Assignment4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn import datasets, linear_model\n",
    "from sklearn.linear_model import Ridge,Lasso\n",
    "from sklearn.metrics import mean_squared_error, r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "diabetes=datasets.load_diabetes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keys of diabetes_dataset: \n",
      "dict_keys(['data', 'target', 'DESCR', 'feature_names'])\n",
      "Diabetes dataset\n",
      "================\n",
      "\n",
      "Notes\n",
      "-----\n",
      "\n",
      "Ten baseline variables, age, sex, body mass index, a\n",
      "[[ 0.03807591  0.05068012  0.06169621  0.02187235 -0.0442235  -0.03482076\n",
      "  -0.04340085 -0.00259226  0.01990842 -0.01764613]\n",
      " [-0.00188202 -0.04464164 -0.05147406 -0.02632783 -0.00844872 -0.01916334\n",
      "   0.07441156 -0.03949338 -0.06832974 -0.09220405]\n",
      " [ 0.08529891  0.05068012  0.04445121 -0.00567061 -0.04559945 -0.03419447\n",
      "  -0.03235593 -0.00259226  0.00286377 -0.02593034]\n",
      " [-0.08906294 -0.04464164 -0.01159501 -0.03665645  0.01219057  0.02499059\n",
      "  -0.03603757  0.03430886  0.02269202 -0.00936191]\n",
      " [ 0.00538306 -0.04464164 -0.03638469  0.02187235  0.00393485  0.01559614\n",
      "   0.00814208 -0.00259226 -0.03199144 -0.04664087]]\n",
      "[ 151.   75.  141.  206.  135.]\n",
      "['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']\n"
     ]
    }
   ],
   "source": [
    "print(\"Keys of diabetes_dataset: \\n{}\".format(diabetes.keys()))\n",
    "print(diabetes['DESCR'][:100])\n",
    "print(diabetes['data'][:5])\n",
    "print(diabetes['target'][:5])\n",
    "print(diabetes['feature_names'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Use only one feature\n",
    "diabetes_X = diabetes.data[:, np.newaxis, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Split the data into training/testing sets\n",
    "diabetes_X_train = diabetes_X[:-20]\n",
    "diabetes_X_test = diabetes_X[-20:]\n",
    "\n",
    "# Split the targets into training/testing sets\n",
    "diabetes_y_train = diabetes.target[:-20]\n",
    "diabetes_y_test = diabetes.target[-20:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create linear regression object\n",
    "regr = linear_model.LinearRegression()\n",
    "# Train the model using the training sets\n",
    "regr.fit(diabetes_X_train, diabetes_y_train)\n",
    "\n",
    "# Make predictions using the testing set\n",
    "diabetes_y_pred = regr.predict(diabetes_X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: \n",
      " [ 938.23786125]\n",
      "Mean squared error: 2548.07\n",
      "Variance score: 0.47\n"
     ]
    }
   ],
   "source": [
    "# The coefficients\n",
    "print('Coefficients: \\n', regr.coef_)\n",
    "# The mean squared error\n",
    "print(\"Mean squared error: %.2f\"\n",
    "      % mean_squared_error(diabetes_y_test, diabetes_y_pred))\n",
    "# Explained variance score: 1 is perfect prediction\n",
    "print('Variance score: %.2f' % r2_score(diabetes_y_test, diabetes_y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WXiZZut5ms6lut8u79QBHlSqQn3vOhHCtJj300PB1Dz1kQvi3vzVrbZa16+XdeoC7LI8r\no9s1QXzllaPXHT5sgvhjHxtPXSuBrhfAgNVP6s3NJW+83X67dP/946kHAM6HE9dv/uQnw//sT3+S\n3vzm8dUCAHmzOpDf9CZp3TrpyBHz+yCQnnqqehf8AKgGqwP5uuvMwxsnTkiXX150NQCQL6sDWZLW\nry+6AgAYj1KcsgCAKiCQAcASlQ5k7hYGYBPrZ8h54W5hALapbIfM3cIAbFPZQOZuYQC2qWwgc7dw\neTH7h6sqG8iu3C1ctXAazP6jKFIcxwuzf9e/b1REHMepf910002xS6ampmLf92PP82Lf9+Opqami\nS8pkamoqrtfrsaSFX/V6feT3Ufbv2ff9s77fwS/f94suDRhK0nScImOtvu0NowVBoCiKlnzu+756\nvd6Sz889WSKZfxWU6brPWq2m5f6b9TxP8/PzBVQEJEt721tlRxYuyLox6cLJEmb/cBmBXGJZw8mF\nkyWuzP6B5RDIJZY1nFzoLnnDClxGIJdY1nBypbvkvYJwVSkCuWpHu7LIEk50l4DdrD9l4cLJAADV\n5swpCxdOBgBAGtYHsgsnAwAgDesD2YWTAQCQhvWB7MrJAABIYnUgh2G4MENetWqVJHEyoCI4WYMq\nsvaNIeeerjh9+vRCZ0wYu423uaCqrD32lvXiHLiDnz1cU/pjb5yuqC5+9qgqawOZ0xXVxc8eVWVt\nIHO6orr42aOqrA1k7l2oLn72qCprN/UAwBWl39QDgKohkAHAEgQyAFiCQAYASxDIAGCJTKcsPM87\nKGnpM60AgFH8OI4vT1qUKZABAPlhZAEAliCQAcASBDIAWIJABgBLEMgAYAkCGQAsQSADgCUIZACw\nBIEMAJb4L/4/ciktfwZ6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f8e24b5a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(diabetes_X_test, diabetes_y_test,  color='black')\n",
    "plt.plot(diabetes_X_test, diabetes_y_pred, color='blue', linewidth=3)\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.47257544798227119"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr.score(diabetes_X_test,diabetes_y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "87.867637731555831"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg=Ridge(alpha=0.3, normalize=True).fit(x_train_scaled,diabetes_y_train)\n",
    "reg.coef_\n",
    "reg.intercept_ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "diabetes_y_pred_reg = reg.predict(diabetes_X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nmTnYFSukJ56Iuppg3v9+E67XXiu9971Sd3fUFUUm6KJe/Hv8kPr7TUfsF8Y7dph/A7aF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\nIAOAJf4fZoBaoqHCt08AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f8e3d0a4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(diabetes_X_test, diabetes_y_test,  color='black')\n",
    "plt.plot(diabetes_X_test, diabetes_y_pred_reg, color='red', linewidth=3)\n",
    "plt.plot(diabetes_X_test, diabetes_y_pred, color='blue', linewidth=3)\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.16564778016286064"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(diabetes_X_test,diabetes_y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "152.98010399209033"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "las = Lasso (alpha=0.3, normalize=True)\n",
    "las.fit (diabetes_X_train, diabetes_y_train) \n",
    "\n",
    "las.coef_\n",
    "\n",
    "las.intercept_ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "diabetes_y_pred_las = las.predict(diabetes_X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f8e3ad52e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(diabetes_X_test, diabetes_y_test,  color='black')\n",
    "plt.plot(diabetes_X_test, diabetes_y_pred_reg, color='red', linewidth=1)\n",
    "plt.plot(diabetes_X_test, diabetes_y_pred, color='blue', linewidth=1)\n",
    "plt.plot(diabetes_X_test, diabetes_y_pred_las, color='green', linewidth=1)\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.43441105306717764"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "las.score(diabetes_X_test,diabetes_y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion:\n",
    "Ridge regression performed the worst, Linear was the best. We can see this from the plots and R-squared value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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
}