Regression(BOSTON).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.datasets import load_boston"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "boston=load_boston()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\".. _boston_dataset:\\n\\nBoston 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\\nThis is a copy of UCI ML housing dataset.\\nhttps://archive.ics.uci.edu/ml/machine-learning-databases/housing/\\n\\n\\nThis dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\\n\\nThe Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\\nprices and the demand for clean air', J. Environ. Economics & Management,\\nvol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\\n...', Wiley, 1980.   N.B. Various transformations are used in the table on\\npages 244-261 of the latter.\\n\\nThe Boston house-price data has been used in many machine learning papers that address regression\\nproblems.   \\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\""
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston.DESCR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "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.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset=boston.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CRIM 0\n",
      "ZN 1\n",
      "INDUS 2\n",
      "CHAS 3\n",
      "NOX 4\n",
      "RM 5\n",
      "AGE 6\n",
      "DIS 7\n",
      "RAD 8\n",
      "TAX 9\n",
      "PTRATIO 10\n",
      "B 11\n",
      "LSTAT 12\n"
     ]
    }
   ],
   "source": [
    "for name, index in enumerate(boston.feature_names): #attributes\n",
    "    print(index,name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6.3200e-03, 1.8000e+01, 2.3100e+00, 0.0000e+00, 5.3800e-01,\n",
       "        6.5750e+00, 6.5200e+01, 4.0900e+00, 1.0000e+00, 2.9600e+02,\n",
       "        1.5300e+01, 3.9690e+02, 4.9800e+00],\n",
       "       [2.7310e-02, 0.0000e+00, 7.0700e+00, 0.0000e+00, 4.6900e-01,\n",
       "        6.4210e+00, 7.8900e+01, 4.9671e+00, 2.0000e+00, 2.4200e+02,\n",
       "        1.7800e+01, 3.9690e+02, 9.1400e+00],\n",
       "       [2.7290e-02, 0.0000e+00, 7.0700e+00, 0.0000e+00, 4.6900e-01,\n",
       "        7.1850e+00, 6.1100e+01, 4.9671e+00, 2.0000e+00, 2.4200e+02,\n",
       "        1.7800e+01, 3.9283e+02, 4.0300e+00]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds=dataset[0:3,12] #checking 12th column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.98, 9.14, 4.03])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=dataset[:,12].reshape(-1,1) #reshaping(one column) a column B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4.98],\n",
       "       [9.14],\n",
       "       [4.03]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0:3,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 13)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(dataset) #shape of dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "tb=boston.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([24. , 21.6, 34.7])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tb[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "target=boston.target.reshape(-1,1) #target or output value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[24. ],\n",
       "       [21.6],\n",
       "       [34.7]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "target[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 1)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(target) #shape of output values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 1)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "plt.scatter(data,target,color='blue')\n",
    "plt.xlabel('Lower income population ')\n",
    "plt.ylabel('Cost of house')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression #linear regression\n",
    "from sklearn.linear_model import Lasso\n",
    "#from sklearn.linear_model import Ridge\n",
    "reg=LinearRegression()#creating model\n",
    "reg.fit(data,target)#fitting the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred=reg.predict(data) #prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[29.8225951 ],\n",
       "       [25.87038979],\n",
       "       [30.72514198]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "plt.scatter(data,target,color='red')\n",
    "plt.plot(data,pred,color='blue')\n",
    "plt.xlabel('Lower Incoome of population')\n",
    "plt.ylabel('Cost of House')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures # fitting of the curve issue using polynomial model\n",
    "from sklearn.pipeline import make_pipeline   #for merging the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=make_pipeline(PolynomialFeatures(10),reg) #creating pipeline with polynomialfeatures of any random number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('polynomialfeatures',\n",
       "                 PolynomialFeatures(degree=10, include_bias=True,\n",
       "                                    interaction_only=False, order='C')),\n",
       "                ('linearregression',\n",
       "                 LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "                                  normalize=False))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(data,target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred1=model.predict(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "plt.scatter(data,target,color='red')\n",
    "plt.plot(data,pred1,color='blue')\n",
    "plt.xlabel('Lower Income of population')\n",
    "plt.ylabel('Cost of House')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1622589284620234"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score#r2(r square value ranges from -1 to 1 ) if it is 1 the model is fitted else not fitted  \n",
    "r2_score(pred,target)#predicton without polynomial features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5388797874712266"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(pred1,target) #prediction with model trained with polynomial features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[6.575]\n",
      " [6.421]\n",
      " [7.185]] [[22.4]\n",
      " [20.6]\n",
      " [23.9]\n",
      " [22. ]]\n"
     ]
    }
   ],
   "source": [
    "#MODEL: AVG NO.OF ROOMS VS COST OF HOUSE\n",
    "x=dataset[:,5].reshape(-1,1)\n",
    "\n",
    "y=boston.target.reshape(-1,1)\n",
    "print(x[0:3],y[-5:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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"
    }
   ],
   "source": [
    "plt.scatter(x,y,color=\"green\")\n",
    "plt.xlabel('Average no.of rooms')\n",
    "plt.ylabel('Cost of House')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr=LinearRegression()\n",
    "lr.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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"
    }
   ],
   "source": [
    "pred2=lr.predict(x)\n",
    "plt.scatter(x,y,color=\"green\")\n",
    "plt.plot(x,pred2,color=\"blue\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.06814344024510333"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(pred2,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "model1=make_pipeline(PolynomialFeatures(35),lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('polynomialfeatures',\n",
       "                 PolynomialFeatures(degree=35, include_bias=True,\n",
       "                                    interaction_only=False, order='C')),\n",
       "                ('linearregression',\n",
       "                 LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "                                  normalize=False))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred3=model1.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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"
    }
   ],
   "source": [
    "plt.scatter(x,y,color=\"green\")\n",
    "plt.plot(x,pred3,color=\"red\")\n",
    "plt.xlabel('Average no.of rooms')\n",
    "plt.ylabel('Cost of House')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.37715243861051506"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(pred3,y)"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}