Untitled8.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt #another way of writing the first line "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coefficients: \n",
      " [0.10976774]\n",
      "intercept: \n",
      " 98.24832962138083\n",
      "0.5808173118722726\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import datasets, linear_model\n",
    "house_price=[245,312,279,308,199,219,405,324,319,255]\n",
    "size = [1400,1600,1700,1875,1100,1550,2350,2450,1425,1700]\n",
    "plt.scatter(size,house_price) #syantax-  plt.scatter(x,y)\n",
    "plt.ylabel('house price')\n",
    "plt.xlabel('size of house ')\n",
    "plt.show()\n",
    "size2 = np.array(size).reshape((-1,1))\n",
    "#fitting into the model\n",
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(size2, house_price)\n",
    "print('coefficients: \\n', regr.coef_) #m\n",
    "print('intercept: \\n', regr.intercept_) #c\n",
    "##############\n",
    "#formula obtained for trained model\n",
    "def graph(formula, x_range):\n",
    "    x=np.array(x_range)\n",
    "    y=eval(formula)\n",
    "    plt.plot(x,y)\n",
    "#plotting prediction line\n",
    "graph('regr.coef_*x +regr.intercept_',range(1000,2700))\n",
    "print(regr.score(size2,house_price))  #score tells us how fit the model is to the given data\n",
    "################\n",
    "plt.scatter(size,house_price,color='black')\n",
    "plt.ylabel('house price')\n",
    "plt.xlabel('size of the house')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[317.78380528]\n"
     ]
    }
   ],
   "source": [
    "print(regr.predict(2000)) #to predict a particular value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "############################## LOGISTIC REGRESSION ##############################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "   "
   ]
  }
 ],
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