LinearRegression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df=pd.read_csv(r'data1.csv',header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data=df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.00000000e+00,   3.38500000e+00,   4.45000000e+01],\n",
       "       [  2.00000000e+00,   4.80000000e-01,   1.55000000e+01],\n",
       "       [  3.00000000e+00,   1.35000000e+00,   8.10000000e+00],\n",
       "       [  4.00000000e+00,   4.65000000e+02,   4.23000000e+02],\n",
       "       [  5.00000000e+00,   3.63300000e+01,   1.19500000e+02],\n",
       "       [  6.00000000e+00,   2.76600000e+01,   1.15000000e+02],\n",
       "       [  7.00000000e+00,   1.48300000e+01,   9.82000000e+01],\n",
       "       [  8.00000000e+00,   1.04000000e+00,   5.50000000e+00],\n",
       "       [  9.00000000e+00,   4.19000000e+00,   5.80000000e+01],\n",
       "       [  1.00000000e+01,   4.25000000e-01,   6.40000000e+00],\n",
       "       [  1.10000000e+01,   1.01000000e-01,   4.00000000e+00],\n",
       "       [  1.20000000e+01,   9.20000000e-01,   5.70000000e+00],\n",
       "       [  1.30000000e+01,   1.00000000e+00,   6.60000000e+00],\n",
       "       [  1.40000000e+01,   5.00000000e-03,   1.40000000e-01],\n",
       "       [  1.50000000e+01,   6.00000000e-02,   1.00000000e+00],\n",
       "       [  1.60000000e+01,   3.50000000e+00,   1.08000000e+01],\n",
       "       [  1.70000000e+01,   2.00000000e+00,   1.23000000e+01],\n",
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       "       [  2.00000000e+01,   2.30000000e-02,   3.00000000e-01],\n",
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       "       [  3.40000000e+01,   3.50000000e+00,   3.90000000e+00],\n",
       "       [  3.50000000e+01,   6.80000000e+00,   1.79000000e+02],\n",
       "       [  3.60000000e+01,   3.50000000e+01,   5.60000000e+01],\n",
       "       [  3.70000000e+01,   4.05000000e+00,   1.70000000e+01],\n",
       "       [  3.80000000e+01,   1.20000000e-01,   1.00000000e+00],\n",
       "       [  3.90000000e+01,   2.30000000e-02,   4.00000000e-01],\n",
       "       [  4.00000000e+01,   1.00000000e-02,   2.50000000e-01],\n",
       "       [  4.10000000e+01,   1.40000000e+00,   1.25000000e+01],\n",
       "       [  4.20000000e+01,   2.50000000e+02,   4.90000000e+02],\n",
       "       [  4.30000000e+01,   2.50000000e+00,   1.21000000e+01],\n",
       "       [  4.40000000e+01,   5.55000000e+01,   1.75000000e+02],\n",
       "       [  4.50000000e+01,   1.00000000e+02,   1.57000000e+02],\n",
       "       [  4.60000000e+01,   5.21600000e+01,   4.40000000e+02],\n",
       "       [  4.70000000e+01,   1.05500000e+01,   1.79500000e+02],\n",
       "       [  4.80000000e+01,   5.50000000e-01,   2.40000000e+00],\n",
       "       [  4.90000000e+01,   6.00000000e+01,   8.10000000e+01],\n",
       "       [  5.00000000e+01,   3.60000000e+00,   2.10000000e+01],\n",
       "       [  5.10000000e+01,   4.28800000e+00,   3.92000000e+01],\n",
       "       [  5.20000000e+01,   2.80000000e-01,   1.90000000e+00],\n",
       "       [  5.30000000e+01,   7.50000000e-02,   1.20000000e+00],\n",
       "       [  5.40000000e+01,   1.22000000e-01,   3.00000000e+00],\n",
       "       [  5.50000000e+01,   4.80000000e-02,   3.30000000e-01],\n",
       "       [  5.60000000e+01,   1.92000000e+02,   1.80000000e+02],\n",
       "       [  5.70000000e+01,   3.00000000e+00,   2.50000000e+01],\n",
       "       [  5.80000000e+01,   1.60000000e+02,   1.69000000e+02],\n",
       "       [  5.90000000e+01,   9.00000000e-01,   2.60000000e+00],\n",
       "       [  6.00000000e+01,   1.62000000e+00,   1.14000000e+01],\n",
       "       [  6.10000000e+01,   1.04000000e-01,   2.50000000e+00],\n",
       "       [  6.20000000e+01,   4.23500000e+00,   5.04000000e+01]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x=data[:,1]\n",
    "y=data[:,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "          6.29000000e+01,  -1.34940000e+02,  -1.76550000e+02,\n",
       "         -1.59440000e+02,   0.00000000e+00,  -1.85100000e+02],\n",
       "       [ -1.57500000e+00,  -6.50000000e-01,   2.28800000e+00,\n",
       "         -3.00000000e-01,  -1.99500000e+00,   3.30000000e+01,\n",
       "         -1.99000000e+00,   2.54500000e+03,  -1.10000000e+00,\n",
       "         -9.60000000e-01,   2.19000000e+00,  -1.97700000e+00,\n",
       "          1.50000000e+00,   1.60000000e+00,   5.35000000e+01,\n",
       "          2.48000000e+02,   5.01600000e+01,   8.55000000e+00,\n",
       "          2.56600000e+01,   1.85100000e+02,   0.00000000e+00]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train=x_train.reshape(-1,1)\n",
    "x_test=x_test.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=x.reshape(-1,1)\n",
    "reg=linear_model.LinearRegression()\n",
    "reg.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.94983143068960507"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   76.46006415,    77.24827655,    79.75180958,    77.54651909,\n",
       "          76.10217311,   105.92216572,    76.10643372,  2246.45142559,\n",
       "          76.86482187,    76.98411888,    79.66830167,    76.1175113 ,\n",
       "          79.08033782,    79.16554997,   123.3906569 ,   289.12829266,\n",
       "         120.54457102,    85.08779454,    99.66759376,   235.52984901,\n",
       "          77.80215554])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  6.40000000e+00,   8.10000000e+00,   3.92000000e+01,\n",
       "         6.30000000e+00,   1.40000000e-01,   5.60000000e+01,\n",
       "         2.50000000e-01,   4.60300000e+03,   2.60000000e+00,\n",
       "         5.50000000e+00,   5.80000000e+01,   4.00000000e-01,\n",
       "         1.08000000e+01,   2.10000000e+01,   1.75000000e+02,\n",
       "         4.90000000e+02,   4.40000000e+02,   1.79500000e+02,\n",
       "         1.15000000e+02,   4.19000000e+02,   1.23000000e+01])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21, 1)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test=x_test.reshape(-1,1)\n",
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.85212152])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "76.097912500235964"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAD8CAYAAACRkhiPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x486fb60b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "temp_x=list()\n",
    "temp_y=list()\n",
    "def graph(formula,rang):\n",
    "    x=np.array(rang)\n",
    "    y=eval(formula)\n",
    "    #temp_x.append(x)\n",
    "    #temp_y.append(y)\n",
    "    plt.plot(x,y)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qzwM3A929y4n3KUHyi0p4ZPFaRj/7CfuOFPK36wfx7HUDadWort+liUg1Uu7h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\nEsX+P42EgY8RytxsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x486ebea5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph('0.85212152*x+76.097912500235964',range(0,7001))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from numpy.linalg import inv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "x1=np.matrix([x_train.shape[0],sum(x_train),sum(x_train),sum(x_train**2)]).reshape(2,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y1=np.matrix([sum(y_train),sum(np.multiply(x_train,y_train))]).reshape(2,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "ans=np.dot(inv(x1),y1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 76.0979125 ],\n",
       "        [  0.85212152]])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ans"
   ]
  }
 ],
 "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}