17MIS1022 (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(47, 3)\n"
     ]
    },
    {
     "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>housesize</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2104</td>\n",
       "      <td>3</td>\n",
       "      <td>399900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1600</td>\n",
       "      <td>3</td>\n",
       "      <td>329900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2400</td>\n",
       "      <td>3</td>\n",
       "      <td>369000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1416</td>\n",
       "      <td>2</td>\n",
       "      <td>232000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3000</td>\n",
       "      <td>4</td>\n",
       "      <td>539900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   housesize  bedrooms   price\n",
       "0       2104         3  399900\n",
       "1       1600         3  329900\n",
       "2       2400         3  369000\n",
       "3       1416         2  232000\n",
       "4       3000         4  539900"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reading data\n",
    "path='C:\\\\Users\\\\student\\\\Downloads\\\\ex1data2.txt'\n",
    "data = pd.read_csv(path, header=None, names=['housesize', 'bedrooms','price'])\n",
    "print(data.shape)   #no of rows and column\n",
    "data.head()   # display top 5 record"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(47, 1)\n",
      "(47,)\n"
     ]
    }
   ],
   "source": [
    "a=data['housesize'].values\n",
    "b=data['bedrooms'].values\n",
    "X=pd.DataFrame(a,b)\n",
    "Y=data['price'].values\n",
    "print(X.shape)\n",
    "print(Y.shape)\n",
    "\n",
    "n=len(X)\n",
    "m=len(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(47, 2)\n",
      "(47,)\n",
      "    housesize  bedrooms\n",
      "0        2104         3\n",
      "1        1600         3\n",
      "2        2400         3\n",
      "3        1416         2\n",
      "4        3000         4\n",
      "5        1985         4\n",
      "6        1534         3\n",
      "7        1427         3\n",
      "8        1380         3\n",
      "9        1494         3\n",
      "10       1940         4\n",
      "11       2000         3\n",
      "12       1890         3\n",
      "13       4478         5\n",
      "14       1268         3\n",
      "15       2300         4\n",
      "16       1320         2\n",
      "17       1236         3\n",
      "18       2609         4\n",
      "19       3031         4\n",
      "20       1767         3\n",
      "21       1888         2\n",
      "22       1604         3\n",
      "23       1962         4\n",
      "24       3890         3\n",
      "25       1100         3\n",
      "26       1458         3\n",
      "27       2526         3\n",
      "28       2200         3\n",
      "29       2637         3\n",
      "30       1839         2\n",
      "31       1000         1\n",
      "32       2040         4\n",
      "33       3137         3\n",
      "34       1811         4\n",
      "35       1437         3\n",
      "36       1239         3\n",
      "37       2132         4\n",
      "38       4215         4\n",
      "39       2162         4\n",
      "40       1664         2\n",
      "41       2238         3\n",
      "42       2567         4\n",
      "43       1200         3\n",
      "44        852         2\n",
      "45       1852         4\n",
      "46       1203         3\n"
     ]
    }
   ],
   "source": [
    "\n",
    "X=data.iloc[:,0:2]\n",
    "Y=data['price'].values\n",
    "print(X.shape)\n",
    "print(Y.shape)\n",
    "print(X)\n",
    "n=len(X)\n",
    "m=len(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Profit')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "import matplotlib.pyplot as plt    # from matplotlib import pyplot as plt\n",
    "\n",
    "plt.plot(X,Y,ls='',marker='*',color='r')  \n",
    "\n",
    "plt.xlabel(\"housesize\" \"bedrooms\")\n",
    "plt.ylabel(\"Profit\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#mean of X and Y\n",
    "mean_x = np.mean(X)\n",
    "mean_y = np.mean(Y)\n",
    "\n",
    "#Total number of values\n",
    "n=len(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.193033644189594 -3.8957808783118537\n"
     ]
    }
   ],
   "source": [
    "#using the formula to calculate b1 and b2\n",
    "numer =0\n",
    "denom = 0\n",
    "\n",
    "numer = sum((X - mean_x)*(Y - mean_y))\n",
    "denom = sum( (X - mean_x)**2)\n",
    "b1 = numer/denom\n",
    "b0 = mean_y -(b1 * mean_x)\n",
    "\n",
    "#print coeff\n",
    "print(b1,b0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ploting values and regression line\n",
    "max_x = np.max(X)\n",
    "min_x = np.min(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.193033644189594 -3.8957808783118537\n"
     ]
    }
   ],
   "source": [
    "#calculating line values x and y\n",
    "x=np.linspace(min_x, max_x,1000)\n",
    "y=b0+b1*x\n",
    "\n",
    "#ploting line\n",
    "plt.plot(x,y, color='r', label='Regression line')\n",
    "\n",
    "#plotting scatter points\n",
    "\n",
    "plt.scatter(X,Y, c='b', label='Scatter plot')\n",
    "plt.xlabel('Population')\n",
    "plt.ylabel('Profit')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "#print coeff\n",
    "print(b1,b0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.9923139460876023\n"
     ]
    }
   ],
   "source": [
    "# Calculating Root Mean Squares Error\n",
    "rmse = 0\n",
    "for i in range(len(X)):\n",
    "    y_pred = b0 + b1 * X[i]\n",
    "    rmse += (Y[i] - y_pred) ** 2\n",
    "rmse = np.sqrt(rmse/n)\n",
    "print(rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7020315537841397\n"
     ]
    }
   ],
   "source": [
    "# Calculating R-Squared Error\n",
    "ss_t = 0\n",
    "ss_r = 0\n",
    "for i in range(len(X)):\n",
    "    y_pred = b0 + b1 * X[i]\n",
    "    ss_t += (Y[i] - mean_y) ** 2\n",
    "    ss_r += (Y[i] - y_pred) ** 2\n",
    "r2 = 1 - (ss_r/ss_t)\n",
    "print(r2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scikit-learn is a powerful Python module for machine learning. It contains function for regression, classification, clustering, model selection and dimensionality reduction. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Linear Regression using Scikit learn \n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63926.2082498693\n",
      "0.7329450180289141\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "Y=Y.reshape(m,1)\n",
    "\n",
    "#creating model\n",
    "reg =LinearRegression()\n",
    "\n",
    "#fitting training data\n",
    "\n",
    "reg = reg.fit(X,Y)\n",
    "\n",
    "# Y prediction\n",
    "y_pred = reg.predict(X)\n",
    "\n",
    "\n",
    "#calculating MSE\n",
    "mse = mean_squared_error(Y,y_pred)\n",
    "r2_score = reg.score(X, Y)\n",
    "\n",
    "\n",
    "print(np.sqrt(mse))\n",
    "print(r2_score)"
   ]
  },
  {
   "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}