Preprocessing-Training/BTC_Prediction (Preprocessing_Training Stage).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BTC Price Prediction (Preprocessing and Training)\n",
    "\n",
    "### This notebook contains:\n",
    "- Use of \"Standard Scaler\" to ensure values are to scale for modeling (no large fluctuations), preventing leakage\n",
    "- Splitting of dataframe into \"testing\" and \"training\" subsets using \"TimeSeriesSplit()\" \n",
    "- Overall preprocessing to prep for modeling and implementation of various classifying/tree based methods\n",
    "- Two baseline linear regression models using \"Ordinary Least Squares(OLS)\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import TimeSeriesSplit\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import linear_model, preprocessing\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn import metrics\n",
    "import statsmodels.api as sm\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/train.csv', index_col='Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>ema_short</th>\n",
       "      <th>ema_long</th>\n",
       "      <th>atr</th>\n",
       "      <th>obv</th>\n",
       "      <th>tweet_sentiment</th>\n",
       "      <th>close_nextday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-09-02</th>\n",
       "      <td>10340.00</td>\n",
       "      <td>44740.25</td>\n",
       "      <td>10164.518939</td>\n",
       "      <td>10452.265343</td>\n",
       "      <td>530.693553</td>\n",
       "      <td>225053.863244</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>10615.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-03</th>\n",
       "      <td>10615.28</td>\n",
       "      <td>47998.38</td>\n",
       "      <td>10207.448563</td>\n",
       "      <td>10458.658074</td>\n",
       "      <td>528.572585</td>\n",
       "      <td>273052.240025</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10567.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-04</th>\n",
       "      <td>10567.02</td>\n",
       "      <td>43943.89</td>\n",
       "      <td>10241.693462</td>\n",
       "      <td>10462.907561</td>\n",
       "      <td>521.468114</td>\n",
       "      <td>229108.350999</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10564.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-05</th>\n",
       "      <td>10564.49</td>\n",
       "      <td>33970.96</td>\n",
       "      <td>10272.435990</td>\n",
       "      <td>10466.891187</td>\n",
       "      <td>516.363249</td>\n",
       "      <td>195137.390360</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10298.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06</th>\n",
       "      <td>10298.73</td>\n",
       "      <td>58799.64</td>\n",
       "      <td>10274.940181</td>\n",
       "      <td>10460.296630</td>\n",
       "      <td>533.470874</td>\n",
       "      <td>136337.749401</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10455.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               close    volume     ema_short      ema_long         atr  \\\n",
       "Date                                                                     \n",
       "2019-09-02  10340.00  44740.25  10164.518939  10452.265343  530.693553   \n",
       "2019-09-03  10615.28  47998.38  10207.448563  10458.658074  528.572585   \n",
       "2019-09-04  10567.02  43943.89  10241.693462  10462.907561  521.468114   \n",
       "2019-09-05  10564.49  33970.96  10272.435990  10466.891187  516.363249   \n",
       "2019-09-06  10298.73  58799.64  10274.940181  10460.296630  533.470874   \n",
       "\n",
       "                      obv  tweet_sentiment  close_nextday  \n",
       "Date                                                       \n",
       "2019-09-02  225053.863244             -1.0       10615.28  \n",
       "2019-09-03  273052.240025              0.5       10567.02  \n",
       "2019-09-04  229108.350999              0.5       10564.49  \n",
       "2019-09-05  195137.390360              0.5       10298.73  \n",
       "2019-09-06  136337.749401              0.0       10455.88  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 441 entries, 2019-09-02 to 2020-11-15\n",
      "Data columns (total 8 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   close            441 non-null    float64\n",
      " 1   volume           441 non-null    float64\n",
      " 2   ema_short        441 non-null    float64\n",
      " 3   ema_long         441 non-null    float64\n",
      " 4   atr              441 non-null    float64\n",
      " 5   obv              441 non-null    float64\n",
      " 6   tweet_sentiment  441 non-null    float64\n",
      " 7   close_nextday    441 non-null    float64\n",
      "dtypes: float64(8)\n",
      "memory usage: 31.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(441, 8)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataframe split (training and testing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               close    volume     ema_short      ema_long         atr  \\\n",
      "Date                                                                     \n",
      "2019-09-02  10340.00  44740.25  10164.518939  10452.265343  530.693553   \n",
      "2019-09-03  10615.28  47998.38  10207.448563  10458.658074  528.572585   \n",
      "2019-09-04  10567.02  43943.89  10241.693462  10462.907561  521.468114   \n",
      "2019-09-05  10564.49  33970.96  10272.435990  10466.891187  516.363249   \n",
      "2019-09-06  10298.73  58799.64  10274.940181  10460.296630  533.470874   \n",
      "\n",
      "                      obv  tweet_sentiment  \n",
      "Date                                        \n",
      "2019-09-02  225053.863244             -1.0  \n",
      "2019-09-03  273052.240025              0.5  \n",
      "2019-09-04  229108.350999              0.5  \n",
      "2019-09-05  195137.390360              0.5  \n",
      "2019-09-06  136337.749401              0.0  \n"
     ]
    }
   ],
   "source": [
    "X = df.drop(labels=['close_nextday'], axis=1)\n",
    "y = df['close_nextday']\n",
    "\n",
    "print(X.head(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Split time series... 70% train data = 70% first months/days of data , 30% test = last 30% of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "tss = TimeSeriesSplit(n_splits = 3) # n_splits = 3-1...2 splits\n",
    "\n",
    "for train_index, test_index in tss.split(X):\n",
    "    X_train, X_test = X.iloc[train_index, :], X.iloc[test_index,:]\n",
    "    y_train, y_test = y.iloc[train_index], y.iloc[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X:\n",
      "                close     volume     ema_short      ema_long         atr  \\\n",
      "Date                                                                      \n",
      "2020-07-29  11100.53   76838.09   9826.875579   9490.443365  354.371960   \n",
      "2020-07-30  11099.61   60794.83   9948.088381   9553.547939  353.273963   \n",
      "2020-07-31  11335.46   70063.66  10080.219011   9623.426844  362.611537   \n",
      "2020-08-01  11801.17   85087.49  10244.119106   9708.828536  382.496427   \n",
      "2020-08-02  11071.35   97553.08  10322.903000   9762.260750  469.815254   \n",
      "...              ...        ...           ...           ...         ...   \n",
      "2020-11-11  15684.24   78469.75  14236.176564  12927.689795  698.560946   \n",
      "2020-11-12  16291.86  102196.36  14431.955939  13059.618039  712.953736   \n",
      "2020-11-13  16320.70   75691.88  14611.836326  13187.503606  699.717755   \n",
      "2020-11-14  16070.45   59116.35  14750.751914  13300.560327  696.665772   \n",
      "2020-11-15  15957.00   43596.84  14865.632684  13404.734432  675.852503   \n",
      "\n",
      "                     obv  tweet_sentiment  \n",
      "Date                                       \n",
      "2020-07-29  1.179829e+06        -0.250000  \n",
      "2020-07-30  1.119035e+06        -0.250000  \n",
      "2020-07-31  1.189098e+06         1.000000  \n",
      "2020-08-01  1.274186e+06         0.500000  \n",
      "2020-08-02  1.176633e+06         0.500000  \n",
      "...                  ...              ...  \n",
      "2020-11-11  1.954724e+06        -0.333333  \n",
      "2020-11-12  2.056921e+06        -0.166667  \n",
      "2020-11-13  2.132613e+06         0.333333  \n",
      "2020-11-14  2.073496e+06         0.250000  \n",
      "2020-11-15  2.029899e+06         1.000000  \n",
      "\n",
      "[110 rows x 7 columns] (110, 7)                close     volume     ema_short      ema_long         atr  \\\n",
      "Date                                                                      \n",
      "2019-09-02  10340.00   44740.25  10164.518939  10452.265343  530.693553   \n",
      "2019-09-03  10615.28   47998.38  10207.448563  10458.658074  528.572585   \n",
      "2019-09-04  10567.02   43943.89  10241.693462  10462.907561  521.468114   \n",
      "2019-09-05  10564.49   33970.96  10272.435990  10466.891187  516.363249   \n",
      "2019-09-06  10298.73   58799.64  10274.940181  10460.296630  533.470874   \n",
      "...              ...        ...           ...           ...         ...   \n",
      "2020-07-24   9537.80   43931.14   9319.820787   9252.782403  206.397467   \n",
      "2020-07-25   9700.42   40679.55   9356.068331   9270.336819  207.361933   \n",
      "2020-07-26   9931.54   65279.27   9410.875156   9296.266355  225.478938   \n",
      "2020-07-27  11029.96  150188.93   9565.073713   9364.254341  314.919728   \n",
      "2020-07-28  10906.27   97267.73   9692.806693   9424.725543  340.799033   \n",
      "\n",
      "                     obv  tweet_sentiment  \n",
      "Date                                       \n",
      "2019-09-02  2.250539e+05        -1.000000  \n",
      "2019-09-03  2.730522e+05         0.500000  \n",
      "2019-09-04  2.291084e+05         0.500000  \n",
      "2019-09-05  1.951374e+05         0.500000  \n",
      "2019-09-06  1.363377e+05         0.000000  \n",
      "...                  ...              ...  \n",
      "2020-07-24  9.441113e+05         1.000000  \n",
      "2020-07-25  9.847908e+05         1.000000  \n",
      "2020-07-26  1.050070e+06         0.000000  \n",
      "2020-07-27  1.200259e+06         0.285714  \n",
      "2020-07-28  1.102991e+06         0.000000  \n",
      "\n",
      "[331 rows x 7 columns] (331, 7)\n"
     ]
    }
   ],
   "source": [
    "print(\"X:\\n\", X_test, X_test.shape, X_train, X_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y:\n",
      " Date\n",
      "2020-07-29    11099.61\n",
      "2020-07-30    11335.46\n",
      "2020-07-31    11801.17\n",
      "2020-08-01    11071.35\n",
      "2020-08-02    11219.81\n",
      "                ...   \n",
      "2020-11-11    16291.86\n",
      "2020-11-12    16320.70\n",
      "2020-11-13    16070.45\n",
      "2020-11-14    15957.00\n",
      "2020-11-15    16713.57\n",
      "Name: close_nextday, Length: 110, dtype: float64 (110,) Date\n",
      "2019-09-02    10615.28\n",
      "2019-09-03    10567.02\n",
      "2019-09-04    10564.49\n",
      "2019-09-05    10298.73\n",
      "2019-09-06    10455.88\n",
      "                ...   \n",
      "2020-07-24     9700.42\n",
      "2020-07-25     9931.54\n",
      "2020-07-26    11029.96\n",
      "2020-07-27    10906.27\n",
      "2020-07-28    11100.53\n",
      "Name: close_nextday, Length: 331, dtype: float64 (331,)\n"
     ]
    }
   ],
   "source": [
    "print(\"y:\\n\", y_test, y_test.shape, y_train, y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshape y train, test to match X train, test\n",
    "\n",
    "y_train = y_train.values.reshape(-1,1)\n",
    "y_test = y_test.values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "ss = StandardScaler()\n",
    "\n",
    "X_train = ss.fit_transform(X_train)\n",
    "X_test = ss.fit_transform(X_test)\n",
    "y_train = ss.fit_transform(y_train)\n",
    "y_test = ss.fit_transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X Train: [[ 1.49929341 -0.40861044  1.57803412 ...  0.9507744   0.17831782\n",
      "  -1.97717629]\n",
      " [ 1.72641589 -0.33062537  1.61962818 ...  0.93249884  0.27043599\n",
      "   0.28715395]\n",
      " [ 1.6865985  -0.42767174  1.6528077  ...  0.87128236  0.18609916\n",
      "   0.28715395]\n",
      " ...\n",
      " [ 1.16228946  0.08300193  0.84783662 ... -1.67914193  1.76168372\n",
      "  -0.4676228 ]\n",
      " [ 2.06855171  2.11535981  0.99723798 ... -0.90846523  2.04992536\n",
      "  -0.0363218 ]\n",
      " [ 1.96650006  0.84866273  1.12099711 ... -0.68547326  1.86324975\n",
      "  -0.4676228 ]] <class 'numpy.ndarray'> (331, 7) 331\n",
      "\n",
      "X Test: [[-5.17734398e-01  7.16505404e-01 -1.52157983e+00 -1.97583424e+00\n",
      "  -8.87472861e-01 -1.17561486e+00 -6.21452070e-01]\n",
      " [-5.18323922e-01  1.37310157e-02 -1.40876707e+00 -1.89442626e+00\n",
      "  -8.98313120e-01 -1.40310289e+00 -6.21452070e-01]\n",
      " [-3.67194292e-01  4.19751758e-01 -1.28579309e+00 -1.80427906e+00\n",
      "  -8.06125517e-01 -1.14093183e+00  1.30515610e+00]\n",
      " [-6.87733460e-02  1.07787005e+00 -1.13325127e+00 -1.69410671e+00\n",
      "  -6.09806823e-01 -8.22543161e-01  5.34512834e-01]\n",
      " [-5.36432565e-01  1.62392475e+00 -1.05992709e+00 -1.62517654e+00\n",
      "   2.52270757e-01 -1.18757680e+00  5.34512834e-01]\n",
      " [-4.41301317e-01 -1.55487113e-01 -9.80426960e-01 -1.55143891e+00\n",
      "   2.99649235e-01 -9.74543673e-01  1.30515610e+00]\n",
      " [-4.59140829e-01 -8.11382658e-02 -9.10965954e-01 -1.48200137e+00\n",
      "   2.57595404e-01 -1.19392782e+00 -2.36130435e-01]\n",
      " [-1.04824027e-01  6.34685183e-01 -7.99108732e-01 -1.38731356e+00\n",
      "   4.11028926e-01 -9.13396786e-01  2.04237147e-01]\n",
      " [-9.35782135e-02  1.33505170e-01 -6.96348979e-01 -1.29545114e+00\n",
      "   3.06380744e-01 -6.75677445e-01 -2.36130435e-01]\n",
      " [-2.01377822e-01  2.31051811e-01 -6.18287457e-01 -1.21570195e+00\n",
      "   3.85816955e-01 -9.21729398e-01 -2.36130435e-01]\n",
      " [-9.42510399e-02 -8.15789087e-01 -5.32841849e-01 -1.13062252e+00\n",
      "   2.53894358e-01 -7.65100439e-01 -2.36130435e-01]\n",
      " [-1.45340993e-01 -8.31781841e-01 -4.62601033e-01 -1.05291308e+00\n",
      "   1.16493992e-01 -9.20363254e-01 -2.36130435e-01]\n",
      " [-9.98113241e-03  1.07194981e+00 -3.80325906e-01 -9.67564415e-01\n",
      "   2.30233385e-01 -6.02480292e-01  3.80384180e-01]\n",
      " [-3.30912928e-01  1.32584074e+00 -3.50280012e-01 -9.10900325e-01\n",
      "   4.71704728e-01 -9.42051094e-01  1.30515610e+00]\n",
      " [-2.20537356e-01  1.93978796e-01 -3.07827718e-01 -8.47744203e-01\n",
      "   4.54412267e-01 -6.99165957e-01  1.30515610e+00]\n",
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      " [ 2.28543349e+00  2.21907525e-01  2.18696013e+00  2.04487491e+00\n",
      "   2.51539425e+00  2.06892266e+00 -2.36130435e-01]\n",
      " [ 2.19143644e+00  2.12431977e+00  2.32141140e+00  2.18254365e+00\n",
      "   2.75192832e+00  1.66114436e+00  9.19834468e-01]\n",
      " [ 2.17144388e+00  5.25899591e-02  2.44029228e+00  2.31323520e+00\n",
      "   2.51536110e+00  1.43033692e+00 -1.26365479e+00]\n",
      " [ 2.41944770e+00  7.87980208e-01  2.58215678e+00  2.45838150e+00\n",
      "   2.51062145e+00  1.72396269e+00 -7.49892615e-01]\n",
      " [ 2.80880274e+00  1.82732345e+00  2.76436865e+00  2.62857538e+00\n",
      "   2.65271797e+00  2.10637101e+00 -4.93011525e-01]\n",
      " [ 2.82728304e+00  6.66295719e-01  2.93178333e+00  2.79355401e+00\n",
      "   2.52204234e+00  2.38960229e+00  2.77631744e-01]\n",
      " [ 2.66692607e+00 -5.97947363e-02  3.06107208e+00  2.93940270e+00\n",
      "   2.49191086e+00  2.16839496e+00  1.49191199e-01]\n",
      " [ 2.59422878e+00 -7.39626276e-01  3.16799162e+00  3.07379238e+00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   2.28642650e+00  2.00526004e+00  1.30515610e+00]] <class 'numpy.ndarray'> (110, 7) 110\n",
      "\n",
      "y Train: [[ 1.71862644e+00]\n",
      " [ 1.67894513e+00]\n",
      " [ 1.67686486e+00]\n",
      " [ 1.45834633e+00]\n",
      " [ 1.58756136e+00]\n",
      " [ 1.52614003e+00]\n",
      " [ 1.46195596e+00]\n",
      " [ 1.29342140e+00]\n",
      " [ 1.34290381e+00]\n",
      " [ 1.55395641e+00]\n",
      " [ 1.49397399e+00]\n",
      " [ 1.48818542e+00]\n",
      " [ 1.46104328e+00]\n",
      " [ 1.41935570e+00]\n",
      " [ 1.36715168e+00]\n",
      " [ 1.34180201e+00]\n",
      " [ 1.41358358e+00]\n",
      " [ 1.35134000e+00]\n",
      " [ 1.20152785e+00]\n",
      " [ 1.23645661e+00]\n",
      " [ 9.67896537e-01]\n",
      " [-2.62822225e-02]\n",
      " [-7.81573521e-02]\n",
      " [-3.79360355e-01]\n",
      " [-2.85476978e-01]\n",
      " [-2.68292159e-01]\n",
      " [-3.95731156e-01]\n",
      " [-1.93854760e-01]\n",
      " [-1.91305816e-01]\n",
      " [-1.35804607e-01]\n",
      " [-2.47612820e-01]\n",
      " [-3.19007930e-01]\n",
      " [-3.28003237e-01]\n",
      " [-5.51603217e-01]\n",
      " [-2.75462093e-01]\n",
      " [-2.93304704e-01]\n",
      " [ 2.93012101e-02]\n",
      " [ 2.70729393e-02]\n",
      " [-2.19212646e-01]\n",
      " [-1.85015679e-01]\n",
      " [-2.05637461e-01]\n",
      " [-1.45457707e-01]\n",
      " [-3.00787089e-01]\n",
      " [-4.38553422e-01]\n",
      " [-3.73728011e-01]\n",
      " [-4.75332223e-01]\n",
      " [-4.74509983e-01]\n",
      " [-2.48114386e-01]\n",
      " [-2.69558409e-01]\n",
      " [-4.15316916e-01]\n",
      " [-8.70328158e-01]\n",
      " [-9.14901795e-01]\n",
      " [ 1.06822009e-01]\n",
      " [ 5.79593637e-01]\n",
      " [ 8.26208119e-01]\n",
      " [ 5.59152748e-01]\n",
      " [ 7.25639929e-01]\n",
      " [ 5.17695401e-01]\n",
      " [ 5.06290930e-01]\n",
      " [ 5.80917444e-01]\n",
      " [ 6.28533370e-01]\n",
      " [ 5.50576783e-01]\n",
      " [ 7.13906562e-01]\n",
      " [ 6.44271046e-01]\n",
      " [ 6.69258923e-01]\n",
      " [ 5.68246724e-01]\n",
      " [ 2.04430135e-01]\n",
      " [ 2.33767662e-01]\n",
      " [ 4.22932226e-01]\n",
      " [ 1.71162299e-01]\n",
      " [ 2.44070331e-01]\n",
      " [ 2.07217529e-01]\n",
      " [ 9.99645266e-02]\n",
      " [-4.38863836e-02]\n",
      " [-2.80253716e-02]\n",
      " [-1.86682790e-02]\n",
      " [-2.77863034e-01]\n",
      " [-3.21877548e-01]\n",
      " [-3.51173964e-01]\n",
      " [-7.37848828e-01]\n",
      " [-1.03345238e+00]\n",
      " [-9.97816489e-01]\n",
      " [-1.33352891e+00]\n",
      " [-1.16390899e+00]\n",
      " [-1.12561727e+00]\n",
      " [-8.35876296e-01]\n",
      " [-9.09080335e-01]\n",
      " [-6.45807269e-01]\n",
      " [-8.08438143e-01]\n",
      " [-9.32596402e-01]\n",
      " [-1.01203302e+00]\n",
      " [-1.01332394e+00]\n",
      " [-1.09422414e+00]\n",
      " [-9.34150436e-01]\n",
      " [-8.20294846e-01]\n",
      " [-8.52575993e-01]\n",
      " [-8.34568934e-01]\n",
      " [-9.75558449e-01]\n",
      " [-1.06971317e+00]\n",
      " [-1.08133142e+00]\n",
      " [-1.09113252e+00]\n",
      " [-1.04146922e+00]\n",
      " [-1.20133737e+00]\n",
      " [-1.15649239e+00]\n",
      " [-1.34303401e+00]\n",
      " [-1.56331214e+00]\n",
      " [-1.02555887e+00]\n",
      " [-1.13041915e+00]\n",
      " [-1.09956048e+00]\n",
      " [-1.14484947e+00]\n",
      " [-8.41697756e-01]\n",
      " [-9.93277724e-01]\n",
      " [-1.04369749e+00]\n",
      " [-1.08574685e+00]\n",
      " [-1.08790934e+00]\n",
      " [-1.04454440e+00]\n",
      " [-9.94058852e-01]\n",
      " [-9.34775339e-01]\n",
      " [-1.05173077e+00]\n",
      " [-1.09347591e+00]\n",
      " [-1.08885492e+00]\n",
      " [-1.28219646e+00]\n",
      " [-9.70361891e-01]\n",
      " [-9.62838394e-01]\n",
      " [-9.59023200e-01]\n",
      " [-6.30743829e-01]\n",
      " [-3.12306673e-01]\n",
      " [-3.85732716e-01]\n",
      " [-5.81606759e-01]\n",
      " [-2.69763969e-01]\n",
      " [-4.15308694e-01]\n",
      " [-2.79663740e-01]\n",
      " [-3.41035743e-01]\n",
      " [ 2.34261006e-01]\n",
      " [ 2.43634544e-01]\n",
      " [ 1.60259395e-01]\n",
      " [ 3.19173744e-01]\n",
      " [ 3.21377348e-01]\n",
      " [ 1.45204178e-01]\n",
      " [ 9.64042268e-02]\n",
      " [ 1.73431682e-01]\n",
      " [ 1.29302054e-01]\n",
      " [-9.91491426e-02]\n",
      " [-7.07983030e-02]\n",
      " [-1.51723176e-01]\n",
      " [ 7.39159593e-02]\n",
      " [ 3.14478753e-01]\n",
      " [ 6.98168886e-01]\n",
      " [ 6.38408474e-01]\n",
      " [ 8.12460264e-01]\n",
      " [ 6.80638727e-01]\n",
      " [ 7.06720183e-01]\n",
      " [ 6.63059233e-01]\n",
      " [ 6.30769863e-01]\n",
      " [ 5.52476158e-01]\n",
      " [ 8.93722255e-01]\n",
      " [ 1.02524779e+00]\n",
      " [ 1.05955987e+00]\n",
      " [ 1.12642443e+00]\n",
      " [ 1.33749347e+00]\n",
      " [ 1.09088722e+00]\n",
      " [ 1.39614386e+00]\n",
      " [ 1.48114705e+00]\n",
      " [ 1.40152954e+00]\n",
      " [ 1.49586515e+00]\n",
      " [ 1.13437550e+00]\n",
      " [ 1.14469461e+00]\n",
      " [ 9.70979938e-01]\n",
      " [ 1.34814971e+00]\n",
      " [ 8.78716373e-01]\n",
      " [ 8.80878865e-01]\n",
      " [ 9.47176086e-01]\n",
      " [ 9.25641617e-01]\n",
      " [ 1.16042406e+00]\n",
      " [ 9.29974823e-01]\n",
      " [ 6.50174730e-01]\n",
      " [ 2.13902341e-01]\n",
      " [ 2.45114576e-01]\n",
      " [ 1.37976688e-01]\n",
      " [-1.22856586e-03]\n",
      " [ 5.57135999e-03]\n",
      " [ 3.20785335e-01]\n",
      " [ 1.93198335e-01]\n",
      " [ 1.85633725e-01]\n",
      " [ 4.35438498e-01]\n",
      " [ 4.98915436e-01]\n",
      " [ 2.97285712e-01]\n",
      " [-4.04372900e-01]\n",
      " [-4.89425419e-01]\n",
      " [-5.18458718e-01]\n",
      " [-4.85602002e-01]\n",
      " [-3.06293012e+00]\n",
      " [-2.42273396e+00]\n",
      " [-2.75700746e+00]\n",
      " [-2.60140674e+00]\n",
      " [-2.87466180e+00]\n",
      " [-2.64141695e+00]\n",
      " [-2.57530884e+00]\n",
      " [-1.94273484e+00]\n",
      " [-1.90492002e+00]\n",
      " [-1.92249951e+00]\n",
      " [-2.22737793e+00]\n",
      " [-1.69200094e+00]\n",
      " [-1.46390331e+00]\n",
      " [-1.51923184e+00]\n",
      " [-1.46995499e+00]\n",
      " [-1.78096732e+00]\n",
      " [-1.88165885e+00]\n",
      " [-2.17374321e+00]\n",
      " [-1.75196691e+00]\n",
      " [-1.73876173e+00]\n",
      " [-1.54760735e+00]\n",
      " [-1.42330931e+00]\n",
      " [-1.47263550e+00]\n",
      " [-1.37159041e+00]\n",
      " [-1.44083125e+00]\n",
      " [-9.82744828e-01]\n",
      " [-1.09175742e+00]\n",
      " [-9.56942933e-01]\n",
      " [-1.02086388e+00]\n",
      " [-1.37000348e+00]\n",
      " [-1.35527716e+00]\n",
      " [-1.33310957e+00]\n",
      " [-1.38727875e+00]\n",
      " [-1.36196197e+00]\n",
      " [-1.56543352e+00]\n",
      " [-1.17018269e+00]\n",
      " [-1.23134913e+00]\n",
      " [-1.04959295e+00]\n",
      " [-1.15472457e+00]\n",
      " [-1.39638917e+00]\n",
      " [-1.38443380e+00]\n",
      " [-1.15110672e+00]\n",
      " [-8.57361431e-01]\n",
      " [-8.38770581e-01]\n",
      " [-8.11085756e-01]\n",
      " [-6.84107214e-01]\n",
      " [-6.17078198e-01]\n",
      " [-6.46382837e-01]\n",
      " [ 2.08409777e-01]\n",
      " [ 7.80271600e-02]\n",
      " [ 2.48197977e-01]\n",
      " [ 3.67496797e-01]\n",
      " [ 3.03452513e-01]\n",
      " [ 2.85198782e-01]\n",
      " [ 4.08427910e-01]\n",
      " [ 5.07992967e-01]\n",
      " [ 1.20153607e+00]\n",
      " [ 1.04827873e+00]\n",
      " [ 8.33994733e-01]\n",
      " [ 1.62528778e-01]\n",
      " [ 2.99425573e-02]\n",
      " [ 2.34902354e-01]\n",
      " [ 6.44854836e-01]\n",
      " [ 1.04167614e+00]\n",
      " [ 6.50651629e-01]\n",
      " [ 7.03973901e-01]\n",
      " [ 9.49634584e-01]\n",
      " [ 9.93945104e-01]\n",
      " [ 1.02815029e+00]\n",
      " [ 8.10996676e-01]\n",
      " [ 4.46925193e-01]\n",
      " [ 5.30259230e-01]\n",
      " [ 5.37782727e-01]\n",
      " [ 1.60530734e-01]\n",
      " [ 3.08542180e-01]\n",
      " [ 2.59890231e-01]\n",
      " [ 5.58272951e-01]\n",
      " [ 8.63998275e-01]\n",
      " [ 7.41632499e-01]\n",
      " [ 9.64171789e-01]\n",
      " [ 7.59063990e-01]\n",
      " [ 1.37779969e+00]\n",
      " [ 8.16431683e-01]\n",
      " [ 9.38287670e-01]\n",
      " [ 1.03927520e+00]\n",
      " [ 9.01221085e-01]\n",
      " [ 9.38337005e-01]\n",
      " [ 1.00468356e+00]\n",
      " [ 1.03347841e+00]\n",
      " [ 1.02560135e+00]\n",
      " [ 1.11816092e+00]\n",
      " [ 6.21034540e-01]\n",
      " [ 7.72926958e-01]\n",
      " [ 7.79677550e-01]\n",
      " [ 6.71766756e-01]\n",
      " [ 7.40769147e-01]\n",
      " [ 8.22639596e-01]\n",
      " [ 7.72935181e-01]\n",
      " [ 7.08126214e-01]\n",
      " [ 6.45561963e-01]\n",
      " [ 6.85621502e-01]\n",
      " [ 6.32784351e-01]\n",
      " [ 9.54280241e-01]\n",
      " [ 9.04288041e-01]\n",
      " [ 6.34264383e-01]\n",
      " [ 5.95619097e-01]\n",
      " [ 5.23853979e-01]\n",
      " [ 4.00345290e-01]\n",
      " [ 4.86146047e-01]\n",
      " [ 5.48808967e-01]\n",
      " [ 5.04399778e-01]\n",
      " [ 5.81238118e-01]\n",
      " [ 4.61635069e-01]\n",
      " [ 4.38382118e-01]\n",
      " [ 5.01859056e-01]\n",
      " [ 4.47550095e-01]\n",
      " [ 6.73493460e-01]\n",
      " [ 6.02114794e-01]\n",
      " [ 7.49024438e-01]\n",
      " [ 5.81591681e-01]\n",
      " [ 6.27563126e-01]\n",
      " [ 5.82907265e-01]\n",
      " [ 6.39411607e-01]\n",
      " [ 5.89970308e-01]\n",
      " [ 6.00848545e-01]\n",
      " [ 5.52953057e-01]\n",
      " [ 5.00428358e-01]\n",
      " [ 5.17366505e-01]\n",
      " [ 5.30489457e-01]\n",
      " [ 5.62318372e-01]\n",
      " [ 5.22678176e-01]\n",
      " [ 7.11152058e-01]\n",
      " [ 8.16530352e-01]\n",
      " [ 8.86511209e-01]\n",
      " [ 8.32679148e-01]\n",
      " [ 9.66391838e-01]\n",
      " [ 1.15642798e+00]\n",
      " [ 2.05959298e+00]\n",
      " [ 1.95789009e+00]\n",
      " [ 2.11761846e+00]] <class 'numpy.ndarray'> (331, 1) 331\n",
      "\n",
      "y Test: [[-0.52956438]\n",
      " [-0.3843199 ]\n",
      " [-0.0975198 ]\n",
      " [-0.54696785]\n",
      " [-0.45554112]\n",
      " [-0.47268594]\n",
      " [-0.13216662]\n",
      " [-0.12135873]\n",
      " [-0.22496051]\n",
      " [-0.12200536]\n",
      " [-0.17110582]\n",
      " [-0.04101702]\n",
      " [-0.34945138]\n",
      " [-0.24337395]\n",
      " [-0.110557  ]\n",
      " [-0.12254114]\n",
      " [-0.06597061]\n",
      " [-0.02988273]\n",
      " [ 0.19805595]\n",
      " [-0.00893822]\n",
      " [-0.12620535]\n",
      " [-0.0652624 ]\n",
      " [-0.26369032]\n",
      " [-0.18263423]\n",
      " [-0.19176705]\n",
      " [-0.13014053]\n",
      " [-0.39481372]\n",
      " [-0.30674328]\n",
      " [-0.38744834]\n",
      " [-0.26641846]\n",
      " [-0.30402745]\n",
      " [-0.15295103]\n",
      " [-0.1909172 ]\n",
      " [-0.02312703]\n",
      " [-0.35163143]\n",
      " [-1.1200015 ]\n",
      " [-0.93192576]\n",
      " [-1.10408835]\n",
      " [-1.04896504]\n",
      " [-0.97676464]\n",
      " [-1.12874635]\n",
      " [-1.0717509 ]\n",
      " [-0.9992857 ]\n",
      " [-0.96786584]\n",
      " [-0.93520816]\n",
      " [-1.00177367]\n",
      " [-0.79304285]\n",
      " [-0.72312105]\n",
      " [-0.61922983]\n",
      " [-0.62786382]\n",
      " [-0.63192833]\n",
      " [-0.5412406 ]\n",
      " [-0.64000191]\n",
      " [-0.94980343]\n",
      " [-0.88058983]\n",
      " [-1.05804243]\n",
      " [-0.75329076]\n",
      " [-0.78386692]\n",
      " [-0.758045  ]\n",
      " [-0.72993217]\n",
      " [-0.77804729]\n",
      " [-0.68914548]\n",
      " [-0.72849112]\n",
      " [-0.82546036]\n",
      " [-0.85546996]\n",
      " [-0.87292269]\n",
      " [-0.79620824]\n",
      " [-0.71887179]\n",
      " [-0.83745065]\n",
      " [-0.79635604]\n",
      " [-0.63674415]\n",
      " [-0.55972178]\n",
      " [-0.41033274]\n",
      " [-0.36365251]\n",
      " [-0.26559324]\n",
      " [-0.33191242]\n",
      " [-0.3335567 ]\n",
      " [-0.27983749]\n",
      " [-0.39425947]\n",
      " [-0.36908417]\n",
      " [-0.28105684]\n",
      " [-0.12812675]\n",
      " [-0.03050473]\n",
      " [ 0.50586832]\n",
      " [ 0.62137416]\n",
      " [ 0.5933845 ]\n",
      " [ 0.70956777]\n",
      " [ 0.65851512]\n",
      " [ 0.672901  ]\n",
      " [ 1.03253581]\n",
      " [ 0.80481883]\n",
      " [ 0.92139623]\n",
      " [ 0.98568931]\n",
      " [ 1.12788541]\n",
      " [ 1.1097183 ]\n",
      " [ 0.97908141]\n",
      " [ 1.27108532]\n",
      " [ 1.34528101]\n",
      " [ 2.23578331]\n",
      " [ 2.22956338]\n",
      " [ 1.7605318 ]\n",
      " [ 2.16501165]\n",
      " [ 2.07467494]\n",
      " [ 2.05546092]\n",
      " [ 2.2938072 ]\n",
      " [ 2.66800033]\n",
      " [ 2.68576098]\n",
      " [ 2.53164849]\n",
      " [ 2.46178211]\n",
      " [ 2.92770373]] <class 'numpy.ndarray'> (110, 1) 110\n"
     ]
    }
   ],
   "source": [
    "print(\"X Train:\", X_train, type(X_train), X_train.shape, len(X_train)) #TrainX\n",
    "print(\"\\nX Test:\", X_test, type(X_test), X_test.shape, len(X_test)) #TestX\n",
    "print(\"\\ny Train:\", y_train, type(y_train), y_train.shape, len(y_train)) #Trainy\n",
    "print(\"\\ny Test:\", y_test, type(y_test), y_test.shape, len(y_test)) #Testy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Multiple Linear Regression Using OLS (Ordinary Least Squares)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared (uncentered):</th>      <td>   0.931</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.930</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>   627.6</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 03 Jan 2021</td> <th>  Prob (F-statistic):</th>          <td>3.22e-184</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>15:33:20</td>     <th>  Log-Likelihood:    </th>          <td> -26.421</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   331</td>      <th>  AIC:               </th>          <td>   66.84</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   324</td>      <th>  BIC:               </th>          <td>   93.46</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     7</td>      <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>     <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th> <td>    0.9412</td> <td>    0.037</td> <td>   25.480</td> <td> 0.000</td> <td>    0.869</td> <td>    1.014</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th> <td>   -0.0060</td> <td>    0.020</td> <td>   -0.307</td> <td> 0.759</td> <td>   -0.045</td> <td>    0.033</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th> <td>    0.0293</td> <td>    0.063</td> <td>    0.463</td> <td> 0.644</td> <td>   -0.095</td> <td>    0.154</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4</th> <td>   -0.0337</td> <td>    0.044</td> <td>   -0.761</td> <td> 0.447</td> <td>   -0.121</td> <td>    0.053</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x5</th> <td>   -0.0118</td> <td>    0.019</td> <td>   -0.613</td> <td> 0.540</td> <td>   -0.050</td> <td>    0.026</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x6</th> <td>    0.0268</td> <td>    0.017</td> <td>    1.555</td> <td> 0.121</td> <td>   -0.007</td> <td>    0.061</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x7</th> <td>    0.0110</td> <td>    0.015</td> <td>    0.736</td> <td> 0.462</td> <td>   -0.018</td> <td>    0.040</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>247.929</td> <th>  Durbin-Watson:     </th> <td>   2.189</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>11304.259</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-2.530</td>  <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>31.179</td>  <th>  Cond. No.          </th> <td>    9.71</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] R² is computed without centering (uncentered) since the model does not contain a constant.<br/>[2] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:                      y   R-squared (uncentered):                   0.931\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.930\n",
       "Method:                 Least Squares   F-statistic:                              627.6\n",
       "Date:                Sun, 03 Jan 2021   Prob (F-statistic):                   3.22e-184\n",
       "Time:                        15:33:20   Log-Likelihood:                         -26.421\n",
       "No. Observations:                 331   AIC:                                      66.84\n",
       "Df Residuals:                     324   BIC:                                      93.46\n",
       "Df Model:                           7                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "x1             0.9412      0.037     25.480      0.000       0.869       1.014\n",
       "x2            -0.0060      0.020     -0.307      0.759      -0.045       0.033\n",
       "x3             0.0293      0.063      0.463      0.644      -0.095       0.154\n",
       "x4            -0.0337      0.044     -0.761      0.447      -0.121       0.053\n",
       "x5            -0.0118      0.019     -0.613      0.540      -0.050       0.026\n",
       "x6             0.0268      0.017      1.555      0.121      -0.007       0.061\n",
       "x7             0.0110      0.015      0.736      0.462      -0.018       0.040\n",
       "==============================================================================\n",
       "Omnibus:                      247.929   Durbin-Watson:                   2.189\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            11304.259\n",
       "Skew:                          -2.530   Prob(JB):                         0.00\n",
       "Kurtosis:                      31.179   Cond. No.                         9.71\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
       "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = sm.OLS(y_train, X_train)\n",
    "\n",
    "results = lr.fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the summary results, we can see that the Adj. R-Squared was 0.930. This shows us that the model explains it variability fairly well. However, this metric solely does not explain the overall performance of the model but it does show us that our predictors are on the right track"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = results.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": [
    "_ = plt.scatter(y_test, y_pred)\n",
    "_ = plt.plot([x for x in range(-2, 4)], [y for y in range(-2, 4)], color='red')\n",
    "\n",
    "_ = plt.title('Model Prediction vs Actual')\n",
    "_ = plt.xlabel('actual values')\n",
    "_ = plt.ylabel('predicted values')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean_absolute_percentage_error(y_true, y_pred):\n",
    "    y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
    "    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of sklearn.metrics:\n",
      "\n",
      "MAPE: 354.1403284538511\n",
      "MAE: 0.133600128054328\n",
      "MSE: 0.039677913740618104\n",
      "RMSE: 0.1991931568619216\n"
     ]
    }
   ],
   "source": [
    "mape = mean_absolute_percentage_error(y_test, y_pred)\n",
    "mae = metrics.mean_absolute_error(y_test, y_pred)\n",
    "mse = metrics.mean_squared_error(y_test, y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "\n",
    "print(\"Results of sklearn.metrics:\\n\")\n",
    "print(\"MAPE:\", mape)\n",
    "print(\"MAE:\", mae)\n",
    "print(\"MSE:\", mse)\n",
    "print(\"RMSE:\", rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.figure(figsize=(10, 6))\n",
    "_ = plt.plot(y_test)\n",
    "_ = plt.plot(y_pred)\n",
    "_ = plt.legend([\"actual\", \"pred\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Avoiding redundancy in linear regression models\n",
    "\n",
    "Since the feature \"close\" is already found to be correlated with our prediction of \"close_nextday\" (from heatmap EDA stage), we can remove \"close\" to see how disposable it really is in relation to the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Highest correlated variable with target:\n",
      " \n",
      " close  close_nextday    0.986686\n",
      "dtype: float64\n",
      "print(c2)print('Highest Correlated Variable:', c2)print(c2)\n"
     ]
    }
   ],
   "source": [
    "# Pull in correlation data gathered from EDA correlation heatmap\n",
    "\n",
    "f = open('data/highest_corr_target.txt', 'r')\n",
    "file_contents = f.read()\n",
    "print('Highest correlated variable with target:\\n', \"\\n\", file_contents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataframe split (training and testing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              volume     ema_short      ema_long         atr            obv  \\\n",
      "Date                                                                          \n",
      "2019-09-02  44740.25  10164.518939  10452.265343  530.693553  225053.863244   \n",
      "2019-09-03  47998.38  10207.448563  10458.658074  528.572585  273052.240025   \n",
      "2019-09-04  43943.89  10241.693462  10462.907561  521.468114  229108.350999   \n",
      "2019-09-05  33970.96  10272.435990  10466.891187  516.363249  195137.390360   \n",
      "2019-09-06  58799.64  10274.940181  10460.296630  533.470874  136337.749401   \n",
      "\n",
      "            tweet_sentiment  \n",
      "Date                         \n",
      "2019-09-02             -1.0  \n",
      "2019-09-03              0.5  \n",
      "2019-09-04              0.5  \n",
      "2019-09-05              0.5  \n",
      "2019-09-06              0.0  \n"
     ]
    }
   ],
   "source": [
    "X2 = df.drop(labels=['close_nextday', 'close'], axis=1)\n",
    "y2 = df['close_nextday']\n",
    "\n",
    "print(X2.head(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Split time series... 70% train data = 70% first months/days of data , 30% test = last 30% of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "tss = TimeSeriesSplit(n_splits = 3)\n",
    "\n",
    "for train_index, test_index in tss.split(X2):\n",
    "    X2_train, X2_test = X2.iloc[train_index, :], X2.iloc[test_index,:]\n",
    "    y2_train, y2_test = y2.iloc[train_index], y2.iloc[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshape y train, test to match X train, test\n",
    "\n",
    "y2_train = y2_train.values.reshape(-1,1)\n",
    "y2_test = y2_test.values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "ss = StandardScaler()\n",
    "\n",
    "X2_train = ss.fit_transform(X2_train)\n",
    "X2_test = ss.fit_transform(X2_test)\n",
    "y2_train = ss.fit_transform(y2_train)\n",
    "y2_test = ss.fit_transform(y2_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared (uncentered):</th>      <td>   0.794</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.790</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>   208.4</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 03 Jan 2021</td> <th>  Prob (F-statistic):</th>          <td>3.46e-108</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>15:38:08</td>     <th>  Log-Likelihood:    </th>          <td> -208.45</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   331</td>      <th>  AIC:               </th>          <td>   428.9</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   325</td>      <th>  BIC:               </th>          <td>   451.7</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     6</td>      <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>     <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th> <td>   -0.1141</td> <td>    0.033</td> <td>   -3.438</td> <td> 0.001</td> <td>   -0.179</td> <td>   -0.049</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th> <td>    1.2756</td> <td>    0.070</td> <td>   18.319</td> <td> 0.000</td> <td>    1.139</td> <td>    1.413</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th> <td>   -0.6110</td> <td>    0.066</td> <td>   -9.297</td> <td> 0.000</td> <td>   -0.740</td> <td>   -0.482</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4</th> <td>   -0.0426</td> <td>    0.033</td> <td>   -1.277</td> <td> 0.203</td> <td>   -0.108</td> <td>    0.023</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x5</th> <td>    0.1383</td> <td>    0.029</td> <td>    4.796</td> <td> 0.000</td> <td>    0.082</td> <td>    0.195</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x6</th> <td>    0.0217</td> <td>    0.026</td> <td>    0.839</td> <td> 0.402</td> <td>   -0.029</td> <td>    0.072</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>57.835</td> <th>  Durbin-Watson:     </th> <td>   0.425</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 374.132</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.498</td> <th>  Prob(JB):          </th> <td>5.73e-82</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 8.112</td> <th>  Cond. No.          </th> <td>    5.80</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] R² is computed without centering (uncentered) since the model does not contain a constant.<br/>[2] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:                      y   R-squared (uncentered):                   0.794\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.790\n",
       "Method:                 Least Squares   F-statistic:                              208.4\n",
       "Date:                Sun, 03 Jan 2021   Prob (F-statistic):                   3.46e-108\n",
       "Time:                        15:38:08   Log-Likelihood:                         -208.45\n",
       "No. Observations:                 331   AIC:                                      428.9\n",
       "Df Residuals:                     325   BIC:                                      451.7\n",
       "Df Model:                           6                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "x1            -0.1141      0.033     -3.438      0.001      -0.179      -0.049\n",
       "x2             1.2756      0.070     18.319      0.000       1.139       1.413\n",
       "x3            -0.6110      0.066     -9.297      0.000      -0.740      -0.482\n",
       "x4            -0.0426      0.033     -1.277      0.203      -0.108       0.023\n",
       "x5             0.1383      0.029      4.796      0.000       0.082       0.195\n",
       "x6             0.0217      0.026      0.839      0.402      -0.029       0.072\n",
       "==============================================================================\n",
       "Omnibus:                       57.835   Durbin-Watson:                   0.425\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              374.132\n",
       "Skew:                          -0.498   Prob(JB):                     5.73e-82\n",
       "Kurtosis:                       8.112   Cond. No.                         5.80\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
       "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr2 = sm.OLS(y2_train, X2_train)\n",
    "\n",
    "results2 = lr2.fit()\n",
    "results2.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time the results of our summary statistics give us an Adj. R-squared measure of 0.790. This was an expected drop from our last model as we removed one of our predictors (\"close\"), which is a highly correlated variable to the target. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "y2_pred = results2.predict(X2_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": [
    "_ = plt.scatter(y2_test, y2_pred)\n",
    "_ = plt.plot([x for x in range(-2, 4)], [y for y in range(-2, 4)], color='red')\n",
    "\n",
    "_ = plt.title('Model 2 Prediction vs Actual')\n",
    "_ = plt.xlabel('actual values')\n",
    "_ = plt.ylabel('predicted values')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean_absolute_percentage_error(y2_test, y2_pred):\n",
    "    y2_test, y2_pred = np.array(y2_test), np.array(y2_pred)\n",
    "    return np.mean(np.abs((y2_test - y2_pred) / y2_test)) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of sklearn.metrics:\n",
      "\n",
      "MAPE: 304.587998861751\n",
      "MAE: 0.29408406499075357\n",
      "MSE: 0.14443887286450433\n",
      "RMSE: 0.1991931568619216\n"
     ]
    }
   ],
   "source": [
    "mape2 = mean_absolute_percentage_error(y2_test, y2_pred)\n",
    "mae2 = metrics.mean_absolute_error(y2_test, y2_pred)\n",
    "mse2 = metrics.mean_squared_error(y2_test, y2_pred)\n",
    "rmse2 = np.sqrt(mse)\n",
    "\n",
    "print(\"Results of sklearn.metrics:\\n\")\n",
    "print(\"MAPE:\", mape2)\n",
    "print(\"MAE:\", mae2)\n",
    "print(\"MSE:\", mse2)\n",
    "print(\"RMSE:\", rmse2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
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    "_ = plt.figure(figsize=(10, 6))\n",
    "_ = plt.plot(y2_test)\n",
    "_ = plt.plot(y2_pred)\n",
    "_ = plt.legend([\"actual\", \"pred\"])\n",
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    "## Initial Findings and Conclusion\n",
    "\n",
    "During this stage (preprocessing and training), we started by splitting the dataframe into two separate ones, training and testing using _TimeSeriesSplit()_. By using _TimeSeriesSplit()_ we are able to split the original dataframe in an ordered time-series way. The train portion is the first 70% of linear data and the test portion is the last 30% of linear data. From there we scaled the train/test splits which standardizes the magnitude of numeric features within our original dataframe, using _StandardScaler()_. This function allows us to normalize the features of each column(X), individually, so that each column/feature/variable will have a mean of 0 and standard deviation of 1.  From there we created a baseline for our predictions or future models by utilizing two simple multi linear regression models using ordinary least squares(OLS). \n",
    "\n",
    "One iteration of our linear regression model, for X contained all available features, aside from the target(y). While the other one for X contained all features except the target and \"close\", with the target(y) remaining the same. We then calculated the _MAPE_, _MAE_, and _MSE_ for each. By comparison, the first linear regression model performed worse as the mean absolute percent error was roughly 354%, meaning the errors are \"much greater\" than the actual values. Whereas the second linear regression model gave us an _MAPE_ of roughly 304%, meaning our errors of that model were \"slightly greater\" than the actual values. This difference in _MAPE_ can be seen as well when comparing _RMSE_ results to _MAE_ results for each model. We can see the first model gave an _RMSE_ output of approx. 0.1999 and an _MAE_ output of approx. 0.1333. Since the two measurements are relatively close to each other it can be implied that the model makes many mistakes, but the mistakes are \"small\". The same can be said for the other model but opposite when comparing metrics. \n",
    "\n",
    "That being said, we can see that the feature \"close\" is fairly important when being utilized in a regression model. As when it was removed, the performance of the model decreased. However, by including the \"close\" price we might assume that it causes the model to be overfit. Because of these brief findings, we have further confirmed the original \"hypothesis\" formed during the EDA stage in exploring the high correlation between \"close_nextday\" and \"close\".\n",
    "\n",
    "During the next stage (modeling), we will be examining other model architecture such as ensemble methods through \"trees\", i.e. random forest regressors(RFR). We will be improving or tuning our different models using optimization methods like _GridSearchCV_. Moreover, optimization will also aid us in the analysis of features (i.e. feature \"impact\" or \"importance\") as well as interpretability as a whole. "
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