missing-category-imputation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('train.csv',usecols=['GarageQual','FireplaceQu','SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>FireplaceQu</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>TA</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  FireplaceQu GarageQual  SalePrice\n",
       "0         NaN         TA     208500\n",
       "1          TA         TA     181500\n",
       "2          TA         TA     223500\n",
       "3          Gd         TA     140000\n",
       "4          TA         TA     250000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FireplaceQu    47.260274\n",
       "GarageQual      5.547945\n",
       "SalePrice       0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().mean()*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of houses')"
      ]
     },
     "execution_count": 26,
     "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": [
    "df['GarageQual'].value_counts().sort_values(ascending=False).plot.bar()\n",
    "plt.xlabel('GarageQual')\n",
    "plt.ylabel('Number of houses')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['GarageQual'].fillna('Missing', inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of houses')"
      ]
     },
     "execution_count": 28,
     "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": [
    "df['GarageQual'].value_counts().sort_values(ascending=False).plot.bar()\n",
    "plt.xlabel('GarageQual')\n",
    "plt.ylabel('Number of houses')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(df.drop(columns=['SalePrice']),df['SalePrice'],test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "imputer = SimpleImputer(strategy='constant',fill_value='Missing')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = imputer.fit_transform(X_train)\n",
    "X_test = imputer.transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Missing', 'Missing'], dtype=object)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.statistics_"
   ]
  },
  {
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}