TimeSeries_Forecasting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mounted-farming"
   },
   "source": [
    "# Financial Time-Series Forecasting Challenge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OYbHxtEfEr6q"
   },
   "source": [
    "### Google Colab Environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "grand-present",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YcNOU9-lEfd5"
   },
   "source": [
    "To work in a Google Colab environment and use the Google GPU Engine uncomment the following cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XhjJFOv52LAJ",
    "outputId": "d782e0cb-6119-48d5-e66a-196b322c3746"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found GPU at: /device:GPU:0\n"
     ]
    }
   ],
   "source": [
    "%tensorflow_version 2.x\n",
    "import tensorflow as tf\n",
    "device_name = tf.test.gpu_device_name()\n",
    "if device_name != '/device:GPU:0':\n",
    "  raise SystemError('GPU device not found')\n",
    "print('Found GPU at: {}'.format(device_name))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "teukICT8E2m6"
   },
   "source": [
    "The train and test dataset should be uploaded to Google Drive, then the drive is mounted so that the files are accessible"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zbG1Q3GV2tSm",
    "outputId": "001d32f0-fa17-41d7-cbc6-e1af2080cb22"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount(\"/content/drive\")\n",
    "\n",
    "test = pd.read_csv(\"/content/drive/My Drive/test.csv\")\n",
    "train = pd.read_csv(\"/content/drive/My Drive/train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 224
    },
    "id": "vbbOtR5M3U10",
    "outputId": "19fe7def-2595-4db3-959d-2e935c984ef5"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>w</th>\n",
       "      <th>y</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "      <th>34</th>\n",
       "      <th>35</th>\n",
       "      <th>36</th>\n",
       "      <th>37</th>\n",
       "      <th>38</th>\n",
       "      <th>39</th>\n",
       "      <th>40</th>\n",
       "      <th>41</th>\n",
       "      <th>42</th>\n",
       "      <th>43</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>46</th>\n",
       "      <th>47</th>\n",
       "      <th>48</th>\n",
       "      <th>49</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.483130</td>\n",
       "      <td>0.790980</td>\n",
       "      <td>0.702555</td>\n",
       "      <td>0.528220</td>\n",
       "      <td>0.298746</td>\n",
       "      <td>0.025488</td>\n",
       "      <td>-0.173480</td>\n",
       "      <td>-0.245290</td>\n",
       "      <td>-0.405057</td>\n",
       "      <td>-0.371818</td>\n",
       "      <td>-0.032334</td>\n",
       "      <td>0.140554</td>\n",
       "      <td>0.390325</td>\n",
       "      <td>0.423380</td>\n",
       "      <td>0.433935</td>\n",
       "      <td>0.422138</td>\n",
       "      <td>0.562708</td>\n",
       "      <td>0.724054</td>\n",
       "      <td>0.870796</td>\n",
       "      <td>0.802751</td>\n",
       "      <td>0.769475</td>\n",
       "      <td>0.901774</td>\n",
       "      <td>0.982177</td>\n",
       "      <td>1.053321</td>\n",
       "      <td>1.161209</td>\n",
       "      <td>1.046285</td>\n",
       "      <td>0.720832</td>\n",
       "      <td>0.531725</td>\n",
       "      <td>0.212623</td>\n",
       "      <td>-0.055058</td>\n",
       "      <td>0.195018</td>\n",
       "      <td>-0.223580</td>\n",
       "      <td>-0.094476</td>\n",
       "      <td>0.415094</td>\n",
       "      <td>0.416219</td>\n",
       "      <td>0.478895</td>\n",
       "      <td>0.807062</td>\n",
       "      <td>0.920355</td>\n",
       "      <td>0.773632</td>\n",
       "      <td>0.892809</td>\n",
       "      <td>1.070225</td>\n",
       "      <td>0.954288</td>\n",
       "      <td>1.143901</td>\n",
       "      <td>1.359252</td>\n",
       "      <td>1.081061</td>\n",
       "      <td>1.364409</td>\n",
       "      <td>1.449354</td>\n",
       "      <td>1.195431</td>\n",
       "      <td>1.195992</td>\n",
       "      <td>1.165327</td>\n",
       "      <td>0.771110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1.135624</td>\n",
       "      <td>0.765286</td>\n",
       "      <td>0.604512</td>\n",
       "      <td>0.414197</td>\n",
       "      <td>0.241638</td>\n",
       "      <td>0.181862</td>\n",
       "      <td>-0.031920</td>\n",
       "      <td>-0.070617</td>\n",
       "      <td>-0.185980</td>\n",
       "      <td>-0.188546</td>\n",
       "      <td>0.275131</td>\n",
       "      <td>0.590515</td>\n",
       "      <td>0.735936</td>\n",
       "      <td>1.082675</td>\n",
       "      <td>1.106204</td>\n",
       "      <td>1.156080</td>\n",
       "      <td>1.238652</td>\n",
       "      <td>0.824779</td>\n",
       "      <td>0.543626</td>\n",
       "      <td>0.106370</td>\n",
       "      <td>0.124514</td>\n",
       "      <td>-0.158819</td>\n",
       "      <td>-0.095093</td>\n",
       "      <td>-0.040372</td>\n",
       "      <td>0.147434</td>\n",
       "      <td>0.439599</td>\n",
       "      <td>0.715045</td>\n",
       "      <td>1.065298</td>\n",
       "      <td>1.253656</td>\n",
       "      <td>1.228927</td>\n",
       "      <td>1.264133</td>\n",
       "      <td>1.125588</td>\n",
       "      <td>0.952086</td>\n",
       "      <td>0.901148</td>\n",
       "      <td>0.494685</td>\n",
       "      <td>0.460681</td>\n",
       "      <td>0.390435</td>\n",
       "      <td>0.423011</td>\n",
       "      <td>0.532402</td>\n",
       "      <td>0.385717</td>\n",
       "      <td>0.599606</td>\n",
       "      <td>0.561545</td>\n",
       "      <td>0.678086</td>\n",
       "      <td>0.848950</td>\n",
       "      <td>1.133852</td>\n",
       "      <td>1.041396</td>\n",
       "      <td>1.242806</td>\n",
       "      <td>1.248121</td>\n",
       "      <td>1.331348</td>\n",
       "      <td>1.267123</td>\n",
       "      <td>1.292718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.686081</td>\n",
       "      <td>0.702834</td>\n",
       "      <td>0.637708</td>\n",
       "      <td>0.798416</td>\n",
       "      <td>0.755065</td>\n",
       "      <td>0.705225</td>\n",
       "      <td>0.535391</td>\n",
       "      <td>0.613129</td>\n",
       "      <td>0.549732</td>\n",
       "      <td>0.472387</td>\n",
       "      <td>0.431216</td>\n",
       "      <td>0.388098</td>\n",
       "      <td>0.136963</td>\n",
       "      <td>0.159388</td>\n",
       "      <td>0.182013</td>\n",
       "      <td>0.079372</td>\n",
       "      <td>0.215777</td>\n",
       "      <td>0.188945</td>\n",
       "      <td>0.093792</td>\n",
       "      <td>0.526586</td>\n",
       "      <td>0.614188</td>\n",
       "      <td>0.640183</td>\n",
       "      <td>0.964396</td>\n",
       "      <td>0.989152</td>\n",
       "      <td>1.248713</td>\n",
       "      <td>1.144618</td>\n",
       "      <td>0.946960</td>\n",
       "      <td>0.940729</td>\n",
       "      <td>0.680542</td>\n",
       "      <td>0.375531</td>\n",
       "      <td>0.066544</td>\n",
       "      <td>-0.038222</td>\n",
       "      <td>-0.095559</td>\n",
       "      <td>0.110763</td>\n",
       "      <td>0.012040</td>\n",
       "      <td>0.398041</td>\n",
       "      <td>0.646913</td>\n",
       "      <td>1.051943</td>\n",
       "      <td>1.215543</td>\n",
       "      <td>1.423341</td>\n",
       "      <td>1.406266</td>\n",
       "      <td>1.328694</td>\n",
       "      <td>1.324254</td>\n",
       "      <td>1.272889</td>\n",
       "      <td>1.074786</td>\n",
       "      <td>0.753950</td>\n",
       "      <td>0.539693</td>\n",
       "      <td>0.402041</td>\n",
       "      <td>0.442759</td>\n",
       "      <td>0.487557</td>\n",
       "      <td>0.699007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>-1.342005</td>\n",
       "      <td>0.018378</td>\n",
       "      <td>-0.097297</td>\n",
       "      <td>-0.020850</td>\n",
       "      <td>-0.083325</td>\n",
       "      <td>-0.268512</td>\n",
       "      <td>-0.486335</td>\n",
       "      <td>-0.731130</td>\n",
       "      <td>-0.924458</td>\n",
       "      <td>-0.995159</td>\n",
       "      <td>-1.352881</td>\n",
       "      <td>-0.903261</td>\n",
       "      <td>-0.982766</td>\n",
       "      <td>-0.544191</td>\n",
       "      <td>-0.129456</td>\n",
       "      <td>-0.228479</td>\n",
       "      <td>-0.147327</td>\n",
       "      <td>-0.201722</td>\n",
       "      <td>-0.137298</td>\n",
       "      <td>-0.192488</td>\n",
       "      <td>-0.292842</td>\n",
       "      <td>-0.211820</td>\n",
       "      <td>-0.296235</td>\n",
       "      <td>-0.229171</td>\n",
       "      <td>-0.371834</td>\n",
       "      <td>-0.513974</td>\n",
       "      <td>-0.854578</td>\n",
       "      <td>-1.066007</td>\n",
       "      <td>-1.246917</td>\n",
       "      <td>-1.334472</td>\n",
       "      <td>-1.279333</td>\n",
       "      <td>-1.174180</td>\n",
       "      <td>-1.017749</td>\n",
       "      <td>-0.706597</td>\n",
       "      <td>-0.556024</td>\n",
       "      <td>-0.336431</td>\n",
       "      <td>-0.300222</td>\n",
       "      <td>-0.576670</td>\n",
       "      <td>-0.411209</td>\n",
       "      <td>-0.403739</td>\n",
       "      <td>-0.604313</td>\n",
       "      <td>-0.386676</td>\n",
       "      <td>-0.357946</td>\n",
       "      <td>-0.612069</td>\n",
       "      <td>-0.698063</td>\n",
       "      <td>-0.891789</td>\n",
       "      <td>-1.127624</td>\n",
       "      <td>-1.535678</td>\n",
       "      <td>-1.490786</td>\n",
       "      <td>-1.856840</td>\n",
       "      <td>-1.441472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.241418</td>\n",
       "      <td>0.463120</td>\n",
       "      <td>0.665307</td>\n",
       "      <td>0.446953</td>\n",
       "      <td>0.480780</td>\n",
       "      <td>0.392500</td>\n",
       "      <td>0.309231</td>\n",
       "      <td>0.158462</td>\n",
       "      <td>0.190963</td>\n",
       "      <td>0.273600</td>\n",
       "      <td>0.198676</td>\n",
       "      <td>0.266028</td>\n",
       "      <td>0.081072</td>\n",
       "      <td>0.081875</td>\n",
       "      <td>-0.064353</td>\n",
       "      <td>-0.455680</td>\n",
       "      <td>-0.497878</td>\n",
       "      <td>-0.698136</td>\n",
       "      <td>-0.764494</td>\n",
       "      <td>-0.622500</td>\n",
       "      <td>-0.424946</td>\n",
       "      <td>-0.450938</td>\n",
       "      <td>-0.072250</td>\n",
       "      <td>-0.037033</td>\n",
       "      <td>0.092286</td>\n",
       "      <td>0.057471</td>\n",
       "      <td>0.010761</td>\n",
       "      <td>-0.004932</td>\n",
       "      <td>0.265042</td>\n",
       "      <td>0.462263</td>\n",
       "      <td>0.372700</td>\n",
       "      <td>0.663679</td>\n",
       "      <td>0.591787</td>\n",
       "      <td>0.625008</td>\n",
       "      <td>0.572981</td>\n",
       "      <td>0.526948</td>\n",
       "      <td>0.150152</td>\n",
       "      <td>0.207772</td>\n",
       "      <td>-0.021719</td>\n",
       "      <td>-0.155733</td>\n",
       "      <td>-0.292606</td>\n",
       "      <td>-0.174944</td>\n",
       "      <td>-0.529541</td>\n",
       "      <td>-0.191921</td>\n",
       "      <td>-0.258093</td>\n",
       "      <td>-0.542403</td>\n",
       "      <td>-0.414866</td>\n",
       "      <td>-0.485580</td>\n",
       "      <td>-0.768820</td>\n",
       "      <td>-0.662573</td>\n",
       "      <td>-0.211837</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   w         y         0         1  ...        46        47        48        49\n",
       "0  1  0.483130  0.790980  0.702555  ...  1.195431  1.195992  1.165327  0.771110\n",
       "1  1  1.135624  0.765286  0.604512  ...  1.248121  1.331348  1.267123  1.292718\n",
       "2  1  0.686081  0.702834  0.637708  ...  0.402041  0.442759  0.487557  0.699007\n",
       "3  1 -1.342005  0.018378 -0.097297  ... -1.535678 -1.490786 -1.856840 -1.441472\n",
       "4  1 -0.241418  0.463120  0.665307  ... -0.485580 -0.768820 -0.662573 -0.211837\n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "burning-chassis"
   },
   "source": [
    "### Data Import & Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "impossible-monte"
   },
   "source": [
    "To make this notebook's output stable across runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "premium-disaster",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "handmade-savannah"
   },
   "source": [
    "*Use only without running on Google Colab*\n",
    "\n",
    "\n",
    "Importing test and train dataset, using `os.getcwd()` to get current working directory and then finding the .csv(s) in the `data` subfolder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "public-seller",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "#test = pd.read_csv(os.getcwd()+\"/data/test.csv\")\n",
    "#train =pd.read_csv(os.getcwd()+\"/data/train.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pretty-deadline"
   },
   "source": [
    "Using `.head()` to preview train and test datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 224
    },
    "id": "demographic-berry",
    "jupyter": {
     "outputs_hidden": true
    },
    "outputId": "bf4ac828-526d-42f3-abf6-02d1e37278c8",
    "scrolled": true
   },
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>w</th>\n",
       "      <th>y</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "      <th>34</th>\n",
       "      <th>35</th>\n",
       "      <th>36</th>\n",
       "      <th>37</th>\n",
       "      <th>38</th>\n",
       "      <th>39</th>\n",
       "      <th>40</th>\n",
       "      <th>41</th>\n",
       "      <th>42</th>\n",
       "      <th>43</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>46</th>\n",
       "      <th>47</th>\n",
       "      <th>48</th>\n",
       "      <th>49</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.483130</td>\n",
       "      <td>0.790980</td>\n",
       "      <td>0.702555</td>\n",
       "      <td>0.528220</td>\n",
       "      <td>0.298746</td>\n",
       "      <td>0.025488</td>\n",
       "      <td>-0.173480</td>\n",
       "      <td>-0.245290</td>\n",
       "      <td>-0.405057</td>\n",
       "      <td>-0.371818</td>\n",
       "      <td>-0.032334</td>\n",
       "      <td>0.140554</td>\n",
       "      <td>0.390325</td>\n",
       "      <td>0.423380</td>\n",
       "      <td>0.433935</td>\n",
       "      <td>0.422138</td>\n",
       "      <td>0.562708</td>\n",
       "      <td>0.724054</td>\n",
       "      <td>0.870796</td>\n",
       "      <td>0.802751</td>\n",
       "      <td>0.769475</td>\n",
       "      <td>0.901774</td>\n",
       "      <td>0.982177</td>\n",
       "      <td>1.053321</td>\n",
       "      <td>1.161209</td>\n",
       "      <td>1.046285</td>\n",
       "      <td>0.720832</td>\n",
       "      <td>0.531725</td>\n",
       "      <td>0.212623</td>\n",
       "      <td>-0.055058</td>\n",
       "      <td>0.195018</td>\n",
       "      <td>-0.223580</td>\n",
       "      <td>-0.094476</td>\n",
       "      <td>0.415094</td>\n",
       "      <td>0.416219</td>\n",
       "      <td>0.478895</td>\n",
       "      <td>0.807062</td>\n",
       "      <td>0.920355</td>\n",
       "      <td>0.773632</td>\n",
       "      <td>0.892809</td>\n",
       "      <td>1.070225</td>\n",
       "      <td>0.954288</td>\n",
       "      <td>1.143901</td>\n",
       "      <td>1.359252</td>\n",
       "      <td>1.081061</td>\n",
       "      <td>1.364409</td>\n",
       "      <td>1.449354</td>\n",
       "      <td>1.195431</td>\n",
       "      <td>1.195992</td>\n",
       "      <td>1.165327</td>\n",
       "      <td>0.771110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1.135624</td>\n",
       "      <td>0.765286</td>\n",
       "      <td>0.604512</td>\n",
       "      <td>0.414197</td>\n",
       "      <td>0.241638</td>\n",
       "      <td>0.181862</td>\n",
       "      <td>-0.031920</td>\n",
       "      <td>-0.070617</td>\n",
       "      <td>-0.185980</td>\n",
       "      <td>-0.188546</td>\n",
       "      <td>0.275131</td>\n",
       "      <td>0.590515</td>\n",
       "      <td>0.735936</td>\n",
       "      <td>1.082675</td>\n",
       "      <td>1.106204</td>\n",
       "      <td>1.156080</td>\n",
       "      <td>1.238652</td>\n",
       "      <td>0.824779</td>\n",
       "      <td>0.543626</td>\n",
       "      <td>0.106370</td>\n",
       "      <td>0.124514</td>\n",
       "      <td>-0.158819</td>\n",
       "      <td>-0.095093</td>\n",
       "      <td>-0.040372</td>\n",
       "      <td>0.147434</td>\n",
       "      <td>0.439599</td>\n",
       "      <td>0.715045</td>\n",
       "      <td>1.065298</td>\n",
       "      <td>1.253656</td>\n",
       "      <td>1.228927</td>\n",
       "      <td>1.264133</td>\n",
       "      <td>1.125588</td>\n",
       "      <td>0.952086</td>\n",
       "      <td>0.901148</td>\n",
       "      <td>0.494685</td>\n",
       "      <td>0.460681</td>\n",
       "      <td>0.390435</td>\n",
       "      <td>0.423011</td>\n",
       "      <td>0.532402</td>\n",
       "      <td>0.385717</td>\n",
       "      <td>0.599606</td>\n",
       "      <td>0.561545</td>\n",
       "      <td>0.678086</td>\n",
       "      <td>0.848950</td>\n",
       "      <td>1.133852</td>\n",
       "      <td>1.041396</td>\n",
       "      <td>1.242806</td>\n",
       "      <td>1.248121</td>\n",
       "      <td>1.331348</td>\n",
       "      <td>1.267123</td>\n",
       "      <td>1.292718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.686081</td>\n",
       "      <td>0.702834</td>\n",
       "      <td>0.637708</td>\n",
       "      <td>0.798416</td>\n",
       "      <td>0.755065</td>\n",
       "      <td>0.705225</td>\n",
       "      <td>0.535391</td>\n",
       "      <td>0.613129</td>\n",
       "      <td>0.549732</td>\n",
       "      <td>0.472387</td>\n",
       "      <td>0.431216</td>\n",
       "      <td>0.388098</td>\n",
       "      <td>0.136963</td>\n",
       "      <td>0.159388</td>\n",
       "      <td>0.182013</td>\n",
       "      <td>0.079372</td>\n",
       "      <td>0.215777</td>\n",
       "      <td>0.188945</td>\n",
       "      <td>0.093792</td>\n",
       "      <td>0.526586</td>\n",
       "      <td>0.614188</td>\n",
       "      <td>0.640183</td>\n",
       "      <td>0.964396</td>\n",
       "      <td>0.989152</td>\n",
       "      <td>1.248713</td>\n",
       "      <td>1.144618</td>\n",
       "      <td>0.946960</td>\n",
       "      <td>0.940729</td>\n",
       "      <td>0.680542</td>\n",
       "      <td>0.375531</td>\n",
       "      <td>0.066544</td>\n",
       "      <td>-0.038222</td>\n",
       "      <td>-0.095559</td>\n",
       "      <td>0.110763</td>\n",
       "      <td>0.012040</td>\n",
       "      <td>0.398041</td>\n",
       "      <td>0.646913</td>\n",
       "      <td>1.051943</td>\n",
       "      <td>1.215543</td>\n",
       "      <td>1.423341</td>\n",
       "      <td>1.406266</td>\n",
       "      <td>1.328694</td>\n",
       "      <td>1.324254</td>\n",
       "      <td>1.272889</td>\n",
       "      <td>1.074786</td>\n",
       "      <td>0.753950</td>\n",
       "      <td>0.539693</td>\n",
       "      <td>0.402041</td>\n",
       "      <td>0.442759</td>\n",
       "      <td>0.487557</td>\n",
       "      <td>0.699007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>-1.342005</td>\n",
       "      <td>0.018378</td>\n",
       "      <td>-0.097297</td>\n",
       "      <td>-0.020850</td>\n",
       "      <td>-0.083325</td>\n",
       "      <td>-0.268512</td>\n",
       "      <td>-0.486335</td>\n",
       "      <td>-0.731130</td>\n",
       "      <td>-0.924458</td>\n",
       "      <td>-0.995159</td>\n",
       "      <td>-1.352881</td>\n",
       "      <td>-0.903261</td>\n",
       "      <td>-0.982766</td>\n",
       "      <td>-0.544191</td>\n",
       "      <td>-0.129456</td>\n",
       "      <td>-0.228479</td>\n",
       "      <td>-0.147327</td>\n",
       "      <td>-0.201722</td>\n",
       "      <td>-0.137298</td>\n",
       "      <td>-0.192488</td>\n",
       "      <td>-0.292842</td>\n",
       "      <td>-0.211820</td>\n",
       "      <td>-0.296235</td>\n",
       "      <td>-0.229171</td>\n",
       "      <td>-0.371834</td>\n",
       "      <td>-0.513974</td>\n",
       "      <td>-0.854578</td>\n",
       "      <td>-1.066007</td>\n",
       "      <td>-1.246917</td>\n",
       "      <td>-1.334472</td>\n",
       "      <td>-1.279333</td>\n",
       "      <td>-1.174180</td>\n",
       "      <td>-1.017749</td>\n",
       "      <td>-0.706597</td>\n",
       "      <td>-0.556024</td>\n",
       "      <td>-0.336431</td>\n",
       "      <td>-0.300222</td>\n",
       "      <td>-0.576670</td>\n",
       "      <td>-0.411209</td>\n",
       "      <td>-0.403739</td>\n",
       "      <td>-0.604313</td>\n",
       "      <td>-0.386676</td>\n",
       "      <td>-0.357946</td>\n",
       "      <td>-0.612069</td>\n",
       "      <td>-0.698063</td>\n",
       "      <td>-0.891789</td>\n",
       "      <td>-1.127624</td>\n",
       "      <td>-1.535678</td>\n",
       "      <td>-1.490786</td>\n",
       "      <td>-1.856840</td>\n",
       "      <td>-1.441472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.241418</td>\n",
       "      <td>0.463120</td>\n",
       "      <td>0.665307</td>\n",
       "      <td>0.446953</td>\n",
       "      <td>0.480780</td>\n",
       "      <td>0.392500</td>\n",
       "      <td>0.309231</td>\n",
       "      <td>0.158462</td>\n",
       "      <td>0.190963</td>\n",
       "      <td>0.273600</td>\n",
       "      <td>0.198676</td>\n",
       "      <td>0.266028</td>\n",
       "      <td>0.081072</td>\n",
       "      <td>0.081875</td>\n",
       "      <td>-0.064353</td>\n",
       "      <td>-0.455680</td>\n",
       "      <td>-0.497878</td>\n",
       "      <td>-0.698136</td>\n",
       "      <td>-0.764494</td>\n",
       "      <td>-0.622500</td>\n",
       "      <td>-0.424946</td>\n",
       "      <td>-0.450938</td>\n",
       "      <td>-0.072250</td>\n",
       "      <td>-0.037033</td>\n",
       "      <td>0.092286</td>\n",
       "      <td>0.057471</td>\n",
       "      <td>0.010761</td>\n",
       "      <td>-0.004932</td>\n",
       "      <td>0.265042</td>\n",
       "      <td>0.462263</td>\n",
       "      <td>0.372700</td>\n",
       "      <td>0.663679</td>\n",
       "      <td>0.591787</td>\n",
       "      <td>0.625008</td>\n",
       "      <td>0.572981</td>\n",
       "      <td>0.526948</td>\n",
       "      <td>0.150152</td>\n",
       "      <td>0.207772</td>\n",
       "      <td>-0.021719</td>\n",
       "      <td>-0.155733</td>\n",
       "      <td>-0.292606</td>\n",
       "      <td>-0.174944</td>\n",
       "      <td>-0.529541</td>\n",
       "      <td>-0.191921</td>\n",
       "      <td>-0.258093</td>\n",
       "      <td>-0.542403</td>\n",
       "      <td>-0.414866</td>\n",
       "      <td>-0.485580</td>\n",
       "      <td>-0.768820</td>\n",
       "      <td>-0.662573</td>\n",
       "      <td>-0.211837</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   w         y         0         1  ...        46        47        48        49\n",
       "0  1  0.483130  0.790980  0.702555  ...  1.195431  1.195992  1.165327  0.771110\n",
       "1  1  1.135624  0.765286  0.604512  ...  1.248121  1.331348  1.267123  1.292718\n",
       "2  1  0.686081  0.702834  0.637708  ...  0.402041  0.442759  0.487557  0.699007\n",
       "3  1 -1.342005  0.018378 -0.097297  ... -1.535678 -1.490786 -1.856840 -1.441472\n",
       "4  1 -0.241418  0.463120  0.665307  ... -0.485580 -0.768820 -0.662573 -0.211837\n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 224
    },
    "id": "average-sample",
    "jupyter": {
     "outputs_hidden": true
    },
    "outputId": "50f1c948-8de7-4655-f609-003275c2d359"
   },
   "outputs": [
    {
     "data": {
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       "      <th>37</th>\n",
       "      <th>38</th>\n",
       "      <th>39</th>\n",
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       "      <th>43</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.670595</td>\n",
       "      <td>-0.839068</td>\n",
       "      <td>-0.734415</td>\n",
       "      <td>-0.587261</td>\n",
       "      <td>-0.788800</td>\n",
       "      <td>-0.975857</td>\n",
       "      <td>-0.774088</td>\n",
       "      <td>-1.021334</td>\n",
       "      <td>-1.110286</td>\n",
       "      <td>-0.893337</td>\n",
       "      <td>-1.064826</td>\n",
       "      <td>-0.535143</td>\n",
       "      <td>-0.451747</td>\n",
       "      <td>-0.319884</td>\n",
       "      <td>-0.230023</td>\n",
       "      <td>-0.309316</td>\n",
       "      <td>-0.423359</td>\n",
       "      <td>-0.483201</td>\n",
       "      <td>-0.287368</td>\n",
       "      <td>-0.269882</td>\n",
       "      <td>-0.038282</td>\n",
       "      <td>0.093233</td>\n",
       "      <td>0.047145</td>\n",
       "      <td>-0.100288</td>\n",
       "      <td>-0.348605</td>\n",
       "      <td>-0.581721</td>\n",
       "      <td>-0.616782</td>\n",
       "      <td>-0.484339</td>\n",
       "      <td>-0.428753</td>\n",
       "      <td>-0.477449</td>\n",
       "      <td>-0.917499</td>\n",
       "      <td>-0.729484</td>\n",
       "      <td>-1.059364</td>\n",
       "      <td>-1.331153</td>\n",
       "      <td>-1.376657</td>\n",
       "      <td>-1.481712</td>\n",
       "      <td>-1.244721</td>\n",
       "      <td>-1.216135</td>\n",
       "      <td>-1.194285</td>\n",
       "      <td>-0.936693</td>\n",
       "      <td>-0.997627</td>\n",
       "      <td>-1.103794</td>\n",
       "      <td>-1.092988</td>\n",
       "      <td>-0.989165</td>\n",
       "      <td>-0.827528</td>\n",
       "      <td>-0.813729</td>\n",
       "      <td>-0.532411</td>\n",
       "      <td>-0.289483</td>\n",
       "      <td>-0.407720</td>\n",
       "      <td>-0.407505</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.188165</td>\n",
       "      <td>0.166410</td>\n",
       "      <td>0.321011</td>\n",
       "      <td>0.318078</td>\n",
       "      <td>0.641710</td>\n",
       "      <td>0.951932</td>\n",
       "      <td>1.170069</td>\n",
       "      <td>1.177711</td>\n",
       "      <td>0.987763</td>\n",
       "      <td>0.981345</td>\n",
       "      <td>0.651526</td>\n",
       "      <td>0.944097</td>\n",
       "      <td>0.665110</td>\n",
       "      <td>0.534558</td>\n",
       "      <td>0.575689</td>\n",
       "      <td>0.563507</td>\n",
       "      <td>0.699194</td>\n",
       "      <td>0.283708</td>\n",
       "      <td>0.277885</td>\n",
       "      <td>0.028674</td>\n",
       "      <td>-0.133431</td>\n",
       "      <td>-0.128956</td>\n",
       "      <td>0.092359</td>\n",
       "      <td>0.388095</td>\n",
       "      <td>0.327306</td>\n",
       "      <td>0.551006</td>\n",
       "      <td>0.593850</td>\n",
       "      <td>0.771223</td>\n",
       "      <td>0.583534</td>\n",
       "      <td>0.587207</td>\n",
       "      <td>0.614839</td>\n",
       "      <td>0.910459</td>\n",
       "      <td>1.191987</td>\n",
       "      <td>1.343534</td>\n",
       "      <td>1.274894</td>\n",
       "      <td>1.460262</td>\n",
       "      <td>1.289660</td>\n",
       "      <td>1.166410</td>\n",
       "      <td>0.974349</td>\n",
       "      <td>1.047257</td>\n",
       "      <td>0.743405</td>\n",
       "      <td>0.916254</td>\n",
       "      <td>0.866453</td>\n",
       "      <td>0.953677</td>\n",
       "      <td>0.716259</td>\n",
       "      <td>0.692816</td>\n",
       "      <td>0.446713</td>\n",
       "      <td>0.539733</td>\n",
       "      <td>0.279293</td>\n",
       "      <td>0.180641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.886510</td>\n",
       "      <td>0.760716</td>\n",
       "      <td>0.751800</td>\n",
       "      <td>0.052198</td>\n",
       "      <td>-0.050958</td>\n",
       "      <td>-0.140734</td>\n",
       "      <td>-0.173480</td>\n",
       "      <td>0.178508</td>\n",
       "      <td>0.198187</td>\n",
       "      <td>0.357906</td>\n",
       "      <td>0.288508</td>\n",
       "      <td>0.385058</td>\n",
       "      <td>0.514924</td>\n",
       "      <td>0.439050</td>\n",
       "      <td>0.907307</td>\n",
       "      <td>1.056502</td>\n",
       "      <td>1.200835</td>\n",
       "      <td>1.038399</td>\n",
       "      <td>0.759615</td>\n",
       "      <td>0.825739</td>\n",
       "      <td>0.463751</td>\n",
       "      <td>0.156036</td>\n",
       "      <td>0.125701</td>\n",
       "      <td>0.116938</td>\n",
       "      <td>0.180331</td>\n",
       "      <td>0.330021</td>\n",
       "      <td>0.337901</td>\n",
       "      <td>0.357731</td>\n",
       "      <td>0.397525</td>\n",
       "      <td>0.600081</td>\n",
       "      <td>0.742583</td>\n",
       "      <td>1.025730</td>\n",
       "      <td>1.323599</td>\n",
       "      <td>1.438235</td>\n",
       "      <td>1.236999</td>\n",
       "      <td>0.983050</td>\n",
       "      <td>0.805176</td>\n",
       "      <td>0.403192</td>\n",
       "      <td>0.398094</td>\n",
       "      <td>0.280344</td>\n",
       "      <td>0.444142</td>\n",
       "      <td>0.492294</td>\n",
       "      <td>0.573348</td>\n",
       "      <td>0.546323</td>\n",
       "      <td>0.373874</td>\n",
       "      <td>0.699132</td>\n",
       "      <td>0.808303</td>\n",
       "      <td>1.118522</td>\n",
       "      <td>1.284887</td>\n",
       "      <td>1.541929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.187722</td>\n",
       "      <td>0.030199</td>\n",
       "      <td>-0.072558</td>\n",
       "      <td>-0.098400</td>\n",
       "      <td>-0.110795</td>\n",
       "      <td>-0.127632</td>\n",
       "      <td>-0.241193</td>\n",
       "      <td>-0.374608</td>\n",
       "      <td>-0.651771</td>\n",
       "      <td>-0.513491</td>\n",
       "      <td>-0.799969</td>\n",
       "      <td>-0.993030</td>\n",
       "      <td>-1.259195</td>\n",
       "      <td>-0.939397</td>\n",
       "      <td>-0.911043</td>\n",
       "      <td>-0.756382</td>\n",
       "      <td>-0.324765</td>\n",
       "      <td>-0.044222</td>\n",
       "      <td>-0.056141</td>\n",
       "      <td>0.057031</td>\n",
       "      <td>0.161459</td>\n",
       "      <td>-0.140535</td>\n",
       "      <td>-0.131395</td>\n",
       "      <td>-0.392840</td>\n",
       "      <td>-0.733964</td>\n",
       "      <td>-0.843351</td>\n",
       "      <td>-1.050084</td>\n",
       "      <td>-0.973848</td>\n",
       "      <td>-0.954250</td>\n",
       "      <td>-0.923576</td>\n",
       "      <td>-1.101400</td>\n",
       "      <td>-0.974370</td>\n",
       "      <td>-0.795471</td>\n",
       "      <td>-0.862259</td>\n",
       "      <td>-0.696164</td>\n",
       "      <td>-0.597515</td>\n",
       "      <td>-0.449067</td>\n",
       "      <td>-0.146809</td>\n",
       "      <td>-0.147430</td>\n",
       "      <td>-0.188590</td>\n",
       "      <td>-0.340927</td>\n",
       "      <td>-0.268253</td>\n",
       "      <td>-0.654777</td>\n",
       "      <td>-1.133722</td>\n",
       "      <td>-1.484557</td>\n",
       "      <td>-1.446644</td>\n",
       "      <td>-1.654337</td>\n",
       "      <td>-1.521009</td>\n",
       "      <td>-1.593825</td>\n",
       "      <td>-1.110684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.248822</td>\n",
       "      <td>0.168815</td>\n",
       "      <td>0.260804</td>\n",
       "      <td>0.505885</td>\n",
       "      <td>0.471486</td>\n",
       "      <td>1.018661</td>\n",
       "      <td>0.971406</td>\n",
       "      <td>1.062348</td>\n",
       "      <td>0.986871</td>\n",
       "      <td>0.947982</td>\n",
       "      <td>0.593847</td>\n",
       "      <td>0.897236</td>\n",
       "      <td>0.581241</td>\n",
       "      <td>0.701187</td>\n",
       "      <td>0.726811</td>\n",
       "      <td>0.488009</td>\n",
       "      <td>0.252971</td>\n",
       "      <td>0.110300</td>\n",
       "      <td>-0.160044</td>\n",
       "      <td>-0.143630</td>\n",
       "      <td>-0.074790</td>\n",
       "      <td>-0.014761</td>\n",
       "      <td>0.166772</td>\n",
       "      <td>0.609277</td>\n",
       "      <td>0.690161</td>\n",
       "      <td>0.570915</td>\n",
       "      <td>0.410520</td>\n",
       "      <td>0.653567</td>\n",
       "      <td>0.666413</td>\n",
       "      <td>0.969931</td>\n",
       "      <td>1.175351</td>\n",
       "      <td>1.504666</td>\n",
       "      <td>1.350586</td>\n",
       "      <td>0.995956</td>\n",
       "      <td>1.154917</td>\n",
       "      <td>0.795522</td>\n",
       "      <td>0.731462</td>\n",
       "      <td>0.753015</td>\n",
       "      <td>0.842744</td>\n",
       "      <td>0.655751</td>\n",
       "      <td>0.422044</td>\n",
       "      <td>0.688196</td>\n",
       "      <td>0.382416</td>\n",
       "      <td>0.344843</td>\n",
       "      <td>0.177595</td>\n",
       "      <td>0.330549</td>\n",
       "      <td>0.595061</td>\n",
       "      <td>0.884860</td>\n",
       "      <td>1.125103</td>\n",
       "      <td>1.220779</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2  ...        47        48        49\n",
       "0 -0.670595 -0.839068 -0.734415  ... -0.289483 -0.407720 -0.407505\n",
       "1  0.188165  0.166410  0.321011  ...  0.539733  0.279293  0.180641\n",
       "2  0.886510  0.760716  0.751800  ...  1.118522  1.284887  1.541929\n",
       "3 -0.187722  0.030199 -0.072558  ... -1.521009 -1.593825 -1.110684\n",
       "4  0.248822  0.168815  0.260804  ...  0.884860  1.125103  1.220779\n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "positive-shipping"
   },
   "source": [
    "We now drop the `w` label (as it is not useful for our model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 224
    },
    "id": "urban-satisfaction",
    "jupyter": {
     "outputs_hidden": true
    },
    "outputId": "18c43cac-da76-42c6-c92f-ea8747b60440"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "      <th>34</th>\n",
       "      <th>35</th>\n",
       "      <th>36</th>\n",
       "      <th>37</th>\n",
       "      <th>38</th>\n",
       "      <th>39</th>\n",
       "      <th>40</th>\n",
       "      <th>41</th>\n",
       "      <th>42</th>\n",
       "      <th>43</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>46</th>\n",
       "      <th>47</th>\n",
       "      <th>48</th>\n",
       "      <th>49</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.483130</td>\n",
       "      <td>0.790980</td>\n",
       "      <td>0.702555</td>\n",
       "      <td>0.528220</td>\n",
       "      <td>0.298746</td>\n",
       "      <td>0.025488</td>\n",
       "      <td>-0.173480</td>\n",
       "      <td>-0.245290</td>\n",
       "      <td>-0.405057</td>\n",
       "      <td>-0.371818</td>\n",
       "      <td>-0.032334</td>\n",
       "      <td>0.140554</td>\n",
       "      <td>0.390325</td>\n",
       "      <td>0.423380</td>\n",
       "      <td>0.433935</td>\n",
       "      <td>0.422138</td>\n",
       "      <td>0.562708</td>\n",
       "      <td>0.724054</td>\n",
       "      <td>0.870796</td>\n",
       "      <td>0.802751</td>\n",
       "      <td>0.769475</td>\n",
       "      <td>0.901774</td>\n",
       "      <td>0.982177</td>\n",
       "      <td>1.053321</td>\n",
       "      <td>1.161209</td>\n",
       "      <td>1.046285</td>\n",
       "      <td>0.720832</td>\n",
       "      <td>0.531725</td>\n",
       "      <td>0.212623</td>\n",
       "      <td>-0.055058</td>\n",
       "      <td>0.195018</td>\n",
       "      <td>-0.223580</td>\n",
       "      <td>-0.094476</td>\n",
       "      <td>0.415094</td>\n",
       "      <td>0.416219</td>\n",
       "      <td>0.478895</td>\n",
       "      <td>0.807062</td>\n",
       "      <td>0.920355</td>\n",
       "      <td>0.773632</td>\n",
       "      <td>0.892809</td>\n",
       "      <td>1.070225</td>\n",
       "      <td>0.954288</td>\n",
       "      <td>1.143901</td>\n",
       "      <td>1.359252</td>\n",
       "      <td>1.081061</td>\n",
       "      <td>1.364409</td>\n",
       "      <td>1.449354</td>\n",
       "      <td>1.195431</td>\n",
       "      <td>1.195992</td>\n",
       "      <td>1.165327</td>\n",
       "      <td>0.771110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.135624</td>\n",
       "      <td>0.765286</td>\n",
       "      <td>0.604512</td>\n",
       "      <td>0.414197</td>\n",
       "      <td>0.241638</td>\n",
       "      <td>0.181862</td>\n",
       "      <td>-0.031920</td>\n",
       "      <td>-0.070617</td>\n",
       "      <td>-0.185980</td>\n",
       "      <td>-0.188546</td>\n",
       "      <td>0.275131</td>\n",
       "      <td>0.590515</td>\n",
       "      <td>0.735936</td>\n",
       "      <td>1.082675</td>\n",
       "      <td>1.106204</td>\n",
       "      <td>1.156080</td>\n",
       "      <td>1.238652</td>\n",
       "      <td>0.824779</td>\n",
       "      <td>0.543626</td>\n",
       "      <td>0.106370</td>\n",
       "      <td>0.124514</td>\n",
       "      <td>-0.158819</td>\n",
       "      <td>-0.095093</td>\n",
       "      <td>-0.040372</td>\n",
       "      <td>0.147434</td>\n",
       "      <td>0.439599</td>\n",
       "      <td>0.715045</td>\n",
       "      <td>1.065298</td>\n",
       "      <td>1.253656</td>\n",
       "      <td>1.228927</td>\n",
       "      <td>1.264133</td>\n",
       "      <td>1.125588</td>\n",
       "      <td>0.952086</td>\n",
       "      <td>0.901148</td>\n",
       "      <td>0.494685</td>\n",
       "      <td>0.460681</td>\n",
       "      <td>0.390435</td>\n",
       "      <td>0.423011</td>\n",
       "      <td>0.532402</td>\n",
       "      <td>0.385717</td>\n",
       "      <td>0.599606</td>\n",
       "      <td>0.561545</td>\n",
       "      <td>0.678086</td>\n",
       "      <td>0.848950</td>\n",
       "      <td>1.133852</td>\n",
       "      <td>1.041396</td>\n",
       "      <td>1.242806</td>\n",
       "      <td>1.248121</td>\n",
       "      <td>1.331348</td>\n",
       "      <td>1.267123</td>\n",
       "      <td>1.292718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.686081</td>\n",
       "      <td>0.702834</td>\n",
       "      <td>0.637708</td>\n",
       "      <td>0.798416</td>\n",
       "      <td>0.755065</td>\n",
       "      <td>0.705225</td>\n",
       "      <td>0.535391</td>\n",
       "      <td>0.613129</td>\n",
       "      <td>0.549732</td>\n",
       "      <td>0.472387</td>\n",
       "      <td>0.431216</td>\n",
       "      <td>0.388098</td>\n",
       "      <td>0.136963</td>\n",
       "      <td>0.159388</td>\n",
       "      <td>0.182013</td>\n",
       "      <td>0.079372</td>\n",
       "      <td>0.215777</td>\n",
       "      <td>0.188945</td>\n",
       "      <td>0.093792</td>\n",
       "      <td>0.526586</td>\n",
       "      <td>0.614188</td>\n",
       "      <td>0.640183</td>\n",
       "      <td>0.964396</td>\n",
       "      <td>0.989152</td>\n",
       "      <td>1.248713</td>\n",
       "      <td>1.144618</td>\n",
       "      <td>0.946960</td>\n",
       "      <td>0.940729</td>\n",
       "      <td>0.680542</td>\n",
       "      <td>0.375531</td>\n",
       "      <td>0.066544</td>\n",
       "      <td>-0.038222</td>\n",
       "      <td>-0.095559</td>\n",
       "      <td>0.110763</td>\n",
       "      <td>0.012040</td>\n",
       "      <td>0.398041</td>\n",
       "      <td>0.646913</td>\n",
       "      <td>1.051943</td>\n",
       "      <td>1.215543</td>\n",
       "      <td>1.423341</td>\n",
       "      <td>1.406266</td>\n",
       "      <td>1.328694</td>\n",
       "      <td>1.324254</td>\n",
       "      <td>1.272889</td>\n",
       "      <td>1.074786</td>\n",
       "      <td>0.753950</td>\n",
       "      <td>0.539693</td>\n",
       "      <td>0.402041</td>\n",
       "      <td>0.442759</td>\n",
       "      <td>0.487557</td>\n",
       "      <td>0.699007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.342005</td>\n",
       "      <td>0.018378</td>\n",
       "      <td>-0.097297</td>\n",
       "      <td>-0.020850</td>\n",
       "      <td>-0.083325</td>\n",
       "      <td>-0.268512</td>\n",
       "      <td>-0.486335</td>\n",
       "      <td>-0.731130</td>\n",
       "      <td>-0.924458</td>\n",
       "      <td>-0.995159</td>\n",
       "      <td>-1.352881</td>\n",
       "      <td>-0.903261</td>\n",
       "      <td>-0.982766</td>\n",
       "      <td>-0.544191</td>\n",
       "      <td>-0.129456</td>\n",
       "      <td>-0.228479</td>\n",
       "      <td>-0.147327</td>\n",
       "      <td>-0.201722</td>\n",
       "      <td>-0.137298</td>\n",
       "      <td>-0.192488</td>\n",
       "      <td>-0.292842</td>\n",
       "      <td>-0.211820</td>\n",
       "      <td>-0.296235</td>\n",
       "      <td>-0.229171</td>\n",
       "      <td>-0.371834</td>\n",
       "      <td>-0.513974</td>\n",
       "      <td>-0.854578</td>\n",
       "      <td>-1.066007</td>\n",
       "      <td>-1.246917</td>\n",
       "      <td>-1.334472</td>\n",
       "      <td>-1.279333</td>\n",
       "      <td>-1.174180</td>\n",
       "      <td>-1.017749</td>\n",
       "      <td>-0.706597</td>\n",
       "      <td>-0.556024</td>\n",
       "      <td>-0.336431</td>\n",
       "      <td>-0.300222</td>\n",
       "      <td>-0.576670</td>\n",
       "      <td>-0.411209</td>\n",
       "      <td>-0.403739</td>\n",
       "      <td>-0.604313</td>\n",
       "      <td>-0.386676</td>\n",
       "      <td>-0.357946</td>\n",
       "      <td>-0.612069</td>\n",
       "      <td>-0.698063</td>\n",
       "      <td>-0.891789</td>\n",
       "      <td>-1.127624</td>\n",
       "      <td>-1.535678</td>\n",
       "      <td>-1.490786</td>\n",
       "      <td>-1.856840</td>\n",
       "      <td>-1.441472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.241418</td>\n",
       "      <td>0.463120</td>\n",
       "      <td>0.665307</td>\n",
       "      <td>0.446953</td>\n",
       "      <td>0.480780</td>\n",
       "      <td>0.392500</td>\n",
       "      <td>0.309231</td>\n",
       "      <td>0.158462</td>\n",
       "      <td>0.190963</td>\n",
       "      <td>0.273600</td>\n",
       "      <td>0.198676</td>\n",
       "      <td>0.266028</td>\n",
       "      <td>0.081072</td>\n",
       "      <td>0.081875</td>\n",
       "      <td>-0.064353</td>\n",
       "      <td>-0.455680</td>\n",
       "      <td>-0.497878</td>\n",
       "      <td>-0.698136</td>\n",
       "      <td>-0.764494</td>\n",
       "      <td>-0.622500</td>\n",
       "      <td>-0.424946</td>\n",
       "      <td>-0.450938</td>\n",
       "      <td>-0.072250</td>\n",
       "      <td>-0.037033</td>\n",
       "      <td>0.092286</td>\n",
       "      <td>0.057471</td>\n",
       "      <td>0.010761</td>\n",
       "      <td>-0.004932</td>\n",
       "      <td>0.265042</td>\n",
       "      <td>0.462263</td>\n",
       "      <td>0.372700</td>\n",
       "      <td>0.663679</td>\n",
       "      <td>0.591787</td>\n",
       "      <td>0.625008</td>\n",
       "      <td>0.572981</td>\n",
       "      <td>0.526948</td>\n",
       "      <td>0.150152</td>\n",
       "      <td>0.207772</td>\n",
       "      <td>-0.021719</td>\n",
       "      <td>-0.155733</td>\n",
       "      <td>-0.292606</td>\n",
       "      <td>-0.174944</td>\n",
       "      <td>-0.529541</td>\n",
       "      <td>-0.191921</td>\n",
       "      <td>-0.258093</td>\n",
       "      <td>-0.542403</td>\n",
       "      <td>-0.414866</td>\n",
       "      <td>-0.485580</td>\n",
       "      <td>-0.768820</td>\n",
       "      <td>-0.662573</td>\n",
       "      <td>-0.211837</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          y         0         1  ...        47        48        49\n",
       "0  0.483130  0.790980  0.702555  ...  1.195992  1.165327  0.771110\n",
       "1  1.135624  0.765286  0.604512  ...  1.331348  1.267123  1.292718\n",
       "2  0.686081  0.702834  0.637708  ...  0.442759  0.487557  0.699007\n",
       "3 -1.342005  0.018378 -0.097297  ... -1.490786 -1.856840 -1.441472\n",
       "4 -0.241418  0.463120  0.665307  ... -0.768820 -0.662573 -0.211837\n",
       "\n",
       "[5 rows x 51 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "financial_train = train.drop(\"w\", axis=1)\n",
    "financial_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "organic-wrestling"
   },
   "source": [
    "We now check so that for `null` values in the dataset.\n",
    "\n",
    "We could also use `.info()` to have more information like the Dtypes of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "exclusive-stopping",
    "jupyter": {
     "outputs_hidden": true
    },
    "outputId": "5e1867c3-3fcd-4cde-95df-a00c2c1962f3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 14,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "financial_train.isnull().values.any()\n",
    "#financial_train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "favorite-chance"
   },
   "source": [
    "We now print the mean and the standard deviation for `financial_train` and after the plot for the correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "royal-atlanta",
    "jupyter": {
     "outputs_hidden": true
    },
    "outputId": "688a4a00-cce1-48b2-aeae-1bf8a569ee18",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean:\n",
      "\n",
      "[0.07981056 0.01690311 0.01897392 0.02320434 0.02763826 0.03106758\n",
      " 0.03990432 0.04564649 0.05125901 0.05523723 0.05998229 0.06348559\n",
      " 0.06059931 0.0595254  0.05751156 0.05432058 0.04932228 0.04643498\n",
      " 0.04403553 0.0406522  0.04198718 0.0409172  0.04040852 0.04092458\n",
      " 0.0464065  0.04816842 0.05133673 0.05514936 0.05567795 0.05729535\n",
      " 0.05941809 0.05451284 0.05246735 0.05084397 0.04789078 0.04369453\n",
      " 0.04131472 0.03917703 0.03669292 0.03443982 0.0305863  0.03662203\n",
      " 0.03902369 0.04360521 0.04540032 0.04947673 0.05575918 0.06071495\n",
      " 0.07043613 0.07828405 0.08102811] \n",
      "\n",
      " std: \n",
      "\n",
      "[0.91249507 0.52541053 0.52491514 0.52848951 0.53464913 0.53880475\n",
      " 0.5417865  0.54870909 0.55419235 0.55770326 0.56149684 0.560994\n",
      " 0.56543377 0.56992756 0.57350887 0.57712409 0.57916214 0.58666059\n",
      " 0.58964479 0.5969637  0.60356411 0.60950638 0.6191625  0.62788191\n",
      " 0.63977145 0.64798546 0.65465379 0.66365014 0.66996123 0.67578322\n",
      " 0.68552744 0.6940925  0.70205383 0.71153066 0.71805973 0.72637984\n",
      " 0.73511858 0.74174153 0.74887765 0.7607051  0.77048763 0.77962479\n",
      " 0.79185982 0.80142961 0.81351217 0.82554335 0.8329938  0.84780252\n",
      " 0.86211745 0.88122666 0.89348236]\n"
     ]
    }
   ],
   "source": [
    "mean = np.array(financial_train.mean(axis=0))\n",
    "std = np.array(financial_train.std(axis=0))\n",
    "print(f\"mean:\\n\\n{mean} \\n\\n std: \\n\\n{std}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hiqrTr34DBqY",
    "outputId": "8d4cddc6-496f-42d9-e541-be0d16415f01"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "y     1.000000\n",
       "49    0.977203\n",
       "48    0.944832\n",
       "47    0.898601\n",
       "46    0.843251\n",
       "31    0.831443\n",
       "30    0.828819\n",
       "32    0.826190\n",
       "29    0.819507\n",
       "33    0.808870\n",
       "28    0.802625\n",
       "45    0.790237\n",
       "27    0.783774\n",
       "34    0.779100\n",
       "26    0.765639\n",
       "44    0.745744\n",
       "25    0.745667\n",
       "35    0.743946\n",
       "24    0.726833\n",
       "23    0.715419\n",
       "36    0.709549\n",
       "43    0.708149\n",
       "22    0.701411\n",
       "21    0.688972\n",
       "42    0.684724\n",
       "20    0.683424\n",
       "37    0.683334\n",
       "18    0.681082\n",
       "19    0.679991\n",
       "17    0.677283\n",
       "16    0.673230\n",
       "41    0.670157\n",
       "15    0.666290\n",
       "38    0.664611\n",
       "40    0.658175\n",
       "14    0.657419\n",
       "39    0.656926\n",
       "13    0.642289\n",
       "12    0.626453\n",
       "2     0.621057\n",
       "1     0.618793\n",
       "3     0.614287\n",
       "0     0.613172\n",
       "11    0.612443\n",
       "4     0.602994\n",
       "10    0.601310\n",
       "5     0.589865\n",
       "9     0.588522\n",
       "6     0.584518\n",
       "7     0.580042\n",
       "8     0.579152\n",
       "Name: y, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix=financial_train.corr()\n",
    "corr_matrix[\"y\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 275
    },
    "id": "passive-clerk",
    "outputId": "c8194764-204e-46b4-8e88-ef821899a10e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(financial_train.corr())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "GDxvKe5tDqxK",
    "outputId": "ae597746-8595-428b-ebf8-48bcb1ac560a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x2160 with 56 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "financial_train.hist(bins=50, figsize=(30,30))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "welcome-above"
   },
   "source": [
    "### Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "chief-milton"
   },
   "source": [
    "We now use `StandardScaler` from `sklearn.preprocessing` to standardize features by removing the mean and scaling to unit variance.\n",
    "\n",
    "The standard score of a sample x is calculated as: $z = \\frac{x-\\mu}{\\sigma}$\n",
    "\n",
    "where $\\mu$ is the mean of the training samples and $\\sigma$ is the standard deviation of the training samples. Then it's printed the first element of the standardized training set that is `fin_train_tr`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "floating-complex",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "import sklearn\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "#print(scaler.fit(financial_train))\n",
    "#print(scaler.transform(financial_train))\n",
    "fin_train_tr = pd.DataFrame(scaler.fit_transform(financial_train))\n",
    "\n",
    "#fin_train_tr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wound-saudi"
   },
   "source": [
    "And we can check the structure of `fin_train_tr` by looking at its shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "governing-release",
    "jupyter": {
     "outputs_hidden": true
    },
    "outputId": "ea02df6e-3631-4982-e4ae-5e82a91ce55c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7326, 51)"
      ]
     },
     "execution_count": 20,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fin_train_tr.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FIpvsrIcH1mO"
   },
   "source": [
    "### Train-Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "Pv5N_4pwHU67"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_set, test_set = train_test_split(fin_train_tr,test_size=0.2,random_state=40)\n",
    "\n",
    "train_set_features = train_set.drop([0], axis =1)\n",
    "train_set_targets = train_set[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "handy-tension"
   },
   "source": [
    "### Linear Regression\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "alpine-dressing"
   },
   "source": [
    "Now, the data prepared before are fitted into the linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "competent-estonia",
    "jupyter": {
     "outputs_hidden": true
    },
    "outputId": "4f793f5d-5c00-4780-af7a-3dd21bbc1f60"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 22,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lin_reg=LinearRegression()\n",
    "lin_reg.fit(train_set_features,train_set_targets) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "primary-chart"
   },
   "source": [
    "and the coefficients obtained regressing are printed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hollywood-prompt",
    "jupyter": {
     "outputs_hidden": true
    },
    "outputId": "be277303-a95b-4a21-85f5-e34c5e716804"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-3.85327481e-02, -2.13185137e-02,  2.94062007e-03,  3.99714823e-03,\n",
       "        7.68857322e-03,  4.28506549e-03, -2.74351676e-02, -9.94729652e-04,\n",
       "       -1.58521265e-02, -6.20880004e-03, -1.16677831e-02,  1.44318608e-02,\n",
       "       -9.95634093e-03, -1.64858430e-03,  1.39945085e-02, -1.71060405e-02,\n",
       "        3.68704125e-03, -5.83617702e-03,  2.93781063e-02,  2.79290434e-02,\n",
       "        2.36986499e-02,  3.53350258e-02,  4.60686579e-03,  4.25972334e-03,\n",
       "        1.73774402e-02,  9.02387495e-03,  1.77086030e-02,  4.28066088e-02,\n",
       "        5.37943454e-02,  2.15126551e-02, -6.45894351e-03,  7.11022744e-04,\n",
       "        4.19033187e-03,  2.05114781e-02,  5.95129298e-02,  6.94893989e-02,\n",
       "        5.14702631e-02, -2.33385249e-02, -4.79341408e-02, -6.59879997e-02,\n",
       "       -7.65239899e-04,  3.67625702e-02,  5.94666495e-02,  2.87991584e-02,\n",
       "       -6.27419063e-03, -1.37474983e-01, -1.89821155e-01, -6.14575902e-02,\n",
       "        2.62431453e-01,  7.79222121e-01])"
      ]
     },
     "execution_count": 23,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "binding-closing"
   },
   "source": [
    "### Error Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "knowing-lexington"
   },
   "source": [
    "We firstly compute the MSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "photographic-affiliate",
    "jupyter": {
     "outputs_hidden": true
    },
    "outputId": "c8ab9996-c33a-45f3-c55c-8b14ce57a0a7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.027527395107276722"
      ]
     },
     "execution_count": 24,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "test_set_features = test_set.drop([0], axis=1)\n",
    "test_set_targets = test_set[0]\n",
    "\n",
    "predictions = lin_reg.predict(test_set_features)\n",
    "lin_mse=mean_squared_error(test_set_targets, predictions)\n",
    "lin_mse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "distant-madonna"
   },
   "source": [
    "and then MAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "organized-clearance",
    "jupyter": {
     "outputs_hidden": true
    },
    "outputId": "7ff93879-1423-466f-9c45-ca83c4c6f6dc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1334700877629156"
      ]
     },
     "execution_count": 25,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "lin_mae = mean_absolute_error(test_set_targets,predictions)\n",
    "lin_mae"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "k5H-NN-hElXB"
   },
   "source": [
    "### Alternative Model Specification\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "75GCRo5KE0fz",
    "outputId": "d37308ba-71d0-4b18-9e38-28a4b5aa1b61"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support Vector Machine Regressor:0.021816756978033817\n",
      "Gradient Boosting Regressor:0.028630600346160023\n",
      "XGBoost Regressor:0.028597322863024355\n",
      "RandomForestRegressor:0.026603789376100393\n",
      "Stochastic Gradient Descent:0.028769777460477475\n",
      "K-Nearest Neighbours Regressor:0.03906336208377472\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.linear_model import SGDRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from xgboost import XGBRegressor\n",
    "\n",
    "models=dict()\n",
    "\n",
    "models[\"Support Vector Machine Regressor\"]=SVR()\n",
    "models[\"Gradient Boosting Regressor\"]=GradientBoostingRegressor()\n",
    "models[\"XGBoost Regressor\"]=XGBRegressor(objective ='reg:squarederror')\n",
    "models[\"RandomForestRegressor\"]=RandomForestRegressor()\n",
    "models[\"Stochastic Gradient Descent\"]=SGDRegressor()\n",
    "models[\"K-Nearest Neighbours Regressor\"]=KNeighborsRegressor()\n",
    "\n",
    "for model in models:\n",
    "    model_inuse=models[model].fit(train_set_features,train_set_targets)\n",
    "    prediction=model_inuse.predict(test_set_features)\n",
    "    print(f\"{model}:{mean_squared_error(test_set_targets,prediction)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DfuP-UoRNde9"
   },
   "source": [
    "As we can see from the MSEs, SVR seems to have a lower loss if compared to the other regressors, so we expand the aforementioned model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8BHRzXa-NcBM",
    "outputId": "6507b422-5d76-4826-a080-480a3d2b8f23"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=1.2, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='scale',\n",
       "    kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 32,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svr_reg=SVR(kernel=\"rbf\", C=1.2, epsilon=0.1)\n",
    "svr_reg.fit(train_set_features, train_set_targets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rmf5rKguPFQf"
   },
   "source": [
    "We can now predict the values for the test in order to compute the error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "hCNkSIuNPNnU"
   },
   "outputs": [],
   "source": [
    "predictions_svr=svr_reg.predict(test_set_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mDbk-NirQCm-"
   },
   "source": [
    "We can now compute the mean squared error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uqypHa7TQQ1L",
    "outputId": "607e726c-9f5c-4a19-9411-6d2c492cffad"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.021693131552361085"
      ]
     },
     "execution_count": 34,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svr_error=mean_squared_error(test_set_targets,predictions_svr)\n",
    "svr_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qw2omlwiQivJ"
   },
   "source": [
    "With hyperparameter optimization and grid search in particular we can try different combinations of parameters and see which one leads to a better performance.\n",
    "\n",
    "*This may take a while!*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "RrTN2ZYBQhaw"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "param_grid={'C': [0.1,1, 10], 'gamma': [1,0.1,0.01],'kernel': ['rbf']}\n",
    "grid=GridSearchCV(SVR(),param_grid, refit=True, verbose=2)\n",
    "grid.fit(train_set_features, train_set_targets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wXiHp7sVSzCg"
   },
   "source": [
    "Let's now see which are the best parameters among the ones we included"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "N8mn90nXS6P5"
   },
   "outputs": [],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "r9DR_hV1c213"
   },
   "source": [
    "### LSTM with Tensorflow and Keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "mcVVqdkVfkgQ"
   },
   "outputs": [],
   "source": [
    "financial_train = train.drop(\"y\", axis=1) # drop labels for training \n",
    "y=train[\"y\"].copy()\n",
    "financial_train2 = financial_train.drop(\"w\", axis=1)\n",
    "labels = np.array(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VUy7Ghx1Fe2u"
   },
   "source": [
    "`MinMaxScaler` is imported from `sklearn.preprocessing` to scale the train set according to the formula:\n",
    "$X'_i = \\frac{X_i-X_{min}}{X_{max}-X_{min}}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "tz3AR_25qqzk"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler(feature_range = (0, 1))\n",
    "fin_array = scaler.fit_transform(financial_train2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OFZG3bLIJNHL"
   },
   "source": [
    "We now transform data to the correct shape for the algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NAzo84BUu0LF",
    "outputId": "0b709d6b-e981-49c5-f734-c5d9a4690fe0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7326, 50, 1)"
      ]
     },
     "execution_count": 37,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_set = np.reshape(fin_array, ( fin_array.shape[0],fin_array.shape[1],1))\n",
    "features_set.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Zu6_iR9hJkGK"
   },
   "source": [
    "From `keras` are imported the functions needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "bktiC2sYu_XT"
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation, TimeDistributed, RepeatVector, LSTM, GRU, Dropout\n",
    "from keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5Jy7VPZgJ3wK"
   },
   "source": [
    "We now define the function `early_stopping` which is a callback function that will allow us to stop training the model to avoid overfitting. The parameters consist of:\n",
    "- monitor: Quantity to be monitored.\n",
    "- min_delta: Minimum change in the monitored quantity to qualify as an improvement\n",
    "- patience: Number of epochs with no improvement after which training will be stopped.\n",
    "- restore_best_weights: allow to restore model weights from the epoch with the best value of the monitored quantity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "_3HlE2Q3d_CY"
   },
   "outputs": [],
   "source": [
    "early_stopping = EarlyStopping(monitor=\"val_loss\",\n",
    "    min_delta=0.004, \n",
    "    patience=10, \n",
    "    restore_best_weights=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FrtYRv1W3XOA"
   },
   "source": [
    "In this cell are implemeted all the steps needed to find the best hyperparametes for the model.\n",
    "First we create a function that builds the model which is structured in the same way as the one in the previous cell. \n",
    "Then we define the parameters and their range, that we want to test using the `GridSearchCV` function.\n",
    "\n",
    "*This may take a while to complete!*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "L-reXN91eU3E"
   },
   "outputs": [],
   "source": [
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "u = 50\n",
    "\n",
    "def build_classifier(optimizer):\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=u, return_sequences=\"True\" ,input_shape=(features_set.shape[1], 1)))\n",
    "    model.add(Dropout(0.2))\n",
    "\n",
    "    model.add(LSTM(units=u, return_sequences=\"True\" ,input_shape=(features_set.shape[1], 1)))\n",
    "    model.add(Dropout(0.2))\n",
    "\n",
    "    model.add(LSTM(units=u, input_shape=(features_set.shape[1], 1)))\n",
    "    model.add(Dropout(0.2))\n",
    "    model.add(Dense(1))\n",
    "\n",
    "    model.compile(loss = 'mse', metrics = ['mean_squared_error'])\n",
    "    return model\n",
    "\n",
    "model = KerasClassifier(build_fn=build_classifier)\n",
    "parameters = {'batch_size' : [28,30,32],#,28,30,32], #40, 43, 45, 50\n",
    "              'epochs' : [100,200],\n",
    "              'optimizer' : ['adam', 'Adadelta'], #'SGD', 'RMSprop', 'Adadelta', 'Adagrad', 'Adamax', 'Nadam', 'Ftrl'],\n",
    "              'verbose': [1]}\n",
    "\n",
    "grid_search  = GridSearchCV(estimator = model,\n",
    "                            param_grid = parameters,\n",
    "                            scoring = 'neg_mean_squared_error',\n",
    "                            cv = 2)\n",
    "\n",
    "\n",
    "grid_search = grid_search.fit(features_set,labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZbDc78hxP_fQ"
   },
   "source": [
    "Once we have obtained the best hyperparameters we print them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "22_9eiqTeUvb",
    "outputId": "de7e1be3-2cd5-422f-f71c-252c52e06ed6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'batch_size': 25, 'epochs': 70, 'optimizer': 'adam', 'verbose': 2}"
      ]
     },
     "execution_count": 118,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_parameters = grid_search.best_params_\n",
    "best_accuracy = grid_search.best_score_\n",
    "best_parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zlisFlqPLEyg"
   },
   "source": [
    "We now start to build the model: we set it up so that it consists of three layers, with first two returning the sequences to the next ones. Dropouts with a $20\\%$ rate are added in order to prevent overfitting. We chose the number of layers by progressively adding more and checking the performance of the model. As optimizer we used `adam` which resulted from the hyperparameters optimization that has been performed before. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "MZOvuxreeFYz"
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(LSTM(units=55, return_sequences=\"True\",input_shape=(features_set.shape[1], 1)))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(LSTM(units=55, return_sequences=\"True\",input_shape=(features_set.shape[1], 1)))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(LSTM(units=55, input_shape=(features_set.shape[1], 1)))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Dense(units = 1))\n",
    "model.compile(optimizer = 'adam', loss = 'mean_squared_error')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x3lNp4_tM_dc"
   },
   "source": [
    "And now we fit the data that have been prepared into the model.\n",
    "(Sometimes) By fitting the model multiple times we achieve better results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Eei7AeATeIpe",
    "outputId": "fde32fb5-e349-44c3-dcf7-03c77377ac34"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "228/228 - 6s - loss: 0.2314 - val_loss: 0.1026\n",
      "Epoch 2/100\n",
      "228/228 - 2s - loss: 0.1204 - val_loss: 0.0928\n",
      "Epoch 3/100\n",
      "228/228 - 2s - loss: 0.1027 - val_loss: 0.1762\n",
      "Epoch 4/100\n",
      "228/228 - 2s - loss: 0.0892 - val_loss: 0.1242\n",
      "Epoch 5/100\n",
      "228/228 - 2s - loss: 0.0794 - val_loss: 0.0802\n",
      "Epoch 6/100\n",
      "228/228 - 2s - loss: 0.0741 - val_loss: 0.0778\n",
      "Epoch 7/100\n",
      "228/228 - 2s - loss: 0.0706 - val_loss: 0.0563\n",
      "Epoch 8/100\n",
      "228/228 - 2s - loss: 0.0597 - val_loss: 0.0754\n",
      "Epoch 9/100\n",
      "228/228 - 2s - loss: 0.0529 - val_loss: 0.0496\n",
      "Epoch 10/100\n",
      "228/228 - 2s - loss: 0.0442 - val_loss: 0.0525\n",
      "Epoch 11/100\n",
      "228/228 - 2s - loss: 0.0405 - val_loss: 0.0628\n",
      "Epoch 12/100\n",
      "228/228 - 2s - loss: 0.0368 - val_loss: 0.0563\n",
      "Epoch 13/100\n",
      "228/228 - 2s - loss: 0.0354 - val_loss: 0.0555\n",
      "Epoch 14/100\n",
      "228/228 - 2s - loss: 0.0352 - val_loss: 0.0367\n",
      "Epoch 15/100\n",
      "228/228 - 2s - loss: 0.0331 - val_loss: 0.0365\n",
      "Epoch 16/100\n",
      "228/228 - 2s - loss: 0.0318 - val_loss: 0.0377\n",
      "Epoch 17/100\n",
      "228/228 - 2s - loss: 0.0313 - val_loss: 0.0146\n",
      "Epoch 18/100\n",
      "228/228 - 2s - loss: 0.0306 - val_loss: 0.0196\n",
      "Epoch 19/100\n",
      "228/228 - 2s - loss: 0.0297 - val_loss: 0.0128\n",
      "Epoch 20/100\n",
      "228/228 - 2s - loss: 0.0295 - val_loss: 0.0209\n",
      "Epoch 21/100\n",
      "228/228 - 2s - loss: 0.0284 - val_loss: 0.0175\n",
      "Epoch 22/100\n",
      "228/228 - 2s - loss: 0.0290 - val_loss: 0.0197\n",
      "Epoch 23/100\n",
      "228/228 - 2s - loss: 0.0279 - val_loss: 0.0280\n",
      "Epoch 24/100\n",
      "228/228 - 2s - loss: 0.0288 - val_loss: 0.0240\n",
      "Epoch 25/100\n",
      "228/228 - 2s - loss: 0.0291 - val_loss: 0.0209\n",
      "Epoch 26/100\n",
      "228/228 - 2s - loss: 0.0285 - val_loss: 0.0151\n",
      "Epoch 27/100\n",
      "228/228 - 2s - loss: 0.0285 - val_loss: 0.0222\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f36692d6a90>"
      ]
     },
     "execution_count": 90,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#n = 1\n",
    "#for i in range(n):\n",
    "model.fit(features_set, labels, epochs = 100, validation_split=0.1, batch_size = 28, verbose=2,  callbacks=[early_stopping])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OMsEwCt6R6sO"
   },
   "source": [
    "We re-apply the same transformations applied before but now on the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "L4jsCQ0ueUl0",
    "outputId": "69bf202e-7341-4dc9-ae47-f0dfe3662166"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.2234717 , 0.16163201, 0.20019499, ..., 0.42314219, 0.39647356,\n",
       "        0.38653022],\n",
       "       [0.54778469, 0.53651845, 0.59138859, ..., 0.64188457, 0.57389185,\n",
       "        0.54498805],\n",
       "       [0.81151686, 0.75810207, 0.75106067, ..., 0.7945658 , 0.83358227,\n",
       "        0.91174531],\n",
       "       ...,\n",
       "       [0.96338652, 0.95140427, 0.94364447, ..., 0.76946144, 0.76684189,\n",
       "        0.77329558],\n",
       "       [0.78042029, 0.78092329, 0.77810149, ..., 0.79887116, 0.79436882,\n",
       "        0.79631411],\n",
       "       [0.87834256, 0.87097847, 0.86740449, ..., 0.79194238, 0.78574633,\n",
       "        0.7925877 ]])"
      ]
     },
     "execution_count": 56,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_scaled=scaler.transform(test)\n",
    "test_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sO9Z1IH7eUbx",
    "outputId": "376f0d49-cb2d-4a67-f4bf-69792783186e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3141, 50, 1)"
      ]
     },
     "execution_count": 57,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_features = np.reshape(test_scaled, (-1, test_scaled.shape[1], 1))\n",
    "test_features.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dOIRX0irSTtP"
   },
   "source": [
    "Finally we can apply the trained model using the `.predict` module to the re-shaped test set and then we print the first 5 predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kysxPDIIeb4d",
    "outputId": "dc1db3eb-1241-4def-d2e4-f200aa8b9e76"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.6401291 ],\n",
       "       [ 0.37648785],\n",
       "       [ 1.634908  ],\n",
       "       ...,\n",
       "       [ 1.09753   ],\n",
       "       [ 0.9398639 ],\n",
       "       [ 1.1545457 ]], dtype=float32)"
      ]
     },
     "execution_count": 72,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = model.predict(test_features)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F75wUgqVROI1"
   },
   "source": [
    "We save the output in a txt file so that it can be uploaded to the platform. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "KpgK0Sltecf0"
   },
   "outputs": [],
   "source": [
    "np.savetxt(\"submissions23.txt\", predictions,delimiter = \",\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zgWkG--PGi35"
   },
   "source": [
    "-----------------"
   ]
  }
 ],
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  "accelerator": "GPU",
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   "collapsed_sections": [],
   "name": "Financial_Time_series_forecasting_challenge.ipynb",
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  },
  "kernelspec": {
   "display_name": "Python 3",
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  "language_info": {
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