Machine Learning Statistics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0a65014f",
   "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": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = [[6], [8], [10], [14], [18], [21]]\n",
    "y = [[7], [9], [13], [17.5], [18], [21]]\n",
    "\n",
    "plt.figure()\n",
    "plt.title('Pizza Price statistics')\n",
    "plt.xlabel('Diameter')\n",
    "plt.ylabel('dollar price')\n",
    "plt.plot(x,y,'.')\n",
    "plt.axis([0,25,0,25])\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6a153ce",
   "metadata": {},
   "source": [
    "## Simple Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ac4ad03e",
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[22.46767241]]\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "x = [[6], [8], [10], [14], [18]]\n",
    "y = [[7], [9], [13], [17.5], [18]]\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(x,y)\n",
    "\n",
    "plt.figure()\n",
    "plt.title('Pizza Price Predictions')\n",
    "plt.xlabel('Diameter')\n",
    "plt.ylabel('dollar price')\n",
    "plt.plot(x,y,'.')\n",
    "plt.plot(x,model.predict(x),'--')\n",
    "plt.axis([0,25,0,25])\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "print (model.predict([[21]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c8d3711",
   "metadata": {},
   "source": [
    "## Loss Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "438b444b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Residual sum of squares =  1.7495689655172406\n",
      "Variance =  23.2\n",
      "Co-variance =  22.650000000000002\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "import numpy as np\n",
    "from numpy.linalg import inv,lstsq\n",
    "from numpy import dot, transpose\n",
    "\n",
    "x = [[6], [8], [10], [14], [18]]\n",
    "y = [[7], [9], [13], [17.5], [18]]\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(x,y)\n",
    "\n",
    "print ('Residual sum of squares = ',np.mean((model.predict(x)- y) ** 2))\n",
    "print ('Variance = ',np.var([6, 8, 10, 14, 18], ddof=1))\n",
    "print ('Co-variance = ',np.cov([6, 8, 10, 14, 18], [7, 9, 13, 17.5, 18])[0][1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7776e419",
   "metadata": {},
   "source": [
    "## R-Sequred Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "72c6dbb3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared score =  0.6620052929422553\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "import numpy as np\n",
    "from numpy.linalg import inv,lstsq\n",
    "from numpy import dot, transpose\n",
    "\n",
    "x = [[6], [8], [10], [14], [18]]\n",
    "y = [[7], [9], [13], [17.5], [18]]\n",
    "model = LinearRegression()\n",
    "model.fit(x,y)\n",
    "\n",
    "x_test = [[8], [9], [11], [16], [12]]\n",
    "y_test = [[11], [8.5], [15], [18], [11]]\n",
    "\n",
    "print ('R-squared score = ',model.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f609c412",
   "metadata": {},
   "source": [
    "## Multiple Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c12aa3da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([10.0625]), [11])\n",
      "(array([10.28125]), [8.5])\n",
      "(array([13.09375]), [15])\n",
      "(array([18.14583333]), [18])\n",
      "(array([13.3125]), [11])\n",
      "[[1.08548851]\n",
      " [0.65517241]]\n",
      "R-squared score =  0.7701677731318468\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from numpy.linalg import lstsq\n",
    "\n",
    "x = [[6, 2], [8, 1], [10, 0], [14, 2], [18, 0]]\n",
    "y = [[7], [9], [13], [17.5], [18]]\n",
    "model = LinearRegression()\n",
    "model.fit(x,y)\n",
    "\n",
    "x1 = [[8, 2], [9, 0], [11, 2], [16, 2], [12, 0]]\n",
    "y1 = [[11], [8.5], [15], [18], [11]]\n",
    "\n",
    "predictions = model.predict(x1)\n",
    "for i, prediction in enumerate(predictions):\n",
    "    print ((prediction, y1[i]))\n",
    "\n",
    "print (lstsq(x, y, rcond=None)[0])\n",
    "\n",
    "print ('R-squared score = ',model.score(x1, y1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "812ed955",
   "metadata": {},
   "source": [
    "## Pandas "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fe9353eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1460, 81)\n",
      "Index(['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',\n",
      "       'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',\n",
      "       'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',\n",
      "       'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',\n",
      "       'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',\n",
      "       'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',\n",
      "       'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',\n",
      "       'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',\n",
      "       'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',\n",
      "       'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',\n",
      "       'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual',\n",
      "       'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',\n",
      "       'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',\n",
      "       'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',\n",
      "       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',\n",
      "       'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',\n",
      "       'SaleCondition', 'SalePrice'],\n",
      "      dtype='object')\n",
      "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
      "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
      "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
      "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
      "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
      "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
      "\n",
      "  LandContour Utilities  ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold  \\\n",
      "0         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
      "1         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      5   \n",
      "2         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      9   \n",
      "3         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
      "4         Lvl    AllPub  ...        0    NaN   NaN         NaN       0     12   \n",
      "\n",
      "  YrSold  SaleType  SaleCondition  SalePrice  \n",
      "0   2008        WD         Normal     208500  \n",
      "1   2007        WD         Normal     181500  \n",
      "2   2008        WD         Normal     223500  \n",
      "3   2006        WD        Abnorml     140000  \n",
      "4   2008        WD         Normal     250000  \n",
      "\n",
      "[5 rows x 81 columns]\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1460 entries, 0 to 1459\n",
      "Data columns (total 81 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Id             1460 non-null   int64  \n",
      " 1   MSSubClass     1460 non-null   int64  \n",
      " 2   MSZoning       1460 non-null   object \n",
      " 3   LotFrontage    1201 non-null   float64\n",
      " 4   LotArea        1460 non-null   int64  \n",
      " 5   Street         1460 non-null   object \n",
      " 6   Alley          91 non-null     object \n",
      " 7   LotShape       1460 non-null   object \n",
      " 8   LandContour    1460 non-null   object \n",
      " 9   Utilities      1460 non-null   object \n",
      " 10  LotConfig      1460 non-null   object \n",
      " 11  LandSlope      1460 non-null   object \n",
      " 12  Neighborhood   1460 non-null   object \n",
      " 13  Condition1     1460 non-null   object \n",
      " 14  Condition2     1460 non-null   object \n",
      " 15  BldgType       1460 non-null   object \n",
      " 16  HouseStyle     1460 non-null   object \n",
      " 17  OverallQual    1460 non-null   int64  \n",
      " 18  OverallCond    1460 non-null   int64  \n",
      " 19  YearBuilt      1460 non-null   int64  \n",
      " 20  YearRemodAdd   1460 non-null   int64  \n",
      " 21  RoofStyle      1460 non-null   object \n",
      " 22  RoofMatl       1460 non-null   object \n",
      " 23  Exterior1st    1460 non-null   object \n",
      " 24  Exterior2nd    1460 non-null   object \n",
      " 25  MasVnrType     1452 non-null   object \n",
      " 26  MasVnrArea     1452 non-null   float64\n",
      " 27  ExterQual      1460 non-null   object \n",
      " 28  ExterCond      1460 non-null   object \n",
      " 29  Foundation     1460 non-null   object \n",
      " 30  BsmtQual       1423 non-null   object \n",
      " 31  BsmtCond       1423 non-null   object \n",
      " 32  BsmtExposure   1422 non-null   object \n",
      " 33  BsmtFinType1   1423 non-null   object \n",
      " 34  BsmtFinSF1     1460 non-null   int64  \n",
      " 35  BsmtFinType2   1422 non-null   object \n",
      " 36  BsmtFinSF2     1460 non-null   int64  \n",
      " 37  BsmtUnfSF      1460 non-null   int64  \n",
      " 38  TotalBsmtSF    1460 non-null   int64  \n",
      " 39  Heating        1460 non-null   object \n",
      " 40  HeatingQC      1460 non-null   object \n",
      " 41  CentralAir     1460 non-null   object \n",
      " 42  Electrical     1459 non-null   object \n",
      " 43  1stFlrSF       1460 non-null   int64  \n",
      " 44  2ndFlrSF       1460 non-null   int64  \n",
      " 45  LowQualFinSF   1460 non-null   int64  \n",
      " 46  GrLivArea      1460 non-null   int64  \n",
      " 47  BsmtFullBath   1460 non-null   int64  \n",
      " 48  BsmtHalfBath   1460 non-null   int64  \n",
      " 49  FullBath       1460 non-null   int64  \n",
      " 50  HalfBath       1460 non-null   int64  \n",
      " 51  BedroomAbvGr   1460 non-null   int64  \n",
      " 52  KitchenAbvGr   1460 non-null   int64  \n",
      " 53  KitchenQual    1460 non-null   object \n",
      " 54  TotRmsAbvGrd   1460 non-null   int64  \n",
      " 55  Functional     1460 non-null   object \n",
      " 56  Fireplaces     1460 non-null   int64  \n",
      " 57  FireplaceQu    770 non-null    object \n",
      " 58  GarageType     1379 non-null   object \n",
      " 59  GarageYrBlt    1379 non-null   float64\n",
      " 60  GarageFinish   1379 non-null   object \n",
      " 61  GarageCars     1460 non-null   int64  \n",
      " 62  GarageArea     1460 non-null   int64  \n",
      " 63  GarageQual     1379 non-null   object \n",
      " 64  GarageCond     1379 non-null   object \n",
      " 65  PavedDrive     1460 non-null   object \n",
      " 66  WoodDeckSF     1460 non-null   int64  \n",
      " 67  OpenPorchSF    1460 non-null   int64  \n",
      " 68  EnclosedPorch  1460 non-null   int64  \n",
      " 69  3SsnPorch      1460 non-null   int64  \n",
      " 70  ScreenPorch    1460 non-null   int64  \n",
      " 71  PoolArea       1460 non-null   int64  \n",
      " 72  PoolQC         7 non-null      object \n",
      " 73  Fence          281 non-null    object \n",
      " 74  MiscFeature    54 non-null     object \n",
      " 75  MiscVal        1460 non-null   int64  \n",
      " 76  MoSold         1460 non-null   int64  \n",
      " 77  YrSold         1460 non-null   int64  \n",
      " 78  SaleType       1460 non-null   object \n",
      " 79  SaleCondition  1460 non-null   object \n",
      " 80  SalePrice      1460 non-null   int64  \n",
      "dtypes: float64(3), int64(35), object(43)\n",
      "memory usage: 924.0+ KB\n",
      "None\n",
      "                Id   MSSubClass  LotFrontage        LotArea  OverallQual  \\\n",
      "count  1460.000000  1460.000000  1201.000000    1460.000000  1460.000000   \n",
      "mean    730.500000    56.897260    70.049958   10516.828082     6.099315   \n",
      "std     421.610009    42.300571    24.284752    9981.264932     1.382997   \n",
      "min       1.000000    20.000000    21.000000    1300.000000     1.000000   \n",
      "25%     365.750000    20.000000    59.000000    7553.500000     5.000000   \n",
      "50%     730.500000    50.000000    69.000000    9478.500000     6.000000   \n",
      "75%    1095.250000    70.000000    80.000000   11601.500000     7.000000   \n",
      "max    1460.000000   190.000000   313.000000  215245.000000    10.000000   \n",
      "\n",
      "       OverallCond    YearBuilt  YearRemodAdd   MasVnrArea   BsmtFinSF1  ...  \\\n",
      "count  1460.000000  1460.000000   1460.000000  1452.000000  1460.000000  ...   \n",
      "mean      5.575342  1971.267808   1984.865753   103.685262   443.639726  ...   \n",
      "std       1.112799    30.202904     20.645407   181.066207   456.098091  ...   \n",
      "min       1.000000  1872.000000   1950.000000     0.000000     0.000000  ...   \n",
      "25%       5.000000  1954.000000   1967.000000     0.000000     0.000000  ...   \n",
      "50%       5.000000  1973.000000   1994.000000     0.000000   383.500000  ...   \n",
      "75%       6.000000  2000.000000   2004.000000   166.000000   712.250000  ...   \n",
      "max       9.000000  2010.000000   2010.000000  1600.000000  5644.000000  ...   \n",
      "\n",
      "        WoodDeckSF  OpenPorchSF  EnclosedPorch    3SsnPorch  ScreenPorch  \\\n",
      "count  1460.000000  1460.000000    1460.000000  1460.000000  1460.000000   \n",
      "mean     94.244521    46.660274      21.954110     3.409589    15.060959   \n",
      "std     125.338794    66.256028      61.119149    29.317331    55.757415   \n",
      "min       0.000000     0.000000       0.000000     0.000000     0.000000   \n",
      "25%       0.000000     0.000000       0.000000     0.000000     0.000000   \n",
      "50%       0.000000    25.000000       0.000000     0.000000     0.000000   \n",
      "75%     168.000000    68.000000       0.000000     0.000000     0.000000   \n",
      "max     857.000000   547.000000     552.000000   508.000000   480.000000   \n",
      "\n",
      "          PoolArea       MiscVal       MoSold       YrSold      SalePrice  \n",
      "count  1460.000000   1460.000000  1460.000000  1460.000000    1460.000000  \n",
      "mean      2.758904     43.489041     6.321918  2007.815753  180921.195890  \n",
      "std      40.177307    496.123024     2.703626     1.328095   79442.502883  \n",
      "min       0.000000      0.000000     1.000000  2006.000000   34900.000000  \n",
      "25%       0.000000      0.000000     5.000000  2007.000000  129975.000000  \n",
      "50%       0.000000      0.000000     6.000000  2008.000000  163000.000000  \n",
      "75%       0.000000      0.000000     8.000000  2009.000000  214000.000000  \n",
      "max     738.000000  15500.000000    12.000000  2010.000000  755000.000000  \n",
      "\n",
      "[8 rows x 38 columns]\n",
      "LotShape\n",
      "IR1    484\n",
      "IR2     41\n",
      "IR3     10\n",
      "Reg    925\n",
      "dtype: int64\n",
      "Id                 0\n",
      "MSSubClass         0\n",
      "MSZoning           0\n",
      "LotFrontage      259\n",
      "LotArea            0\n",
      "                ... \n",
      "MoSold             0\n",
      "YrSold             0\n",
      "SaleType           0\n",
      "SaleCondition      0\n",
      "SalePrice          0\n",
      "Length: 81, dtype: int64\n",
      "LotFrontage      259\n",
      "Alley           1369\n",
      "MasVnrType         8\n",
      "MasVnrArea         8\n",
      "BsmtQual          37\n",
      "BsmtCond          37\n",
      "BsmtExposure      38\n",
      "BsmtFinType1      37\n",
      "BsmtFinType2      38\n",
      "FireplaceQu      690\n",
      "GarageType        81\n",
      "GarageYrBlt       81\n",
      "GarageFinish      81\n",
      "GarageQual        81\n",
      "GarageCond        81\n",
      "PoolQC          1453\n",
      "Fence           1179\n",
      "MiscFeature     1406\n",
      "dtype: int64\n",
      "(1460, 63)\n",
      "MSZoning         0\n",
      "Street           0\n",
      "LotShape         0\n",
      "LandContour      0\n",
      "Utilities        0\n",
      "LotConfig        0\n",
      "LandSlope        0\n",
      "Neighborhood     0\n",
      "Condition1       0\n",
      "Condition2       0\n",
      "BldgType         0\n",
      "HouseStyle       0\n",
      "RoofStyle        0\n",
      "RoofMatl         0\n",
      "Exterior1st      0\n",
      "Exterior2nd      0\n",
      "ExterQual        0\n",
      "ExterCond        0\n",
      "Foundation       0\n",
      "Heating          0\n",
      "HeatingQC        0\n",
      "CentralAir       0\n",
      "Electrical       1\n",
      "KitchenQual      0\n",
      "Functional       0\n",
      "PavedDrive       0\n",
      "SaleType         0\n",
      "SaleCondition    0\n",
      "dtype: int64\n",
      "Index(['MSZoning', 'Street', 'LotShape', 'LandContour', 'Utilities',\n",
      "       'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2',\n",
      "       'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st',\n",
      "       'Exterior2nd', 'ExterQual', 'ExterCond', 'Foundation', 'Heating',\n",
      "       'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual', 'Functional',\n",
      "       'PavedDrive', 'SaleType', 'SaleCondition'],\n",
      "      dtype='object')\n",
      "['RL', 'RM', 'FV', 'RH', 'C (all)']\n",
      "['Pave', 'Grvl']\n",
      "['Reg', 'IR1', 'IR2', 'IR3']\n",
      "['Lvl', 'Bnk', 'HLS', 'Low']\n",
      "['AllPub', 'NoSeWa']\n",
      "['Inside', 'Corner', 'CulDSac', 'FR2', 'FR3']\n",
      "['Gtl', 'Mod', 'Sev']\n",
      "['NAmes', 'CollgCr', 'OldTown', 'Edwards', 'Somerst', 'Gilbert', 'NridgHt', 'Sawyer', 'NWAmes', 'SawyerW', 'BrkSide', 'Crawfor', 'Mitchel', 'NoRidge', 'Timber', 'IDOTRR', 'ClearCr', 'StoneBr', 'SWISU', 'MeadowV', 'Blmngtn', 'BrDale', 'Veenker', 'NPkVill', 'Blueste']\n",
      "['Norm', 'Feedr', 'Artery', 'RRAn', 'PosN', 'RRAe', 'PosA', 'RRNn', 'RRNe']\n",
      "['Norm', 'Feedr', 'Artery', 'RRNn', 'PosN', 'PosA', 'RRAn', 'RRAe']\n",
      "['1Fam', 'TwnhsE', 'Duplex', 'Twnhs', '2fmCon']\n",
      "['1Story', '2Story', '1.5Fin', 'SLvl', 'SFoyer', '1.5Unf', '2.5Unf', '2.5Fin']\n",
      "['Gable', 'Hip', 'Flat', 'Gambrel', 'Mansard', 'Shed']\n",
      "['CompShg', 'Tar&Grv', 'WdShngl', 'WdShake', 'Metal', 'Membran', 'Roll', 'ClyTile']\n",
      "['VinylSd', 'HdBoard', 'MetalSd', 'Wd Sdng', 'Plywood', 'CemntBd', 'BrkFace', 'WdShing', 'Stucco', 'AsbShng', 'BrkComm', 'Stone', 'AsphShn', 'ImStucc', 'CBlock']\n",
      "['VinylSd', 'MetalSd', 'HdBoard', 'Wd Sdng', 'Plywood', 'CmentBd', 'Wd Shng', 'Stucco', 'BrkFace', 'AsbShng', 'ImStucc', 'Brk Cmn', 'Stone', 'AsphShn', 'Other', 'CBlock']\n",
      "['TA', 'Gd', 'Ex', 'Fa']\n",
      "['TA', 'Gd', 'Fa', 'Ex', 'Po']\n",
      "['PConc', 'CBlock', 'BrkTil', 'Slab', 'Stone', 'Wood']\n",
      "['GasA', 'GasW', 'Grav', 'Wall', 'OthW', 'Floor']\n",
      "['Ex', 'TA', 'Gd', 'Fa', 'Po']\n",
      "['Y', 'N']\n",
      "['SBrkr', 'FuseA', 'FuseF', 'FuseP', 'Mix']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['TA', 'Gd', 'Ex', 'Fa']\n",
      "['Typ', 'Min2', 'Min1', 'Mod', 'Maj1', 'Maj2', 'Sev']\n",
      "['Y', 'N', 'P']\n",
      "['WD', 'New', 'COD', 'ConLD', 'ConLI', 'ConLw', 'CWD', 'Oth', 'Con']\n",
      "['Normal', 'Partial', 'Abnorml', 'Family', 'Alloca', 'AdjLand']\n",
      "Id               0\n",
      "MSSubClass       0\n",
      "LotArea          0\n",
      "OverallQual      0\n",
      "OverallCond      0\n",
      "YearBuilt        0\n",
      "YearRemodAdd     0\n",
      "BsmtFinSF1       0\n",
      "BsmtFinSF2       0\n",
      "BsmtUnfSF        0\n",
      "TotalBsmtSF      0\n",
      "1stFlrSF         0\n",
      "2ndFlrSF         0\n",
      "LowQualFinSF     0\n",
      "GrLivArea        0\n",
      "BsmtFullBath     0\n",
      "BsmtHalfBath     0\n",
      "FullBath         0\n",
      "HalfBath         0\n",
      "BedroomAbvGr     0\n",
      "KitchenAbvGr     0\n",
      "TotRmsAbvGrd     0\n",
      "Fireplaces       0\n",
      "GarageCars       0\n",
      "GarageArea       0\n",
      "WoodDeckSF       0\n",
      "OpenPorchSF      0\n",
      "EnclosedPorch    0\n",
      "3SsnPorch        0\n",
      "ScreenPorch      0\n",
      "PoolArea         0\n",
      "MiscVal          0\n",
      "MoSold           0\n",
      "YrSold           0\n",
      "SalePrice        0\n",
      "dtype: int64\n",
      "Index([], dtype='object')\n",
      "(1460, 63)\n",
      "(1460, 65)\n",
      "(1460, 59)\n",
      "MSSubClass           0.084284\n",
      "LotArea              0.263843\n",
      "OverallQual          0.790982\n",
      "OverallCond          0.077856\n",
      "BsmtFinSF1           0.386420\n",
      "BsmtFinSF2           0.011378\n",
      "BsmtUnfSF            0.214479\n",
      "TotalBsmtSF          0.613581\n",
      "1stFlrSF             0.605852\n",
      "2ndFlrSF             0.319334\n",
      "LowQualFinSF         0.025606\n",
      "GrLivArea            0.708624\n",
      "BsmtFullBath         0.227122\n",
      "BsmtHalfBath         0.016844\n",
      "FullBath             0.560664\n",
      "HalfBath             0.284108\n",
      "BedroomAbvGr         0.168213\n",
      "KitchenAbvGr         0.135907\n",
      "TotRmsAbvGrd         0.533723\n",
      "Fireplaces           0.466929\n",
      "GarageCars           0.640409\n",
      "GarageArea           0.623431\n",
      "WoodDeckSF           0.324413\n",
      "OpenPorchSF          0.315856\n",
      "EnclosedPorch        0.128578\n",
      "3SsnPorch            0.044584\n",
      "ScreenPorch          0.111447\n",
      "PoolArea             0.092404\n",
      "MiscVal              0.021190\n",
      "YrSold               0.028923\n",
      "SalePrice            1.000000\n",
      "Years Before Sale    0.523350\n",
      "Years Since Remod    0.509079\n",
      "Name: SalePrice, dtype: float64\n",
      "(1460, 38)\n",
      "Index(['MSZoning', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',\n",
      "       'LandSlope', 'Condition1', 'Condition2', 'BldgType', 'HouseStyle',\n",
      "       'OverallQual', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd',\n",
      "       'ExterQual', 'ExterCond', 'TotalBsmtSF', 'HeatingQC', 'CentralAir',\n",
      "       'Electrical', '1stFlrSF', 'GrLivArea', 'FullBath', 'KitchenQual',\n",
      "       'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'GarageCars', 'GarageArea',\n",
      "       'PavedDrive', 'SalePrice', 'Years Before Sale', 'Years Since Remod'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('input_data.csv')\n",
    "\n",
    "# Understanding data\n",
    "print (df.shape)\n",
    "print (df.columns)\n",
    "print(df.head(5))\n",
    "print(df.info())\n",
    "print(df.describe())\n",
    "print(df.groupby('LotShape').size())\n",
    "\n",
    "# Dropping null value columns which cross the threshold\n",
    "a = df.isnull().sum()\n",
    "print (a)\n",
    "b =  a[a>(0.05*len(a))]\n",
    "print (b)\n",
    "df = df.drop(b.index, axis=1)\n",
    "print (df.shape)\n",
    "\n",
    "# Replacing null value columns (text) with most used value\n",
    "a1 = df.select_dtypes(include=['object']).isnull().sum()\n",
    "print (a1)\n",
    "print (a1.index)\n",
    "for i in a1.index:\n",
    "    b1 = df[i].value_counts().index.tolist()\n",
    "    print (b1)\n",
    "    df[i] = df[i].fillna(b1[0])\n",
    "\n",
    "# Replacing null value columns (int, float) with most used value\n",
    "a2 = df.select_dtypes(include=['integer','float']).isnull().sum()\n",
    "print (a2)\n",
    "b2 = a2[a2!=0].index \n",
    "print (b2)\n",
    "modes = df[b2].mode().to_dict(orient='records')\n",
    "if len(modes) > 0:\n",
    "    df = df.fillna(modes[0])\n",
    "\n",
    "# Creating new columns from existing columns\n",
    "print (df.shape)\n",
    "a3 = df['YrSold'] - df['YearBuilt']\n",
    "b3 = df['YrSold'] - df['YearRemodAdd']\n",
    "df['Years Before Sale'] = a3\n",
    "df['Years Since Remod'] = b3\n",
    "print (df.shape)\n",
    "\n",
    "# Dropping unwanted columns\n",
    "df = df.drop([\"Id\", \"MoSold\", \"SaleCondition\", \"SaleType\", \"YearBuilt\", \"YearRemodAdd\"], axis=1) \n",
    "print (df.shape)\n",
    "\n",
    "# Dropping columns which has correlation with target less than threshold\n",
    "target='SalePrice'\n",
    "x = df.select_dtypes(include=['integer','float']).corr()[target].abs()\n",
    "print (x)  \n",
    "df=df.drop(x[x<0.4].index, axis=1)\n",
    "print (df.shape)\n",
    "\n",
    "# Checking for the necessary features after dropping some columns\n",
    "l1 = [\"PID\",\"MS SubClass\",\"MS Zoning\",\"Street\",\"Alley\",\"Land Contour\",\"Lot Config\",\"Neighborhood\",\n",
    "      \"Condition 1\",\"Condition 2\",\"Bldg Type\",\"House Style\",\"Roof Style\",\"Roof Matl\",\"Exterior 1st\",\n",
    "      \"Exterior 2nd\",\"Mas Vnr Type\",\"Foundation\",\"Heating\",\"Central Air\",\"Garage Type\",\"Misc Feature\",\n",
    "      \"Sale Type\",\"Sale Condition\"]\n",
    "l2 = []\n",
    "for i in l1:\n",
    "    if i in df.columns:\n",
    "        l2.append(i)\n",
    "       \n",
    " #Getting rid of nominal columns with too many unique values\n",
    "for i in l2:\n",
    "    len(df[i].unique())>10\n",
    "    df=df.drop(i, axis=1)\n",
    "print (df.columns)\n",
    "\n",
    "df.to_csv('output_data.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e038bc60",
   "metadata": {},
   "source": [
    "## Model input/output data and pickle file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5d841c1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['OverallQual', 'TotalBsmtSF', '1stFlrSF', 'GrLivArea', 'FullBath',\n",
      "       'TotRmsAbvGrd', 'Fireplaces', 'GarageCars', 'GarageArea',\n",
      "       'Years Before Sale', 'Years Since Remod', 'SalePrice'],\n",
      "      dtype='object')\n",
      "Number of predictions: 365\n",
      "Mean Squared Error: 1683466974.746262\n",
      "Root Mean Squared Error: 41030.0740280378\n",
      "Scoring: 0.7767124842469847\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "['output_salepricemodel.pkl']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split,cross_val_score\n",
    "#from sklearn.externals import joblib\n",
    "import joblib\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import matplotlib.pyplot as plt\n",
    "from math import sqrt\n",
    "import os\n",
    "\n",
    "df = pd.read_csv('output_data.csv')\n",
    "\n",
    "i = list(df.columns.values)\n",
    "i.pop(i.index('SalePrice'))\n",
    "df0 = df[i+['SalePrice']]\n",
    "df = df0.select_dtypes(include=['integer','float'])\n",
    "print (df.columns)\n",
    "\n",
    "X = df[list(df.columns)[:-1]]\n",
    "y = df['SalePrice']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)\n",
    "\n",
    "y_predictions = regressor.predict(X_test)\n",
    "\n",
    "meanSquaredError=mean_squared_error(y_test, y_predictions)\n",
    "rootMeanSquaredError = sqrt(meanSquaredError)\n",
    "\n",
    "print(\"Number of predictions:\",len(y_predictions))\n",
    "print(\"Mean Squared Error:\", meanSquaredError)\n",
    "print(\"Root Mean Squared Error:\", rootMeanSquaredError)\n",
    "print (\"Scoring:\",regressor.score(X_test, y_test))\n",
    "\n",
    "plt.plot(y_predictions,y_test,'r.') \n",
    "plt.plot(y_predictions,y_predictions,'k-') \n",
    "plt.title('Parity Plot - Linear Regression')\n",
    "plt.show()\n",
    "\n",
    "plot = plt.scatter(y_predictions, (y_predictions - y_test), c='b')\n",
    "plt.hlines(y=0, xmin= 100000, xmax=400000)\n",
    "plt.title('Residual Plot - Linear Regression')\n",
    "plt.show()\n",
    "\n",
    "joblib.dump(regressor, 'output_salepricemodel.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f57e131b",
   "metadata": {},
   "source": [
    "## Prediction on input/output and pickle, json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "de81b3fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JSON file created.\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "# Prepare the data\n",
    "data = {\n",
    "    \"OverallQual\":[7],\n",
    "    \"TotalBsmtSF\":[856],\n",
    "    \"1stFlrSF\":[856],\n",
    "    \"GrLivArea\":[1710],\n",
    "    \"FullBath\":[2],\n",
    "    \"TotRmsAbvGrd\":[8],\n",
    "    \"Fireplaces\":[0],\n",
    "    \"GarageCars\":[2],\n",
    "    \"GarageArea\":[548],\n",
    "    \"Years Before Sale\":[5],\n",
    "    \"Years Since Remod\":[5]    \n",
    "}\n",
    "\n",
    "# Specify the file path and name\n",
    "file_path = \"C:/Users/Admin/input.json\"\n",
    "\n",
    "# Open the file in write mode\n",
    "with open(file_path, 'w') as json_file:\n",
    "    # Write the data as JSON\n",
    "    json.dump(data, json_file)\n",
    "\n",
    "# File created successfully\n",
    "print(\"JSON file created.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4ff6fbc9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[210550.8027625]\n"
     ]
    }
   ],
   "source": [
    "import os \n",
    "import json\n",
    "import pandas as pd\n",
    "import numpy\n",
    "#from sklearn.externals import joblib\n",
    "import joblib\n",
    "\n",
    "s = pd.read_json('input.json')\n",
    "p = joblib.load(\"output_salepricemodel.pkl\")\n",
    "r = p.predict(s)\n",
    "print (str(r))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6c8fe5ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * Serving Flask app \"__main__\" (lazy loading)\n",
      " * Environment: production\n",
      "\u001b[31m   WARNING: This is a development server. Do not use it in a production deployment.\u001b[0m\n",
      "\u001b[2m   Use a production WSGI server instead.\u001b[0m\n",
      " * Debug mode: on\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " * Restarting with watchdog (windowsapi)\n"
     ]
    },
    {
     "ename": "SystemExit",
     "evalue": "1",
     "output_type": "error",
     "traceback": [
      "An exception has occurred, use %tb to see the full traceback.\n",
      "\u001b[1;31mSystemExit\u001b[0m\u001b[1;31m:\u001b[0m 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3377: UserWarning: To exit: use 'exit', 'quit', or Ctrl-D.\n",
      "  warn(\"To exit: use 'exit', 'quit', or Ctrl-D.\", stacklevel=1)\n"
     ]
    }
   ],
   "source": [
    "import os \n",
    "import json\n",
    "import pandas as pd\n",
    "import numpy\n",
    "from flask import Flask, render_template, request, jsonify\n",
    "from pandas.io.json import json_normalize\n",
    "#from sklearn.externals import joblib\n",
    "import joblib\n",
    "\n",
    "app = Flask(__name__)\n",
    "port = int(os.getenv('PORT', 5500))\n",
    "\n",
    "@app.route('/')\n",
    "def home():\n",
    "    return render_template('index.html')\n",
    "\n",
    "@app.route('/api/salepricemodel', methods=['POST'])\n",
    "def salepricemodel():\n",
    "    if request.method == 'POST':\n",
    "        try:\n",
    "            post_data = request.get_json()\n",
    "            json_data = json.dumps(post_data)\n",
    "            s = pd.read_json(json_data)          \n",
    "            p = joblib.load(\"output_salepricemodel.pkl\")\n",
    "            r = p.predict(s)\n",
    "            return str(r) \n",
    "        \n",
    "        except Exception as e:\n",
    "            return (e)\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    app.run(host='0.0.0.0', port=port, debug=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "32ee5a41",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score, RMSE values\n",
      "Linear = (0.8182178182166804, 34866.60296321034)\n",
      "Ridge = (0.8054207260367505, 36073.000666625376)\n",
      "Lasso = (0.8182178103192395, 34866.60372059187)\n",
      "ElasticNet = (0.8181192239106916, 34876.05708706729)\n",
      "Selected Features for RandomForest: [0.63450578 0.09381529 0.03323273 0.11711888 0.00878517 0.00959487\n",
      " 0.01408235 0.01359338 0.030312   0.02054806 0.02441148]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_base.py:141: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2.\n",
      "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
      "\n",
      "from sklearn.pipeline import make_pipeline\n",
      "\n",
      "model = make_pipeline(StandardScaler(with_mean=False), Ridge())\n",
      "\n",
      "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
      "\n",
      "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
      "model.fit(X, y, **kwargs)\n",
      "\n",
      "Set parameter alpha to: original_alpha * n_samples. \n",
      "  warnings.warn(\n",
      "C:\\Users\\Admin\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_base.py:141: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2.\n",
      "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
      "\n",
      "from sklearn.pipeline import make_pipeline\n",
      "\n",
      "model = make_pipeline(StandardScaler(with_mean=False), Lasso())\n",
      "\n",
      "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
      "\n",
      "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
      "model.fit(X, y, **kwargs)\n",
      "\n",
      "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
      "  warnings.warn(\n",
      "C:\\Users\\Admin\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Admin\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:400: FutureWarning: Criterion 'mse' was deprecated in v1.0 and will be removed in version 1.2. Use `criterion='squared_error'` which is equivalent.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForest = (0.8662460090756656, 29907.998842545683)\n",
      "Coefficients of Perceptron: [array([[ 1.39564275e+00, -1.52133160e-02,  1.92897073e+00, ...,\n",
      "         5.61071656e-03, -2.80500921e-02,  1.36665239e+00],\n",
      "       [ 6.46592926e-02, -6.91687997e-83,  1.15377885e-02, ...,\n",
      "         3.25020006e-03,  1.63515296e-02,  6.34744429e-02],\n",
      "       [ 1.00848016e-01, -1.53519902e-02,  7.58186224e-02, ...,\n",
      "         8.47476312e-03, -3.48477055e-02,  1.37871774e-01],\n",
      "       ...,\n",
      "       [ 1.84486542e-01,  7.13464229e-82,  2.37438780e-01, ...,\n",
      "         7.59617254e-03, -2.31767999e-02,  2.00608352e-01],\n",
      "       [-2.90547063e-01, -1.53452587e-02, -5.45801285e-01, ...,\n",
      "         2.00391408e-03,  1.79968408e-02, -3.30870828e-01],\n",
      "       [-4.91170769e-01, -1.59450655e-02, -8.00754734e-01, ...,\n",
      "        -4.03756807e-02, -1.63766576e-02, -5.09656818e-01]]), array([[ 0.14248838],\n",
      "       [ 0.01506398],\n",
      "       [ 0.17189591],\n",
      "       ...,\n",
      "       [-0.004931  ],\n",
      "       [-0.01005716],\n",
      "       [ 0.14383741]])]\n",
      "Perceptron = (0.818230796240589, 34865.35832064121)\n",
      "Selected Features for DecisionTree: [0.72244936 0.04066149 0.03398669 0.09980533 0.00200302 0.00828614\n",
      " 0.002423   0.02163101 0.00674598 0.01456358 0.04744441]\n",
      "DecisionTree = (0.8146797259640887, 35204.27844887174)\n",
      "Selected Features for AdaBoost: [0.38733683 0.09984917 0.04751726 0.16182717 0.0180535  0.10376595\n",
      " 0.01369684 0.02875624 0.01147719 0.05699498 0.07072487]\n",
      "AdaBoost = (0.825929787018412, 34118.99348019598)\n",
      "Selected Features for ExtraTrees: [0.34092261 0.0604644  0.0469877  0.12135108 0.05500338 0.02662429\n",
      " 0.04641567 0.14738737 0.04950729 0.05920269 0.04613352]\n",
      "ExtraTrees = (0.8759923670398962, 28797.725615763575)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_gb.py:294: FutureWarning: The loss 'ls' was deprecated in v1.0 and will be removed in version 1.2. Use 'squared_error' which is equivalent.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected Features for GradientBoosting: [0.53209641 0.11476383 0.04737377 0.13751723 0.01817214 0.00578659\n",
      " 0.02359445 0.03125981 0.02464976 0.03027731 0.03450871]\n",
      "GradientBoosting = (0.884506377360196, 27791.56365592052)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso, ElasticNet\n",
    "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, ExtraTreesRegressor, GradientBoostingRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt\n",
    "\n",
    "df = pd.read_csv('output_data.csv')\n",
    "\n",
    "i = list(df.columns.values)\n",
    "i.pop(i.index('SalePrice'))\n",
    "df0 = df[i + ['SalePrice']]\n",
    "df = df0.select_dtypes(include=['integer', 'float'])\n",
    "\n",
    "X = df[list(df.columns)[:-1]]\n",
    "y = df['SalePrice']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)\n",
    "\n",
    "def linear():\n",
    "    regressor = LinearRegression()\n",
    "    regressor.fit(X_train, y_train)\n",
    "    y_predictions = regressor.predict(X_test)\n",
    "    return regressor.score(X_test, y_test), sqrt(mean_squared_error(y_test, y_predictions))\n",
    "\n",
    "def ridge():\n",
    "    regressor = Ridge(alpha=0.3, normalize=True)\n",
    "    regressor.fit(X_train, y_train)\n",
    "    y_predictions = regressor.predict(X_test)\n",
    "    return regressor.score(X_test, y_test), sqrt(mean_squared_error(y_test, y_predictions))\n",
    "\n",
    "def lasso():\n",
    "    regressor = Lasso(alpha=0.00009, normalize=True)\n",
    "    regressor.fit(X_train, y_train)\n",
    "    y_predictions = regressor.predict(X_test)\n",
    "    return regressor.score(X_test, y_test), sqrt(mean_squared_error(y_test, y_predictions))\n",
    "\n",
    "def elasticnet():\n",
    "    regressor = ElasticNet(alpha=1, l1_ratio=0.5, normalize=False)\n",
    "    regressor.fit(X_train, y_train)\n",
    "    y_predictions = regressor.predict(X_test)\n",
    "    return regressor.score(X_test, y_test), sqrt(mean_squared_error(y_test, y_predictions))\n",
    "\n",
    "def randomforest():\n",
    "    regressor = RandomForestRegressor(n_estimators=15, min_samples_split=15, criterion='mse', max_depth=None)\n",
    "    regressor.fit(X_train, y_train)\n",
    "    y_predictions = regressor.predict(X_test)\n",
    "    print(\"Selected Features for RandomForest:\", regressor.feature_importances_)\n",
    "    return regressor.score(X_test, y_test), sqrt(mean_squared_error(y_test, y_predictions))\n",
    "\n",
    "def perceptron():\n",
    "    regressor = MLPRegressor(hidden_layer_sizes=(5000,), activation='relu', solver='adam', max_iter=1000)\n",
    "    regressor.fit(X_train, y_train)\n",
    "    y_predictions = regressor.predict(X_test)\n",
    "    print(\"Coefficients of Perceptron:\", regressor.coefs_)\n",
    "    return regressor.score(X_test, y_test), sqrt(mean_squared_error(y_test, y_predictions))\n",
    "\n",
    "def decisiontree():\n",
    "    regressor = DecisionTreeRegressor(min_samples_split=30, max_depth=None)\n",
    "    regressor.fit(X_train, y_train)\n",
    "    y_predictions = regressor.predict(X_test)\n",
    "    print(\"Selected Features for DecisionTree:\", regressor.feature_importances_)\n",
    "    return regressor.score(X_test, y_test), sqrt(mean_squared_error(y_test, y_predictions))\n",
    "\n",
    "def adaboost():\n",
    "    regressor = AdaBoostRegressor(random_state=8, loss='exponential')\n",
    "    regressor.fit(X_train, y_train)\n",
    "    y_predictions = regressor.predict(X_test)\n",
    "    print(\"Selected Features for AdaBoost:\", regressor.feature_importances_)\n",
    "    return regressor.score(X_test, y_test), sqrt(mean_squared_error(y_test, y_predictions))\n",
    "\n",
    "def extratrees():\n",
    "    regressor = ExtraTreesRegressor(n_estimators=50)\n",
    "    regressor.fit(X_train, y_train)\n",
    "    y_predictions = regressor.predict(X_test)\n",
    "    print(\"Selected Features for ExtraTrees:\", regressor.feature_importances_)\n",
    "    return regressor.score(X_test, y_test), sqrt(mean_squared_error(y_test, y_predictions))\n",
    "\n",
    "def gradientboosting():\n",
    "    regressor = GradientBoostingRegressor(loss='ls', n_estimators=500, min_samples_split=15)\n",
    "    regressor.fit(X_train, y_train)\n",
    "    y_predictions = regressor.predict(X_test)\n",
    "    print(\"Selected Features for GradientBoosting:\", regressor.feature_importances_)\n",
    "    return regressor.score(X_test, y_test), sqrt(mean_squared_error(y_test, y_predictions))\n",
    "\n",
    "print(\"Score, RMSE values\")\n",
    "print(\"Linear =\", linear())\n",
    "print(\"Ridge =\", ridge())\n",
    "print(\"Lasso =\", lasso())\n",
    "print(\"ElasticNet =\", elasticnet())\n",
    "print(\"RandomForest =\", randomforest())\n",
    "print(\"Perceptron =\", perceptron())\n",
    "print(\"DecisionTree =\", decisiontree())\n",
    "print(\"AdaBoost =\", adaboost())\n",
    "print(\"ExtraTrees =\", extratrees())\n",
    "print(\"GradientBoosting =\", gradientboosting())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6e72865",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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