Third Week/LinearRegressionExample.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "from sklearn import linear_model\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(442L, 10L)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes = datasets.load_diabetes()\n",
    "diabetes.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes_X = diabetes.data[:, np.newaxis, 2]\n",
    "diabetes_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "diabetes_X_train = diabetes_X[:-20]\n",
    "diabetes_X_test = diabetes_X[-20:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "diabetes_Y_train = diabetes.target[:-20]\n",
    "diabetes_Y_test = diabetes.target[-20:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "regr = linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr.fit(diabetes_X_train, diabetes_Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "diabetes_Y_pred = regr.predict(diabetes_X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted\tTrue Value\n",
      "225.97\t\t233\n",
      "115.75\t\t91\n",
      "163.28\t\t111\n",
      "114.74\t\t152\n",
      "120.80\t\t120\n",
      "158.22\t\t67\n",
      "236.09\t\t310\n",
      "121.82\t\t94\n",
      "99.57\t\t183\n",
      "123.84\t\t66\n",
      "204.74\t\t173\n",
      "96.53\t\t72\n",
      "154.17\t\t49\n",
      "130.92\t\t64\n",
      "83.39\t\t48\n",
      "171.37\t\t178\n",
      "138.00\t\t104\n",
      "138.00\t\t132\n",
      "189.57\t\t220\n",
      "84.40\t\t57\n"
     ]
    }
   ],
   "source": [
    "print(\"Predicted\\tTrue Value\")\n",
    "for i in range(0,len(diabetes_Y_pred)):\n",
    "    print(\"%.2f\\t\\t%d\" % (diabetes_Y_pred[i], diabetes_Y_test[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: 938.238\n"
     ]
    }
   ],
   "source": [
    "print('Coefficients: %.3f' % (regr.coef_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared error: 2548.07\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean Squared error: %.2f\" % mean_squared_error(diabetes_Y_test, diabetes_Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variance score: 0.47\n"
     ]
    }
   ],
   "source": [
    "print('Variance score: %.2f' % r2_score(diabetes_Y_test, diabetes_Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YxJ/4RDZ1TQBdL4ABs5/Um5+P3ni77Tbp/vuzqQcAzoEd12/+7Gej/9lf/iK95S3Z1QIA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\nAXHPb7urVHqIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa2d3080>"
      ]
     },
     "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='red', linewidth=3)\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(442L, 10L)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20L, 10L)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.data[-20:].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}