Lesson14-Python进阶ML-Polynomial (Linear) Regression多项式(线性)回归.ipynb

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  {
   "cell_type": "markdown",
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   "source": [
    "# 第十四课 Python进阶ML-Polynomial (Linear) Regression多项式线性回归\n",
    "### 对于一元线性回归的提升\n",
    "\n",
    "源文件下载链接: https://pan.baidu.com/s/1yuNlG6u9_C31fzhzbzqASA 提取码: ebrh\n",
    "\n",
    "Python零基础学习：https://www.ixigua.com/home/77346806707?utm_source=xiguastudio\n",
    "\n",
    "y=p+p1X1=ax+b Linear Regression一元线性回归参照第12节课\n",
    "\n",
    "y=p+p1X1+p2X2+p3X3+...+pnXn Multiple Linear Regression多元线性回归参照第13节课\n",
    "\n",
    "y=p+p1X1+p2X1(2)+p3X1(3)+...+pnX1(n) Polynomial Linear Regression多项式回归\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraries \n",
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the Height Weight Dataset \n",
    "data = pd.read_excel('Lesson14-age-height.xlsx') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "<p>71 rows × 2 columns</p>\n",
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      "text/plain": [
       "    Age  Height\n",
       "0    10     138\n",
       "1    11     138\n",
       "2    12     138\n",
       "3    13     139\n",
       "4    14     139\n",
       "..  ...     ...\n",
       "66   76     204\n",
       "67   77     205\n",
       "68   78     206\n",
       "69   79     207\n",
       "70   80     208\n",
       "\n",
       "[71 rows x 2 columns]"
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    "data"
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   "cell_type": "code",
   "execution_count": 229,
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   "source": [
    "#Store the data in the form of dependent and independent variables separately\n",
    "X = data.iloc[:, 0:1].values \n",
    "y = data.iloc[:, 1].values"
   ]
  },
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   ],
   "source": [
    "X"
   ]
  },
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   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.iloc[:, :-1].values "
   ]
  },
  {
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    {
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       "(71, 1)"
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     },
     "execution_count": 234,
     "metadata": {},
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    "X.shape"
   ]
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    "X"
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      "text/plain": [
       "(71,)"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([138, 138, 138, 139, 139, 139, 140, 140, 140, 141, 141, 141, 142,\n",
       "       142, 142, 143, 143, 143, 144, 144, 145, 145, 146, 146, 147, 147,\n",
       "       148, 148, 149, 149, 150, 150, 151, 152, 153, 155, 156, 157, 158,\n",
       "       159, 160, 161, 162, 163, 164, 164, 165, 166, 167, 168, 169, 170,\n",
       "       171, 172, 173, 175, 178, 180, 183, 185, 188, 190, 193, 195, 199,\n",
       "       203, 204, 205, 206, 207, 208], dtype=int64)"
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     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Split the Dataset into Training and Test Dataset\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49, 1)"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22, 1)"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fit the Simple Linear Regression Model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "LinReg = LinearRegression()\n",
    "LinReg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([152.90005813, 153.8194466 , 173.1266044 , 149.22250426,\n",
       "       156.577612  , 175.8847698 , 135.43167726, 183.23987754,\n",
       "       160.25516587, 193.35315067, 180.48171214, 154.73883506,\n",
       "       157.49700047, 167.6102736 , 159.3357774 , 179.56232367,\n",
       "       192.43376221, 185.99804294, 168.52966207, 132.67351186,\n",
       "       188.75620834, 174.96538134])"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LinReg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([148, 148, 167, 146, 150, 170, 140, 185, 153, 208, 178, 149, 150,\n",
       "       162, 152, 175, 207, 193, 163, 139, 203, 169], dtype=int64)"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "score1=r2_score(y_test,LinReg.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8727873738671587"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "r2_score?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualise the Linear Regression Result\n",
    "plt.scatter(X_train, y_train, color = 'green') \n",
    "  \n",
    "plt.plot(X_test, LinReg.predict(X_test), color = 'blue') \n",
    "plt.plot(X_train, LinReg.predict(X_train), color = 'red') \n",
    "plt.title('Linear Regression') \n",
    "plt.xlabel('Age') \n",
    "plt.ylabel('Height') \n",
    "  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.000e+00, 1.200e+01, 1.440e+02],\n",
       "       [1.000e+00, 5.000e+01, 2.500e+03],\n",
       "       [1.000e+00, 2.100e+01, 4.410e+02],\n",
       "       [1.000e+00, 1.300e+01, 1.690e+02],\n",
       "       [1.000e+00, 6.400e+01, 4.096e+03],\n",
       "       [1.000e+00, 5.500e+01, 3.025e+03],\n",
       "       [1.000e+00, 2.000e+01, 4.000e+02],\n",
       "       [1.000e+00, 5.100e+01, 2.601e+03],\n",
       "       [1.000e+00, 5.900e+01, 3.481e+03],\n",
       "       [1.000e+00, 6.300e+01, 3.969e+03],\n",
       "       [1.000e+00, 6.700e+01, 4.489e+03],\n",
       "       [1.000e+00, 4.200e+01, 1.764e+03],\n",
       "       [1.000e+00, 2.400e+01, 5.760e+02],\n",
       "       [1.000e+00, 7.800e+01, 6.084e+03],\n",
       "       [1.000e+00, 2.900e+01, 8.410e+02],\n",
       "       [1.000e+00, 3.900e+01, 1.521e+03],\n",
       "       [1.000e+00, 6.200e+01, 3.844e+03],\n",
       "       [1.000e+00, 4.500e+01, 2.025e+03],\n",
       "       [1.000e+00, 2.800e+01, 7.840e+02],\n",
       "       [1.000e+00, 1.000e+01, 1.000e+02],\n",
       "       [1.000e+00, 2.500e+01, 6.250e+02],\n",
       "       [1.000e+00, 1.500e+01, 2.250e+02],\n",
       "       [1.000e+00, 2.600e+01, 6.760e+02],\n",
       "       [1.000e+00, 3.000e+01, 9.000e+02],\n",
       "       [1.000e+00, 7.600e+01, 5.776e+03],\n",
       "       [1.000e+00, 1.800e+01, 3.240e+02],\n",
       "       [1.000e+00, 2.300e+01, 5.290e+02],\n",
       "       [1.000e+00, 3.500e+01, 1.225e+03],\n",
       "       [1.000e+00, 4.700e+01, 2.209e+03],\n",
       "       [1.000e+00, 2.700e+01, 7.290e+02],\n",
       "       [1.000e+00, 7.000e+01, 4.900e+03],\n",
       "       [1.000e+00, 5.600e+01, 3.136e+03],\n",
       "       [1.000e+00, 7.300e+01, 5.329e+03],\n",
       "       [1.000e+00, 4.900e+01, 2.401e+03],\n",
       "       [1.000e+00, 4.800e+01, 2.304e+03],\n",
       "       [1.000e+00, 1.100e+01, 1.210e+02],\n",
       "       [1.000e+00, 6.800e+01, 4.624e+03],\n",
       "       [1.000e+00, 2.200e+01, 4.840e+02],\n",
       "       [1.000e+00, 7.100e+01, 5.041e+03],\n",
       "       [1.000e+00, 3.400e+01, 1.156e+03],\n",
       "       [1.000e+00, 1.600e+01, 2.560e+02],\n",
       "       [1.000e+00, 3.300e+01, 1.089e+03],\n",
       "       [1.000e+00, 4.600e+01, 2.116e+03],\n",
       "       [1.000e+00, 3.100e+01, 9.610e+02],\n",
       "       [1.000e+00, 1.900e+01, 3.610e+02],\n",
       "       [1.000e+00, 7.700e+01, 5.929e+03],\n",
       "       [1.000e+00, 7.400e+01, 5.476e+03],\n",
       "       [1.000e+00, 5.700e+01, 3.249e+03],\n",
       "       [1.000e+00, 5.400e+01, 2.916e+03]])"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add the polynomial term to the equation/model\n",
    "from sklearn.preprocessing import PolynomialFeatures \n",
    "  \n",
    "polynom = PolynomialFeatures(degree = 2) \n",
    "X_polynom = polynom.fit_transform(X_train) \n",
    "  \n",
    "X_polynom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49, 4)"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_polynom.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fit the Polynomial Regression Model \n",
    "PolyReg = LinearRegression() \n",
    "PolyReg.fit(X_polynom, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "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": [
    "# Visualise the Polynomial Regression Results \n",
    "plt.scatter(X_train, y_train, color = 'green') \n",
    "  \n",
    "plt.plot(X_train, PolyReg.predict(polynom.fit_transform(X_train)), color = 'red') \n",
    "plt.plot(X_test, PolyReg.predict(polynom.fit_transform(X_test)), color = 'blue') \n",
    "plt.title('Polynomial Regression') \n",
    "plt.xlabel('Age') \n",
    "plt.ylabel('Height') \n",
    "  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-Square Error associated with Simple Linear Regression: 0.8727873738671587\n"
     ]
    }
   ],
   "source": [
    "#Predicted Height from test dataset w.r.t Simple Linear Regression\n",
    "y_predict_slr = LinReg.predict(X_test)\n",
    "\n",
    "#Model Evaluation using R-Square for Simple Linear Regression\n",
    "from sklearn import metrics\n",
    "r_square = metrics.r2_score(y_test, y_predict_slr)\n",
    "print('R-Square Error associated with Simple Linear Regression:', r_square)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "r2_score?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-Square Error associated with Polynomial Regression is: 0.992763409209586\n"
     ]
    }
   ],
   "source": [
    "#Predicted Height from test dataset w.r.t Polynomial Regression\n",
    "y_predict_pr = PolyReg.predict(polynom.fit_transform(X_test))\n",
    "\n",
    "#Model Evaluation using R-Square for Polynomial Regression\n",
    "#from sklearn import metrics\n",
    "r_square = metrics.r2_score(y_test, y_predict_pr)\n",
    "print('R-Square Error associated with Polynomial Regression is:', r_square)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([150.14189273])"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predicting Height based on Age using Linear Regression \n",
    "LinReg.predict([[33]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([146.46115558])"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predicting Height based on Age using Polynomial Regression \n",
    "PolyReg.predict(polynom.fit_transform([[33]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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