Evaluation of Regression Models in scikit-learn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt \n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.linear_model import LinearRegression \n",
    "from sklearn import metrics\n",
    "from sklearn import datasets\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "boston_data = datasets.load_boston()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['data', 'target', 'feature_names', 'DESCR', 'filename'])\n"
     ]
    }
   ],
   "source": [
    "print(boston_data.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(506, 13)\n"
     ]
    }
   ],
   "source": [
    "print(boston_data.data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _boston_dataset:\n",
      "\n",
      "Boston house prices dataset\n",
      "---------------------------\n",
      "\n",
      "**Data Set Characteristics:**  \n",
      "\n",
      "    :Number of Instances: 506 \n",
      "\n",
      "    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\n",
      "\n",
      "    :Attribute Information (in order):\n",
      "        - CRIM     per capita crime rate by town\n",
      "        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\n",
      "        - INDUS    proportion of non-retail business acres per town\n",
      "        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
      "        - NOX      nitric oxides concentration (parts per 10 million)\n",
      "        - RM       average number of rooms per dwelling\n",
      "        - AGE      proportion of owner-occupied units built prior to 1940\n",
      "        - DIS      weighted distances to five Boston employment centres\n",
      "        - RAD      index of accessibility to radial highways\n",
      "        - TAX      full-value property-tax rate per $10,000\n",
      "        - PTRATIO  pupil-teacher ratio by town\n",
      "        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n",
      "        - LSTAT    % lower status of the population\n",
      "        - MEDV     Median value of owner-occupied homes in $1000's\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "    :Creator: Harrison, D. and Rubinfeld, D.L.\n",
      "\n",
      "This is a copy of UCI ML housing dataset.\n",
      "https://archive.ics.uci.edu/ml/machine-learning-databases/housing/\n",
      "\n",
      "\n",
      "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n",
      "\n",
      "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n",
      "prices and the demand for clean air', J. Environ. Economics & Management,\n",
      "vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n",
      "...', Wiley, 1980.   N.B. Various transformations are used in the table on\n",
      "pages 244-261 of the latter.\n",
      "\n",
      "The Boston house-price data has been used in many machine learning papers that address regression\n",
      "problems.   \n",
      "     \n",
      ".. topic:: References\n",
      "\n",
      "   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n",
      "   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(boston_data.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Input Data \n",
    "x = boston_data.data    \n",
    "\n",
    "# Output Data \n",
    "y = boston_data.target "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(x,y,test_size=1/3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predicted = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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cxRyqWc4qtDQQqJgUbSesgI6nrMz7Y82s4T8cOY+Jlz/Mki4XNTsbOSdHmHmljeLlWX5nMYdqlrMKMWNMu/8aOXKkUSqYFi92m649D5veU1abgfPeML2nrDZdex42ixe7I920VmnyeKqqjLn1VmMGDTJm3756r8nIrDYSZ30P9Nibe11r96uCD8g3AZxjI36SD+RLA4EKhWg7Yfk8nq1bjTnpJOu/Ohhz0UXGuN1Rd+zKt0ADgWYNKRWF8vIMn970D+b/MJvuHKz9xYQJLLnkFWbeao+ajCnln2YNKdVBtXV+w5KnSnFfPYOHf5jmDQLV2Pl8yt2wYgW/uTMhqjKmVNtpYq9S7Uibl51cu5YTZk3hyOqN3k3bk/tw7Qnz2Zh/Odvi4ijcKgyY7CPD6EUd0I1VekegVDvS6vkNxsBDD8FJJ9ULAkuPGcN5M/7ChmFHeFNJoy1jSrWdBgKl2pHWzG/IyzPM7LsU5syxqocCZfZEbjx3LnPPu4nShM71TvSa4qka0q4hpdqR9MEuKnam1CvRUHsSb/zf1duVNK4PP88/kfFb/sNaWxaXJfydoq6lJLhrB4PvX2Cd6EO1kI3quPxmDYnI64DfUSpjzKQmdyzSCVgFJGD9Bf/TGHO7iAwClgApwOfAZcaYqqb2pVlDKla0pmZQTW2fHuUHmFGwnPv7Xcvhj04lKcm6w0gf7GJ+rp7oY1GgWUNN3RHc18Y2VALjjDGlIuIAPhKRFcCNwAJjzBIR+RtwFfBYG99LqagQ0NX69u1w553w8MMUbnV4B373de7Ogp/kIK5S9r5sY4+r5jX+/5vn5Rluy3VpwIhxfv9CjDEftGXHnskMpZ6HDs+XAcYBl3q2LwJy0UCglFdOjpCTU/Nfs8F/0RdfhJkz4cAB6NKF9MF3t6grqa42ZyipqNHsYLGIDBGRf4rIehHZWvMVyM5FxCYia4E9wLvAFmC/McbpecpOoJ+f184UkXwRyS8qKgrsaJSKVmVlcM01cMklVhAAePhhFvxyZ6OB3+LlWWzfEtfsHIRoq8CqWi+QweJngNuBBcBY4AogoMsFY4wLyBKRZGApcLSvp/l57UJgIVhjBIG8n1JR6YsvYMoU+Oab2m2DBsHzz1O2OZ2EOBd7Xs7GVNuIS3DSZcQ2kkdtbvYKv3CrTecTKCCw9NFEY8xKrIHlQmNMLlb3TsCMMfuB94GTgWQRqQlA/YFd/l6nVEwzBh55BE46qX4QmDoV1qwhb/OJzJpbhW1sPgPmvEPvKZ8hCdXEp5X5vMJvOGM5rbfOJ1CWQALBYRGJAzaJyC9F5EKgV3MvEpGenjsBRCQROBPYALwHXOR52nTgtVa1XKkICvkyl8XFMHkyXH89VFZa2zp3hmeegbw86N7dZ9dO2jnrOLA607ubmjkIvpaQPFTq5uDb/ktKq9gRSNfQDUBn4FfAn7HuBqYH8Lo+wCIRsWEFnBeNMctFZD2wRETmA2uAp1rVcqUiJOSDrN9+C+PGwfff127LyoIlS+Coo7yb/HXtVBd38T6uucK/LRdv0ACs7+d+juu9bBLzdT5BrGs2EBhj/uv5sRRrfCAgxph1wHAf27cCJwa6H6Xam7pX4uA5qZ6xhttys+tk+7RBRgb06VMbCObMgbvvhoSEek/zN/nM3r0c45J6E8kuuxyfQWNHkY2i/zWfZqqiWyBZQ++JyL8bfoWjcUq1R/7KQGzfbAtON1F8PDz/vDUg/Prr8OCDjYIA+C4VcfDtLJITEtjxwEQS87O9E9G0vpBqSiBjBPOAmz1fvwfWAjrNV4VdyPvlA+TvpOpILaUiO59Zc6ta1rYPPgC3u/62zEyri+i88/y+LCdHeGxBPIn52d4T/xMPJ1D0Pztul7Btk93bzaP1hVRTWrUwjYh8YIw5PQTt8UlLTKi8PMM111fS7ay13tILB9/O4omHE0Lep91w9u3ECXEsfqm6XhmI4hVDSR69kaRjdnG4MJXE/Gy2bWqmq6W8HObOhYULYcECuOGGsB6HziKOfoGWmGg2EIhI3UufOGAk8JAx5ig/Lwk6DQSqV18ncafn1+sPP1yYivuDbPbsCl3ftr/aP9N+7mDFO262b7bhSC2l+6mbSTrGyoQ2LmHHAxNxu5o4yX75pTU3YP1667HDAZ99BsMbDasp1WrBqDVUowBr0pcATmAbVn0gpcKmaLeN1EMJ7HpqNNXFXXCkltLtpM0U7w5t14a/geEV71hX/FbRt68DL/FgDDz2GNx4Y21aKMCFF1pjAkpFQCCB4GhjzOG6G0Sk8ciVUqFkc7H/w6NIO2ed98p875tDwdZ8TZ22aG727fxcm5U62uCOoabkcz0lJXDVVfDqq7XbEhPh4YfhyitBtJtGRUYgg8Wf+Ni2OtgNUaopNoch7Zx1jSZP2RyhHTBuLtvG14Ctz5LRq1bBsGH1g8DQoVBQYAWHMAaB9jLortoPv4FARI4QkZFAoogMF5ERnq8xWBPMlAobd6XdZ8qmuzK0ue+BZNvk5FgZOg0zdQBwOiE3F8aOhZ07a7dff701JnC0r/JbtYJ90vY1w7jFWU4q6jS1MM10YAaQDfyX2kJzB4FFxphXwtFA0MFiVX8BlhoBZ+e0kjfLZosNe6KT6gobGUe6W5Ztc/CgdSewfbv1OCXFKhMxqcl1nbzv35JFagIRic9RRU4ws4Z+Zox5OWgtawUNBCoUJ8Wwvd+nn8Jpp1lfeXnQz2fl9UZCcdKOsxkG3LgCsdX+vw8oy0l1SIEGgkDGCEbWFI/z7LiHp06QUmETcF98kLS6Vn91deNtJ59sTRpbuTLgIACtW8i+OTrDWPkSSCCY6CkjDYAxZh9wTuiapJRvTfbFB1mrTsJffQUjRsDLPm6gR40CW8tO4KE4aesMY+VLIIHAVjdd1FNSWtNHVVRr0UnYGPjb3+CEE6xgcPXV8N13bW5DKE7a4b6zUh1DIB2Ni4GVIvKM5/EVWGsNKxW1Ap4fUFJiLSH5Sp3cicpKWLsWBg5sUxsCWsi+lfv1uyayiknN3hEYY+4B5mMtM3kM8BaQHuJ2KRVULU3DDOjK+aOPrHUC6gaB44+H/PyAs4Kaa1Og3WE6N0C1RaCXA/8D3MDFWCUmIppFpFRLtHYhGX9Xzv/4u5P/3TCfOSV/xkadqqGzZ8N991mzhUPUplDvS8WmpiaU/UhE/iAiG4BHgB1Y6aZjjTGPhK2FSrVC3SvkK2Y6A84Ayssz9OrrRMQQF++k5xH1r65feWgHA68ay40lf/QGgX3Sgw9ueAUefTSgIAC+s5Iks5ArZrb8qr7VGU5KeTTVNfQNcAZwvjHmNGPMw4D+ZamIa64bpOHs2eoK37OSG2YA1ZS6jjs9n4HzVtDrZ/nsq6ji6lnVzL7OTWbmYY6fM47Tqj/yvuazAcdxxnnPMGP5+S06hoZZSWXr+1K2vj8pkwpaPOM3FGmmKrY0FQh+htUl9J6IPCEiZ1A7u1ipiAikRELDK2RHamlAGUC35brodtbaRvWMnLZqFj7tovSEtdzC3QC4JI4HTsth0rBn+OqTcd7VyWZf5653R9Gtu9Mqod0gaDXMSjqwOpO0ietadVWvcwNUW/kNBMaYpcaYS4AfA+8Dc4HeIvKYiEwIU/uUqieQbpCGV8jdT9nM3hVDm03D9Hdl7TzQGVelnU7pxbyRNp77jr2KKVP/jzt73ETJh8eQMv5rBs5bQUnPrSxcVOW9o+iavZUyl7WOQsOg1TA1tLq4S6uv6nVugGqrQBavLwPygDzPIjU/B24F3glx25RqpLmy0NB4UfekY3ZRtTeJkmUjcR62+03DrPc6Y5jyxdt85TqeXd3LEZs1HtD9lM38ZtVDpB6/jgOfZJLquYoHqNjUh7Tz19Z73LPO47qL3FslImpTQx2JTip9LUTfyUmczd7kimI5OcLHnzh48tmRVJfbcXR2cvUMXX1MBS6QCWVexpgSY8zjxphxoWqQUk0JpBvE1xWy2ZzOMwubTsOcn2vj4NtZxG+K55FX7+autx/hkZV30LW8jMQf7QasoJI8eiPFbx3f6Cq+ucdQ/yq/bmroMwvtHHw7q16b976eReKwbc2OGeTlGRa/VE3KpAIGzltByqQCFr9UrSmkKmAtCgSq42kv+eX+2tHS9s3PtXHgjRF8//gYCu85h+8fH8OBN0Y0KgsdyOzZhu8NsPT6At599RrO+9YaEM4w3/G3PvdgNqd7T9K2pEowgq17Rb2g1HAsItCxiRrGGUfxW8fz3f0TKX7reNxuiE8rq9cFNvfmxq/VrCHVVjqtMIq1l/xyf+34+BOHdxH4lrRP7G5Sz/qy3iL2DTU3e7Zhm/Z/NIhvZrzE7c4/Ya+THLfoyMnccuB3TLvY0/VSYceRWkry6I0A7H1zqHfVtMQhuyl6PYue56/1+bip1ctuuMmF01aN80Bnaw3kUzZjS6qk5F/HetdCTuhfwne7beTlmXqfTyDdZUo1pdky1O2BlqFunfZSe95fO0qWjSRlUkGL2hesY6q7n64Fdh56707Guj7w/n4fydw48ne8d+bR3v1bZSfql6be88oI4uIE12ErQHRK38vh79KsdZVTSqkuTqJbiotD+61+/okT4ljxjpvCrTZvvz/A5TMr6TmpNmAUrxhK99M2UvxmFum3vOk9zuK3jqd/WkK9Y20v/86q/Qnm4vWqg2ovV4r+2lFd7ie/v4n2BeuYavZzxubPuHvlX0kztSfR//Y7hmtH3MGXq8+gL6u8+6+p/TP35my+223DnlxOyvivsXetZO+rI0kZ/3Wjk7H7g2z27HIAkJdn83ln1Mluo+ek+oPKqRPXUfzW8di7l2Ncwv6PMzn0+SBMlZ3CMle9u4IWrZuslA86RhDF2kt+ub92ODo7A25fTX++ccOuJ0+nbH3fZl/TXJtuWf4Pnnr5z94g4JI4/nLqVKZceid7j4qnurhLo/3n5AhJSdB7ymf0+8X7dDluF53Si0kavo3i5VmNUjgX3Ft7MvbXl1+020/a6v7OuKtsfHff2ZSuG0ivCwsYeJM10a3uwLFWFFVtpYEgioUrv7y5AV9/7bh6RmDtqzuJbOC8FaSe/SX7PjiK0q/6tvqY5ufaKNwxwvt4V2JPLp1yBwt+koMrzmYFqpRSDhemcuCNEZSV4T2+7VviGp24k0dtxlnW9Ml4+5Y4St49lsJ7zmHXU6MpW9+XhP4liMN3oOyW4iQpSbAnV3jTUP0NBodzrQYVfbRrKIqFqoxxXYEMSDfVjlGnNt++ulfSgHfG756Xs0lPp8lj8q47vNVGly4uyquM1Z/f2cnoU+ew8pN/c6jcxkzbI8SZ70lwWV0rRa9n4S6Lx/VeNmJ3Enf65wzobx2f/X9Z7P84kx6jN3nfp3JnChlHuuv0ydsbtcOeVEXK+K/rjQNU7U0iNQXKVzbu2nnsIWsf06bFt7gLTamW0MFi1SbhGKhs7Tq7dYOU7etEHJsTOXxBifdku/f1LK671MmJoztz9WynN2vH3r0cu8vBk485uC3X5fP49rycjS2pku6jvsXetbLZ9Yz9fU57Xx3Joietz6kmYDWcPKaDwaq1grlmsVJ+haPgWWvHOube7EIyC8lcUcZHX07m5aopxB2Kq60jdP5aHstLIGdaHE8+5qB/WgIi0D8tgScfc5CTIxRu8X18ptqG80BnSv51HOVvNt8n7+9zclfaPamu/rt2tISECjUNBKpNwjEg3ZoTYV6eYe9u+GXBEt4/eBaD2M5I1xrmvZvnHWiuyVwCq/tqfq51NV641cZtuS5mX+cmLsH3gLY9udwauL2wgAq3s9lj8Ps5Hdn856SDwSrUNBCoNgnH1WprToQP3baDd+Qs7qi6HbuxTrYHE5LYkN2LA6szgdrMJfBd1XTh0y4rG6hBwbq9bw4l+Sffeu8sUs9b2+ws3rZ+TjoYrEJJOxhVm4RjQLrmfZpbZ7dmYPjYLW+x3FxBT/Z6f5ff72jmnH8zO7v0pnp1F75/fAzO/Z3pluL0vq7hgLSr0k7yqM3Ep5VR8q9jqS7ugthdpEz40jvbFwIbuA3X56RUa+hgseoQak7W27fE4Uh04aywk35knZm6W2wkOMq40/lbbnA/7H2dG+GRUy7mL6ddiivOZg30Lh1JrwsLSOgaAC6yAAAa1klEQVRfwv6PMylbM8g7M7j7qZsBa32A6r1dsCeXk/yTb70n/u8fH0Pq2V/qwK3qECI+s1hEBgDPAUdgrXe80BjzF08p6xeADGA7cLExZl+o2qE6hrppng2zZmq6bSSzEFtRf1ImWrV9Sj7OZOGigaSdv5bRo7/koaX3MXTft9597nL04jLbc3yd3p0EU0JloZUW2nXENjqlF1O2vi/l6/uTNrmgNpPozWG4q+PodcGaOtuGYtxg71qJVDvY+3oWaQHUD1KqowjlJYwTuMkY87mIdAUKRORdYAaw0hhzl4jcirW2wa9D2A7Vzs2+zs0Tz1WRet5an3MRarptSv51rHcVL6hf///iD96pFwTezTyRWybOYWv+IA4tzcBUObB3L8ddFk/yqNqr/rrrCVjzE76g+K3j/c5ZeOZx64SvXTwqmoQsEBhjdgO7PT8fEpENQD/gAmCM52mLsFY/00AQo/LyDAufdpE22fcCLjk5dgq3WHWBmqr3v+C0HE7bvpajirYzz3U/r/90MIiQPGozB1cPQeKstNCyRLd3ARh/6wU4D3RutM1U29i2qfZk39x4hVIdSViyhkQkAxgOfAb09gSJmmDRKxxtUO1HXp7xrut72ZVOXId959hv32yVXLZ7Vu+qV9/f1F+LuNrm4PpJt3DOmU/xqGM2u5626hFV7kwhI9PlzbYZelwcRa9bNYEcKb7XC7B3L2+0TRxa219Fr5BfzohIF+Bl4AZjzEGRwG6hRWQmMBNg4MCBoWugCqu8PMM111fS7ay1DOxfW87BV8kGe3I5s+baqS53sHfFUJKO2Unxm8fxh0G3ckLZ51ySOaRef/3Gg8ezd/VQUiZ8aVUEfXMo7sN2/v50bf/9qo9ddBn2nScLKKnRegF73xiGu9IaVK47RpCa4utolIoOIc0aEhEHsBx42xjzgGfbRmCMMWa3iPQB3jfGHNXUfjRrKHr4K5dQN5OnJjikjNtgLc6ybCSJw7bRbWMcT5dcy3hWAnCj434OX3MDK95xs32zrVGGT00JB2eFw/teIoaB82rLVZSt78uBTzKt9QPSSkkcsptD+YOxJVU2KjcB/stAKNUeRbzEhFiX/k8BG2qCgMcyYLrn5+nAa6FqgwqvQJad9FdqwVTZKXn3WL67fyJ7lo4k6ajdJB2zy+qzr7Bz5tffsKbsRG8QAPj1kcsYdXLte/S9+oNG+f3uyvo3vQ1nCicds4uU8V/jSC0l5cyvMZvTufaauEblJoBGE878rSGsVEcTyjGCUcBlwDgRWev5Oge4CxgvIpuA8Z7Hqh1oy/rGvmbm+jpRpvX2XWpB4p1UF3fB3r2c1AlfkTJ+PQCmsAsLu9/IywcvIK3SyjJ2I3x1/q2svPUdZt1k3WH4Wx8Yu6vesbgrbd4xgpoZvkWvZ1FdnOSdsfzXR+MazeLVdYFVNAtl1tBHgL/75jNC9b6qddq6vrGvmbl1M39q3uNQqZvqN4aRdu4X9frgwXDGeMN/1tqxJVViXELfDWX89e3fM9y5xvs+u+jDzX2f5ZxLxnve0+pm6n7qZopXDCXVM8egpnsp6dgdVPz4B++xZGS6+UFKKHp1BO7DDuI6VZMwYC+9Ta8mJ4S1l9XelAoFnVmsgLaXOg6kVPSgIU5Kem61llystCMOF3GdqugxZiO2pErvusC35boYvSWPv3IdSabMu7837Wcxd8wtHEjrTPnK4ZTujWfATU3391ds6kPfq1Y1ue5wcyWkg/H5KBUJER8jUB1LW8tJB1KFdPuWOMrW97eWXJxnLbkocfXfKydH2HbjEywyM7xBoCrOzh/HXsOVP7udrf89GVdZAp3PWONNK63h7e9PK6XvVatIHrXZu9xk3f23ppKnloJW0UwDgQLaXk46kBOlI9HlnRnsrdw5cR0HVmfWf6+pUyE9HYAtXQdw4WX38cyJF9Apo8T7/IT+JVRXNH7P4hVD6X7KZm/7HamljY6lNZU8tRS0imYaCBQQnDLJzZ0onRX2RncdzkMJOA8k8sOSkygrs8YRSE6G55/naa7gvCsW8PURmd7nJ/S3ZhjXLA352IJ43B9k8919VrZR52N20vmo3Vbq6BvD6HbS5oDXL2huoFxLQatopWMEystb+G2LDXuik+oKGxlHuv3myzdVKM6Xhv3sZev74ngvmcuOeowlY8c06q/31y9f/NbxJDrs9QJNXp7hhptcFJeAqbbRLcVJvEMoLmq+bXUHylsybqBUe6djBKrFalbp6pJWRcqkAgbe9JbfNNBA00Xrmp9r4+DbtambJ/z7O76oHsndBfdy9uaPG6Vk+rpLKVqWRaI7wedJuktXwGUjI9PFXx+yU/S/wK7e596sqaEqtukdgaon0OyY1mbR9DzCSdnhQ/zxwB3czP3e7fs6deW0a5+i1J5UL9No9nVuFj7twlVpx5FSSuKPdlP+5UCcZfHeuxWg1Vf0eXmGadOoN9sYGmc8KdUR6R2BaqQtM38bZg/VfV7Z+r7semo0P7xwEoWFNHlXkLxnG6s7nVQvCBQlJXP9pFsoS+jcaIB6xTtu0iYXkH7Lm/S9ehU9Rm8i9by1OFLKvHchrbmir/ksLrvChT25POTrLivVnmkgiBGBduUEmj1U87yy9X3Zv+ooUs782lrM/WdNdBHl5bGGEQz7oba43Fv28Ywdn8eHA0f4HNT1F5iqS7p4T/hFu1uW+lr3szBOG91HfdtoXeKiZVmaGqpihgaCGBFoiYRAs4dqnrf/wx95F3fxu99Dh2D6dJg2jS7GSuesirPz5zFXMyX7Ib55azw77vedaeQvMNWkhSb0L0EcLUt9rftZOFJLsXetJHn0Rkr+ZdU6Kn7reFK6alaQih0aCGJEoF0+gebL1zzPub9z0/v96isYORKee877+4O9M/lZ34+4fdVCeuzNZNGTdtxu34O6vgJTw7kCqSm0KPW17mfR/RSrNIUtqZI+Mz6k9yWfkeiw8+D9ejegYocGghjRkgljNfnyf/ecuy+7HJ9jCjk5QkZmM/vt1g2Kimp/efnldNv0Oa/vOCmgjJ6agFOybKS3MmnduQLFy7N48H6b3+Dla1yk7meRdMwukkdvpPit4/nuPp0opmKTZg3FiJbmygf6/ICe98orVtfQY4/BtGmtan9NLaPyjX04sNpTTyillOqSJIzb9/WMv7ZN+7mDxS9V67wBFfUCzRrSQBBDWjIBrCXpoTX73b4ljsEJ29hWOZj0Ixvsv6gIevZsdVtLD4FtbMvSVZs6hpridrrIjIpmmj6qGmlJiYSWFqE7fLCau2y3sKHqx1ww9YHGWUl1gkBzaaw1V/IlPbdi71HK9i02SvYbSl4d2aISGE0dg5aLUKqWBgLlU6BjCnl5hrt/9Q2vFY/m1877iXc7eXj5PfTqXegzKymQNNbbcl1IZiHl6/uTMt5KS+15YQFumwvXe4EXfWtrIT2lYoUGAuVToGmkn92Ux8cHsznRVdt1tzWlH3a3y+cdRHNprLOvs9Yfrvi2T6O01LTz19KlKwFfxWvpaKUCo4GgFdqypGNH0WwaaWkpXHklD/1wGV2d5QBUi43/G3MF0y/+IyWdu/u8+m6qu2b2dW4WLqrCnlxOdUmXNq2PENAxKKWAEC5VGa3auqRjR5KTI95lJuv9qaxZY60ZsHGjd9O2zv25VP7BtiMcJLitTJyDb2fhircyfmoGZNMHu6jYmVJvALcmYDz5rCFt0lpcZQkUv3MclX6e15I/W7/HoJTyito7glBdtcf0IubGwF/+AiefXC8IvJAwhfETn2XTmC7efPw9L4+ksjwO29j6YwETJ8T57a6pLrfWK0g6Zhddjt3ZaJF57dZRKjSiMhC0pkRyoNq6pGN70pJgmZdnGJexgcobboGqKmtjUhIsWoTzqX/g+uJ0St7MwhyOp9spm7B3O0zPCz9vFDBXvOP2213j6Fy79GTK+PWkjNugE72UCoOoDAShvGqPlkyU5oJl3SDR8wgn11xfyfpTD3LX2OkArLEPZ1luAVx+uXefxoB0qiI+razJPn5/qZtXz7Cxt85dgC2pElNtY9ZsoymeSoVQVHaaFm61MWCyj5PQi22/ap+fa7PGBBrMSr1/Qce6I6gbLAHr+xlruC03G7DVGwfZ9eTppJ79JZ3Si3lm4CQq4hP4R7efU3HfkcgDTop227AnV5J67rfYu1ZatXu6VLS4j/+vj8YB8Tz57Eiqy+04OjuZOcPm2d7yFdGUUoGJyjuCUF61R0smSlNdXLfluvjxSW/w4qezyTj4Pc4DdQrLibAk62zKy7tScshJ3On5DJy3gtSzv+TAR0fhKksgdeI6iKNRaedA+vj/+mgcVWUOjBGqyhz1gkCouvuUinVRWWJC16BtXlPlF07c8jJPOK6kW1U5XxwxhFOrV9N1/KZ6z/3+8THeu4S6ry/517H0mfEh390/kW4nb+LQ54Ogyt645EQQ29vUimhKxbKYLjERLVftoeRrshX/+hHv9r+WF8wUulVZcwOO2bOV8T96mb1v1r+691d+urq4C5U7UxC7i4pv+2CqbH5LTLdENA3SK9XeRO2llOaPN806Kccz9+ZsvtttY5j9c140o8h8/xvvcwqT+vKryTfxmWsojq/suD/IZscPVv98zz4un2MA9u7lFK8YSurZX2JLqiQxP5tgXG80Nf9A/32VapuovCNQgTtc7eQ3I+bxqTmFH7lqg8D2U6Zyfq91vP78XBLzs3nyMQd7dtVm+iy4t/EdRdGyLNxVNrqfthFbUmVQ8/61XIRSIWSMafdfI0eONKqxxYvdJiOz2kic9X3xYneLXp816H9mRb+fGGNlfhoDptSeaG7q9aQx7ub31fD9Z812tak9LX2/1uw/GPtQqqMA8k0A59ioHCyOBW0eED94kB3dj2UAO72bvup9JNefewsfPHsNblf0jadoEoGKNbowTZQLRhbNkz3mcvX+BwF4KvsC7j59Bge/PyJqM3E080jFmpjOGooFwciiSXrwLt51TGDa6Pv405iZHPz+iKjud9fMI6V800DQQbV40tzSpbB3b71NU6cnsOeZt/h415wWp9l2xFLc0VIeRKlg00DQQQWcRVNeDjNnwk9/CldeaQ0J19GaJRs76ixfzTxSyjcNBCEQ6NVyW66qA5o0t24dZGfDE09Yj19/HZ58si2HBnTcUtw60VAp3zQQBFmgV8vBuKr2ezVvDDz6KJx4ImzYUPuCiy+Gn/+8zcfYlr72SHcp6aL1SjUWsqwhEXkaOA/YY4w5zrMtBXgByAC2AxcbY/Y1t6+OlDUUaGZKyDJYiovhqqvgtddqt3XuDA89ZHUNSdtPfK1tu6ZvKhVe7SFr6Fng7AbbbgVWGmOGACs9j6NKoFfLIclgWbUKsrLqB4FhwyA/3woOQQgC0Pq+9o7apaRUtAtZIDDGrAJKGmy+AFjk+XkRMDlU7x8pgWamtCWDpVH3yt9dkJsLY8fCztoJYlx/PXz6KRx9dKuPx5fW9rVr+qZS7VO4xwh6G2N2A3i+9/L3RBGZKSL5IpJfVFQUtga2VaBXy629qvY5tnBjNYUr1oPbbT0pNRWWLbO6gzp1CslxtqavXdM3lWqfQjqzWEQygOV1xgj2G2OS6/x+nzGmR3P76UhjBBD4SlqtWXHLX/98r88yWefOhkGDYPFi6Ncv6MfVVjpGoFR4tYsSEz4CwUZgjDFmt4j0Ad43xhzV3H46WiAIpTibIfP6VxGbodKRAIBxCTsemIh72w4rANjab1eLLjepVPi0h8FiX5YB0z0/Twdea+K5USHY6ZLj+33Bqwt/zQ1/XUHhPeew66nR7P840+peGTiwXQcB0PRNpdqjkAUCEXkeWA0cJSI7ReQq4C5gvIhsAsZ7Hkcd78k/zjD9aiclPbe2fQauMfC3v/H6rlM4pmITsw4/yTWTfk/KmV9Ttm4gEydEZkpIpOcFKKXaLpRZQ1ONMX2MMQ5jTH9jzFPGmGJjzBnGmCGe7w2zijq8eoO5N60gbXIB5ev7U76xT+vTJUtK4KKLYNYs4l2HAaiwJ9DFWU6n9GLSzl/LinfcLW5nW0/gHbXUhFKqPi1DHWT+BnNL/nUsfa9aVdufH2i9/48+gksvhR07vJs2pGVw/QW3sDltIECL9xmsQVst66xU+9Zexwiinr9c+eriLkAL0iVdLvjTn+D00+sFgee6z2LimGe8QaBF+/QI1sQunRegVHTQQBBk/nLlHSmlgVe73LkTxo2D22+vnRvQowcsXYrt0Ucp+eCUNlXQbMkJvKkuJJ0XoFR00EAQRHl5htJDULQsq96Junh5FtUlSYFXu5w92yoXUWP0aPjiC5g8OSgVNAM9gTc3BqBlnZWKEoEsbBzpr46weP3ixW7Ttedh03vKapN67ufGnlxqwG169ml+gfSGC6ovfXCbMcnJxsTFGfOnPxnjdAb82kAWY6/b1oHz3jC9p6w2XXsebvTajMxq03vKapP+6+Xer95TVpuMzOo2vb9SKjzQxevDK9gVOV+7/F3GTk6G005r8WsDuUMIZGJXnM0w4MYViK32b6TFg91KqYhpFzOLg6UjBIJWnTSN4Te9H8fRfwPPTZjg3Rxo5k2os3Y0K0ipjk2zhsKsxQOn+/bBxRdzZ9Esfv/Fo2Tt2uj9VaCZN6HO2tExAKVigwaCIGnRSfOTT6x1A/75TwAcbhe//GSJ99eBZt6EOmtHl3ZUKjbo/X2QWCfHeG7LzabwRavf/f4FDfrdXS64805r7QBX7cn6yU4zmT98KsZV7u3nv39B81fd83NtzJo7HBqMEQTy2pYcV05OzZ+J/rkoFY30f3YQNXnS3LkTpk2DDz6o3ZacDE89RWLFhUiui+9e8RNAmni/ZoOPUko1QweLw2HZMrjiCqtmUI3TToO8PKtiaIhoyWelYpsOFrcXd90FF1xQGwTi4qwZw++9F/IgoAXhlFKB0EAQahMmgMNh/dy/vxUAcnPBHtpeOV0oXikVKA0EoTZiBNxzD0yeDGvXWuUiwkALwimlAqWBIBzmzIFXXrEWlQ8TLQinlAqUBoJwELG+wkgngymlAqXpo1FKU0uVUoHSQBDFdDKYUioQ2jWklFIxTgOBUkrFOA0ESikV4zQQKKVUjNNAoJRSMa5DFJ0TkSKgsBUvTQP2Brk57Z0ec2zQY44NbT3mdGNMz+ae1CECQWuJSH4glfeiiR5zbNBjjg3hOmbtGlJKqRingUAppWJctAeChZFuQAToMccGPebYEJZjjuoxAqWUUs2L9jsCpZRSzYjKQCAiZ4vIRhHZLCK3Rro9oSIiT4vIHhH5qs62FBF5V0Q2eb73iGQbg01EBojIeyKyQUS+FpE5nu1Re9wi0klE/iMiX3iO+Y+e7YNE5DPPMb8gIvGRbmswiYhNRNaIyHLP46g+XgAR2S4iX4rIWhHJ92wL+d921AUCEbEBjwITgWOAqSJyTGRbFTLPAmc32HYrsNIYMwRY6XkcTZzATcaYo4GTges8/77RfNyVwDhjzDAgCzhbRE4G7gYWeI55H3BVBNsYCnOADXUeR/vx1hhrjMmqkzYa8r/tqAsEwInAZmPMVmNMFbAEuCDCbQoJY8wqoKTB5guARZ6fFwGTw9qoEDPG7DbGfO75+RDWiaIfUXzcxlLqeejwfBlgHPBPz/aoOmYR6Q+cCzzpeSxE8fE2I+R/29EYCPoBO+o83unZFit6G2N2g3XSBHpFuD0hIyIZwHDgM6L8uD3dJGuBPcC7wBZgvzHG6XlKtP2dPwjcArg9j1OJ7uOtYYB3RKRARGZ6toX8bzsaVyvxtQSXpkZFGRHpArwM3GCMOShhXgo03IwxLiBLRJKBpcDRvp4W3laFhoicB+wxxhSIyJiazT6eGhXH28AoY8wuEekFvCsi34TjTaPxjmAnMKDO4/7Argi1JRJ+EJE+AJ7veyLcnqATEQdWEMgzxrzi2Rz1xw1gjNkPvI81PpIsIjUXc9H0dz4KmCQi27G6dsdh3SFE6/F6GWN2eb7vwQr4JxKGv+1oDAT/BYZ4MgzigSnAsgi3KZyWAdM9P08HXotgW4LO01f8FLDBGPNAnV9F7XGLSE/PnQAikgiciTU28h5wkedpUXPMxpjfGGP6G2MysP7//tsYk0OUHm8NEUkSka41PwMTgK8Iw992VE4oE5FzsK4gbMDTxpg7ItykkBCR54ExWBUKfwBuB14FXgQGAt8BPzfGNBxQ7rBE5DTgQ+BLavuPf4s1ThCVxy0iQ7EGCW1YF28vGmP+JCKDsa6YU4A1wDRjTGXkWhp8nq6hecaY86L9eD3Ht9Tz0A78wxhzh4ikEuK/7agMBEoppQIXjV1DSimlWkADgVJKxTgNBEopFeM0ECilVIzTQKCUUjFOA4GKCSLi8lR0/EpEXhKRzm3Y15g6FTEnNVXhVkSSRWR2K94jV0TmtbaNSrWEBgIVKyo8FR2PA6qAa+v+Uiwt/v9gjFlmjLmriackAy0OBEqFkwYCFYs+BDJFJMOzrsFfgc+BASIyQURWi8jnnjuHLuBd4+IbEfkI+GnNjkRkhog84vm5t4gs9awb8IWInArcBRzpuRu51/O8m0XkvyKyrmZtAc/234m1jsa/gKPC9mmomKeBQMUUT62aiVgzk8E64T5njBkOlAG3AWcaY0YA+cCNItIJeAI4H/gJcISf3T8EfOBZN2AE8DVW7fgtnruRm0VkAjAEq4ZMFjBSREaLyEiscgrDsQLNCUE+dKX8isbqo0r5kugp4wzWHcFTQF+g0BjzqWf7yViLGX3sqWYaD6wGfgxsM8ZsAhCRxcBMGhsHXA7eaqEHfKwmNcHztcbzuAtWYOgKLDXGlHveI5bqY6kI00CgYkWFMSar7gbPyb6s7ibgXWPM1AbPyyJ4JY8FuNMY83iD97ghiO+hVIto15BStT4FRolIJoCIdBaRHwHfAINE5EjP86b6ef1KYJbntTYR6QYcwrrar/E2cGWdsYd+ntrzq4ALRSTRU4Hy/CAfm1J+aSBQysMYUwTMAJ4XkXVYgeHHxpjDWF1Bb3gGiwv97GIOMFZEvgQKgGONMcVYXU1fici9xph3gH8Aqz3P+yfQ1bP85gvAWqy1Fj4M2YEq1YBWH1VKqRindwRKKRXjNBAopVSM00CglFIxTgOBUkrFOA0ESikV4zQQKKVUjNNAoJRSMU4DgVJKxbj/B0xDLS8+0PevAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(y_predicted, y_test, edgecolors=(0, 0, 1))\n",
    "ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--', lw=3)\n",
    "ax.set_xlabel('Predicted')\n",
    "ax.set_ylabel('Actual')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model performance for testing set\n",
      "--------------------------------------\n",
      "MAE is 3.555474556662591\n",
      "MSE is 26.361863287968156\n",
      "R2 score is 0.6694221114991397\n"
     ]
    }
   ],
   "source": [
    "# model evaluation for testing set\n",
    "\n",
    "mae = metrics.mean_absolute_error(y_test, y_predicted)\n",
    "mse = metrics.mean_squared_error(y_test, y_predicted)\n",
    "r2 = metrics.r2_score(y_test, y_predicted)\n",
    "\n",
    "\n",
    "print(\"The model performance for testing set\")\n",
    "print(\"--------------------------------------\")\n",
    "print('MAE is {}'.format(mae))\n",
    "print('MSE is {}'.format(mse))\n",
    "print('R2 score is {}'.format(r2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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