linear_regression implementation from scratch .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(99,) (99,)\n"
     ]
    }
   ],
   "source": [
    "#Data Processing and Reshaping\n",
    "dfx = pd.read_csv('Datasets/linearX-Copy1.csv')\n",
    "dfy = pd.read_csv('Datasets/linearY-Copy1.csv')\n",
    "\n",
    "dfx.head(n=5)\n",
    "dfy.head(n=5)\n",
    "\n",
    "x = dfx.values\n",
    "y = dfy.values\n",
    "\n",
    "x = x.reshape((-1,))\n",
    "y = y.reshape((-1,))\n",
    "\n",
    "print(x.shape,y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   9.1\n",
      "0  8.0\n",
      "1  9.1\n",
      "2  8.4\n",
      "3  6.9\n",
      "4  7.7\n",
      "   0.99523\n",
      "0  0.99007\n",
      "1  0.99769\n",
      "2  0.99386\n",
      "3  0.99508\n",
      "4  0.99630\n"
     ]
    }
   ],
   "source": [
    "print(dfx.head())\n",
    "print(dfy.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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l9/fntgfwOmAK2bKZ3cAvJR0K/FZEPBjZkm23AgtGdCZmZjYiZQJCL/Bs7vNA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\nmSUOCGZmBsD/Bxj267mrCaOSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20a1b0e12e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y)\n",
    "X = (x-x.mean())/x.std()\n",
    "Y = y\n",
    "plt.scatter(X,Y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Dn0y7jwN+nPWt8yJwbK5f5FPAVZI+B9wH/GdV52BtaLScsJmNSg1XSBwtvEKi\nmVnriq6Q6CfbzcysFAcSMzMrxYHEzMxKcSAxM7NSHEjMzKwUBxIzMyvFgcTMzEpxIDEzs1IcSMzM\nrBQHEjMzK8WBxMzMSnEgMTOzUhxIzMysFAcSMzMrxYHEzMxKqTSQSJoj6WFJyyWdXmP7NEm3SLpf\n0m2SpuS2fVHSg5KWSTpXaZWrlO9hSUvS621VnoOZmTVWWSCRNAY4DzgImAnMlTRzSLYvAZdExG7A\nWcDZad8/APYlW2L3XcCewP65/Y5J67nPiohnqjoHMzNrrsoayWxgeUQ8GhGvA1cBhw7JMxO4Jb2/\nNbc9gC2A8cDmZEvvPl1hWc3MrE1VBpIdgCdzn/tTWt5S4Ij0/iPA1pImRcQdZIFlZXotjohluf0u\nTM1anx5s8jIzs+6oMpDUusAPXSD+VGB/SfeRNV09BayR9A5gV2AKWfA5UNL70j7HRMS7gf3S67ia\nXy7Nk9QnqW9gYKD82ZiZWU1VBpJ+YGru8xRgRT5DRKyIiMMj4j3AmSntBbLayZ0R8XJEvAzcCOyd\ntj+Vfr4EXEHWhLaBiFgQEb0R0dvT09PZMzMzszdVGUjuBmZI2knSeOAoYFE+g6TJkgbLcAawML1/\ngqymMlbSOLLayrL0eXLadxzwYeCBCs/BzMyaqCyQRMQa4BRgMbAMuDoiHpR0lqRDUrYDgIcl/QJ4\nOzA/pV8D/BL4GVk/ytKIuJ6s432xpPuBJWRNYd+o6hzMzKw5RQztthh9ent7o6+vr9vFMDMbUSTd\nExG9zfL5yXYzMyvFgcTMzEpxIDEzs1IcSMzMrBQHEjMzK8WBxMzMSnEgMTOzUhxIzMysFAcSMzMr\nxYHEzMxKcSAxM7NSHEjMzKwUBxIzMyvFgcTMzEpxIDEzs1IcSMzMrJRKA4mkOZIelrRc0uk1tk+T\ndIuk+yXdJmlKbtsXJT0oaZmkcyUppb9X0s/SMd9MNzOz7qgskEgaA5wHHATMBOZKmjkk25eASyJi\nN+As4Oy07x8A+wK7Ae8C9iRbtx3gfGAeMCO95lR1DmZm1lyVNZLZwPKIeDQiXgeuAg4dkmcmcEt6\nf2tuewBbAOPJ1mkfBzwtaTtgm4i4I7I1gi8BDqvwHMzMrIkqA8kOwJO5z/0pLW8pcER6/xFga0mT\nIuIOssCyMr0WR8SytH9/k2MCIGmepD5JfQMDA6VPxszMaqsykNTqu4ghn08F9pd0H1nT1VPAGknv\nAHYFppAFigMlva/gMbPEiAUR0RsRvT09Pe2eg5mZNTG2wmP3A1Nzn6cAK/IZImIFcDiApK2AIyLi\nBUnzgDsj4uW07UZgb+DSdJzVfAdBAAAHKElEQVS6xzQzs42ryhrJ3cAMSTtJGg8cBSzKZ5A0WdJg\nGc4AFqb3T5DVVMZKGkdWW1kWESuBlyTtnUZrHQ98t8JzMDOzJioLJBGxBjgFWAwsA66OiAclnSXp\nkJTtAOBhSb8A3g7MT+nXAL8EfkbWj7I0Iq5P2/4K+CawPOW5sapzMDOz5pQNfhrdent7o6+vr9vF\nMDMbUSTdExG9zfL5yXYzMyvFgcTMzEpxIDEzs1IcSMzMrBQHEjMzK8WBxMzMSnEgMTOzUhxIzMys\nFAcSMzMrxYHEzMxKcSAxM7NSHEjMzKwUBxIzMyvFgcTMzEpxIDEzs1IqDSSS5kh6WNJySafX2D5N\n0i2S7pd0m6QpKf2PJC3JvV6VdFjadpGkx3LbZlV5DmZm1lhla7ZLGgOcB3yAbP32uyUtioif57J9\nCbgkIi6WdCBwNnBcRNwKzErHmUi2GuJNuf1Oi4hrqiq7mZkVV2WNZDawPCIejYjXgauAQ4fkmQnc\nkt7fWmM7wJHAjRGxurKSmplZ26oMJDsAT+Y+96e0vKXAEen9R4CtJU0akuco4MohafNTc9g5kjav\n9eWS5knqk9Q3MDDQ3hmYmVlTVQYS1UgbukD8qcD+ku4D9geeAta8eQBpO+DdwOLcPmcAuwB7AhOB\nT9X68ohYEBG9EdHb09PT9kmYmVljlfWRkNVApuY+TwFW5DNExArgcABJWwFHRMQLuSz/G/hORPw2\nt8/K9PY1SReSBSMzM+uSKmskdwMzJO0kaTxZE9WifAZJkyUNluEMYOGQY8xlSLNWqqUgScBhwAMV\nlN3MzApSxNDWpg4eXDoY+AowBlgYEfMlnQX0RcQiSUeSjdQK4HbgkxHxWtp3OvD/gKkR8UbumD8E\nesiazpYAJ0fEy03KMQA83uHTq8pk4NluF6JDRtO5wOg6n9F0LjC6zmc4ncu0iGjaN1BpILHWSeqL\niN5ul6MTRtO5wOg6n9F0LjC6zmcknoufbDczs1IcSMzMrBQHkuFnQbcL0EGj6VxgdJ3PaDoXGF3n\nM+LOxX0kZmZWimskZmZWigOJmZmV4kAyDEn6M0kPSnpD0ogaBjio2RICI4mkhZKekTTiH36VNFXS\nrZKWpX9jf9vtMrVL0haS7pK0NJ3Lv3S7TGVJGiPpPknf63ZZWuFAMjw9QDZ1zO3dLkg7cksIHEQ2\nw/NcSTO7W6pSLgLmdLsQHbIG+IeI2BXYG/jkCP7bvAYcGBG7ky07MUfS3l0uU1l/CyzrdiFa5UAy\nDEXEsoh4uNvlKKHIEgIjRkTcDqzqdjk6ISJWRsS96f1LZBetobNyjwiRGZzVYlx6jdjRQ2lhvw8B\n3+x2WVrlQGJVKLKEgHVZmoboPcBPu1uS9qWmoCXAM8DNETFiz4VsOql/BN5olnG4cSDpEkk/kPRA\njdeIvXPPKbKEgHVRmm37WuDvIuLFbpenXRGxNiJmkc0uPlvSu7pdpnZI+jDwTETc0+2ytKPKaeSt\ngYh4f7fLUKGmSwhY90gaRxZELo+Ib3e7PJ0QEc9Luo2sL2skDorYFzgkTXS7BbCNpMsi4tgul6sQ\n10isCk2XELDuSMsv/CewLCK+3O3ylCGpR9K26f2WwPuBh7pbqvZExBkRMSUippP9f/nhSAki4EAy\nLEn6iKR+YB/gvyQtbrbPcBIRa4BTyFa2XAZcHREPdrdU7ZN0JXAH8PuS+iWd1O0ylbAvcBxwoKQl\n6XVwtwvVpu2AWyXdT3bzcnNEjKhhs6OFp0gxM7NSXCMxM7NSHEjMzKwUBxIzMyvFgcTMzEpxIDEz\ns1IcSMxKSsO1Q9IuTfKdJWmDB1ElHTA426ukQwZnS5Z02AieUNE2IQ4kZuXNBf6b7EGyuiLiMxHx\ngyZ5FkXE59PHw8hmTzYb1hxIzEpIc1btC5xELpBI+kdJP0trZXw+pV0k6cj0fo6khyT9N9mSAYP7\nfUzS1yT9AXAI8G/pocGdJd2byzdD0oicl8lGH8+1ZVbOYcD3I+IXklZJ2gN4e0rfKyJWS5qY30HS\nFsA3gAOB5cC3hh40In4iaRHwvYi4Ju33gqRZEbEEOJFsnRSzrnONxKycuWTrrZB+ziWb8+nCiFgN\nEBFD1zLZBXgsIh6JbGqJywp+1zeBE9PCYR8FrihbeLNOcI3ErE2SJpHVKt4lKYAxZNPlX0vzafPb\nmZvoWuCzwA+BeyLi120cw6zjXCMxa9+RwCURMS0ipkfEVOAxstUU/1zSBIChTVtkM9TuJGnn9Hlu\nneO/BGw9+CEiXiWbCPN84MLOnYZZOQ4kZu2bC3xnSNq1wPZk0+b3pdX7Ts1nSAFhHtnMzv8NPF7n\n+FcBp0m6Lxd0LierzdzUmVMwK8+z/5qNIJJOBX4nIj7d7bKYDXIfidkIIek7wM5k/TJmw4ZrJGZm\nVor7SMzMrBQHEjMzK8WBxMzMSnEgMTOzUhxIzMyslP8PKI2QCHhToNAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20a1cdec4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Normalisation \n",
    "#plt.scatter(x,y)\n",
    "x = (x - x.mean())/x.std()\n",
    "plt.scatter(x,y,color='orange')\n",
    "plt.xlabel(\"Acidity\")\n",
    "plt.ylabel(\"Density\")\n",
    "plt.title(\"Milk Acidity vs Density Plot\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# hypothesis equations\n",
    "def hypothesis(x,theta):\n",
    "    return theta[0] + x*theta[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Error calculation\n",
    "\n",
    "def error(X,Y,theta):\n",
    "    m = Y.shape[0]\n",
    "    total_e = 0\n",
    "    \n",
    "    for i in range(m):\n",
    "        h = hypothesis(X[i],theta)\n",
    "        e = (Y[i] - h)**2\n",
    "        total_e += e\n",
    "        \n",
    "    return total_e/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Gradient function returnin Slope of the equation\n",
    "def gradient(X,Y,theta):\n",
    "    \n",
    "    m = Y.shape[0]\n",
    "    grad = np.zeros((2,))\n",
    "    \n",
    "    for i in range(m):\n",
    "        h = hypothesis(X[i],theta)\n",
    "        grad[0] += (Y[i]-h)\n",
    "        grad[1] += (Y[i] - h)*X[i]\n",
    "        \n",
    "    \n",
    "    return grad*.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Implimenting Gradient Descent\n",
    "\n",
    "\n",
    "def batch_gradient(X,Y,theta,batch_size=30):\n",
    "    \n",
    "    m = Y.shape[0]\n",
    "    indices = np.arange(m)\n",
    "    np.random.shuffle(indices)\n",
    "    indices = indices[:batch_size]\n",
    "    grad = np.zeros((2,))\n",
    "    for i in indices:\n",
    "        h = hypothesis(X[i],theta)\n",
    "        grad[0] += (Y[i]-h)\n",
    "        grad[1] += (Y[i] - h)*X[i]\n",
    "    \n",
    "    return grad*0.5\n",
    "        \n",
    "    \n",
    "\n",
    "def gradientDescent(X,Y,learning_rate=0.01):\n",
    "    theta = np.zeros((2,))\n",
    "    \n",
    "    e = error(X,Y,theta)\n",
    "    de =e\n",
    "    error_list = [e]\n",
    "    theta_list = []\n",
    "    itr = 0\n",
    "    \n",
    "    while(itr<=10):\n",
    "        grad = gradient(X,Y,theta)\n",
    "        theta = theta + learning_rate*grad\n",
    "        ne = error(X,Y,theta)\n",
    "        theta_list.append((theta[0],theta[1]))\n",
    "        error_list.append(ne)\n",
    "        de = ne  - e\n",
    "        e = ne\n",
    "        itr += 1\n",
    "    \n",
    "    return error_list,theta\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "not enough values to unpack (expected 3, got 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-41-54fd877d03a6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfinal_theta\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtheta_list\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgradientDescent\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mY\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m: not enough values to unpack (expected 3, got 2)"
     ]
    }
   ],
   "source": [
    "final_theta, error_list,theta_list = gradientDescent(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Lkk+KmtkqM9toZhs7OqJ9ZltTWcEPP72MEz193PXwZi1VJyITylgD/YCZZQGCx/aRDnT3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\n/+yWNfugOXkSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20a1bb17e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "e,theta = gradientDescent(x,y)\n",
    "\n",
    "plt.plot(e)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'xtest' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-40-591532b69380>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mY\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Training Data'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxtest\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mhypothesis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxtest\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfinal_theta\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'orange'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"Prediction\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'xtest' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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r3S5IqxwIHZL0j5KerPGZtnfSOUWGPrcukvRG4P8An46IX3a7PO2KiFciYjHZ\naMrnSHp7t8vUDknvB56LiMe7XZZ2NJ1C0xqLiN/rdhkmUNOhz617JFXIwuD2iPh2t8tThog4IOlB\nsrae6dj4fx5wqaSLgeOBN0n6m4j4aJfLVYifEKyRpkOfW3ekYeO/AeyMiL/odnk6IWlQ0kD6uR/4\nPeDH3S1VeyJiTUTMi4gFZP9evj9dwgAcCBNK0uWSRoD/AHxP0qZm+0wl9YY+726p2ifpDuBh4HRJ\nI5I+0e0ydeA84CpgmaRt6XNxtwvVplOALZJ2kN2EPBAR06q7Zq/wm8pmZgb4CcHMzBIHgpmZAQ4E\nMzNLHAhmZgY4EMzMLHEgmJkZ4EAwM7PEgWBmZgD8f6KIBjtjGEn8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20a1b011978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X,Y,label='Training Data')\n",
    "plt.scatter(xtest,hypothesis(xtest,final_theta),color='orange',label=\"Prediction\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HgpmZJRwIZmYGOBDMzCzhQDAzMyBjIEhaJmmjpDZJlxZZP0/SPZI2SLpfUnNq\n3T9KelzSE5Kuk6Rk+VslPZocc3C5mZnVRsVAkFQHfAc4C1gEXChpUcFm1wI3RsTxwBXAVcm+bweW\nAMcDxwInkZtGE+C7wApgfvJZNtKTMTOz4cvyhHAy0BYRT0fETuBm4NyCbRYB9yR/35daH8B0YBq5\naTPrgRckzQEOjYiHIjdl243A8hGdiZmZjUiWQGgCnkt9b0+Wpa0HLkj+Pg84RNJhEfEQuYDYknxW\nRcQTyf7tFY5pZmZjKEsgFKvbL5yI+fPA6ZLWkqsS6gAGJL0ReDPQTO6Cv1TSn2c8Zu7HpRWSWiW1\ndnV1ZSiumZkNR5ZAaAeOSH1vBjrTG0REZ0ScHxEnAJcny3rJPS2sjohXIuIV4E7g1OSYzeWOmTr2\n9RHREhEtjY2NGU/LzMyGKksgPALMl3SUpGnAB4Db0xtImi0pf6zLgJXJ35vJPTlMlVRP7unhiYjY\nArws6dSkd9FHgF9U4XzMzGyYKgZCRAwAFwOrgCeAn0TE45KukPTeZLMzgI2SNgGvA65Mlt8KPAU8\nSq6dYX1E3JGs+xvg+0Bbss2dVTkjMzMbFuU6+UwMLS0t0draWutimJlNKJLWRERLpe38prKZmQEO\nBDMzSzgQzMwMcCCYmVnCgWBmZoADwczMEg4EMzMDHAhmZpZwIJiZGeBAMDOzhAPBzMwAB4KZmSUc\nCGZmBjgQzMws4UAwMzMgYyBIWiZpo6Q2SZcWWT9P0j2SNki6X1JzsvxMSetSnz9JWp6su0HSM6l1\ni6t7amZmNhRTK20gqQ74DvBOcnMhPyLp9oj4n9Rm1wI3RsQPJS0FrgIuioj7gMXJcWaRmx3trtR+\nl0TErdU5FTMzG4ksTwgnA20R8XRE7ARuBs4t2GYRcE/y931F1gO8D7gzIrYPt7BmZjZ6sgRCE/Bc\n6nt7sixtPXBB8vd5wCGSDivY5gPATQXLrkyqmb4p6YCMZTYzs1GQJRBUZFnhRMyfB06XtBY4HegA\nBgYPIM0BjgNWpfa5DHgTcBIwC/hi0R+XVkhqldTa1dWVobhmZjYcWQKhHTgi9b0Z6ExvEBGdEXF+\nRJwAXJ4s601t8n7g5xHRn9pnS+TsAH5ArmpqHxFxfUS0RERLY2NjppMyM7OhyxIIjwDzJR0laRq5\nqp/b0xtImi0pf6zLgJUFx7iQguqi5KkBSQKWA48NvfhmZlYtFQMhIgaAi8lV9zwB/CQiHpd0haT3\nJpudAWyUtAl4HXBlfn9JR5J7wvhtwaF/JOlR4FFgNvD1EZ2JmZmNiCIKmwPGL0ldwLPD3H028Mcq\nFme0TaTyuqyjZyKVdyKVFSadk7zsAAADTElEQVRWeUda1nkRUbHOfUIFwkhIao2IllqXI6uJVF6X\ndfRMpPJOpLLCxCrvWJXVQ1eYmRngQDAzs8RkCoTra12AIZpI5XVZR89EKu9EKitMrPKOSVknTRuC\nmZmVN5meEMzMrIxJFQiSrpH0+2T8pJ9LmlHrMpUi6S8lPS5pt6Rx2xOi0tDo44WklZK2SpoQL0BK\nOkLSfZKeSP4/+Ltal6kUSdMl/bek9UlZv1rrMlUiqU7SWkm/rHVZKpH0B0mPJtMEtI7mb02qQADu\nBo6NiOOBTeTeqh6vHgPOBx6odUFKSQ2Nfha5EW8vlLSotqUq6QZgWa0LMQQDwOci4s3AqcCnx/G/\n2x3A0oh4C7nh7pdJOrXGZark78i9aDtRnBkRi0e76+mkCoSIuCt58xpgNblxmcaliHgiIjbWuhwV\nZBkafVyIiAeA7lqXI6tkrK/fJX+/TO7iVTjK8LiQjEn2SvK1PvmM28bJZAKvc4Dv17os482kCoQC\nnwDurHUhJrgsQ6PbCCXDv5wAPFzbkpSWVMGsA7YCd0fEuC0r8M/AF4DdtS5IRgHcJWmNpBWj+UMV\nZ0ybaCT9Bnh9kVWXR8Qvkm0uJ/dI/qOxLFuhLGUd57IMjW4jIOlg4KfAZyLipVqXp5SI2AUsTtrl\nfi7p2IgYd+01kt4DbI2INZLOqHV5MloSEZ2SXgvcLen3yRNv1e13gRARf1FuvaSPAu8B3hE17nNb\nqawTQMWh0W34JNWTC4MfRcTPal2eLCKiR9L95Nprxl0gAEuA90o6G5gOHCrp3yPiwzUuV0kR0Zn8\nc6ukn5Orqh2VQJhUVUaSlpGbiOe9nsqzKioOjW7DkwwL/2/AExHxT7UuTzmSGvM99iQ1AH8B/L62\npSouIi6LiOaIOJLc/6/3jucwkHSQpEPyfwPvYhSDdlIFAvBt4BByj13rJH2v1gUqRdJ5ktqBtwG/\nkrSq0j5jrdTQ6LUtVXGSbgIeAhZKapf0yVqXqYIlwEXA0uT/1XXJXe14NAe4T9IGcjcJd0fEuO/O\nOUG8Dvh/ktYD/w38KiL+Y7R+zG8qm5kZMPmeEMzMrAQHgpmZAQ4EMzNLOBDMzAxwIJiZWcKBYGZm\ngAPBzMwSDgQzMwPg/wNEXQvmP09UFAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20a1bb175f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Drawing result \n",
    "def drawLine(theta):\n",
    "    x_pts = np.linspace(-2,5,10)\n",
    "    y_pts = hypothesis(x_pts,theta)\n",
    "\n",
    "    plt.scatter(x,y)\n",
    "    plt.plot(x_pts,y_pts,color='black')\n",
    "\n",
    "drawLine(theta)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualising Gradient Descent\n",
    "\n",
    "### Plotting Error Surface and Contours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.contour(T0,T1,J)\n",
    "plt.title(\"2D Contour\")\n",
    "th = np.array(theta_list)\n",
    "plt.scatter(th[:,0],th[:,1],marker='>',label='Trajectory')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Working on Housing Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "\n",
    "data =pd.read_csv('Housing Data.csv')\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# no any null values so no need of fillna"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QAQAADBKoAAAABglUAAAAgwQqAACAQQIVAADAIIEKAABgkEAFAAAwSKACAAAYJFABAAAM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b1a99b7898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting correlations\n",
    "num_feat=data.columns[data.dtypes!=object]\n",
    "num_feat=num_feat[1:-1] \n",
    "labels = []\n",
    "values = []\n",
    "for col in num_feat:\n",
    "    labels.append(col)\n",
    "    values.append(np.corrcoef(data[col].values, data.Price.values)[0,1])\n",
    "\n",
    "ind = np.arange(len(labels))\n",
    "width = 0.9\n",
    "fig, ax = plt.subplots(figsize=(12,8))\n",
    "rects = ax.barh(ind, np.array(values), color='red')\n",
    "ax.set_yticks(ind+((width)/2.))\n",
    "ax.set_yticklabels(labels, rotation='horizontal')\n",
    "ax.set_xlabel(\"Correlation coefficient\")\n",
    "ax.set_title(\"Correlation Coefficients w.r.t y\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9GmjNp9vk5ltJJV4rrt1hPzxwufjYBO0O+0FdR7fQ9P4L9VxJFzXsXEtT3/lc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\nItb/AKTAg4R8d7oqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b1a9bbd048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Heatmap\n",
    "import seaborn as sns\n",
    "corrMatrix=data[num_feat].corr()\n",
    "sns.set(font_scale=1.10)\n",
    "plt.figure(figsize=(10, 10))\n",
    "sns.heatmap(corrMatrix, vmax=.8, linewidths=0.01,\n",
    "            square=True,annot=True,cmap='viridis',linecolor=\"white\")\n",
    "plt.title('Correlation between features');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train.hist(bins=10, figsize=(20,15), color='#E14906')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b1a9bbd400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's see how the numeric data is distributed.\n",
    "\n",
    "data.hist(bins=10, figsize=(20,15), color='#E14906')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Dropping not nessesary col\n",
    "feature=data.drop(['Price','Address'],axis=1).values\n",
    "target =data['Price'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#making train and test data\n",
    "from sklearn.model_selection import train_test_split\n",
    "train,test,train_label,test_label=train_test_split(feature,target,test_size=0.33,random_state=222)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Implimenting Linear Regression from inbuilt function of sklearn\n",
    "from sklearn.linear_model import LinearRegression\n",
    "reg=LinearRegression(fit_intercept=True)\n",
    "model = reg.fit(train,train_label)\n",
    "predict = model.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.91514416048\n"
     ]
    }
   ],
   "source": [
    "#Predicting Score\n",
    "from sklearn.metrics import r2_score\n",
    "print(r2_score(test_label,predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
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