House_test1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c4caccdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "617ed6c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Housing.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7182deee",
   "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>price</th>\n",
       "      <th>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>stories</th>\n",
       "      <th>mainroad</th>\n",
       "      <th>guestroom</th>\n",
       "      <th>basement</th>\n",
       "      <th>hotwaterheating</th>\n",
       "      <th>airconditioning</th>\n",
       "      <th>parking</th>\n",
       "      <th>prefarea</th>\n",
       "      <th>furnishingstatus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13300000</td>\n",
       "      <td>7420</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12250000</td>\n",
       "      <td>8960</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12250000</td>\n",
       "      <td>9960</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>semi-furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12215000</td>\n",
       "      <td>7500</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11410000</td>\n",
       "      <td>7420</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      price  area  bedrooms  bathrooms  stories mainroad guestroom basement  \\\n",
       "0  13300000  7420         4          2        3      yes        no       no   \n",
       "1  12250000  8960         4          4        4      yes        no       no   \n",
       "2  12250000  9960         3          2        2      yes        no      yes   \n",
       "3  12215000  7500         4          2        2      yes        no      yes   \n",
       "4  11410000  7420         4          1        2      yes       yes      yes   \n",
       "\n",
       "  hotwaterheating airconditioning  parking prefarea furnishingstatus  \n",
       "0              no             yes        2      yes        furnished  \n",
       "1              no             yes        3       no        furnished  \n",
       "2              no              no        2      yes   semi-furnished  \n",
       "3              no             yes        3      yes        furnished  \n",
       "4              no             yes        2       no        furnished  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4c20153d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(545, 13)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "791307b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price               0\n",
       "area                0\n",
       "bedrooms            0\n",
       "bathrooms           0\n",
       "stories             0\n",
       "mainroad            0\n",
       "guestroom           0\n",
       "basement            0\n",
       "hotwaterheating     0\n",
       "airconditioning     0\n",
       "parking             0\n",
       "prefarea            0\n",
       "furnishingstatus    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5c50b93c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price                int64\n",
       "area                 int64\n",
       "bedrooms             int64\n",
       "bathrooms            int64\n",
       "stories              int64\n",
       "mainroad            object\n",
       "guestroom           object\n",
       "basement            object\n",
       "hotwaterheating     object\n",
       "airconditioning     object\n",
       "parking              int64\n",
       "prefarea            object\n",
       "furnishingstatus    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df. dtypes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4b1091f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['mainroad',\n",
       " 'guestroom',\n",
       " 'basement',\n",
       " 'hotwaterheating',\n",
       " 'airconditioning',\n",
       " 'prefarea',\n",
       " 'furnishingstatus']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_cols = [i for i in df.columns if df[i].dtypes == 'object'] #categorical column and this is a list comprehension\n",
    "cat_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "32394aa6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "yes    468\n",
       "no      77\n",
       "Name: mainroad, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['mainroad'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "97e92802",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no     448\n",
       "yes     97\n",
       "Name: guestroom, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['guestroom'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cd92431b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no     354\n",
       "yes    191\n",
       "Name: basement, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['basement'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c5a6237b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no     520\n",
       "yes     25\n",
       "Name: hotwaterheating, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['hotwaterheating'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "75d94243",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no     373\n",
       "yes    172\n",
       "Name: airconditioning, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['airconditioning'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c9450c42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no     417\n",
       "yes    128\n",
       "Name: prefarea, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['prefarea'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a4bcb254",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "semi-furnished    227\n",
       "unfurnished       178\n",
       "furnished         140\n",
       "Name: furnishingstatus, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['furnishingstatus'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b2ec6ae4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "97fb213c",
   "metadata": {},
   "outputs": [],
   "source": [
    "lb= LabelEncoder()\n",
    "for i in cat_cols:\n",
    "    df[i] = lb.fit_transform(df[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "063f4cfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price               int64\n",
       "area                int64\n",
       "bedrooms            int64\n",
       "bathrooms           int64\n",
       "stories             int64\n",
       "mainroad            int32\n",
       "guestroom           int32\n",
       "basement            int32\n",
       "hotwaterheating     int32\n",
       "airconditioning     int32\n",
       "parking             int64\n",
       "prefarea            int32\n",
       "furnishingstatus    int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "06202d94",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "<class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "x = df.drop('price',axis=1)\n",
    "y = df['price']\n",
    "print(type(x))\n",
    "print(type(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a68c100b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(545, 12)\n",
      "(545,)\n"
     ]
    }
   ],
   "source": [
    "print(x.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "26ef2f52",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "de2102d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(408, 12)\n",
      "(137, 12)\n",
      "(408,)\n",
      "(137,)\n"
     ]
    }
   ],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.25)\n",
    "print(x_train.shape)\n",
    "print(x_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9ba64c0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "72268ea4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m1 = LinearRegression()\n",
    "m1.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b46a729b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training score 0.6940896400463534\n",
      "Testing score 0.5747109471210592\n"
     ]
    }
   ],
   "source": [
    "print('Training score',m1.score(x_train,y_train))\n",
    "print('Testing score',m1.score(x_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "adfbef4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4152667.54481737 3573903.8779347  4169568.93650542 8176220.64316395\n",
      " 3155981.54806679 5026568.18929726 5665662.52721574 3629346.43951177\n",
      " 2154967.45671478 2695436.27238911 2804856.24522613 4227010.37239009\n",
      " 6381005.96342528 3873744.60009244 5520563.86821458 4849598.90730404\n",
      " 5836860.38115967 4914542.65936529 4366527.36305697 7801924.97428026\n",
      " 2695436.27238911 4068482.15515803 6393237.78378539 3353208.36123571\n",
      " 2569603.30362654 6606209.15005424 4858615.7441814  7869486.43176751\n",
      " 6770139.85733684 6472710.39675647 3085948.14140106 5171878.44410516\n",
      " 3807501.63638106 3827283.7803806  6641478.23234392 4504615.94437672\n",
      " 6533185.69139623 2788238.23737882 6324741.57833641 6725282.61345059\n",
      " 4058700.28520632 2088654.20119724 5290179.08779373 5841952.98283618\n",
      " 3712350.14015784 6776258.12374427 4168135.44057291 4307596.15437618\n",
      " 6439454.00907692 3511016.02815276 5044048.12678383 3202791.25540887\n",
      " 6467274.42987774 3909784.48703458 4441186.59461187 2689965.27374726\n",
      " 6259951.30163608 4621301.5738277  5077593.40893289 4997132.00513071\n",
      " 3973193.32841249 3090656.49192356 4335854.69681742 6657846.66955571\n",
      " 7984291.74156699 3617431.83023916 4460703.27616703 4823678.66246674\n",
      " 5343014.47876961 5229601.37904758 3862282.09699014 4306295.17242682\n",
      " 7304286.93032515 3071213.58748847 2881991.95033663 4294090.94588336\n",
      " 4649640.07712966 5119143.00202665 4216916.45580001 6935355.03477815\n",
      " 3312773.81488269 3837244.3879021  4112714.43077515 6069997.61403255\n",
      " 2222303.25816956 3112164.3030474  5087154.84410332 4220485.15489396\n",
      " 4235811.9002168  3167313.95510353 2621771.04906042 4993903.83146696\n",
      " 5791637.96671786 6033992.10122449 3376017.39290669 5135673.65221881\n",
      " 4092584.54576647 3767658.0700372  5132303.3953514  4313569.01075227\n",
      " 2204086.89680069 3239607.53569131 5634407.66945724 3455410.18932385\n",
      " 4492175.1550039  7264670.21978069 6467517.8831888  3903241.75771194\n",
      " 3996761.22935311 2709265.08336529 5097246.56566029 3035673.75001862\n",
      " 4915144.81876465 2857221.14207675 2692700.77306819 2739204.26152392\n",
      " 6696188.21876243 4931972.21992857 4465727.51239722 4537362.93695047\n",
      " 4733133.19909644 5372239.21773562 8509320.52083191 4020355.68562606\n",
      " 3935544.65830255 4474283.44852527 6803247.93467724 7201732.07111047\n",
      " 3235121.00649232 2903334.22077945 3271072.90741738 5044677.06391905\n",
      " 3878667.28746902 3692403.68988978 7147135.79516572 3508941.80064715\n",
      " 4360202.59527357]\n"
     ]
    }
   ],
   "source": [
    "ypred_m1 = m1.predict(x_test)\n",
    "print(ypred_m1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "439d189b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score,mean_absolute_error,mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b08e6bd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE 1107214481558.741\n",
      "RMSE 1052242.5963430395\n",
      "MAE 793170.6449500028\n",
      "r2_Score 0.5747109471210592\n"
     ]
    }
   ],
   "source": [
    "print('MSE',mean_squared_error(y_test,ypred_m1))\n",
    "print('RMSE',np.sqrt(mean_squared_error(y_test,ypred_m1)))\n",
    "print('MAE',mean_absolute_error(y_test,ypred_m1))\n",
    "print('r2_Score',r2_score(y_test,ypred_m1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8b2dde6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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