frequent-value-imputation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('train.csv',usecols=['GarageQual','FireplaceQu','SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FireplaceQu    47.260274\n",
       "GarageQual      5.547945\n",
       "SalePrice       0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().mean()*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d85fc6b730>"
      ]
     },
     "execution_count": 42,
     "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().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    TA\n",
       "dtype: object"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['GarageQual'].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'GarageQual')"
      ]
     },
     "execution_count": 44,
     "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": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "df[df['GarageQual']=='TA']['SalePrice'].plot(kind='kde', ax=ax)\n",
    "\n",
    "df[df['GarageQual'].isnull()]['SalePrice'].plot(kind='kde', ax=ax, color='red')\n",
    "\n",
    "lines, labels = ax.get_legend_handles_labels()\n",
    "labels = ['Houses with TA', 'Houses with NA']\n",
    "ax.legend(lines, labels, loc='best')\n",
    "\n",
    "plt.title('GarageQual')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = df[df['GarageQual']=='TA']['SalePrice']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['GarageQual'].fillna('TA', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d85fdf90d0>"
      ]
     },
     "execution_count": 47,
     "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().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'GarageQual')"
      ]
     },
     "execution_count": 48,
     "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": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "\n",
    "temp.plot(kind='kde', ax=ax)\n",
    "\n",
    "# distribution of the variable after imputation\n",
    "df[df['GarageQual'] == 'TA']['SalePrice'].plot(kind='kde', ax=ax, color='red')\n",
    "\n",
    "lines, labels = ax.get_legend_handles_labels()\n",
    "labels = ['Original variable', 'Imputed variable']\n",
    "ax.legend(lines, labels, loc='best')\n",
    "\n",
    "# add title\n",
    "plt.title('GarageQual')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d860f11b20>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tfeXpU5IfTPIZ4GHgkST3J3lJ37mGWZFnyyT5WDd5HjAJfJaZT9X+EHBfVf1YX9nOtiRvYKbE1wC7usvdVXVJr8F6luRngHcCLwCOAC8E9lXVpb0G60GSPweeB1wPfBfwJ8A/V9Vv9hqsB0n+DfitqvpYN3858I6q+tFegw2xVF8/sKxV1SsBktwFbKuqz3XzLwFW2hP2PcCngNdX1RRAkpX3in+63wNeDny0qi5L8kpW6P9kqur1SX4B+BzwX8A1VfWvPcfqy7NOFDtAVX08ybP6DDSbFXlYZsD3nyh2gKp6GNjYY54+rGHmWzvf1X3//u8B5/ScaTn4ZlV9GXhakqd1/6BX2nMDgCTrgTcC7wcOANcm+Y5eQ/XnC0l+J8m67vLbwON9hxpmpZf7viR/nOTyJD+Z5HZgX9+hzrKPVNVtVfUTwBXAfwJHkuxL8o6es/Xpq90bZp8A7kzyh8CxnjP15W+B36mqG4CfBB5j5itGVqJfASaAD3SXC4Bf7jXRLFbkMfcTkpwH/Drw493QJ4Dbqupof6nOriSfqarLhoy/mJn/fr+9h1i9SfLdVfVE91/t/2ZmB+gXge8E7uz25leUJM+tqq+dMra+qh7rK9PZ1nXFrwHfy8zhqTuq6pv9pprbiiz3JFuANVX1nm5+DzOvxgW8uar+us98Z1OSg8C7ZlteVbMua1GSB6rqZd30+6vqZ/vO1Jckb66q3++mr66qvxpY9o6qekt/6c6uJH8JfBP4F2Z+Ye5AVb2p31RzW6mHZd7M//+WymcAPwxczsye/EqyCng28Jwhl2V9Hu8SGfwtgu/pLcXysHVg+pZTlm0+m0GWgQ1V9UtV9UfAzwE/0Xeg+azIs2WAZ1TVkwPzn6yqrwBfWa7vfC+hw1X1u32HWEZqlumVKLNMD5tv3f8dgum+9bbPLCNZqeV+/uDM4M//MXN4ZiVZ/s/Ss+ulSb7GzOPyzG6abr6q6rn9RTvr5nqhW2kvfC895bnwzIHnybJ8XqzUcr8vyRuq6vbBwSQ3AHt6ytSXK/oOsJxU1aq+Mywjc73QnddfrLPv2/F5sVLfUL0Q+BvgKPBAN/zDwLnAVVX1VF/ZJGkxrMhyPyHJq4ATHyffW1X/1GceSVosK7rcJalVK/VUSElqmuUuSQ2y3CWpQZa7JDXofwHXrbaRK+VEEAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['FireplaceQu'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Gd\n",
       "dtype: object"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['FireplaceQu'].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'FireplaceQu')"
      ]
     },
     "execution_count": 51,
     "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": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "df[df['FireplaceQu']=='Gd']['SalePrice'].plot(kind='kde', ax=ax)\n",
    "\n",
    "df[df['FireplaceQu'].isnull()]['SalePrice'].plot(kind='kde', ax=ax, color='red')\n",
    "\n",
    "lines, labels = ax.get_legend_handles_labels()\n",
    "labels = ['Houses with Gd', 'Houses with NA']\n",
    "ax.legend(lines, labels, loc='best')\n",
    "\n",
    "plt.title('FireplaceQu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = df[df['FireplaceQu']=='Gd']['SalePrice']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['FireplaceQu'].fillna('Gd', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d860fc6970>"
      ]
     },
     "execution_count": 54,
     "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['FireplaceQu'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'FireplaceQu')"
      ]
     },
     "execution_count": 55,
     "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": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "\n",
    "temp.plot(kind='kde', ax=ax)\n",
    "\n",
    "# distribution of the variable after imputation\n",
    "df[df['FireplaceQu'] == 'Gd']['SalePrice'].plot(kind='kde', ax=ax, color='red')\n",
    "\n",
    "lines, labels = ax.get_legend_handles_labels()\n",
    "labels = ['Original variable', 'Imputed variable']\n",
    "ax.legend(lines, labels, loc='best')\n",
    "\n",
    "# add title\n",
    "plt.title('FireplaceQu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "imputer = SimpleImputer(strategy='most_frequent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = imputer.fit_transform(X_train)\n",
    "X_test = imputer.transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Gd', 'TA'], dtype=object)"
      ]
     },
     "execution_count": 60,
     "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
}