LinearRegression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston\n",
    "boston = load_boston()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(506, 13)\n"
     ]
    }
   ],
   "source": [
    "print(boston.data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['CRIM' 'ZN' 'INDUS' 'CHAS' 'NOX' 'RM' 'AGE' 'DIS' 'RAD' 'TAX' 'PTRATIO'\n",
      " 'B' 'LSTAT']\n"
     ]
    }
   ],
   "source": [
    "print(boston.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 24.   21.6  34.7  33.4  36.2  28.7  22.9  27.1  16.5  18.9  15.   18.9\n",
      "  21.7  20.4  18.2  19.9  23.1  17.5  20.2  18.2  13.6  19.6  15.2  14.5\n",
      "  15.6  13.9  16.6  14.8  18.4  21.   12.7  14.5  13.2  13.1  13.5  18.9\n",
      "  20.   21.   24.7  30.8  34.9  26.6  25.3  24.7  21.2  19.3  20.   16.6\n",
      "  14.4  19.4  19.7  20.5  25.   23.4  18.9  35.4  24.7  31.6  23.3  19.6\n",
      "  18.7  16.   22.2  25.   33.   23.5  19.4  22.   17.4  20.9  24.2  21.7\n",
      "  22.8  23.4  24.1  21.4  20.   20.8  21.2  20.3  28.   23.9  24.8  22.9\n",
      "  23.9  26.6  22.5  22.2  23.6  28.7  22.6  22.   22.9  25.   20.6  28.4\n",
      "  21.4  38.7  43.8  33.2  27.5  26.5  18.6  19.3  20.1  19.5  19.5  20.4\n",
      "  19.8  19.4  21.7  22.8  18.8  18.7  18.5  18.3  21.2  19.2  20.4  19.3\n",
      "  22.   20.3  20.5  17.3  18.8  21.4  15.7  16.2  18.   14.3  19.2  19.6\n",
      "  23.   18.4  15.6  18.1  17.4  17.1  13.3  17.8  14.   14.4  13.4  15.6\n",
      "  11.8  13.8  15.6  14.6  17.8  15.4  21.5  19.6  15.3  19.4  17.   15.6\n",
      "  13.1  41.3  24.3  23.3  27.   50.   50.   50.   22.7  25.   50.   23.8\n",
      "  23.8  22.3  17.4  19.1  23.1  23.6  22.6  29.4  23.2  24.6  29.9  37.2\n",
      "  39.8  36.2  37.9  32.5  26.4  29.6  50.   32.   29.8  34.9  37.   30.5\n",
      "  36.4  31.1  29.1  50.   33.3  30.3  34.6  34.9  32.9  24.1  42.3  48.5\n",
      "  50.   22.6  24.4  22.5  24.4  20.   21.7  19.3  22.4  28.1  23.7  25.\n",
      "  23.3  28.7  21.5  23.   26.7  21.7  27.5  30.1  44.8  50.   37.6  31.6\n",
      "  46.7  31.5  24.3  31.7  41.7  48.3  29.   24.   25.1  31.5  23.7  23.3\n",
      "  22.   20.1  22.2  23.7  17.6  18.5  24.3  20.5  24.5  26.2  24.4  24.8\n",
      "  29.6  42.8  21.9  20.9  44.   50.   36.   30.1  33.8  43.1  48.8  31.\n",
      "  36.5  22.8  30.7  50.   43.5  20.7  21.1  25.2  24.4  35.2  32.4  32.\n",
      "  33.2  33.1  29.1  35.1  45.4  35.4  46.   50.   32.2  22.   20.1  23.2\n",
      "  22.3  24.8  28.5  37.3  27.9  23.9  21.7  28.6  27.1  20.3  22.5  29.\n",
      "  24.8  22.   26.4  33.1  36.1  28.4  33.4  28.2  22.8  20.3  16.1  22.1\n",
      "  19.4  21.6  23.8  16.2  17.8  19.8  23.1  21.   23.8  23.1  20.4  18.5\n",
      "  25.   24.6  23.   22.2  19.3  22.6  19.8  17.1  19.4  22.2  20.7  21.1\n",
      "  19.5  18.5  20.6  19.   18.7  32.7  16.5  23.9  31.2  17.5  17.2  23.1\n",
      "  24.5  26.6  22.9  24.1  18.6  30.1  18.2  20.6  17.8  21.7  22.7  22.6\n",
      "  25.   19.9  20.8  16.8  21.9  27.5  21.9  23.1  50.   50.   50.   50.\n",
      "  50.   13.8  13.8  15.   13.9  13.3  13.1  10.2  10.4  10.9  11.3  12.3\n",
      "   8.8   7.2  10.5   7.4  10.2  11.5  15.1  23.2   9.7  13.8  12.7  13.1\n",
      "  12.5   8.5   5.    6.3   5.6   7.2  12.1   8.3   8.5   5.   11.9  27.9\n",
      "  17.2  27.5  15.   17.2  17.9  16.3   7.    7.2   7.5  10.4   8.8   8.4\n",
      "  16.7  14.2  20.8  13.4  11.7   8.3  10.2  10.9  11.    9.5  14.5  14.1\n",
      "  16.1  14.3  11.7  13.4   9.6   8.7   8.4  12.8  10.5  17.1  18.4  15.4\n",
      "  10.8  11.8  14.9  12.6  14.1  13.   13.4  15.2  16.1  17.8  14.9  14.1\n",
      "  12.7  13.5  14.9  20.   16.4  17.7  19.5  20.2  21.4  19.9  19.   19.1\n",
      "  19.1  20.1  19.9  19.6  23.2  29.8  13.8  13.3  16.7  12.   14.6  21.4\n",
      "  23.   23.7  25.   21.8  20.6  21.2  19.1  20.6  15.2   7.    8.1  13.6\n",
      "  20.1  21.8  24.5  23.1  19.7  18.3  21.2  17.5  16.8  22.4  20.6  23.9\n",
      "  22.   11.9]\n"
     ]
    }
   ],
   "source": [
    "print(boston.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Boston House Prices dataset\n",
      "===========================\n",
      "\n",
      "Notes\n",
      "------\n",
      "Data Set Characteristics:  \n",
      "\n",
      "    :Number of Instances: 506 \n",
      "\n",
      "    :Number of Attributes: 13 numeric/categorical predictive\n",
      "    \n",
      "    :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",
      "http://archive.ics.uci.edu/ml/datasets/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",
      "**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",
      "   - many more! (see http://archive.ics.uci.edu/ml/datasets/Housing)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(boston.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        0     1     2    3      4      5     6       7    8      9     10  \\\n",
      "0  0.00632  18.0  2.31  0.0  0.538  6.575  65.2  4.0900  1.0  296.0  15.3   \n",
      "1  0.02731   0.0  7.07  0.0  0.469  6.421  78.9  4.9671  2.0  242.0  17.8   \n",
      "2  0.02729   0.0  7.07  0.0  0.469  7.185  61.1  4.9671  2.0  242.0  17.8   \n",
      "3  0.03237   0.0  2.18  0.0  0.458  6.998  45.8  6.0622  3.0  222.0  18.7   \n",
      "4  0.06905   0.0  2.18  0.0  0.458  7.147  54.2  6.0622  3.0  222.0  18.7   \n",
      "\n",
      "       11    12  \n",
      "0  396.90  4.98  \n",
      "1  396.90  9.14  \n",
      "2  392.83  4.03  \n",
      "3  394.63  2.94  \n",
      "4  396.90  5.33  \n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "bos = pd.DataFrame(boston.data)\n",
    "print(bos.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bos['PRICE'] = boston.target\n",
    "\n",
    "X = bos.drop('PRICE', axis = 1)\n",
    "Y = bos['PRICE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(339, 13)\n",
      "(167, 13)\n",
      "(339,)\n",
      "(167,)\n"
     ]
    }
   ],
   "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, random_state = 5)\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(Y_train.shape)\n",
    "print(Y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# code source:https://medium.com/@haydar_ai/learning-data-science-day-9-linear-regression-on-boston-housing-dataset-cd62a80775ef\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lm = LinearRegression()\n",
    "lm.fit(X_train, Y_train)\n",
    "\n",
    "Y_pred = lm.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OuUMfIPn7tpHgOEV1ym3d2l/vY0DXD8UcIPLCNLqN52V9USG8iRT0AVL35Z8wK/6M/eYd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\nCSnoQgjhJf4/+7iHpqIXAlQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bbbaba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "delta_y = Y_test - Y_pred;\n",
    "\n",
    "import seaborn as sns;\n",
    "import numpy as np;\n",
    "sns.set_style('whitegrid')\n",
    "sns.kdeplot(np.array(delta_y), bw=0.5)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+iu/Nm8Cp+mY+OHSmw/P+9nkpxZVWnrh5qvuuTbfNHMvCKfH86v1CKc07DHkN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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c114208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.kdeplot(np.array(Y_pred), bw=0.5)\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",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}