House prediction Linear Regression Implementation

Linear regression .ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#importing datasets from scikit learn\n",
+    "from sklearn import datasets , linear_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#load the dataset\n",
+    "house_price = [245, 312, 279, 308, 199, 219, 405, 325, 319, 255]\n",
+    "size = [1400, 1600, 1700, 1875, 1100, 1550, 2350, 2450, 1425, 1700]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#reshape the input to your regression\n",
+    "# -1 means create a arrray and 1 means each having 1 element. all elements in size2 are [[1400][1600] ..] \n",
+    "size2 = np.array(size).reshape((-1,1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#linear regression classifier\n",
+    "regr = linear_model.LinearRegression()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#fit module used to fit data frequently and quickly\n",
+    "regr.fit(size2, house_price)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "coefficient : \n",
+      " [0.11023544]\n",
+      "intercept : \n",
+      " 97.54621380846325\n"
+     ]
+    }
+   ],
+   "source": [
+    "#printing coefficient and intercept\n",
+    "\n",
+    "print(\"coefficient : \\n\", regr.coef_ )\n",
+    "print(\"intercept : \\n\", regr.intercept_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "by formula  [251.87583519]\n",
+      "[251.87583519]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# checking prediction by formula a + b(size) = price\n",
+    "size_new = 1400\n",
+    "price = regr.coef_ * size_new + regr.intercept_\n",
+    "print(\"by formula \", price)\n",
+    "# By inbuilt function predict\n",
+    "print(regr.predict([[size_new]]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Formula obtained for the trained model\n",
+    "def graph(formula, x_range):\n",
+    "    # x_range array converted to np array\n",
+    "    x = np.array(x_range)\n",
+    "    # formula is evaluated as y\n",
+    "    y = eval(formula)\n",
+    "    #plotting graph\n",
+    "    plt.plot(x, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Plotting the prediction line\n",
+    "graph('regr.coef_*x + regr.intercept_', range(1000,2700))\n",
+    "plt.scatter(size, house_price, color='black')\n",
+    "plt.ylabel('House Price')\n",
+    "plt.xlabel('Size of Houses')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "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",
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+}