Power_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_selection import VarianceThreshold\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import seaborn as sns\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score as acc\n",
    "from mlxtend.feature_selection import SequentialFeatureSelector as sfs\n",
    "\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining modle metrics\n",
    "from sklearn.metrics import r2_score\n",
    "def R2(y_true, y_pred):\n",
    "    return r2_score(y_true, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_log_error\n",
    "def MSLE(y_true, y_pred):\n",
    "    return mean_squared_log_error(y_true, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import explained_variance_score\n",
    "def EX_VAR(y_true, y_pred):\n",
    "    return explained_variance_score(y_true, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "def MAE(y_true, y_pred):\n",
    "    return mean_absolute_error(y_true, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "def RMSE(y_true, y_pred):\n",
    "    return mean_squared_error(y_true, y_pred,squared=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading Input data\n",
    "data=pd.read_csv('final_data_2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RangeIndex(start=0, stop=1382, step=1)\n",
      "Index(['avg_outflow', 'avg_inflow', 'total_grid', 'Am', 'BOD', 'COD', 'TN',\n",
      "       'T', 'TM', 'Tm', 'SLP', 'H', 'PP', 'VV', 'V', 'VM', 'VG', 'year',\n",
      "       'month', 'day'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(data.index)\n",
    "print(data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['avg_inflow', 'total_grid', 'Am', 'BOD', 'TN', 'T', 'TM', 'H', 'PP',\n",
       "       'year', 'month'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selected Features= {month,year,TN,Am,BOD,T,TM,H,PP,inflow }\n",
    "data=data.drop(columns=['avg_outflow', 'COD','Tm', 'SLP', 'VV', 'V', 'VM', 'VG', 'day'])\n",
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()\n",
    "scaler.fit(data)\n",
    "normdata = pd.DataFrame(scaler.transform(data.values), index=data.index, columns=data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\Software\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "normdata2=normdata[normdata['total_grid']>0.4]\n",
    "normdata3=normdata2[normdata['total_grid']<0.77]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x16e6948a0c8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(normdata3['total_grid'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=normdata3['total_grid']\n",
    "x=normdata3.drop(columns=['total_grid'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#x_train,x_test,y_train,y_test= train_test_split(x,y,test_size=0.2,random_state=20)\n",
    "x_train,x_test,y_train,y_test=x[:840],x[840:],y[:840],y[840:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([   5,   16,   17,   20,   21,   31,   38,   39,   42,   46,\n",
       "            ...\n",
       "            1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381],\n",
       "           dtype='int64', length=1039)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2_GBM_Test=  0.016116712248002707\n",
      "R2_GBM_ALL=  0.6625576451523152\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "GB=GradientBoostingRegressor(n_estimators=6000,learning_rate=0.001\n",
    "                                ,min_samples_leaf=20 ,min_samples_split=30, max_depth=15)\n",
    "GB.fit(x_train,y_train)\n",
    "y_pred_GBM=GB.predict(x_test)\n",
    "y_pred_GBM_train=GB.predict(x_train)\n",
    "y_pred_GBM2=GB.predict(x)\n",
    "print ( 'R2_GBM_Test= ',R2(y_test,y_pred_GBM.flatten()) )\n",
    "print ( 'R2_GBM_ALL= ',R2(y, y_pred_GBM2.flatten()) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE =  0.00860360455524536\n",
      "MAE =  0.07515200967088977\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print('MSE = ',metrics.mean_squared_error(y_test,y_pred_GBM))\n",
    "print('MAE = ', metrics.mean_absolute_error(y_test,y_pred_GBM))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2_RF_Test=  -0.010209030894103632\n",
      "R2_RF_ALL=  0.5346695056050697\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "regressior= RandomForestRegressor(bootstrap=True,max_depth= 30, max_features='auto'\n",
    "                                  ,min_samples_leaf= 5,min_samples_split= 5,n_estimators= 2000)\n",
    "regressior.fit(x_train,y_train)\n",
    "y_pred_RF=regressior.predict(x_test)\n",
    "y_pred_RF2=regressior.predict(x)\n",
    "y_pred_RF_train=regressior.predict(x_train)\n",
    "print ( 'R2_RF_Test= ',R2(y_test,y_pred_RF.flatten()) )\n",
    "print ( 'R2_RF_ALL= ',R2(y, y_pred_RF2.flatten()) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE_test =  0.00883381101005277\n",
      "MAE_test =  0.07587723766517798\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print('MSE_test = ',metrics.mean_squared_error(y_test,y_pred_RF))\n",
    "print('MAE_test = ', metrics.mean_absolute_error(y_test,y_pred_RF))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(840, 10)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense , LSTM\n",
    "# Initialising the ANN\n",
    "model = Sequential()\n",
    "\n",
    "# Adding the input layer and the first hidden layer\n",
    "model.add(Dense(16, input_dim = 10, activation='relu'))\n",
    "\n",
    "# Adding the second hidden layer\n",
    "model.add(Dense(units = 10, activation = 'relu'))\n",
    "# Adding the second hidden layer\n",
    "model.add(Dense(units = 4, activation = 'relu'))\n",
    "\n",
    "# Adding the output layer\n",
    "model.add(Dense(units = 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/80\n",
      "27/27 - 0s - loss: 0.3702 - mae: 0.3702\n",
      "Epoch 2/80\n",
      "27/27 - 0s - loss: 0.1189 - mae: 0.1189\n",
      "Epoch 3/80\n",
      "27/27 - 0s - loss: 0.1007 - mae: 0.1007\n",
      "Epoch 4/80\n",
      "27/27 - 0s - loss: 0.0944 - mae: 0.0944\n",
      "Epoch 5/80\n",
      "27/27 - 0s - loss: 0.0899 - mae: 0.0899\n",
      "Epoch 6/80\n",
      "27/27 - 0s - loss: 0.0869 - mae: 0.0869\n",
      "Epoch 7/80\n",
      "27/27 - 0s - loss: 0.0851 - mae: 0.0851\n",
      "Epoch 8/80\n",
      "27/27 - 0s - loss: 0.0840 - mae: 0.0840\n",
      "Epoch 9/80\n",
      "27/27 - 0s - loss: 0.0830 - mae: 0.0830\n",
      "Epoch 10/80\n",
      "27/27 - 0s - loss: 0.0824 - mae: 0.0824\n",
      "Epoch 11/80\n",
      "27/27 - 0s - loss: 0.0816 - mae: 0.0816\n",
      "Epoch 12/80\n",
      "27/27 - 0s - loss: 0.0811 - mae: 0.0811\n",
      "Epoch 13/80\n",
      "27/27 - 0s - loss: 0.0810 - mae: 0.0810\n",
      "Epoch 14/80\n",
      "27/27 - 0s - loss: 0.0809 - mae: 0.0809\n",
      "Epoch 15/80\n",
      "27/27 - 0s - loss: 0.0818 - mae: 0.0818\n",
      "Epoch 16/80\n",
      "27/27 - 0s - loss: 0.0799 - mae: 0.0799\n",
      "Epoch 17/80\n",
      "27/27 - 0s - loss: 0.0797 - mae: 0.0797\n",
      "Epoch 18/80\n",
      "27/27 - 0s - loss: 0.0792 - mae: 0.0792\n",
      "Epoch 19/80\n",
      "27/27 - 0s - loss: 0.0788 - mae: 0.0788\n",
      "Epoch 20/80\n",
      "27/27 - 0s - loss: 0.0789 - mae: 0.0789\n",
      "Epoch 21/80\n",
      "27/27 - 0s - loss: 0.0787 - mae: 0.0787\n",
      "Epoch 22/80\n",
      "27/27 - 0s - loss: 0.0779 - mae: 0.0779\n",
      "Epoch 23/80\n",
      "27/27 - 0s - loss: 0.0791 - mae: 0.0791\n",
      "Epoch 24/80\n",
      "27/27 - 0s - loss: 0.0777 - mae: 0.0777\n",
      "Epoch 25/80\n",
      "27/27 - 0s - loss: 0.0778 - mae: 0.0778\n",
      "Epoch 26/80\n",
      "27/27 - 0s - loss: 0.0771 - mae: 0.0771\n",
      "Epoch 27/80\n",
      "27/27 - 0s - loss: 0.0770 - mae: 0.0770\n",
      "Epoch 28/80\n",
      "27/27 - 0s - loss: 0.0770 - mae: 0.0770\n",
      "Epoch 29/80\n",
      "27/27 - 0s - loss: 0.0769 - mae: 0.0769\n",
      "Epoch 30/80\n",
      "27/27 - 0s - loss: 0.0765 - mae: 0.0765\n",
      "Epoch 31/80\n",
      "27/27 - 0s - loss: 0.0765 - mae: 0.0765\n",
      "Epoch 32/80\n",
      "27/27 - 0s - loss: 0.0766 - mae: 0.0766\n",
      "Epoch 33/80\n",
      "27/27 - 0s - loss: 0.0759 - mae: 0.0759\n",
      "Epoch 34/80\n",
      "27/27 - 0s - loss: 0.0762 - mae: 0.0762\n",
      "Epoch 35/80\n",
      "27/27 - 0s - loss: 0.0761 - mae: 0.0761\n",
      "Epoch 36/80\n",
      "27/27 - 0s - loss: 0.0753 - mae: 0.0753\n",
      "Epoch 37/80\n",
      "27/27 - 0s - loss: 0.0771 - mae: 0.0771\n",
      "Epoch 38/80\n",
      "27/27 - 0s - loss: 0.0766 - mae: 0.0766\n",
      "Epoch 39/80\n",
      "27/27 - 0s - loss: 0.0758 - mae: 0.0758\n",
      "Epoch 40/80\n",
      "27/27 - 0s - loss: 0.0758 - mae: 0.0758\n",
      "Epoch 41/80\n",
      "27/27 - 0s - loss: 0.0753 - mae: 0.0753\n",
      "Epoch 42/80\n",
      "27/27 - 0s - loss: 0.0747 - mae: 0.0747\n",
      "Epoch 43/80\n",
      "27/27 - 0s - loss: 0.0748 - mae: 0.0748\n",
      "Epoch 44/80\n",
      "27/27 - 0s - loss: 0.0746 - mae: 0.0746\n",
      "Epoch 45/80\n",
      "27/27 - 0s - loss: 0.0742 - mae: 0.0742\n",
      "Epoch 46/80\n",
      "27/27 - 0s - loss: 0.0748 - mae: 0.0748\n",
      "Epoch 47/80\n",
      "27/27 - 0s - loss: 0.0743 - mae: 0.0743\n",
      "Epoch 48/80\n",
      "27/27 - 0s - loss: 0.0749 - mae: 0.0749\n",
      "Epoch 49/80\n",
      "27/27 - 0s - loss: 0.0739 - mae: 0.0739\n",
      "Epoch 50/80\n",
      "27/27 - 0s - loss: 0.0741 - mae: 0.0741\n",
      "Epoch 51/80\n",
      "27/27 - 0s - loss: 0.0738 - mae: 0.0738\n",
      "Epoch 52/80\n",
      "27/27 - 0s - loss: 0.0737 - mae: 0.0737\n",
      "Epoch 53/80\n",
      "27/27 - 0s - loss: 0.0730 - mae: 0.0730\n",
      "Epoch 54/80\n",
      "27/27 - 0s - loss: 0.0750 - mae: 0.0750\n",
      "Epoch 55/80\n",
      "27/27 - 0s - loss: 0.0749 - mae: 0.0749\n",
      "Epoch 56/80\n",
      "27/27 - 0s - loss: 0.0735 - mae: 0.0735\n",
      "Epoch 57/80\n",
      "27/27 - 0s - loss: 0.0735 - mae: 0.0735\n",
      "Epoch 58/80\n",
      "27/27 - 0s - loss: 0.0751 - mae: 0.0751\n",
      "Epoch 59/80\n",
      "27/27 - 0s - loss: 0.0734 - mae: 0.0734\n",
      "Epoch 60/80\n",
      "27/27 - 0s - loss: 0.0742 - mae: 0.0742\n",
      "Epoch 61/80\n",
      "27/27 - 0s - loss: 0.0732 - mae: 0.0732\n",
      "Epoch 62/80\n",
      "27/27 - 0s - loss: 0.0726 - mae: 0.0726\n",
      "Epoch 63/80\n",
      "27/27 - 0s - loss: 0.0732 - mae: 0.0732\n",
      "Epoch 64/80\n",
      "27/27 - 0s - loss: 0.0731 - mae: 0.0731\n",
      "Epoch 65/80\n",
      "27/27 - 0s - loss: 0.0726 - mae: 0.0726\n",
      "Epoch 66/80\n",
      "27/27 - 0s - loss: 0.0726 - mae: 0.0726\n",
      "Epoch 67/80\n",
      "27/27 - 0s - loss: 0.0729 - mae: 0.0729\n",
      "Epoch 68/80\n",
      "27/27 - 0s - loss: 0.0742 - mae: 0.0742\n",
      "Epoch 69/80\n",
      "27/27 - 0s - loss: 0.0724 - mae: 0.0724\n",
      "Epoch 70/80\n",
      "27/27 - 0s - loss: 0.0744 - mae: 0.0744\n",
      "Epoch 71/80\n",
      "27/27 - 0s - loss: 0.0726 - mae: 0.0726\n",
      "Epoch 72/80\n",
      "27/27 - 0s - loss: 0.0725 - mae: 0.0725\n",
      "Epoch 73/80\n",
      "27/27 - 0s - loss: 0.0727 - mae: 0.0727\n",
      "Epoch 74/80\n",
      "27/27 - 0s - loss: 0.0725 - mae: 0.0725\n",
      "Epoch 75/80\n",
      "27/27 - 0s - loss: 0.0721 - mae: 0.0721\n",
      "Epoch 76/80\n",
      "27/27 - 0s - loss: 0.0726 - mae: 0.0726\n",
      "Epoch 77/80\n",
      "27/27 - 0s - loss: 0.0718 - mae: 0.0718\n",
      "Epoch 78/80\n",
      "27/27 - 0s - loss: 0.0718 - mae: 0.0718\n",
      "Epoch 79/80\n",
      "27/27 - 0s - loss: 0.0725 - mae: 0.0725\n",
      "Epoch 80/80\n",
      "27/27 - 0s - loss: 0.0730 - mae: 0.0730\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x16e7e541688>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(loss='mae', optimizer='adam', metrics=['mae'])\n",
    "model.fit(x_train, y_train , epochs=80, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2_ANN_Test=  -0.2679469086517807\n",
      "R2_ANN_ALL=  0.09656406787634864\n"
     ]
    }
   ],
   "source": [
    "y_pred_ANN=model.predict(x_test)\n",
    "y_pred_ANN_train=model.predict(x_train)\n",
    "y_pred_ANN2=model.predict(x)\n",
    "print ( 'R2_ANN_Test= ',R2(y_test,y_pred_ANN.flatten()) )\n",
    "print ( 'R2_ANN_ALL= ',R2(y, y_pred_ANN2.flatten()) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "xx=x_train.to_numpy()\n",
    "x_train2=np.reshape(xx,(xx.shape[0],xx.shape[1],1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(840, 10, 1)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Dropout\n",
    "\n",
    "# Initialising the RNN\n",
    "model = Sequential()\n",
    "\n",
    "model.add(LSTM(units = 10, return_sequences =True, batch_input_shape=(None, 10,1)))\n",
    "#model.add(Dropout(0.2))\n",
    "\n",
    "# Adding a second LSTM layer and Dropout layer\n",
    "#model.add(LSTM(units = 10, return_sequences = True))\n",
    "#model.add(Dropout(0.2))\n",
    "\n",
    "# Adding a third LSTM layer and Dropout layer\n",
    "#model.add(LSTM(units = 50, return_sequences =True))\n",
    "#model.add(Dropout(0.2))\n",
    "\n",
    "# Adding a fourth LSTM layer and and Dropout layer\n",
    "model.add(LSTM(units = 10, return_sequences =False))\n",
    "#model.add(Dropout(0.2))\n",
    "\n",
    "# Adding the output layer\n",
    "# For Full connection layer we use dense\n",
    "# As the output is 1D so we use unit=1\n",
    "model.add(Dense(units = 1))\n",
    "model.compile(loss='mse', optimizer='adam', metrics=['mse', 'mae'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/60\n",
      "27/27 [==============================] - 5s 50ms/step - loss: 0.2120 - mse: 0.2120 - mae: 0.4388 - val_loss: 0.0162 - val_mse: 0.0162 - val_mae: 0.1026\n",
      "Epoch 2/60\n",
      "27/27 [==============================] - 0s 14ms/step - loss: 0.0147 - mse: 0.0147 - mae: 0.1005 - val_loss: 0.0108 - val_mse: 0.0108 - val_mae: 0.0875\n",
      "Epoch 3/60\n",
      "27/27 [==============================] - 0s 14ms/step - loss: 0.0114 - mse: 0.0114 - mae: 0.0904 - val_loss: 0.0108 - val_mse: 0.0108 - val_mae: 0.0878\n",
      "Epoch 4/60\n",
      "27/27 [==============================] - 0s 14ms/step - loss: 0.0119 - mse: 0.0119 - mae: 0.0927 - val_loss: 0.0105 - val_mse: 0.0105 - val_mae: 0.0860\n",
      "Epoch 5/60\n",
      "27/27 [==============================] - 0s 14ms/step - loss: 0.0108 - mse: 0.0108 - mae: 0.0869 - val_loss: 0.0106 - val_mse: 0.0106 - val_mae: 0.0869\n",
      "Epoch 6/60\n",
      "27/27 [==============================] - 0s 14ms/step - loss: 0.0107 - mse: 0.0107 - mae: 0.0873 - val_loss: 0.0103 - val_mse: 0.0103 - val_mae: 0.0855\n",
      "Epoch 7/60\n",
      "27/27 [==============================] - 0s 14ms/step - loss: 0.0110 - mse: 0.0110 - mae: 0.0884 - val_loss: 0.0103 - val_mse: 0.0103 - val_mae: 0.0855\n",
      "Epoch 8/60\n",
      "27/27 [==============================] - 0s 14ms/step - loss: 0.0107 - mse: 0.0107 - mae: 0.0875 - val_loss: 0.0101 - val_mse: 0.0101 - val_mae: 0.0850\n",
      "Epoch 9/60\n",
      "27/27 [==============================] - 0s 14ms/step - loss: 0.0109 - mse: 0.0109 - mae: 0.0880 - val_loss: 0.0100 - val_mse: 0.0100 - val_mae: 0.0843\n",
      "Epoch 10/60\n",
      "27/27 [==============================] - 0s 14ms/step - loss: 0.0103 - mse: 0.0103 - mae: 0.0861 - val_loss: 0.0099 - val_mse: 0.0099 - val_mae: 0.0838\n",
      "Epoch 11/60\n",
      "27/27 [==============================] - 0s 15ms/step - loss: 0.0101 - mse: 0.0101 - mae: 0.0859 - val_loss: 0.0098 - val_mse: 0.0098 - val_mae: 0.0835\n",
      "Epoch 12/60\n",
      "27/27 [==============================] - 0s 14ms/step - loss: 0.0105 - mse: 0.0105 - mae: 0.0863 - val_loss: 0.0098 - val_mse: 0.0098 - val_mae: 0.0835\n",
      "Epoch 13/60\n",
      "27/27 [==============================] - 0s 14ms/step - loss: 0.0101 - mse: 0.0101 - mae: 0.0854 - val_loss: 0.0096 - val_mse: 0.0096 - val_mae: 0.0827\n",
      "Epoch 14/60\n",
      "27/27 [==============================] - 0s 14ms/step - loss: 0.0105 - mse: 0.0105 - mae: 0.0876 - val_loss: 0.0096 - val_mse: 0.0096 - val_mae: 0.0823\n",
      "Epoch 15/60\n",
      "27/27 [==============================] - 0s 14ms/step - loss: 0.0101 - mse: 0.0101 - mae: 0.0842 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0820\n",
      "Epoch 16/60\n",
      "27/27 [==============================] - 0s 15ms/step - loss: 0.0102 - mse: 0.0102 - mae: 0.0861 - val_loss: 0.0094 - val_mse: 0.0094 - val_mae: 0.0817\n",
      "Epoch 17/60\n",
      "27/27 [==============================] - 0s 14ms/step - loss: 0.0100 - mse: 0.0100 - mae: 0.0855 - val_loss: 0.0094 - val_mse: 0.0094 - val_mae: 0.0815\n",
      "Epoch 18/60\n",
      "27/27 [==============================] - 0s 15ms/step - loss: 0.0096 - mse: 0.0096 - mae: 0.0834 - val_loss: 0.0094 - val_mse: 0.0094 - val_mae: 0.0813\n",
      "Epoch 19/60\n",
      "27/27 [==============================] - 0s 17ms/step - loss: 0.0094 - mse: 0.0094 - mae: 0.0821 - val_loss: 0.0092 - val_mse: 0.0092 - val_mae: 0.0808\n",
      "Epoch 20/60\n",
      "27/27 [==============================] - 0s 17ms/step - loss: 0.0099 - mse: 0.0099 - mae: 0.0848 - val_loss: 0.0091 - val_mse: 0.0091 - val_mae: 0.0805\n",
      "Epoch 21/60\n",
      "27/27 [==============================] - 0s 15ms/step - loss: 0.0100 - mse: 0.0100 - mae: 0.0856 - val_loss: 0.0092 - val_mse: 0.0092 - val_mae: 0.0802\n",
      "Epoch 22/60\n",
      "27/27 [==============================] - 0s 14ms/step - loss: 0.0100 - mse: 0.0100 - mae: 0.0843 - val_loss: 0.0090 - val_mse: 0.0090 - val_mae: 0.0798\n",
      "Epoch 23/60\n",
      "27/27 [==============================] - 0s 15ms/step - loss: 0.0097 - mse: 0.0097 - mae: 0.0844 - val_loss: 0.0091 - val_mse: 0.0091 - val_mae: 0.0799\n",
      "Epoch 24/60\n",
      "27/27 [==============================] - 1s 23ms/step - loss: 0.0102 - mse: 0.0102 - mae: 0.0857 - val_loss: 0.0089 - val_mse: 0.0089 - val_mae: 0.0792\n",
      "Epoch 25/60\n",
      "27/27 [==============================] - 1s 28ms/step - loss: 0.0096 - mse: 0.0096 - mae: 0.0829 - val_loss: 0.0091 - val_mse: 0.0091 - val_mae: 0.0794\n",
      "Epoch 26/60\n",
      "27/27 [==============================] - 0s 17ms/step - loss: 0.0097 - mse: 0.0097 - mae: 0.0835 - val_loss: 0.0088 - val_mse: 0.0088 - val_mae: 0.0786\n",
      "Epoch 27/60\n",
      "27/27 [==============================] - 0s 14ms/step - loss: 0.0095 - mse: 0.0095 - mae: 0.0834 - val_loss: 0.0089 - val_mse: 0.0089 - val_mae: 0.0787\n",
      "Epoch 28/60\n",
      "27/27 [==============================] - 0s 13ms/step - loss: 0.0094 - mse: 0.0094 - mae: 0.0829 - val_loss: 0.0087 - val_mse: 0.0087 - val_mae: 0.0779\n",
      "Epoch 29/60\n",
      "27/27 [==============================] - 0s 13ms/step - loss: 0.0094 - mse: 0.0094 - mae: 0.0815 - val_loss: 0.0088 - val_mse: 0.0088 - val_mae: 0.0782\n",
      "Epoch 30/60\n",
      "27/27 [==============================] - 0s 15ms/step - loss: 0.0091 - mse: 0.0091 - mae: 0.0811 - val_loss: 0.0087 - val_mse: 0.0087 - val_mae: 0.0777\n",
      "Epoch 31/60\n",
      "27/27 [==============================] - 0s 13ms/step - loss: 0.0090 - mse: 0.0090 - mae: 0.0810 - val_loss: 0.0085 - val_mse: 0.0085 - val_mae: 0.0771\n",
      "Epoch 32/60\n",
      "27/27 [==============================] - 0s 12ms/step - loss: 0.0091 - mse: 0.0091 - mae: 0.0809 - val_loss: 0.0085 - val_mse: 0.0085 - val_mae: 0.0767\n",
      "Epoch 33/60\n",
      "27/27 [==============================] - 0s 15ms/step - loss: 0.0091 - mse: 0.0091 - mae: 0.0817 - val_loss: 0.0084 - val_mse: 0.0084 - val_mae: 0.0762\n",
      "Epoch 34/60\n",
      "27/27 [==============================] - 0s 12ms/step - loss: 0.0089 - mse: 0.0089 - mae: 0.0808 - val_loss: 0.0083 - val_mse: 0.0083 - val_mae: 0.0759\n",
      "Epoch 35/60\n",
      "27/27 [==============================] - 0s 14ms/step - loss: 0.0090 - mse: 0.0090 - mae: 0.0811 - val_loss: 0.0083 - val_mse: 0.0083 - val_mae: 0.0755\n",
      "Epoch 36/60\n",
      "27/27 [==============================] - 0s 13ms/step - loss: 0.0090 - mse: 0.0090 - mae: 0.0808 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0755\n",
      "Epoch 37/60\n",
      "27/27 [==============================] - 0s 13ms/step - loss: 0.0090 - mse: 0.0090 - mae: 0.0813 - val_loss: 0.0083 - val_mse: 0.0083 - val_mae: 0.0753\n",
      "Epoch 38/60\n",
      "27/27 [==============================] - 0s 12ms/step - loss: 0.0091 - mse: 0.0091 - mae: 0.0815 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0747\n",
      "Epoch 39/60\n",
      "27/27 [==============================] - 0s 12ms/step - loss: 0.0086 - mse: 0.0086 - mae: 0.0786 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0744\n",
      "Epoch 40/60\n",
      "27/27 [==============================] - 0s 13ms/step - loss: 0.0085 - mse: 0.0085 - mae: 0.0787 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0743\n",
      "Epoch 41/60\n",
      "27/27 [==============================] - 0s 15ms/step - loss: 0.0086 - mse: 0.0086 - mae: 0.0791 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0741\n",
      "Epoch 42/60\n",
      "27/27 [==============================] - 0s 13ms/step - loss: 0.0086 - mse: 0.0086 - mae: 0.0786 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0741\n",
      "Epoch 43/60\n",
      "27/27 [==============================] - 0s 15ms/step - loss: 0.0088 - mse: 0.0088 - mae: 0.0799 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0741\n",
      "Epoch 44/60\n",
      "27/27 [==============================] - 0s 15ms/step - loss: 0.0083 - mse: 0.0083 - mae: 0.0764 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0746\n",
      "Epoch 45/60\n",
      "27/27 [==============================] - 0s 15ms/step - loss: 0.0082 - mse: 0.0082 - mae: 0.0763 - val_loss: 0.0090 - val_mse: 0.0090 - val_mae: 0.0772\n",
      "Epoch 46/60\n",
      "27/27 [==============================] - 0s 15ms/step - loss: 0.0082 - mse: 0.0082 - mae: 0.0767 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0745\n",
      "Epoch 47/60\n",
      "27/27 [==============================] - 0s 13ms/step - loss: 0.0082 - mse: 0.0082 - mae: 0.0764 - val_loss: 0.0083 - val_mse: 0.0083 - val_mae: 0.0748\n",
      "Epoch 48/60\n",
      "27/27 [==============================] - 0s 13ms/step - loss: 0.0087 - mse: 0.0087 - mae: 0.0790 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0738\n",
      "Epoch 49/60\n",
      "27/27 [==============================] - 0s 13ms/step - loss: 0.0083 - mse: 0.0083 - mae: 0.0770 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0736\n",
      "Epoch 50/60\n",
      "27/27 [==============================] - 0s 13ms/step - loss: 0.0097 - mse: 0.0097 - mae: 0.0831 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0741\n",
      "Epoch 51/60\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27/27 [==============================] - 0s 13ms/step - loss: 0.0086 - mse: 0.0086 - mae: 0.0784 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0735\n",
      "Epoch 52/60\n",
      "27/27 [==============================] - 0s 13ms/step - loss: 0.0088 - mse: 0.0088 - mae: 0.0793 - val_loss: 0.0083 - val_mse: 0.0083 - val_mae: 0.0748\n",
      "Epoch 53/60\n",
      "27/27 [==============================] - 0s 13ms/step - loss: 0.0084 - mse: 0.0084 - mae: 0.0770 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0735\n",
      "Epoch 54/60\n",
      "27/27 [==============================] - 0s 12ms/step - loss: 0.0082 - mse: 0.0082 - mae: 0.0757 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0734\n",
      "Epoch 55/60\n",
      "27/27 [==============================] - 0s 12ms/step - loss: 0.0080 - mse: 0.0080 - mae: 0.0750 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0734\n",
      "Epoch 56/60\n",
      "27/27 [==============================] - 0s 12ms/step - loss: 0.0082 - mse: 0.0082 - mae: 0.0757 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0756\n",
      "Epoch 57/60\n",
      "27/27 [==============================] - 0s 12ms/step - loss: 0.0090 - mse: 0.0090 - mae: 0.0803 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0734\n",
      "Epoch 58/60\n",
      "27/27 [==============================] - 0s 12ms/step - loss: 0.0084 - mse: 0.0084 - mae: 0.0763 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0740\n",
      "Epoch 59/60\n",
      "27/27 [==============================] - 0s 12ms/step - loss: 0.0078 - mse: 0.0078 - mae: 0.0748 - val_loss: 0.0084 - val_mse: 0.0084 - val_mae: 0.0750\n",
      "Epoch 60/60\n",
      "27/27 [==============================] - 0s 15ms/step - loss: 0.0081 - mse: 0.0081 - mae: 0.0757 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0743\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x16e0129dd48>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xxt=x_test.to_numpy()\n",
    "x_test2=np.reshape(xxt,(xxt.shape[0],xxt.shape[1],1))\n",
    "model.fit(x_train2,y_train, validation_data=(x_test2,y_test), epochs=60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2_RNN_Test=  0.058407424796035556\n",
      "R2_RNN_Train=  0.11316337938160914\n"
     ]
    }
   ],
   "source": [
    "y_pred_RNN_train=model.predict(x_train2)\n",
    "y_pred_RNN=model.predict(x_test2)\n",
    "#y_pred_RNN2=model.predict(x)\n",
    "print ( 'R2_RNN_Test= ',R2(y_test,y_pred_RNN.flatten()) )\n",
    "print ( 'R2_RNN_Train= ',R2(y_train,y_pred_RNN_train.flatten()) )\n",
    "#print ( 'R2_RNN_ALL= ',R2(y, y_pred_RNN2) )\n",
    "#y_pred_RNN[:,0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(199,)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_RNN.flatten().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([   5,   16,   17,   20,   21,   31,   38,   39,   42,   46,\n",
       "            ...\n",
       "            1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381],\n",
       "           dtype='int64', length=1039)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x16e7acb6448>]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.rcParams.update({'font.size': 14})\n",
    "plt.scatter(y_pred_RNN.flatten(),y_test)\n",
    "plt.ylim((0,1))\n",
    "plt.xlim((0,1))\n",
    "plt.xlabel(\"RNN Prediction\")\n",
    "plt.ylabel(\"Real values\")\n",
    "plt.title('Prediction Vs. Real for RNN ')\n",
    "plt.plot((0,1),(0,1), color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x16e7ad0c8c8>]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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V10DftCl88AE8+CDMnJlViQWs5GKMMamzfDlcd51ruG/fHp57Dho3TndUSWElF2OMSbbSUtdA37w5TJ3qvv7ii6xNLGAlF2OMSa5vvnETTU6d6nqDPfssHHhguqNKOiu5GGNMMhQXu/aUww+HRYvgxRfduis5kFjASi7GGJN4s2e7qVvmzoXzzoMnnoD99kt3VCllJRdjjEmUoiI3VuWoo1xX4zFj4K23ci6xgJVcjDEmMb780pVWli51o+0HD4ZatdIdVdpYycUYYyrizz/dYMjjj4etW1034+efz+nEApZcjDEmfh995LoXP/WUm8l4wQLo1CndUVUKllyMMSZWv/7q1lg57TSoUcNViQ0dCrvvnu7IKg1LLsYY45eqa6Bv2hRefdWtEjl3Lhx7bLojq3SsQd8YY/xYtQp69oSxY6FNG9e2cvjh6Y6q0rKSizHGRKPqGuibNHFtLA89BF99ZYmlHFZyMcaYSL7/Hrp1g4kTXW+w556DQw9Nd1QZwUouxhgTqqTENdA3bw4zZrjeYJ9/boklBilPLiLSU0S+F5HNIjJLRI4vZ//OIjJdRP4UkbUi8q6I2G/YGJMcixa5UkqvXnDiifD119CjB1Sx9+KxSOndEpELgKHAg0ArYBowXkQaRtj/IOBdYIq3/ylAPvBhSgI2xuSOrVvhgQfcol1Ll8KoUTBuHBxwQLojy0ipTsW3AiNVdbiqLlbVG4FVQI8I+7cB8oA7VPV/qjoXGAAcIiK1UxKxMSb7FRTAkUfC3XfD3/7mSi8XXwwi6Y4sY6UsuYhIdVyymBDy0gQgUifxAqAYuEZEqorIHsDlwH9VdW3SgjXG5IaiIujTB44+Gtascd2MX38d9t033ZFlvFSWXGoDVYHVIdtXA3XDHaCqy4FOwL3AFuB3oAVwRrj9RaSbiBSISMGaNWsSFLYxJitNngwtW8KgQXDVVa60cvbZ6Y4qa6SjhUpDvpcw29wLInWBEcBLwJFAB+BP4E0RKRO7qg5T1baq2rZOnToJDdoYkyX++MM10Hfo4JYfnjgRhg+HmjXTHVlWSeU4l7VACWVLKftStjQTcD2wUVX7BDaIyCXAClxV2pdJiNMYk60+/BCuuw5WroRbb4X77oPddkt3VFkpZSUXVd0KzMJVcwXrhOs1Fk4NXEIKFvje+gUaY/xZuxYuuQS6dIE994Rp09x6K5ZYkibVI/SHAC+LyExgKtAdqA88AyAiA4CjVPVkb/9xwC0i0g94FdgD1415BS5RGWOSaOycQgZ9vISV64uoXzOf3p0P45xWDdIdln+q8MYbcOONsH499OvnVorcZZd0R5b1UppcVPUNEdkHuAuoBywETlfVH7xd6gGHBO3/mYhcBPQBegNFwFfAqaq6MZWxG5PN7hq7gFdm/Ih6rZ818qrQtc3+jJlVSFGxqywoXF/EHW8vAMiMBFNY6CaafO89aNvWzQ/WokW6o8oZohq2LT3jtW3bVgsKCtIdhjGV3l1jFzDqqx9979+gZj5T+3ZMYkQVpOrmALv99h0DI2++GarZVIp+iMgsVW1b0fPY3TYmx702Y0VM+69cXxRTdVlKq9a++w6uvRYmTXK9wYYPh7/8JTnXMlFZcjEmx5XEWHuxV34evUfPo7jEHVe4vojeo+cBZavLxs4p5I63FyS/ai0w0eRdd0FeHjz7LFxzjc0HlkZ2543JcVVjmOIkP68qxSWl2xNLQHGJcu/7X++0beycQm57c972xBJQVFzCoI+XxB9wqIUL3UqQt90GJ5/sJprs1s0SS5rZ3TcmR42dU0j7gZ/5LrnUzM9jQNcWbNwaOjrAWbepeKdz3/H2gojnXrm+KPaAQ23dCvfeC61bw7Jlbtnh996D/fev+LlNhVm1mDE5KLS6yo+NW7f53nfQx0uinrtmjTzf5wpr5ky4+mpXarnoInj0UbBZOSoVK7kYk4PKe/iHU1yiDPp4CTXzwyeG4O3llUw2bN7G2DmFMV0fgE2bXC+wdu1g3TpXUnnlFUsslZAlF2NyULzVUivXF9H/rGbkVdm5nSavinDG4fVoP/AzDuo7jirltOMUl2rs7S6TJrlxKoMHux5hX38NZ54Z649gUsSqxYzJQfVr5lMYR4KpXzN/ey+v4O7FJzWuwxv/XbG9od9PO47vBPf7725a/GHD4JBDdnQzNpWaJRdjclDvzofF3OYSOA5cN+LgrsSt7ptQpgdZeerXzC9/p/ffh+7d4eefXXXYvfdCjRoxXcekh1WLGZODzmnVgAFdW9CgZj6Cv+7INfPzwo5NGTuncKeeYqEuOaYh+XlVd9qWn1d1e6IKa80a+Mc/4KyzYJ994Kuv3LorllgyhiUXY3LUOa0aMLVvR74f2IXB5x9OtPSSV0Xof1azMtsDvc6imfTNmp0SWYOa+Qzo2iL8IEpV16W4SRMYM8aVVAJLEJuMYtVixhjOadWAgh9+izjHWPVqVcImAz+9zlauLypTjRbWihVuEa9x49yywyNGQLMdCS3jZ2jOMZZcjDHl2ri1hFb3TWD9puKdHux+GuXLbVspLXVzgPXu7aZxeeQRN0V+1R1VaSmbRsYkjFWLGWMYO6eQV8qZGXndpmKUHQ/2sXMKfTXKb9wSZUzLt99Cx46u0f6oo2DBAujVa6fEAuFLSAmfRsYklCUXYwyDPl5CLH29Ag/23p0PK9NYH2p9UfH2ZLTdtm2ugb5lS5g7102R/8kncPDBYc8RqYSUkGlkTFJYcjHGxPWQDrSlhDbWhxvBv1MpY/58N8K+Tx/o3BkWLXJTuUTpsRaphOSrO7NJC0suxuSowMSVB/UdF+25HlGkB/v6ovDdkteu/QPuuQfatIEffnDLD7/zDtSvX+61wpWQyu3ObNLKGvSNyUFj5xTS+615FJe6yrBYF6QV3AM/XEN7OK0Kv2HIhMfhlx/g0ktdo/0++/i+XrhZAay3WOVmyxwbk0X8dtc94t4JEUsYfglQRSTqVC/5Wzdz+5SXubLgPTbXrUeN55+D006r0HVNcqV9mWMRyVPViv11GmMSJpbuuhVNLABK9DnEjl0+l4EfPU7D31fz1akXcMwbw2DPPSt8XZMZfLW5iMhNInJu0PcjgCIRWSIiVulpTCVQWbrr7rl5AwPHP8arb9zFtipVOf+igVx4+KW0f6ogvmn2TUbyW3K5CbgKQEROAM4HLgLOBQYDZyQlOmOMb7F0161VIy/qfGDx+r+l07n/k6fZZ+N6nj76PB5t/w+25O0C2MDHXOO3t1gDYLn39ZnAW6r6JtAfOCbxYRljYhVLd90uLesl9Nq1N67jibEDGfbOv/m1xl6cc9kQHupwxfbEEmADH3OH3+TyBxBY6q0T8Kn3dTGwa6KDMsbEzm933bFzChkzK0HVU6r8beFnfPJcTzr97ysGHX8pZ132CAvr/iXiITbwMTf4rRabAAwXkTnAX4Dx3vZmwPfJCMyYyqayT5zot7tuPEsch1P/j1/498dPctKyWcyq35g+p93Md7UPKP84G/iYE/wml+uBfwMNgfNU9Tdve2vgtWQEZkxlkikTJ/qZfbiiJQfRUi6eM547Jo8EVfqfch0vtTqd0irRp4EBG/iYS3wlF1X9A7gxzPZ+CY/ImEooWk+sypRc/Ih3iWOAg3/9iQEfPc7RP33NL8ecwL6vvci2uX9SWs6klwER13ExWcf3OBcR2Q+4FDgEuFtV14pIe2ClqlrVmMlq2TRxYjxLHFctLaHbzLfp9eWrbK5WndtP78Xo5idT66WlvnudNaiZb4klh/hKLiLSBteI/z2unWUQsBbXuH8orluyMVkr0rv9TGw/CG6b8VOCabp6GQ+NH0qL1d/x0aHtuLtTD9bsvjdATN2ZT2pcp/ydTNbw21vsYWCoqrYCtgRt/xhon/CojKlksm3ixMASx+FmMA7YZdtWbv/iJd57sRd1N/xK93PuoPvf7tyeWGI16Zs18YZrMpDfarE2wNVhtq8C9ktcOMZUTtk6cWKkaWBa/7SY/4wfyl9++4nRzU/m/o7X8Hv+HhW6ViZWIZr4+U0uRUCtMNsbA78kLhxjKi9f68BnuBpbi+j9xUtcPusDVu5Zm8v+fi9fHNwmIeeuWSNyKclkH7/VYu8C/UQkMNxWRaQR8BAwJhmBGWOSr1bQA//472czYcT1XD7rA15q3YXOVz2ZsMQCsGFzlOWOTdbxNeW+iOwJfAi0BHYDfsZVh00FTlfVjb4vKNIT6A3UA74GeqnqlCj7C3Az0B04CPgNeFFV+0a7jk25b8zOggeB1qyRh6qrFtur6E/u+mwEf184ke/23p9/nnYjBfs3S0oMVUUoVc2aasVslNIp971xLseJSEfcwMkqwGxVnRjLxUTkAmAo0BP40vs8XkSaqmqkjvKBiTF7AwuAvXCJyRjjU+gg0EAvr1OXTOX+T56m1qY/eKLd+Tx+7IVsqVY9aXEEpuivrINQTeKkdLEwEZkBzFfVa4O2fQuMVtU7wux/GLAQaKmqi2O5lpVcjNmh/cDPdup2XGfDOu795GlOXzqNhfsdQp/TbmZlo8MSss5LLBrUzGdq344pvaaJLqUlFxG5NdrrqjrExzmq43qdPRzy0gTg2AiHnQ0sA04VkXG4EtNkoLeqlulIICLdgG4ADRs2LC8kY7JeoCpse2JR5byFn3LXZ8+RX7yFh068nOFH/o1tVauBz8QiuIXCEsF6kGUvv73FQqd+ycNVTRXheouVm1yA2kBVYHXI9tXAKRGOORg4ELgQuAL3N/0w8L6ItFPV0uCdVXUYMAxcycVHTMZkrdCqsP1/X82DHz3BCcvnMHP/pvQ99SaW7bN/zOetWSOPzcWlYUf4x5p4MnEQqvHHb5vLQaHbvOlgXgCGx3jN0L+9aH+PVYBdgEtVdal33UuBJcCRwIwYr21MVvAzQ3NgPjTRUi6bPY4+k19ERbi7U3dGtTodFb+dRXe2blMxlxzTkFFh5hOLllhC/9EzeRCqKZ/vucVCqepqEbkTeBN4x8cha4ESoG7I9n0pW5oJWAVsCyQWz7fANtwMzZZcTM7xO0PzyvVFHLJ2BQ999BhtCxcz+aDW/KvzDRTutW+Fri9Q7nowoYkk8H1VEUpUaWC9xbJefG9ddj7e1wh9Vd0KzMLNRxasEzAtwmFTgWoickjQtoNxSfGH2EI1JjtEm6F5u+Ji+s55mw9H3sghv/7ErV1u4fK/31vhxAIuSZQ36eXFxzSkgVflFZxoAr3FNm3dVuE4TOXmt0G/a+gmXJvL9UDEMSphDAFeFpGZuMTRHagPPONdZwBwlKqe7O0/EZgNPC8ivbxtj+JKLNYVzOSkcmdonj0brr6a6+bOZXyT47n75G6s3S3cBBvJ887sQr6+79QyvdQC1m0qtq7IWc5vtdjokO8VWAN8Btzm92Kq+oaI7APchUtOC3GDMAOlkHq4Kf0D+5eKyBnAY8AXuA4EnwC3hjbmG5MrIs3Q3Gi3KnDHHTBoENSpA2+/zZZGR7HLx0sQr20m3nVcYrVxawkXD58e9XqZuh6O8Sel41xSyca5mGwV2uYC0H7VYp6Z/DR7/LAMrroKHn4YapUtrUQqSVQRKE3Do0CA7wd2Sf2FTUSJGudS0TYXY0yKndOqAQO6tqBBzXx237KJwZOH88pLvdlDSuGTT2DEiLCJBSIvHZCOxALWFTmbRawWK2/gZDA/gyiNMYlzTqsGnPPzfLjuNvjpJ7j5ZnjgAdh993KPg7JLB9z5zgI2bvW/MmUiWFfk7BatzSV04GQkir9BlMZkBT9jTJLq11/hllvg5ZehSROYOhXatfN9eOjSAXeNTX1iqZmfR/+zmll7SxaLmFzCDZw0Jtf5HWOSFKowejTccAP89hvcfTfceSfsskv5x0bxSpjBkMm2ZZv1x8l21uZiTAx8jTFJhlWroGtXOP98OOAAKCiA++6rUGIZO6eQpnePT9g8YbFIyT0zaeV7hL6I7A2cihsZv9Oc3Kp6X4LjMqZSKneMSaKpwgsvwK23wpYt8J//uCqxanFPrgEEl8DSV4KwSSuzm99BlMcA44AtQB2gEDcmZQuwHLDkYnJCpLEiSen1tGwZXHcdTJwIJ5wAw4fDoYcm5NThSmB+VK0ilCSoa5n1FMtufqvFBgGvAA2AzUBHXAmmALfUsTE5IVJX3oT2eiopgUcfhRYtYMYMePppmDQpYYkF4i81JCqxWE+x7Oc3ubQEnlA34rIE2EVVVwP/BPonKTZjKp3gMSaCW+xqQNcWiWvMX7QIjjvOVX116ABffw3du0OVxDaPprvUkNB7ZiolvxW3W4O+Xo1bY2UxsAE3N5gxOSO0K29CbN0KDz3kxqrssQeMGgUXXQQiib2Op3fnw8qM8k8lSyzZz29ymY1bP2Up8DnwgLeeyyXA/OSEZkyOKCiAq6+G+fPhwgth6FDYt+KzF0cTPJgyVfONBdTMz0vp9Ux6+C1r3wms9L6+Czdp5eNALbxlhY0xMSoqgj594OijYe1aePddeO21pCeWgHNaNQjbhlSeqlUqVpo64/B6FTreZAZfyUVVC1R1kvf1GlU9TVX3VNW2qroguSEak4UmT4aWLd0Mxldf7dpWzjor5WHE02ts8N8Pr9A1J32zpkLHm8zgK7mIyCMi0jrZwRiT9f74A3r0cI31paXw6acwbBjUrJmWcGLtNdagZj7ntGqwfSGwVFzTZCa/1WJHAwUislhE/iUijZIYkzHZadw4aNbMJZNbb4UFC6Bjx7SGFGuvsUD34d6dDyOvanzVY+nuqWZSw2+12LG4RbxewTXifyciU0TkOhFJ7RJ3xmSatWvhkkvgjDNgr71g2jQYPBhq1EhrWGPnFMa83HCvN+bSqO847n3/a+JJLTa+JXf47jyvqt+r6gOq2hTXc2wGcDc7GvqNMcFU4fXX3czFb74J/fq5JYiPPjrdkW2f/mXdpuKdttfMz6Oajwb7dZuK2Vrif0BlUsYEmUot3gmK8oBdcHOMpaejvDGVWWEh9OwJ770HRx7pFvBq0SLdUW0XqSF/t13cI2F9UXGZ1+LVoGY+U/umt/rPpJ7vkouIHCoi94rIt8CXwGHA7cB+yQrOmIyj6uYAa9rUrQr58MMwfXqlSiwQfQLO3xOYWKwaLHf5nbiyAGgFzAOeBl5V1Z+TGZgxGee77+Daa908YB06uCTzl7+kO6qwypuAMxEDKwU4t00SZjMwGcFvyWUC0FxVW6vqEEssxgQpKYEhQ1zpZNYs1xvss88qbWKByBNwntS4TsyN/JEoNqYll/kquajqv5IdiDEZaeFCNwhy5kw480w3g3GDyvdOPdzSzAO6tthp20mN6zBmVmFC5xuzMS25q2IrDhmTq7ZuhQED4N//dt2LX3sNLrggaRNNBoRLEuVVO0VamnlA1xY7NbS3H/hZ1MSSn1eVc9s04LUZKyhRfz3FbExL7rJljo2J1cyZ0KYN9O8Pf/87LF7sJpxMQWK54+0FFK4vQtmRJMbOKYx6nN+lmaOVMhrUzOfcNg0YM6vQd2KxxvzcZsnFGL82bYLbboN27WDdOnj/fXjlFahdOyWX95skQvldmnmvCLMVVxWhd+fDmPTNGt9VZoHGfHAlooP6jqP9wM/KTYQme1i1mDF+TJoE11zjlh7u3h0GDnTVYSnkN0mE8rM089g5hWyM0JBfokrv0fMojmHQpAIfzFu1UxtOoKQFtp5LLohYchGRvf1+pDJgY1Lq99+hWzc3B1iVKvD5567RPsWJBSK3X5TXruFnaeZBHy+JmjxiSSwB64uK4yppmewQrVpsLW7dlmgfgX2MyT7vv+8GQ44YAb17w7x5cOKJaQvHT5IIx8/SzKns1WU9yHJDtGqxk1IWhTGVyZo1cNNNbl6wFi3cIl5t26Y7qp1Wj4ylt1jg2Gj7Rao6i1deFaG4NHxpx3qQ5QZRnz0/Mk3btm21oKAg3WGYTKIKr74KN9/s1l25+2745z+hevV0R5ZQ4bozAzt1V45HrRp5rN9UTP2a+Wzauq3MpJjgGvofueAIa3OpxERklqpW+N1UzL3FRKSuiDQM/qhoEMak3YoVbhDkJZe4kfVz5rjkkoWJJVx3ZmCnqrNaNfKomZ+3/eu8cmZKbn/I3vQ7sxn1a+azcn1R2MQCrqHfEktu8Du32F7AY8D5uJmQQ8W2CLcxlUVpqZuupU8fN43LI4/AjTdC1ez8k47WnXlq344RH/zBpZ1d86pQVFy60+tTv/uN6ct+I0JN2HYVWcHSZBa/JZeHgcOBc4DNwEVAb+An4IJYLigiPUXkexHZLCKzROR4n8f9VUT+FJENsVzPmIi+/db1AuvRA446yq0M2atX1iYWiL0789g5hbQf+Bm3vDEXcFVau1QLf3/KSywAJzWu4ytOk/n8JpfTgBtV9WPc+i2zVHUI0Be4zu/FROQCYCjwIG6W5WnA+PKq1kSkOvA68IXfaxkT0bZtMGgQtGwJc+e63mCffAIHH5zuyJIulu7M4arQer81r0JrvdhElrnDb3KpCfzgff07sI/39XTg2BiudyswUlWHq+piVb0RWAX0KOe4h4D5wFsxXMuYsubPdyPs+/SBzp1h0SK46qqkT91SWcTSnTlcFVqkHmB+WTfk3OE3uXwHBN7WLQYuFBEBugK/+TmBV/pog5u+P9gEoiQoEekCnAHc5DNWY8rasgXuucfNCfbjj27Z4Xfegfr10x1ZSvkZ8xKQjERg3ZBzh9/pX0YCLYHPgYHAB8ANuOR0s89z1MY1/K8O2b4aOCXcASJSDxgOdFXVP6Wcd5ci0g3oBtCwoXViM57p0920+IsXw6WXukb7ffYp/7gsVd6Yl4BYx77kVRHyqgqbQhr7A2wiy9ziq+Siqo+o6mPe158BjXEN+Ueo6hMxXjO0XC1htgWMAp5W1a98xjlMVduqats6dazhMOdt3Oga6Nu3hw0b4MMP4aWXcjqxxCJcFVoggYSqmZ/HoL8fzqL7T+PRC47Y3iusqveGMFoJyWSnuCauVNUfgR9jPGwtrjNA3ZDt+1K2NBPQEThRRPp53wtQRUS2AT1VdViMMZhcMXGiW3J4+XK4/nq39soee6Q7qowSaUaAcNuCk4bfkpHJbr6Ti4j0BK4HDsItebxMRPoCy1T1zfKOV9WtIjIL6MTODfOdgDERDmsR8v3ZwJ3AUYDN3W3KWr/eTYv//PPw17/CF1/A8b56u5swIiUKSx6mPL6qxUSkF3AXMAxXeggoxLW9+DUEuEJErhGRJiIyFKgPPONdZ4CIfBrYWVUXBn941yv1vl8Xw3VNLhg71k00+eKL0Levm2jSEosxaeG35NIduFZVx4nIA0HbZwPN/F5MVd8QkX1wiaoesBA4XVUD3ZzrAYf4PZ8xAKxe7UbVv/UWHH64m824TZt0R2VMTvPbFflAXCIIVQzE1LdQVZ9S1UaquouqtlHVL4Jeu0JVG0U5dqSq7h7L9UwWU3UN9E2auJmL//1v+O9/LbEYUwn4TS7LgNZhtp8OLEpcOMb49OOPcPrpcPnlLrnMmwf/+hfkhV+q1xiTWn6rxR4GnhCRGrg2l3YicinQB7gqWcEZU0ZpqVsJsm9fV3J57DHXG6xKzBN8G2OSyFdyUdUXRKQabk6wGsDLuMb1m1T1jSTGZ8wOS5a4dey//BI6dXKzGTdqlO6ojDFh+H67580HdiBuXEpdVT1AVUeIyAHJC88Y3ESTAwe6xvqFC+GFF+Djjy2xGFOJxVyXoKprVfUXb9GwJ4GlSYjLGGfuXDj6aLjjDujSxU3hcsUVOTPRpDGZKmpyEZGaIvKKiKwRkZUicpM4/XCN/EdhbS4mGTZvhjvvdGvXFxbC6NEwZgzUDZ3gwRhTGZXX5vIgcALwInAq8AhuRP1uwGmqOjm54ZmcNHWqm2hyyRJXShk8GPbeO91RGWNiUF61WBfgSlW9HTgL11PsO1XtaInFJNyGDXDTTW5U/ebNrl3lhRcssRiTgcpLLvXxxrGo6jLcEsfDkx2UyUETJkDz5vDEE3DDDa7h/v/+L91RGWPiVF5yqYIbhR9QAmxKXjgm5/z2G1x5pVsVctddYcoUN3Zld5uIwZhMVl6biwCjRGSL9/2uwHAR2SnBqOpZyQjOZLkxY9wAyLVr3ej6u+92CcYYk/HKSy4vhnw/KlmBmByyapWr+nr7bWjVCj76CI44It1RGWMSKGpyUdUrUxWIyQGqbjr8W26BoiI3MPK226BaXGvWGWMqMfuvNqmxfDl06waffALHHQfPPQeH2XrqxmQrm+3PJFdpKTz+uOsJNn06PPkkTJ5sicWYLGclF5M8ixe7iSanTYNTT4VnnoEDD0x3VMaYFLCSi0m84mJ48EHXSP/NN25Brw8/tMRiTA6xkotJrNmz4aqr3OJd55/vxqzst1+6ozLGpJiVXExiFBW5BbyOOsqtaf/OO/DGG5ZYjMlRVnIxFTdlimtbWbrUTTg5aBDUqpXuqIwxaWQlFxO/P/90I+xPOAG2bnXdjJ97zhKLMcaSi4nT+PHQrJlbz75XLzfR5CmnpDsqY0wlYcnFxObXX+Gyy+D002GPPdzaK488Arvtlu7IjDGViCUX448qvPkmNGkCr73mJpmcPRvatUt3ZMaYSsga9E35Vq50bStjx0KbNjBxIrRsme6ojDGVmJVcTGSqMGIENG3qZi7+z3/gq68ssRhjymUlFxPesmVuoslPP3W9wZ57Dv7613RHZYzJEFZyMTsrKYFHH4UWLWDmTNcbbNIkSyzGmJhYycXssGiRGwT51VfQpYtLLAcckO6ojDEZyEouxg2AvP9+N9Hkt9/CK6/A++9bYjHGxM1KLrnuv/91pZUFC+DCC91Ek3XqpDsqY0yGs5JLrtq0Cfr0gWOOcQMj333XjV+xxGKMSQArueSiyZPdRJP/+x9ce62baHKvvdIdlTEmi6S85CIiPUXkexHZLCKzROT4KPt2EJF3RWSViGwSkfkiclUq480qf/wBPXpAhw5u+eFPP4VhwyyxGGMSLqXJRUQuAIYCDwKtgGnAeBFpGOGQY4EFwHlAc+BpYJiIXJSCcLPLuHFuoslhw+C221wbS8eO6Y7KGJOlRFVTdzGRGcB8Vb02aNu3wGhVvcPnOd4EqqrqudH2a9u2rRYUFFQo3qywZo2btfjVV6F5czfi/qij0h2VMaaSEpFZqtq2oudJWclFRKoDbYAJIS9NwJVQ/NoTWBfhGt1EpEBECtasWRNfoNlCFV5/3U3d8tZb0L8/zJplicUYkxKprBarDVQFVodsXw3U9XMCETkDOBkYFu51VR2mqm1VtW2dXO71VFgIZ58N//gHHHywm724Xz+oXj3dkRljckQ6uiKH1sNJmG1liEh74FXgJlWdmYzAMp4qDB/uSisTJ8LgwTBtmqsOM8aYFEplV+S1QAllSyn7UrY0sxMROQ74ELhHVZ9OTngZ7rvvXLfiSZPgpJNckjnkkHRHZYzJUSkruajqVmAW0CnkpU64XmNhicgJwHjgXlV9NGkBZqqSEldCadHCtakMH+66GFtiMcakUaoHUQ4BXhaRmcBUoDtQH3gGQEQGAEep6sne9x2AccBTwCsiEij1lKhqjrfY49atv+oqN4XLmWe6iSYbNEh3VMYYk9rkoqpviMg+wF1APWAhcLqq/uDtUg8Ifst9BVADuN37CPgBaJTseCutrVvhwQfdR82arlfY+eeDSLojM8YYIMXjXFIpa8e5zJzpSitffw0XX+zWXqldO91RGWOyRMaNczEVtGmTG1nfrh38/jt88AGMGmWJxRhTKdnElZlg0iQ30eSyZdC9Ozz0EOy5Z7qjMsaYiKzkUpmtX+/Wse/YEapUgc8/d432lliMMZWcJZfK6r333ESTI0a4dVfmz4cTT0x3VMYY44sll8rml1/cipBnnw377AMzZrhqsPz8dEdmjDG+WXKpLFTd2vVNm8I777g17QsKoG2FO20YY0zKWYN+ZbBihVvEa9w4t+zwiBEuyRhjTIaykks6lZbCM8+4tpVJk9yYlS+/tMRijMl4VnJJl2+/dd2Lv/gCTjnFrRB50EHpjsoYYxLCSi6ptm0b/Oc/0LIlzJvnqsAmTLDEYozJKlZySaV58+Dqq93sxeecA08+CfXrpzsqY4xJOCu5pMKWLXD33a7n14oV8Oab8PbblliMMVnLSi7JNn26K60sXgyXXQZDhrjxK8YYk8Ws5JIsGzdCr17Qvr37evx4ePFFSyzGmJxgJZdkmDjRLTm8fDlcfz0MGAB77JHuqIwxJmWs5JJI69a5KrBOnaB6ddfN+IknLLEYY3KOJZdEeecdN/jxxRehb1/XM+z449MdlTHGpIVVi1XU6tVw443w1ltwxBFuCpfWrdMdlTHGpJWVXOKlCi+9BE2auOnxH3zQLUFsicUYY6zkEpcff4TrroOPPoJjj3Wj7Bs3TndUxhhTaVjJJRalpW5UfbNmMGUKPP64+2yJxRhjdmIlF7+WLHETTX75Jfzf/8Gzz0KjRumOyhhjKiUruZSnuBgGDoTDD4evv4aRI111mCUWY4yJyEou0cyZ48atzJkD557rxqzUrZvuqIwxptKzkks4mzfDnXfCkUfCypUwerT7sMRijDG+WMkl1NSprrSyZAlceSUMHgy1aqU7KmOMyShWcgnYsAFuusmNqt+8GT7+GJ5/3hKLMcbEwZILuETSrJlrU7nxRli40PUIM8YYE5fcTi6//QZXXAGnngo1argxK0OHwu67pzsyY4zJaLmbXMaMcRNNjhrlGu/nzHFrrxhjjKmw3GvQX7UKbrjBLTPcurUbs3LEEemOyhhjskrulFxU3QDIpk3dzMUDB8KMGZZYjDEmCVKeXESkp4h8LyKbRWSWiERd9EREWojIZBEpEpFCEblHRCSmiy5fDp07u67FLVrA/Pnwz39CtdwruBljTCqkNLmIyAXAUOBBoBUwDRgvIg0j7L8n8AmwGjgSuAnoDdzq64IlJfDYY9C8OUyf7iad/PxzOPTQCv8sxhhjIhNVTd3FRGYA81X12qBt3wKjVfWOMPv3AB4C9lPVIm/bXUAPYH+NEnzb5s21YK+9YNo01xvs2WehYdgcZowxxiMis1S1bUXPk7KSi4hUB9oAE0JemgAcG+GwdsCUQGLxfAzUBxpFveCiRfDNN25Brw8/tMRijDEplMpGh9pAVVwVV7DVwCkRjqkL/BRm/8Br3we/ICLdgG7et1vkt98WctllcNllcQedJWoDa9MdRCVh92IHuxc72L3Y4bBEnCQdLdqhVVkSZlt5+4fbjqoOA4YBiEhBIop22cDuxQ52L3awe7GD3YsdRKQgEedJZYP+WqAEV+IIti9lSzMBP0fYnyjHGGOMSbOUJRdV3QrMAjqFvNQJ12ssnOnA8SKya8j+K4HliY7RGGNMYqR6nMsQ4AoRuUZEmojIUFzj/DMAIjJARD4N2v9VYBMwUkSai0hXoC8wJFpPMc+wJMSfqexe7GD3Yge7FzvYvdghIfcipV2RwQ2iBPoA9YCFwC2q+oX32kigg6o2Ctq/BfAkcBSwDpeI7vORXIwxxqRJypOLMcaY7Jc7c4sZY4xJGUsuxhhjEi5jk0taJsCspGK5FyLSQUTeFZFVIrJJROaLyFWpjDeZYv27CDruryLyp4hsSHaMqRLH/4iISC8R+UZEtnh/IwNTFW8yxXEvOovIdO9vYq33P5PxkxKKyAki8p73DFQRucLHMXE9OzMyuaR8AsxKLNZ7gZtqZwFwHtAceBoYJiIXpSDcpIrjXgSOqw68DnyR9CBTJM57MRjoCfwTaAKcThbckzieFwcB7wJTvP1PAfKBD1MScHLtjutIdTNQVM6+FXt2qmrGfQAzgOEh274FBkTYvwfwB5AftO0uoBCvU0OmfsR6LyKc401gTLp/lnTdC+AR4AXgCmBDun+OdNwL3JQfxUCTdMdeCe7FebgB31WDtp2EmxWkdrp/ngTelw3AFeXsE/ezM+NKLimfALMSi/NehLMnrpt3xor3XohIF+AM3DuyrBDnvTgbWAacKiLLRGS5iLwoIvtG2D8jxHkvCnCJ9hoRqSoiewCXA/9V1VybfyzuZ2fGJReiT4AZOlVMQN0I+wdey1Tx3IudiMgZwMlk/iCymO+FiNQDhgOXquqfyQ0vpeL5uzgYOBC4EFeCuxRoDLwvIpn4nAiI+V6o6nLcTCD3AluA34EWuDchuSbuZ2cm/9EkbQLMDBTrvXA7ibTHzYJwk6rOTEZgaRDLvRgFPK2qXyU3pLSJ5V5UAXbBJdovVHUKLsEchatrz3S+74WI1AVGAC/hfvYOwJ/AmxmeaOMV17MzE2+UTYC5Qzz3AgAROQ4YD9yjqk8nJ7yUiudedAT6icg2EdmGe6Ds5n3fLcIxmSCee7EK2KaqS4O2fQtsAzJ5MaR47sX1wEZV7aOqc9TNIHIJcCKxVTdng7ifnRmXXNQmwNwuznuBiJyASyz3quqjSQswheK8Fy2AI4I+7sH1oDkCeCvxUaZGnPdiKlBNRA4J2nYwblmOHxIeZIrEeS9q4BJSsMD3GffMrKD4n53p7rEQZy+HC4CtwDW4LpNDcT0fDvReHwB8GrT/XrgM/Dqu+21XXA+I29L9s6ThXnQANgKDcO9IAh910v2zpPpehDn+CrKnt1isfxdVcA/hybjut628r78CqqT750nxvegIlAL9gL8CrYGPgB+B3dL981TwXuzOjjdTm3BvqI4AGka4F3E/O9P+w1bgJvXEZc4t3j/FCUGvjQSWh+zfAtdnfzOuCqAfGd4NOZ574X2vYT6WpzrudN+LMMdmTXKJ517gJpN9C9e+8AvwCrBfun+ONN2LC4HZXhJaA7wPNE33z5GA+9Ahwv//yCj3Iq5np01caYwxJuFyrf7QGGNMClhyMcYYk3CWXIwxxiScJRdjjDEJZ8nFGGNMwllyMcYYk3CWXIypxESkrbeoUyPv+w7e97UrcM5G3jnaJixQY0JYcjEZRURaiUiJiEyN8LqKyFYROThk+0gR+SDo+/7evs+F7FfugzfoWPViWSEiz4lInYr+fD5Mww12/NXPziLyuYg8EbJ5hXeOuYkNzZgdLLmYTHMt8BTQXESaRNinBPi3j3NtBq4QkWZxxLEE94BuiFtQ6UzcLLpliEgVEakaxzXKUNWtqvqzVmD0s6qWeOfYloiYjAnHkovJGCKSD1yEW4NlNHB1hF0fB84XkTblnPI73MJHA+IIZ5v3gC5U1Q+Ax4D/E5F8EblCRDaIyOkishA3r1UTEakuIg+JyE8islFE/isinUN+xlO9New3i8gU4NCQ18tUi4nIMSLymXfO30XkUxGpLyIjcTP5Xh9U0moUrnTmra0+w7vuahF5xFtoK/D65yLylIg8KG5N+V9E5OEcnYLe+GB/GCaTnAf8oKrzgZeBy0QkL8x+M4ExwH98nLMv0EVEjq9gbEW4/6dq3ve74paDvQ5oiptZ+AXcw/4i3HxNL+IW4zocQEQOAMbi1iw/Apcko/4M3rGTgP8B7YFjcMtWV8Otkz7du24972NFmHM0wM2SPQc3YeXVwD8om3Qvxk3BfyxwA9ALNymkMWVUK38XYyqNa3BJBdyMvZuAs3CJJNS/gEUicqqqfhTphKq6QERewj3E28UTlIg0xlWNzVTVP0UE3OqHN6rqLG+fQ3AP7Eaq+qN36BMicgouAfX0zvEjbvE2Bb4RkUOB+6Ncvg8wT1WD159ZHBTbVmCTqv4ctC30HD1xExL2VNVSYLGI9AWeFZG7VXWTt98iVb3H+3qpiFyLW8X0tXJukclBVnIxGUFE/oJ7Z/4qgPfwfQWXcMpQ1f/hqs8G+qi6uQc4QkS6xhBSE6/qqwhYhCsRXBz0+jZ2bjBvjVvBb5F33AYR2QB0AQJrqDQBvgppT5leThytgE9jiDucJsB0L7EEfAlUB/4StG1+yHEr2bFwlDE7sZKLyRTX4EoDPwa98xZw1UmqWqa6B7cG+nfs/NAvQ1VXiMjjuGqgLj7j+Q44Hdd5YKWqbgl5fYuqBi84VQU3tfmRQHHIvkXe5zJFCh/iOSbcOSJ1EAjeHhq3Ym9QTQT2h2EqPRGpBlwO3MHOK0cejns3fWW441T1F+BhXLXSLuVcZgBQhwgloTC2qur/VPX7MIklnDm4h3hd77jgj0Jvn0XA0bJzvdUx5Zx3Nm5xq4hx4pJyNIuAdiElvOO8Y78r51hjwrLkYjJBF6A2MFxVFwZ/4FbIuypK1ddgXOP6OdEuoKrrgAdxjeAJp25t+leAkSJynogc7A2QvD2oOu4ZoBHwqIgcJiLnAd3LOfUgoJWIDBORw73jrhGRwLr3y4GjvB5itSPcp6eA+sBTItJERLoAA4EngtpbjImJJReTCa4GJqlquIGDbwEHAqeEO1BVN+Cqx3YN93qIx3ErMCbLlbieW/8BvgE+AE7AW6Pea+jvCpwKzANuwfVmi0hV5+J+9sa4JYln4FZRDFRhPYwrgSzCrajYMMw5CoHTcO03c4HncY30/4rvxzQGW4nSGGNM4lnJxRhjTMJZcjHGGJNwllyMMcYknCUXY4wxCWfJxRhjTMJZcjHGGJNwllyMMcYknCUXY4wxCff/PTeG0y785FAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update({'font.size': 14})\n",
    "plt.scatter(y_pred_ANN.flatten(),y_test)\n",
    "plt.ylim((0,1))\n",
    "plt.xlim((0,1))\n",
    "plt.xlabel(\"ANN Prediction\")\n",
    "plt.ylabel(\"Real values\")\n",
    "plt.title('Prediction Vs. Real for ANN ')\n",
    "plt.plot((0,1),(0,1), color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x16e7ad71d48>]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.rcParams.update({'font.size': 14})\n",
    "plt.scatter(GB.predict(x_test),y_test)\n",
    "plt.ylim((0,1))\n",
    "plt.xlim((0,1))\n",
    "plt.xlabel(\"GBM Prediction\")\n",
    "plt.ylabel(\"Real values\")\n",
    "plt.title('Prediction Vs. Real for GBM ')\n",
    "plt.plot((0,1),(0,1), color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x16e7add81c8>]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.rcParams.update({'font.size': 14})\n",
    "plt.scatter(y_pred_RF.flatten(),y_test)\n",
    "plt.ylim((0,1))\n",
    "plt.xlim((0,1))\n",
    "plt.xlabel(\"RF Prediction\")\n",
    "plt.ylabel(\"Real values\")\n",
    "plt.title('Prediction Vs. Real for RF ')\n",
    "plt.plot((0,1),(0,1), color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler_y = MinMaxScaler()\n",
    "y_for_scale=pd.DataFrame(data['total_grid'])\n",
    "scaler_y.fit(y_for_scale)\n",
    "def invers_result(y_pred):\n",
    "    y_pred=pd.DataFrame(y_pred)\n",
    "    return scaler_y.inverse_transform(y_pred)/1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "time=list(range(len(y_test)))\n",
    "plt.rcParams.update({'font.size': 18})\n",
    "fig, ax1 = plt.subplots(figsize=(18,6), )\n",
    "\n",
    "\n",
    "#plt.xlim(700,832), plt.ylim(40, 160)\n",
    "#plt.plot([800,800],[0,500],  linestyle=':',color = 'k')\n",
    "plt.plot(time,invers_result(y_test), color = 'gray', label = 'Real')\n",
    "plt.plot(time,invers_result(y_pred_RNN.flatten()), color = 'y', label = 'RNN')\n",
    "plt.plot(time,invers_result(y_pred_ANN.flatten()), color = 'green', label = 'ANN')\n",
    "plt.plot(time,invers_result(y_pred_RF.flatten()), color = 'red', label = 'RF')\n",
    "plt.plot(time,invers_result(GB.predict(x_test)), color = 'blue', label = 'GBM')\n",
    "\n",
    "\n",
    "plt.xlabel(\"Time (day)\")\n",
    "plt.ylabel(\"Power (MWh)\")\n",
    "#plt.title('Daily Prediction ')\n",
    "plt.ylim((150,450))\n",
    "plt.legend(loc='upper center', mode='expand',ncol=5)\n",
    "plt.savefig('zoom-Scenario I.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\Software\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'RNN Error Distribution')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"white\")\n",
    "ax = sns.distplot(y_test-y_pred_RNN.flatten(), color='blue')\n",
    "plt.rcParams.update({'font.size': 12})\n",
    "plt.xlabel('Error')\n",
    "plt.title('RNN Error Distribution')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\Software\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'ANN Error Distribution')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.set_style(\"white\")\n",
    "ax = sns.distplot(y_test-y_pred_ANN.flatten(), color='blue')\n",
    "plt.rcParams.update({'font.size': 14})\n",
    "plt.xlabel('Error')\n",
    "plt.title('ANN Error Distribution')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\Software\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'RF Error Distribution')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AmbzwcGDOHDEAzNALQLbQUHGmMnOm3EnI1LEIkEmLjhYfqN26icsrxlAAANFdtV8/MY1FQoLcaciUce4gMkjvugoYABw5IqZk8PISBSA1VSvR9CYkBFi+XJwNREbKnYZMFYsAGaS3XQUs27174sOzbFmga1exMLynp9bi6UXlymIZym+/FV1HK1WSOxGZIl4OIpOTmgpERYkJ4IYOFYu4GKvQUNGwPWuW3EnIVLEIkEmRJNEOcPeuuKZuYyN3ondTpQrQu7dY4vL+fbnTkCliESCTcugQcPw40K4dUKeO3Gm0Y9QoMdX0okVyJyFTxCJAJuP2bWDjRqB+faBNG7nTaI+DA9ChA7BkCfD8udxpyNSwCJBJyMwE1qwR1//79jXc0cBvKyQEePJE9BYi0iYT+1Uhc7Vnj+hP3707ULy43Gm0r1EjoGlTYN48MaUEkbawCJDR++cf4KefADc3wNVV7jS6ExIiLnlt2CB3EjIlLAJk1LIvAxUrJqaEMGVt2oj2jvBw0QuKSBtYBMio/f67mGSta1egZEm50+hW9hKYFy6I6bCJtIFFgIzWixfAjh1ArVpiRS5zEBgoRg6Hh8udhEwFiwAZrR9/BFJSgC5djGdiuHdVpAgwYgTw229iPATRu2IRIKN0756YD8jHx/zm1OnfX6yJzLMB0gYWATJKMTFA0aLAJ5/InUT/ihcHhgwBtm4F/vpL7jRk7FgEyOhcuiQaR9u2BUqUkDuNPIYNE+sjz5kjdxIydiwCZFQkSTQG29gAfn5yp5FP2bJiZPSaNVyQnt4NiwAZlQsXgOvXgYAAMVW0ORs5EsjIABYulDsJGTMWATIa2WcBtrZA48Zyp5Ff9erAp5+KVcfedRU2Ml8sAmQ0zp0T8wMFBACFCsmdxjCMHg08fSoW0SF6GywCZBQkCdi5E7CzM75lInXJzQ1o0QKYP9/41lAmw6DXInDt2jX06dMHLi4u8PPzw4oVK/S5ezJiZ88Ct26JHkE8C1AVEiIah9evlzsJGSO9FYH09HT0798fH374IbZt24YJEyYgIiICO3bs0FcEMmJ79oi2gIYN5U5iePz9ARcXYPZsICtL7jRkbPRWBO7fvw8nJydMnDgR9vb28PPzQ+PGjXHy5El9RSAjdfUq8Pff4sOOZwE5KRTibODKFdFwTqQJvRWBihUrYsGCBShWrBgkSUJcXBxOnjwJLy8vfUUgI7V3L2BlxR5B+encGahWDZg1i9NMk2ZkaRhu2rQpunfvDhcXF7Ru3VqOCGQk7t0TvYJ8fcU0EZS7woXFuIFjx8T02kTqkqUIREREICIiAhcvXsTMmTPliEBGYt8+8QFnzqOD1dWnj+g9NWuW3EnImMhSBBwdHdGiRQuEhoZiw4YNSEtLkyMGGbinT8U328aNzXeOIE1YWoo5hXbtAs6flzsNGQu9Ngzv379fZVv16tWRnp6O58+f6ysGGZFDh8Tykf7+cicxHkOGiPYTTjNN6tJbEbh27Rq++OILPHr0SLnt4sWLsLGxgY2Njb5ikJFITwcOHwbq1ROTpZF6bGyAAQOA778Xy24SFURvU3B5eHigevXqCA0NRWhoKBISEjB37lwMGjRIXxHIiOzfDzx5AnTvLncS/crIAG7efLf36NoVWLIEGDsWmDFDrL1curR28pHp0VsRKFKkCKKiojBlyhT873//g5WVFXr37o1evXrpKwIZkfXrxbdaR0e5k+jXixeiHeRdeXoCGzcCTk5iXWIWAcqLXifjLV++PJYuXarPXZIRio8Xl4LatwcsOLvVW/noI/FvuGePKAJEeeGvGBmcqCjRLdTbW+4kxqtMGcDLSxSCBw/kTkOGjEWADMrLl8CqVUDr1oC1tdxpjNtHH4k2huXL5U5ChoxFgAxKTAzw+DHQo4fcSYxf2bJiwr3164HERLnTkKFiESCDEhkJODiISxn07gICgFevxHoDRLlhESCD8ccfomfMoEFiZkx6dx98INZgWLxYnGERvYlFgAzG0qVi6oPeveVOYlqGDgWePQO++UbuJGSIWATIICQni2vX7NOufbVrA506AQsWAK8N2CcCwCJABmL9ejFQavBguZOYpsmTxdnAnDlyJyFDwyJAspMkcSnIzQ3w8JA7jWmqXx/o1g1YuFCs0UCUjUWAZBcbK6Y+5jRSujVpEpCaCoSFyZ2EDInaReCXX37hvP+kE5GRYmBYt25yJzFtNWuKhWciI4Fbt+ROQ4ZC7SIwZcoUeHt74+uvv0ZsbCyysrJ0mYvMRGKiGCDWq5eYB590a/x4cflt+nS5k5ChULsI/Pbbb4iIiIClpSVGjx4NHx8fTJ06FadPn9ZlPjJxq1YBaWm8FKQv9vZivYFvvwX+/lvuNGQI1C4CCoUCHh4emDBhAg4dOoR58+ZBoVAgKCgIzZs3x9y5c3GDq1iQBrKyxGRxTZsCdevKncZ8jB0rJuibPFnuJGQING4YfvXqFfbs2YPvv/8eW7duRdmyZdGuXTs8evQInTp1wqpVq3SRk0zQvn3i2yi7herXhx+KAWTr1wOXLsmdhuSm9noCP//8M3bv3o1Dhw7BysoKAQEBWLVqFZycnJTPcXR0xJw5c9C3b1+dhCXTEhkpJjnr1EnuJKYtt9XKuncXZ2Fffvl2s4xytTLToXYRGD9+PFq2bImIiAh4enpCkcvkLvXq1cNnn32m1YBkmm7dAnbuBEJCgPfekzuNactrtbLmzYHt28VSlDVqaPaerVuzCJgKtS8HjRw5EhMnToSXl5dKAUhJScG6desAAE5OThgyZIj2U5LJWbFC9FIZMEDuJObL3x8oVQrYtEn8X5B5yrcIPHr0CLdu3cKtW7cwadIkXLlyRXk/+3b48GHMnj1bX3nJBKSni0sQbdoAVarIncZ8vfce0K4dcP06cOaM3GlILvleDjp16hSGDx+u/ObfpUsXlbMA6d+vDx07dtRhRDI1O3YAd+8Cy5bJnYS8vIBffgG2bgWcnYFCheRORPqWbxFo3bo1Dhw4gKysLPj7+yMmJgY2NjbKxxUKBd5//32UKlVK1znJhERGiv7qbdrInYQKFQI6dgQiIsR6xM2ayZ2I9K3AhuHy5csDAC5fvqzzMGT6/voL2L9fjFjlt07D4OQkppTYuRNo1AgoVkzuRKRP+RaBHj16IDIyEiVLlkSPAhZ9jY6O1mowMk1RUWKgUlCQ3Ekom0IBdO4sJpbbt0+0E5D5yLcIeHl5oUiRIsq/E72Lly/FNBGdOollD8lwVK0qpvLet0+M4La2ljsR6Uu+RWDo0KG5/j3b48ePUbp06VzHDBC9aeNGICmJI4QNVceOYp3nHTuAnj3lTkP6ovY4gYcPHyI4OBh//vkn0tLS0Lt3b3h7e8Pf3x9Xr17VZUYyERERYqlDNj4aJjs7wNcXOHIEuHNH7jSkL2oXgcmTJ+PevXsoWbIktm/fjosXL2L9+vVo0qQJpkyZosuMZAJOnBC3oUPFNWgyTG3bApaWwObNcichfVF72ojY2Fhs3LgRFSpUwL59++Dn5wc3NzflBHJE+Vm8GChRQqwbQIbLygoICBCjiC9d4uyu5kDtM4HChQtDkiSkpKTg+PHjaPbvOf3Dhw9hxdVAKB8PHgA//CBWtSpRQu40VBBfX8DWVhQCrh1l+tQ+E/Dy8sLYsWPx/vvvo2jRovDz80NsbCymTp2KFi1a6DIjGblly8TCMZ9/LncSUkeRIqIH17JlwNGjgLe33IlIl9Q+E5g2bRocHR1haWmJZcuWwcrKCvHx8fD19cWYMWN0mZGMWHo6sHQp0KoV4OAgdxpSl6srUK2amGU0NVXuNKRLap8JFC9eHOPGjVPZ1rt3b60HItOybZvoaRIZKXcS0oRCAXz6KRAeDuzdywFkpkztIpCWloYtW7bg/PnzyMjIUE4ely08PFzr4cj4ffONGIgUECB3EtJU9epiANnevRxAZsrUvhw0btw4hIWFITk5GRYWFihUqJDKjehNR4+KPufDh3OeIGPVsSOQmSkGkJFpUvtM4LfffsO8efPQvHlzXeYhEzJnjlh9iovNGa/sAWQHDoiVyCpUkDsRaZvaZwLFihVDpUqVdJmFTMjVq2KO+sGDgeLF5U5D7yJ7ANmmTXInIV1Quwh8/vnnmDFjBq5evYrU1FRkZWWp3IheN2+e6GqYy5RTZGSsrEQhuHQJuHhR7jSkbWpfDlqyZAkePXqU5+jgP//8s8D3SEhIwIwZMxAXFwdLS0sEBARgxIgRKFq0qPqJyeAlJorZQnv2BD78UO40pA3NmgEHD4rpJOrUkTsNaZPaReBd1xFOS0vDoEGDUKNGDWzYsAGPHj1Sji8IDQ19p/cmeSUlAcnJ/91fsAB49Qro1g24efPt3vPlS61EIy15fQBZbCxXhTMlaheBhg0bAgDu37+P69evo0GDBnj+/DlsbW3Vev25c+eQkJCAmJgYWFlZoXr16hg+fDjCwsJYBIxccjKwZ4/4+8uX4oPCyQm4dk3c3oanp/bykXZkDyDbsQPgr6zpULtN4MWLFxg2bBiaNWuGoKAgJCYmYvz48QgMDMSjR48KfH21atWUI42zKRQKpKWlvV1yMkgHDwIpKeIaMpmW7AFkT58Cy5fLnYa0Re0iMGvWLCQlJWH//v3Ka/jZ3+CnTZtW4OttbGzQuHFj5f2srCysX78ebm5ummYmA/XypViZyskJqFJF7jSkC9kDyKKigH/+kTsNaYPaReDAgQP4+uuvUeG1jsL29vaYNGkSjhw5ovGOZ86ciT///BPBwcEav5YM04ED4iyAUwyYto4dgYwMYMIEuZOQNqhdBF69eqVcb/h1aWlpOaaQyI8kSZg2bRq+++47zJ07FzVr1lT7tWS4Xr4EfvkFcHYGKleWOw3pkp0d0Ls3sHIlcO6c3GnoXandMNyiRQvMnTtXZY6gGzduYOrUqfD19VXrPbKysjB27Fjs3LkT8+fPh7+/v8aByTDt3y/OAj7+WO4kpA+DBwMxMcAXXwBr12rnPUuWFCPMSb/ULgLjx4/HmDFj4OnpiaysLLRv3x4pKSnw8fHB2LFj1XqPsLAw7Ny5E4sWLYKfn99bhybD8vChaAto0IBnAeaicGExPXhMjBgYWK/eu79n69YsAnJQuwgkJSWhY8eOqFevHhwcHHDz5k34+PigWrVqar3+jz/+wJo1axAcHIz69esjMTFR+ZidnZ3myclgzJsnFo3p2FHuJKRPvr7Ar7+K6STq1AEs1L64TIakwCJw9OhRzJw5E/Hx8SrX/hUKBXbu3InQ0FC4u7sXuKM9/3Yknzt3LubOnavy2MWLF1G4sNr1iAzI+fPAhg3iA+GDD+ROQ/pUuLAo/NkDyJo0kTsRvY18P3kPHz6MgQMHom3btpgwYQJq1KiBEiVK4Pnz57h8+TI2b96Mvn37Yu3atXBxccl3RyEhIQgJCdFqeJKXJAHBwWLdYLYFmCdXV9FtdPt2wN0dKFZM7kSkqXxP4JYsWYI+ffogPDwc7u7uKFWqFAoVKgRra2s0atQI4eHhCAwMRCSXjTJLu3aJtoDhw8UkY2R+sgeQJSeLnwUyPvkWgcuXL6NjARd6//e//+HSpUtaDUWG79Ur4KuvgJo1gf/7P7nTkJyqVftvBbKnT+VOQ5rKtwi8evUK1gWsKVe6dGm1po0g0zJtGvDXX8CiRcB778mdhuSWPYDs55/lTkKayrcISJIEiwKa/BUKhVYDkeE7dw6YNQvo1Ut06yOysxOT/v3+O88GjE2BXXJ+/PFHlUnf3vT8+XOtBiLDlpEhlossXVp0DSXKFhAAHDsmzga6dpU7Dakr3yJQvnx5rFmzpsA3+ZArh5iNb74BTp0S3ULLlJE7DRkSOzvAyws4dEgMJOPAL+OQbxE4cOCAvnKQEbhwARg3TkwQ16WL3GnIEAUEAEePirOBbt3kTkPq4Bg/UktKijjFt7YWc8mzKYhyY2sLNG4MHD4sVpwjw8ciQGoZMUIsNL52LVCunNxpyJC1aQNkZXHcgLFgEaACbdwopgYICRHXeonyY2sLNGwoegqx34jhYxGgfF25AvTvDzRqBEydKncaMhatW4tJBdmsaPhYBChPT54An3wCFC0K/PADkMuaQkS5Kl9eTC1+8KAYXU6Gi0WAcpWZKXp3/P03sHkzYG8vdyIyNh99JDoUHDokdxLKD+dvNjNJSWKyr4LMnCm6+U2fLhaKuXkz7+e+fKm9fGQ6qlYFatcWDcR+fjyTNFQsAmYmORn4d2mHPP3+O7B+PdCsmWjkK+j5np7ay0em5aOPgAULxEhiHx+501BueDmIVFy4AHz3nVgukEP/6V3Vrg1UqiTWoM7KkjsN5YZFgJRu3hRdQStUAAYMAAoVkjsRGTuFAvD3B+7eFeNMyPCwCBAAsVj84sVA8eLAF19whSjSHnd3oFQpDh4zVCwChBcvxLoAGRmiABSwhASRRgoXFg3Dly8Dt27JnYbexCJg5tLTgchIcSYwZAjACWFJF3x8xOJDv/widxJ6E4uAGcvKAtasAeLjgT59xFKRRLpgZSUmljt5UgxCJMPBImDGduwQv5QdOgAeHnKnIVPn7y++eBw8KHcSeh2LgJk6cgTYvRto0kT05SbSNTs7wNlZjCBOTZU7DWVjETBDly6JwWB16wLdu3NtANKfli3FVBJHj8qdhLKxCJiZ+HggKko0AHMsAOlb9epAlSocPGZIWATMyJMn4oO/SBFg6FDA0lLuRGRusgePPXggRqeT/FgEzERWFvB//yf6aQ8cCNjYyJ2IzJWrqxg8tn+/3EkIYBEwGxMnAj/9JP5kV1CSU6FCgK+vGDx2547caYhFwAz8+CMwbRoQFCTOBojk5uMjLkty5TH5sQiYuNu3xUCwBg2AJUvYE4gMQ/HiYsnS48e5DrHcWARMWGam+Ob/6hWwYQMnhSPD0qKFmLaEK4/Ji0XAhE2fDvz2mzgDcHCQOw2RqvLlgTp1xM9oZqbcacwXi4CJOnIEmDxZnAn06iV3GqLctWghui7HxcmdxHyxCJigFy9EO4C9PRARwXYAMlz16gFly7KBWE4sAibo66+Bq1eBVauAEiXkTkOUNwsLoHlz4Pp14PRpudOYJxYBE/Prr2KBmGHDxELxRIbOy0uMXl+1Su4k5olFwIQ8fw707QvUqAHMmCF3GiL1FCsGeHsDu3aJLs2kX4Xl2GlaWho6deqEMWPGoHHjxnJEMApJSUBysvrPnzRJLBYfEyNWCnv4MOdzXr7UWjwirfHzE+0CERH8AqNvei8CqampCA4ORnx8vL53bXSSk4E9e9R77o0bYpWwZs2AR4/yfp2np9biEWmNra2YZjoqChg3Dnj/fbkTmQ+9Xg66evUqunTpgoSEBH3u1uRlZor1AaytxSphRMYoKAh4/Fj8LJP+6LUInDp1Ct7e3vjhhx/0uVuTd+CAmB20a1dOD03Gq2FDMb3JN98AkiR3GvOh18tBgYGB+tydWXj8WKwV7OgIuLjInYbo7SkUwJdfijEuv/wiLg+R7rF3kJHLPqnq1o2Dwsj4BQaKwWPffCN3EvPBImDELl0C/vgDaNMGKFNG7jRE765oUWDwYLH2xV9/yZ3GPLAIGKmMDHEWULYsT5vJtAwaJNYaWLRI7iTmgUXASB08CNy7B3TpIn5hiEzFBx+Iy5urVonJ5Ui3WASM0NOnYrUwR0dxIzI1w4eLiRBXrpQ7ieljETBCW7aIy0FdusidhEg3XF3FEpSLFnGtAV2TrQhcuXKFU0a8hWvXgGPHAH9/0R5AZKq+/FKMhN++Xe4kpo1nAkYkK0ssE1m6NBAQIHcaIt1q3x6oUgUID+fgMV1iETAihw8DCQlA586iKx2RKStUCBg9WixG/+uvcqcxXSwCRuLFC2DbNqBmTcDdXe40RPrRty9QrhxnFtUlFgEj8eOPQEqKmB+II4PJXBQrBgQHi2kkTpyQO41pYhEwAnfuiNPhpk2BSpXkTkOkX4MGiXYwng3oBouAgZMkMTK4WDHgk0/kTkOkfyVKiOVSt28HLlyQO43pYREwcGfOAFeuiAJQvLjcaYjk8cUXgJUVMG2a3ElMD4uAAXv1Cti0CahQQVwKIjJXZcqIs4EffgDOnpU7jWlhETBgUVFiqciuXUV3OSJzNmqUWD1v/Hi5k5gWFgEDlZAAREYCbm6Ag4PcaYjkV7q0GDewcydw9KjcaUwHi4CBGjVK/Nm5s7w5iAzJsGFiupQxYziKWFtYBAzQgQPAxo2iaxwXiyH6T/HiwNixosv0/v1ypzENLAIG5tUr8eFfvbr4k4hUDRwIVK4sLg1xhtF3xyJgYMLDgfh4ICJCjA0gIlVFiwKzZonu0ytWyJ3G+LEIGJD4eDEqMjAQaNVK7jREhqtrV9FteuxY4PFjudMYNxYBAyFJwJAh4lvOvHlypyEybAoFsHAhkJQETJggdxrjxiJgINauFZNkzZgBfPih3GmIDJ+zMzB4sOhKfe6c3GmMF4uAAbhzR6yp2qSJ+KEmIvVMmSLGDwwezEbit8UiIDNJAgYMANLSgFWrAAv+jxCpzcZGXD6NjQUWLJA7jXHiR47MVq8Gdu0CwsKAGjXkTkNkfHr2FBMsjh0L/Pmn3GmMD4uAjG7dAkaMEL0chg6VOw2RcVIoxDxbVlZA795ARobciYwLi4BM0tOBbt3EdcyVK3kZiOhdfPCBaCA+eVKcVZP6+NEjkwkTgCNHgOXLxehgIno3XbqIMTYTJ4qpV0g9heUOYI527xbfVgYMED+0RCQu49y8+W7vMW4ccOoU8OmnYn4hJyetRDNpLAJ6dvs20KuX+OFkbwai/7x4ARw79u7v06uXGG/Tvbu4PGRp+e7vacp4OUiPnj0DPv4YSE0Vs4Tyh5NI+8qVAz77DLh4EejXD8jKkjuRYWMR0JOMDDHfyYULQEwMF4oh0iUnJ7Emx3ffiTUIuPZA3ng5SA8kSfwg7t4turK1bi13IiLTN2SI+N2bM0d0Hw0LE91JSRWLgI5JkhjaHhkpvpkMGCB3IiLzoFCIqdlfvBB/WlqKnkMsBKpYBHRIksSi2NOni8Yq9l8m0i+FAli8GHj5Epg8Gbh7V9wvUkTuZIaDRUBHJAkICQFmzxaNU1FRHBBGJAcLC+Dbb8XsvDNnAjduiI4Z1tZyJzMM/FjSgVevgKAgUQCGDGEBIJKbhYXoNrpihRhI1rgxp5/Oxo8mLUtIAHx8xMRw48eLU08WACLD8NlnwM8/A48eAe7uoq3A3Keg5seTFu3aBbi5AVeuAFu3igZhNkIRGZYWLURX7XbtxCXbZs2A06flTiUfFgEtuHtXTP/Qti1Qtixw4gTQoYPcqYgoL7a2wKZNwJo14kubm5uYkjohQe5k+sci8A6ePxd9kOvUAbZtE9/8T58GateWOxkRFUShEL32rl4FQkNFUahZE+jTBzhzRu50+qPXIpCWlobx48fDw8MD3t7eWL58uT53rzWJiaK7WeXKou+/p6doZBo/XiwUT0TGw9pa9Bq6cgXo318UA1dX0bYXFSV+302ZXotAeHg4zpw5g1WrVmHy5MmIjIzETz/9pM8Iby05GYiOBgICRFezSZPEYjDHjomGplq15E5IRO+icmXRkeP2bWDuXODBA2DQIPH73rKlOOs/fdr05iLS2ziBlJQUbNy4EUuXLkX9+vVRv3599OvXD+vXr0fbtm31FUMtmZnA33+LxqOjR8WUtHFx4j+/cmVg5EhxGlm3rtxJiUjbSpUCvvpKrPp37pwYU7BlizjrB8TC9m5ugIuLuNWqBVSrJrYbI70VgcuXLyMtLQ1ubm7KbW5uboiIiEBGRgYKF9Z+lJQU0Wc/NfW/P1NTxfakJNXb3buiUejWLSA+XowwBMTIQk9PsX5pq1aifzG7fBKZPoUCcHYWt+nTgX/+EV8If/1VnBF88w2Qlvbf862tRTGoXFk0PL9+K1FCzF/0/vvilv33okWBQoWAwoVVb4UK6e9zRm9FIDExEdbW1ij62kVzW1tbpKen4/HjxyhbtqxW97dypegTrK4yZYBKlQB7e9GFzNERqF9f3DjlMxGVLy/WKOjeXdxPSxPtCNeuiSsHf/8NXL8u/jxxAnj4UCwj+7ZKlRIN1FWqaCN93hSSpJ9JVrdt24a5c+fi999/V267desW/P39sX//flSsWDHX1zVq1AgVKlTQR0QiIpNx584dHD9+vMDn6e1MoGjRokh7/dwJUN63zOertjoHQUREb0dvV7fLlSuH5ORklUKQmJiI9957D9acyYmISBZ6KwJ16tRBkSJFcOa1URhxcXGoV6+eThqFiYioYHorApaWlujQoQMmT56Mc+fOYf/+/Vi5ciV69eqlrwhERPQGvTUMA8DLly8xadIk7N27F1ZWVggKCkJQUJC+dk9ERG/QaxEgIiLDwmFPRERmzCyLgCRJmD9/Pry8vODh4YFZs2YhM5+VJQ4ePIh27drByckJ7du3x2+//abHtHnT9DiOHj2Kzp07w8XFBa1bt0ZMTIwe0+ZN0+PIdvPmTTg5OSEjI0MPKfOmycSIly9fRteuXeHs7IxOnTrhnAEtb/U2EzyeOnUKvr6+ug+nIU2OZdeuXfj444/RoEEDfPLJJzhw4IAek+ZPk+PYvHkzWrZsCScnJwQGBqr/syWZoZUrV0pNmjSRjh8/Lh07dkxq0qSJFBUVletz4+Pjpfr160vr1q2TEhISpBUrVkj16tWTEhIS9Jw6J02O4/r165Kjo6MUGRkp3bhxQ9q+fbtUv359af/+/XpOnZMmx5Htn3/+kVq3bi3VqlVLSk9P11PS3E2dOlVq27atdP78eWnfvn2Si4uL9OOPP+Z43osXLyRvb29p+vTp0tWrV6Vp06ZJnp6e0rNnz2RInZO6x5Ht8uXLUuPGjSUfHx89plSPusdy8uRJqV69etIPP/wg3bhxQ1qzZo1Ut25d6eLFizKkzknd4zh8+LDk6Ogo/fTTT9LNmzelGTNmSA0bNlTrZ8ssi0CzZs2kjRs3Ku9v27ZNatq0aa7PPXTokBQWFqayzcPDQ9qxY4dOM6pDk+NYsmSJ1KVLF5Vt48aNk7788kudZlSHJschSZK0b98+ydPTU2rXrp3sReDFixeSo6OjdOTIEeW2JUuWSIGBgTmeGxMTI/n6+kqZmZmSJElSVlaW1LJlS5Vjl4smxyFJkvT9999LDRo0kNq1a2dwRUCTYxkzZow0YsQIlW19+/aVZs+erfOcBdHkOLZt2yYtW7ZMef/Zs2dSrVq1pLi4uAL3Y3aXg+7fv4+7d+/C3d1duc3NzQ337t3D3bt3czzfx8cHISEhAID09HTExMQgLS0NDRo00FfkXGl6HG3atMH48eNVtikUCqSmpuo8a340PQ4A+P333zFixAiMHTtWXzHzlNfEiOfPn89xmers2bNwdXWFxb8zgykUCri6uqqMnZGLJscBALGxsQgPD0efPn30mFI9mhxLz549MWTIEJVthvB7AWh2HO3bt0f//v0BAK9evcLq1atRpkwZ1FJjjnuzKwKJ/64Q8fqEdba2tgCAe/fu5fm6a9euwdnZGePGjcOQIUNQqVIl3QYtgKbHUbVqVdSvX195/+HDh/jpp5/g4eGh46T5e5v/j8mTJ6NLly66D6eGgiZGfPO5b06UWKZMGdy/f18vWfOjyXEAwMKFC9GyZUt9RlSbJsdSu3Zt1KhRQ3k/Pj4eR48elf33AtD8/wQQX5AaNGiAxYsXY8yYMShevHiB+zHJobqpqal5foC8evUKAPDee+8pt2X//c25jV5nZ2eHTZs24fTp0wgLC4O9vT1at26txdQ56eI4ALG2w9ChQ1G2bFkEBgZqKW3edHUchuDly5cq2YG88+f1XEM4Tk2Ow9C97bE8evQIQ4cOhZubG/z9/XWaUR1vcxy1a9fG1q1bsX//foSGhqJixYoFXrUwySJw/vx59OjRI9fHRv27MkRaWhqKFCmi/DuQ/0R2JUuWRN26dVG3bl389ddfWL9+vc6LgC6O49mzZxg4cCBu376N7777Lt/naosujsNQaDIxYl7PLVasmG5DquFtJ3g0RG9zLPfu3UNQUBAsLCywcOFC5SU7Ob3NcdjZ2cHOzg516tTBmTNnsGHDBvMsAu7u7rhy5Uquj92/fx+zZ8/Gw4cPYWVlBeC/SxJ2dnY5nn/58mWkpKTA1dVVua1GjRo4ffq0DpKr0uZxAMDjx4/x2Wef4eHDh1i7di0qV66sm+Bv0PZxGJLXJ0bM/paW18SI5cqVUx5btocPHxrEcWpyHIZO02O5desWevfuDUtLS6xduxalDWSJME2O48yZM7C0tETt2rWV22rUqIEbN24UuB/5y52elStXDuXLl0dcXJxyW1xcHMqWLYsPP/wwx/N3796NSZMmqWy7ePEiqlWrpuuo+dL0ONLS0jBo0CAkJSUhOjpa9vzZND0OQ6PJxIjOzs44c+YMpH8H6UuShDNnzsjeyQAwrQkeNTmWJ0+eoG/fvihRogTWrVunbI8yBJocR3R0NBYsWKCyTe3PKW10ZTI2UVFRkre3txQbGysdP35c8vHxUele9ejRI+n58+eSJElSQkKC5OzsLM2bN0+6fv26tGbNGqlevXrShQsX5IqvpMlxREVFSXXr1pViY2OlBw8eKG9JSUkypf+PJsfxumPHjsneRVSSJGn8+PFSmzZtpLNnz0q//PKL5OrqKv3000+SJEnSgwcPpJcvX0qSJLrteXp6SpMnT5bi4+Ol6dOnS15eXgYzTkDd43jd5s2bDa6LqCSpfywTJkyQXF1dpUuXLqn8XiQnJ8sZX0nd4zh9+rRUp04dad26ddL169elefPmSQ0aNJDu3r1b4D7MsghkZGRIM2fOlNzd3aWGDRtKYWFhyr7bkiRJfn5+0sKFC5X3T548KXXq1ElydHSUAgICDGKAlSRpdhwdO3aUatWqleOWVz9wfdL0/yOboRSBlJQUafTo0VKDBg0kb29v6dtvv1U+VqtWLWnz5s3K+2fPnpU6dOgg1a9fX+rcubN0/vx5OSLnSpPjyGaoRUDdY2nYsGGuvxfBwcFyRVehyf/J3r17pYCAAOXPljpjBCRJkjiBHBGRGTO7NgEiIvoPiwARkRljESAiMmMsAkREZoxFgIjIjLEIEBGZMeMaCkikA82bN8edO3dyfWzJkiUGMZkYka6wCBABCA0Nxccff5xju7HNm0OkKRYBIgDFixc3iInciPSNbQJEBWjevDnCw8PRpEkTBAQEIDY2Fk2bNsWUKVPg5uaGRYsWAQC2bNmCgIAAODk5oVOnTjh+/Hie75Hbal1EcuCZAJEaduzYgRUrVkCSJCQnJ+P+/ft4/vw5tm7dCgsLC2zZsgVTpkzBpEmT4OzsjC1btmDAgAHYvXs3ypcvn+M9jG1mTjJdPBMgAjBlyhS4uLio3Jo2bap8vF27dqhduzbq1Kmj3NavXz9UrlwZFStWxLp169CjRw906NABVatWRXBwMGrXro1169bl+x5EcuPXESIAQ4cOxUcffaSy7fXVpSpUqJDjNa9vu3btGgYPHqzyeIMGDfD333/n+x5EcmMRIAJgY2MDe3v7PB9/fbHv3LbltkRkZmYmMjMz830PIrnxchCRFlSrVg1nz55V2Xb27FlUrVpVpkRE6uGZABGA58+f51j/F1B/kfW+ffsiNDQUNWvWVDYM//nnn5gxY4a2oxJpFYsAEYCwsDCEhYXl2B4UFKTW61u3bo3ExEQsXLgQiYmJqFOnDlatWoWaNWtqOyqRVnFlMSIiM8Y2ASIiM8YiQERkxlgEiIjMGIsAEZEZYxEgIjJjLAJERGaMRYCIyIyxCBARmbH/BxGM6O/FX+s4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update({'font.size': 14})\n",
    "\n",
    "sns.set_style(\"white\")\n",
    "ax = sns.distplot(y_test-y_pred_RF.flatten(), color='blue')\n",
    "plt.xlabel('Error')\n",
    "plt.title('RF Error Distribution')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\Software\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'GBM Error Distribution')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update({'font.size': 14})\n",
    "\n",
    "sns.set_style(\"white\")\n",
    "ax = sns.distplot(y_test-GB.predict(x_test), color='blue')\n",
    "plt.xlabel('Error')\n",
    "plt.title('GBM Error Distribution')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "result=pd.DataFrame()\n",
    "result['ML_Type']=['GBM','RF','ANN','RNN']\n",
    "\n",
    "result['RMSE_Test']=[RMSE(y_test,GB.predict(x_test)),RMSE(y_test,y_pred_RF.flatten())\n",
    "                    ,RMSE(y_test,y_pred_ANN.flatten()),RMSE(y_test,y_pred_RNN.flatten())]\n",
    "result['RMSE_Train']=[RMSE(y_train,GB.predict(x_train)),RMSE(y_train,y_pred_RF_train.flatten())\n",
    "                     ,RMSE(y_train,y_pred_ANN_train.flatten()),RMSE(y_train,y_pred_RNN_train.flatten())]\n",
    "j2=[]\n",
    "for i in range(len(result[\"RMSE_Test\"])):\n",
    "    j2.append(result[\"RMSE_Test\"][i]**2/result[\"RMSE_Train\"][i]**2)\n",
    "    \n",
    "result['J_2']=j2\n",
    "\n",
    "result['MAE_Test']=[MAE(y_test,GB.predict(x_test)),MAE(y_test,y_pred_RF.flatten())\n",
    "                    ,MAE(y_test,y_pred_ANN.flatten()),MAE(y_test,y_pred_RNN.flatten())]\n",
    "result['MAE_Train']=[MAE(y_train,GB.predict(x_train)),MAE(y_train,y_pred_RF_train.flatten())\n",
    "                     ,MAE(y_train,y_pred_ANN_train.flatten()),MAE(y_train,y_pred_RNN_train.flatten())]\n",
    "\n",
    "result['MSLE_Test']=[MSLE(y_test,GB.predict(x_test)),MSLE(y_test,y_pred_RF.flatten())\n",
    "                     ,MSLE(y_test,y_pred_ANN.flatten()),MSLE(y_test,y_pred_RNN.flatten())]\n",
    "result['MSLE_Train']=[MSLE(y_train,GB.predict(x_train)),MSLE(y_train,y_pred_RF_train.flatten())\n",
    "                      ,MSLE(y_train,y_pred_ANN_train.flatten()),MSLE(y_train,y_pred_RNN_train.flatten())]\n",
    "\n",
    "result['Ex_Var_Test']=[EX_VAR(y_test,GB.predict(x_test)),EX_VAR(y_test,y_pred_RF.flatten())\n",
    "                       ,EX_VAR(y_test,y_pred_ANN.flatten()),EX_VAR(y_test,y_pred_RNN.flatten())]\n",
    "result['Ex_Var_Train']=[EX_VAR(y_train,GB.predict(x_train)),EX_VAR(y_train,y_pred_RF_train.flatten())\n",
    "                        ,EX_VAR(y_train,y_pred_ANN_train.flatten()),EX_VAR(y_train,y_pred_RNN_train.flatten())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ML_Type</th>\n",
       "      <th>RMSE_Test</th>\n",
       "      <th>RMSE_Train</th>\n",
       "      <th>J_2</th>\n",
       "      <th>MAE_Test</th>\n",
       "      <th>MAE_Train</th>\n",
       "      <th>MSLE_Test</th>\n",
       "      <th>MSLE_Train</th>\n",
       "      <th>Ex_Var_Test</th>\n",
       "      <th>Ex_Var_Train</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GBM</td>\n",
       "      <td>0.008604</td>\n",
       "      <td>0.001793</td>\n",
       "      <td>23.035587</td>\n",
       "      <td>0.075152</td>\n",
       "      <td>0.033805</td>\n",
       "      <td>0.003481</td>\n",
       "      <td>0.000719</td>\n",
       "      <td>0.070185</td>\n",
       "      <td>0.806399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RF</td>\n",
       "      <td>0.008834</td>\n",
       "      <td>0.003190</td>\n",
       "      <td>7.669029</td>\n",
       "      <td>0.075877</td>\n",
       "      <td>0.046005</td>\n",
       "      <td>0.003580</td>\n",
       "      <td>0.001282</td>\n",
       "      <td>0.046477</td>\n",
       "      <td>0.655493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ANN</td>\n",
       "      <td>0.011088</td>\n",
       "      <td>0.007630</td>\n",
       "      <td>2.111909</td>\n",
       "      <td>0.085611</td>\n",
       "      <td>0.071007</td>\n",
       "      <td>0.004495</td>\n",
       "      <td>0.003057</td>\n",
       "      <td>0.046771</td>\n",
       "      <td>0.178008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RNN</td>\n",
       "      <td>0.008054</td>\n",
       "      <td>0.008458</td>\n",
       "      <td>0.906622</td>\n",
       "      <td>0.074787</td>\n",
       "      <td>0.077782</td>\n",
       "      <td>0.003226</td>\n",
       "      <td>0.003389</td>\n",
       "      <td>0.079010</td>\n",
       "      <td>0.088490</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ML_Type  RMSE_Test  RMSE_Train        J_2  MAE_Test  MAE_Train  MSLE_Test  \\\n",
       "0     GBM   0.008604    0.001793  23.035587  0.075152   0.033805   0.003481   \n",
       "1      RF   0.008834    0.003190   7.669029  0.075877   0.046005   0.003580   \n",
       "2     ANN   0.011088    0.007630   2.111909  0.085611   0.071007   0.004495   \n",
       "3     RNN   0.008054    0.008458   0.906622  0.074787   0.077782   0.003226   \n",
       "\n",
       "   MSLE_Train  Ex_Var_Test  Ex_Var_Train  \n",
       "0    0.000719     0.070185      0.806399  \n",
       "1    0.001282     0.046477      0.655493  \n",
       "2    0.003057     0.046771      0.178008  \n",
       "3    0.003389     0.079010      0.088490  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "result.to_csv('metric_results.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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