house_pred.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "32b622d8-440a-466b-8d5b-b173dd269945",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing the libraries \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "68339b0e-ff18-4e76-b3e9-d368dd63b61e",
   "metadata": {},
   "outputs": [],
   "source": [
    "House = pd.read_excel(r'C:\\Users\\Jiya Sharma\\Dropbox\\PC\\Downloads\\HousePricePrediction.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "170a9594-5436-43cc-8569-4e5e31120e57",
   "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",
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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>Id</th>\n",
       "      <th>SIZE</th>\n",
       "      <th>PRICE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>8450</td>\n",
       "      <td>208500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>9600</td>\n",
       "      <td>181500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>11250</td>\n",
       "      <td>223500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>9550</td>\n",
       "      <td>140000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>14260</td>\n",
       "      <td>250000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id   SIZE     PRICE\n",
       "0   0   8450  208500.0\n",
       "1   1   9600  181500.0\n",
       "2   2  11250  223500.0\n",
       "3   3   9550  140000.0\n",
       "4   4  14260  250000.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "House.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dafd9455-d6d2-4425-9820-8770233854a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2919 entries, 0 to 2918\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Id      2919 non-null   int64  \n",
      " 1   SIZE    2919 non-null   int64  \n",
      " 2   PRICE   1460 non-null   float64\n",
      "dtypes: float64(1), int64(2)\n",
      "memory usage: 68.5 KB\n"
     ]
    }
   ],
   "source": [
    "House.info() #for judging the datatype of the column data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "42c1c988-7f7f-493d-8ba4-711385457081",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'SIZE', 'PRICE'], dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "House.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c895356a-df97-4055-9028-e267f0ffa422",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=House[['SIZE']]\n",
    "y=House['PRICE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3a466562-1699-465a-8d06-ce337e8a5e70",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a51f532f-fcc5-4098-b2a1-f33c537a35b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,random_state = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7df123da-34d3-47e8-ae58-9fd2e1d2e3cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = y_train.dropna() #removes the rows from y_train where target values are missing (NaN)\n",
    "X_train = X_train.loc[y_train.index] #will select the rows with non-missing values\n",
    "try:\n",
    "    lm = LinearRegression()\n",
    "    lm.fit(X_train, y_train)\n",
    "except Exception as e:\n",
    "    print(f\"An error occurred: {e}\") #here the error occured of the target variable y_train having missing values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "42cebacb-8bab-4680-a550-022ce596fae5",
   "metadata": {},
   "outputs": [],
   "source": [
    "coeff_df=pd.DataFrame(lm.coef_,X.columns,columns=['Coefficient'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6824a436-de1a-4bb2-aed1-367c5c2b4a4f",
   "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>Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SIZE</th>\n",
       "      <td>2.409456</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Coefficient\n",
       "SIZE     2.409456"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeff_df #this means that the price is going to increase by 1  then the size increases by 2.409456 when other features like iD are constant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c9cad71d-ebf0-48e7-8f0d-dadcb342e87d",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = lm.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7d87b5dc-e29d-463a-920f-68510617dc68",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_test, predictions)\n",
    "plt.xlabel('SIZE')\n",
    "plt.ylabel('PRICE')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "83d5beca-fdc6-48ca-8571-6fc4dabb6dc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.40945585])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.coef_ #coefficient m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "88be8a8b-4354-4aa2-85fd-0880004c5ffb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "154977.78486428637"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.intercept_ #intercept c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e08a70da-11d4-4e40-ba7c-0d87d2c2ade3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "160519.53331241733\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Jiya Sharma\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\base.py:465: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "sample = np.array([[2300]])\n",
    "prediction_new = lm.predict(sample)\n",
    "print(prediction_new[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1903252b-e71f-42bf-b6c1-b89093e7b2ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "160519.53331928636"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2.40945585*2300 + 154977.78486428637 #for checking if the predicted value is correct or not using y=m*x+c\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92107abb-c17b-489e-a3ea-0d6a1ed935bc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}