price_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple house Prediction with  area as a parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import linear_model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "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>area</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2600</td>\n",
       "      <td>550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3000</td>\n",
       "      <td>565000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3200</td>\n",
       "      <td>610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3600</td>\n",
       "      <td>680000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4000</td>\n",
       "      <td>725000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   area   price\n",
       "0  2600  550000\n",
       "1  3000  565000\n",
       "2  3200  610000\n",
       "3  3600  680000\n",
       "4  4000  725000"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/homeprices.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# %matplotlib.inlin\n",
    "plt.plot(df.area,df.price,'b+')\n",
    "plt.xlabel(\"area(sqr_ft)\")\n",
    "plt.ylabel('price(US$)')\n",
    "plt.scatter(df.area,df.price,color='red',marker=\"+\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = linear_model.LinearRegression()# fitting the model using linear regression\n",
    "reg.fit(df[['area']],df.price)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Give values inside the predict function to predict the values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([628715.75342466])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict([[3300]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using the line eqation to predict the values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "m=reg.coef_\n",
    "b=reg.intercept_\n",
    "x=3300"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([628715.75342466])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = m*x+b\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}