform product type with label-encoder\n",
    "        self.majority_product_type = new_df[\"Product Type\"].mode()[0] # obtain majority product type code\n",
    "        new_df[\"Product Type\"].fillna(self.majority_product_type,inplace=True) # fill in missing product type with majority product type code\n",
    "        new_df[\"Cost\"] = X[\"cost\"].str.strip(\"$\").str.strip(\"k\").astype(float)*1000 # convert cost to numerical value\n",
    "        self.cost_imputer = XGBRegressor() # create a XGBoost imputer for cost\n",
    "        df_for_imputing_cost = new_df.dropna() # create training data for cost imputer by dropping missing data\n",
    "        self.cost_imputer.fit(df_for_imputing_cost[[\"Weight\",\"Month\",\"Weekday\",\"Ingredient Number\",\"Product Level\",\"Product Type\"]], df_for_imputing_cost[\"Cost\"]) # fit cost imputer\n",
    "        \n",
    "    def transform(self, X, y=None):\n",
    "        new_df = pd.DataFrame()\n",
    "        new_df[\"Weight\"] = X[\"weight\"].map(self.weight2num) # convert weight to numerical value\n",
    "        new_df[\"Weight\"].fillna(self.mean_weight,inplace=True) # fill in missing weight with mean weight\n",
    "        new_df[\"Month\"] = pd.to_datetime(X[\"purchase_date\"]).dt.month # convert purchase date to purchase month\n",
    "        new_df[\"Month\"].fillna(self.majority_month,inplace=True) # fill in missing purchase month with majority purchase month\n",
    "        new_df[\"Weekday\"] = pd.to_datetime(X[\"purchase_date\"]).dt.weekday # convert purchase date to purchase weekday\n",
    "        new_df[\"Weekday\"].fillna(self.majority_weekday,inplace=True) # fill in missing purchase weekday with majority purchase weekday\n",
    "        new_df['Ingredient Number'] = X[\"ingredient\"].map(self.get_numbers) # obtain number of ingredients in recipe\n",
    "        new_df['Ingredient Number'].fillna(self.mean_ingredient_number,inplace=True) # fill in missing ingredient number with mean ingredient number\n",
    "        new_df[\"Product Level\"] = self.pl_le.transform(X[\"product_level\"]) # transform product level with label-encoder\n",
    "        new_df[\"Product Level\"].fillna(self.majority_product_level,inplace=True) # fill in missing product level with majority product level code\n",
    "        new_df[\"Product Type\"] = self.pt_le.transform(X[\"product_type\"]) # transform product type with label-encoder\n",
    "        new_df[\"Product Type\"].fillna(self.majority_product_type,inplace=True) # fill in missing product type with majority product type code\n",
    "        new_df[\"Cost\"] = X[\"cost\"].str.strip(\"$\").str.strip(\"k\").astype(float)*1000 # convert cost to numerical value\n",
    "        imputed_cost = pd.Series(self.cost_imputer.predict(new_df[new_df[\"Cost\"].isnull()][[\"Weight\",\"Month\",\"Weekday\",\"Ingredient Number\",\"Product Level\",\"Product Type\"]])) # obtain imputed cost\n",
    "        imputed_cost.index = new_df[new_df[\"Cost\"].isnull()][\"Cost\"].index # set index of imputed cost\n",
    "        new_df[\"Cost\"].fillna(imputed_cost,inplace=True) # fill in missing cost with imputed cost\n",
    "        return new_df # return new_df\n",
    "    \n",
    "    def fit_transform(self, X, y=None):\n",
    "        self.fit(X)\n",
    "        return self.transform(X)\n",
    "    \n",
    "    def weight2num(self, x): # function to convert weight to number\n",
    "        if type(x) == str:\n",
    "            x = x.strip('Kg').split(' Ton ')\n",
    "            return float(x[0])*1000+float(x[1])\n",
    "        else:\n",
    "            return x\n",
    "        \n",
    "    def get_numbers(self, x): # function to get number of ingredients in recipe\n",
    "        if type(x) == str:\n",
    "            return len(x.split(','))\n",
    "        else:\n",
    "            return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_dtf = Mean_Data_Transformer()\n",
    "X_mean_imputed = mean_dtf.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cost</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Month</th>\n",
       "      <th>Weekday</th>\n",
       "      <th>Ingredient Number</th>\n",
       "      <th>Product Level</th>\n",
       "      <th>Product Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>333000.000000</td>\n",
       "      <td>3090.0</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>169019.448213</td>\n",
       "      <td>3030.0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>270000.000000</td>\n",
       "      <td>3040.0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>169019.448213</td>\n",
       "      <td>3050.0</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>97000.000000</td>\n",
       "      <td>2970.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>3181</th>\n",
       "      <td>169019.448213</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>3182</th>\n",
       "      <td>169019.448213</td>\n",
       "      <td>2830.0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3183</th>\n",
       "      <td>169019.448213</td>\n",
       "      <td>3080.0</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3184</th>\n",
       "      <td>44000.000000</td>\n",
       "      <td>2950.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3185</th>\n",
       "      <td>128000.000000</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>4.709413</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3186 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Cost  Weight  Month  Weekday  Ingredient Number  Product Level  \\\n",
       "0     333000.000000  3090.0     12        4           2.000000              0   \n",
       "1     169019.448213  3030.0      9        2           5.000000              1   \n",
       "2     270000.000000  3040.0      9        2           3.000000              1   \n",
       "3     169019.448213  3050.0     12        4           3.000000              0   \n",
       "4      97000.000000  2970.0      1        2           3.000000              0   \n",
       "...             ...     ...    ...      ...                ...            ...   \n",
       "3181  169019.448213  3000.0     11        4           5.000000              0   \n",
       "3182  169019.448213  2830.0      5        4           6.000000              2   \n",
       "3183  169019.448213  3080.0     12        4           9.000000              2   \n",
       "3184   44000.000000  2950.0      4        4           1.000000              0   \n",
       "3185  128000.000000  2980.0      6        4           4.709413              3   \n",
       "\n",
       "      Product Type  \n",
       "0               40  \n",
       "1               23  \n",
       "2               23  \n",
       "3               55  \n",
       "4                9  \n",
       "...            ...  \n",
       "3181             6  \n",
       "3182            22  \n",
       "3183             5  \n",
       "3184            44  \n",
       "3185            31  \n",
       "\n",
       "[3186 rows x 7 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_mean_imputed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "median_dtf = Median_Data_Transformer()\n",
    "X_median_imputed = median_dtf.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cost</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Month</th>\n",
       "      <th>Weekday</th>\n",
       "      <th>Ingredient Number</th>\n",
       "      <th>Product Level</th>\n",
       "      <th>Product Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>333000.0</td>\n",
       "      <td>3090.0</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>122000.0</td>\n",
       "      <td>3030.0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>270000.0</td>\n",
       "      <td>3040.0</td>\n",
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       "      <td>23</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>122000.0</td>\n",
       "      <td>3050.0</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>97000.0</td>\n",
       "      <td>2970.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3181</th>\n",
       "      <td>122000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3182</th>\n",
       "      <td>122000.0</td>\n",
       "      <td>2830.0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3183</th>\n",
       "      <td>122000.0</td>\n",
       "      <td>3080.0</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3184</th>\n",
       "      <td>44000.0</td>\n",
       "      <td>2950.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3185</th>\n",
       "      <td>128000.0</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3186 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Cost  Weight  Month  Weekday  Ingredient Number  Product Level  \\\n",
       "0     333000.0  3090.0     12        4                2.0              0   \n",
       "1     122000.0  3030.0      9        2                5.0              1   \n",
       "2     270000.0  3040.0      9        2                3.0              1   \n",
       "3     122000.0  3050.0     12        4                3.0              0   \n",
       "4      97000.0  2970.0      1        2                3.0              0   \n",
       "...        ...     ...    ...      ...                ...            ...   \n",
       "3181  122000.0  3000.0     11        4                5.0              0   \n",
       "3182  122000.0  2830.0      5        4                6.0              2   \n",
       "3183  122000.0  3080.0     12        4                9.0              2   \n",
       "3184   44000.0  2950.0      4        4                1.0              0   \n",
       "3185  128000.0  2980.0      6        4                4.0              3   \n",
       "\n",
       "      Product Type  \n",
       "0               40  \n",
       "1               23  \n",
       "2               23  \n",
       "3               55  \n",
       "4                9  \n",
       "...            ...  \n",
       "3181             6  \n",
       "3182            22  \n",
       "3183             5  \n",
       "3184            44  \n",
       "3185            31  \n",
       "\n",
       "[3186 rows x 7 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_median_imputed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgboost_dtf = XGBoost_Data_Transformer()\n",
    "X_xgboost_imputed = xgboost_dtf.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Weight</th>\n",
       "      <th>Month</th>\n",
       "      <th>Weekday</th>\n",
       "      <th>Ingredient Number</th>\n",
       "      <th>Product Level</th>\n",
       "      <th>Product Type</th>\n",
       "      <th>Cost</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>3184</th>\n",
       "      <td>2950.0</td>\n",
       "      <td>4</td>\n",
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       "      <th>3185</th>\n",
       "      <td>2980.0</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
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       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>128000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3186 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Weight  Month  Weekday  Ingredient Number  Product Level  Product Type  \\\n",
       "0     3090.0     12        4           2.000000              0            40   \n",
       "1     3030.0      9        2           5.000000              1            23   \n",
       "2     3040.0      9        2           3.000000              1            23   \n",
       "3     3050.0     12        4           3.000000              0            55   \n",
       "4     2970.0      1        2           3.000000              0             9   \n",
       "...      ...    ...      ...                ...            ...           ...   \n",
       "3181  3000.0     11        4           5.000000              0             6   \n",
       "3182  2830.0      5        4           6.000000              2            22   \n",
       "3183  3080.0     12        4           9.000000              2             5   \n",
       "3184  2950.0      4        4           1.000000              0            44   \n",
       "3185  2980.0      6        4           4.709413              3            31   \n",
       "\n",
       "               Cost  \n",
       "0     333000.000000  \n",
       "1     246373.187500  \n",
       "2     270000.000000  \n",
       "3     104642.882812  \n",
       "4      97000.000000  \n",
       "...             ...  \n",
       "3181   53999.269531  \n",
       "3182  185979.156250  \n",
       "3183  465221.593750  \n",
       "3184   44000.000000  \n",
       "3185  128000.000000  \n",
       "\n",
       "[3186 rows x 7 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_xgboost_imputed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "pristine_cost = 1000*X[\"cost\"].str.strip(\"$\").str.strip(\"k\").astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 600000.0)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(pristine_cost, bw_adjust=0.8, label='Pristine Data', fill=True, alpha=0.3)\n",
    "sns.kdeplot(data=X_mean_imputed, x=\"Cost\", bw_adjust=0.8, label='Imputed with Mean', fill=True, alpha=0.3)\n",
    "sns.kdeplot(data=X_median_imputed, x=\"Cost\", bw_adjust=0.8, label='Imputed with Median', fill=True, alpha=0.3)\n",
    "sns.kdeplot(data=X_xgboost_imputed, x=\"Cost\", bw_adjust=0.8, label='Imputed with XGBoost', fill=True, alpha=0.3)\n",
    "plt.xlabel('Cost')\n",
    "plt.legend()\n",
    "plt.xlim(0,600000)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.11.4"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}