F743_Final_Project_V4.ipynb

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      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.6"
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    "colab": {
      "name": "F743 - Final Project V4.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/FowadIjaz/Portfolio/blob/main/F743_Final_Project_V4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zG9GbWj1u9rh"
      },
      "source": [
        "### **Project Overview**\r\n",
        "\r\n",
        "*This project was completed by Fowad Ijaz, Bishal Podder, Akshay Hegde, Prathamesh Sinha, and Sourav Dasgupta for F743: Big Data in Finance taught by A. Mahmood at the DeGroote School of Business in Fall 2020.*\r\n",
        "\r\n",
        "The purpose of this project was to predict stock prices 60 days in the future for all stocks in the S$P 500 Index.\r\n",
        "\r\n",
        "\r\n",
        "### **Methods**\r\n",
        "\r\n",
        "The 5-year Stock Price data was taken from the following [Kaggle Dataset](https://www.kaggle.com/camnugent/sandp500). It contained stock prices for all S&P 500 stocks from Feb 2013 to Feb 2018. In total, the dataset had close to 500,000 rows and 6 columns.\r\n",
        "\r\n",
        "The machine learning algorithms chosen for this particular assignment were RandomForest, ADAboost, and GradientBoost Regressors. Of the 3, the ML model that was most successful was GradientBoost Regressor, with a score of over 98%.\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TQ5Cpgy10DCE"
      },
      "source": [
        "# from google.colab import drive\n",
        "# drive.mount('/content/drive')"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9tFqjhyy3vhf"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ajn1eNfQxJnu"
      },
      "source": [
        "### **Data Cleaning**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EOlIcenOug_F"
      },
      "source": [
        "# Importing relevant libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "#ScikitLearn: \n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "#Matplotlib and Seaborn:\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "from matplotlib.pylab import rcParams\n",
        "import seaborn as sns"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
        },
        "id": "zTxb3uB0ug_I",
        "outputId": "807f7be5-ee45-4ec2-cef1-814e8e5d9b9c"
      },
      "source": [
        "# Reading DataFrame from CSV\r\n",
        "df = pd.read_csv(r'/content/drive/MyDrive/MBA/MBA Term 7 - Fall 2020/F743/Final Project Raw Data/Stock_Data.csv', header = 0, sep = ',')\r\n",
        "df"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>a_date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>Name</th>\n",
              "      <th>52 week high</th>\n",
              "      <th>% Over/Under 52WKH</th>\n",
              "      <th>20 Day MA</th>\n",
              "      <th>% Over Under 20DMA</th>\n",
              "      <th>50 Day MA</th>\n",
              "      <th>% Over Under 50DMA</th>\n",
              "      <th>100 Day MA</th>\n",
              "      <th>% Over Under 100DMA</th>\n",
              "      <th>200 Day MA</th>\n",
              "      <th>% Over Under 200DMA</th>\n",
              "      <th>Average Gain/Loss</th>\n",
              "      <th>Average Gains2</th>\n",
              "      <th>Av Losses2</th>\n",
              "      <th>RSI V2</th>\n",
              "      <th>S&amp;P500_open</th>\n",
              "      <th>S&amp;P500_high</th>\n",
              "      <th>S&amp;P500_low</th>\n",
              "      <th>S&amp;P500_close</th>\n",
              "      <th>S&amp;P500_52 week high</th>\n",
              "      <th>S&amp;P500_% Over/Under 52WKH</th>\n",
              "      <th>S&amp;P500_20 Day MA</th>\n",
              "      <th>S&amp;P500_% Over Under 20DMA</th>\n",
              "      <th>S&amp;P500_50 Day MA</th>\n",
              "      <th>S&amp;P500_% Over Under 50DMA</th>\n",
              "      <th>S&amp;P500_100 Day MA</th>\n",
              "      <th>S&amp;P500_% Over Under 100DMA</th>\n",
              "      <th>S&amp;P500_200 Day MA</th>\n",
              "      <th>S&amp;P500_% Over Under 200DMA</th>\n",
              "      <th>S&amp;P500_Average Gain/Loss</th>\n",
              "      <th>S&amp;P500_Average Gains2</th>\n",
              "      <th>S&amp;P500_Av Losses2</th>\n",
              "      <th>S&amp;P500_RSI V2</th>\n",
              "      <th>Alpha</th>\n",
              "      <th>Signal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2014-02-20</td>\n",
              "      <td>34.43</td>\n",
              "      <td>35.6694</td>\n",
              "      <td>34.43</td>\n",
              "      <td>35.66</td>\n",
              "      <td>9289448.0</td>\n",
              "      <td>AAL</td>\n",
              "      <td>35.67</td>\n",
              "      <td>-0.000280</td>\n",
              "      <td>33.78775</td>\n",
              "      <td>0.052503</td>\n",
              "      <td>29.7528</td>\n",
              "      <td>0.165653</td>\n",
              "      <td>25.9190</td>\n",
              "      <td>0.273163</td>\n",
              "      <td>21.73880</td>\n",
              "      <td>0.390387</td>\n",
              "      <td>0.030934</td>\n",
              "      <td>0.008443</td>\n",
              "      <td>-0.004493</td>\n",
              "      <td>65.269321</td>\n",
              "      <td>1829.24</td>\n",
              "      <td>1842.79</td>\n",
              "      <td>1824.58</td>\n",
              "      <td>1839.78</td>\n",
              "      <td>1848.38</td>\n",
              "      <td>-0.004653</td>\n",
              "      <td>1798.9785</td>\n",
              "      <td>0.022680</td>\n",
              "      <td>1813.7914</td>\n",
              "      <td>0.014328</td>\n",
              "      <td>1782.7328</td>\n",
              "      <td>0.032000</td>\n",
              "      <td>1720.68075</td>\n",
              "      <td>0.069216</td>\n",
              "      <td>0.006031</td>\n",
              "      <td>0.004559</td>\n",
              "      <td>-0.002723</td>\n",
              "      <td>62.607123</td>\n",
              "      <td>++++</td>\n",
              "      <td>Bullish</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2014-02-21</td>\n",
              "      <td>35.78</td>\n",
              "      <td>36.4500</td>\n",
              "      <td>35.75</td>\n",
              "      <td>36.17</td>\n",
              "      <td>7821334.0</td>\n",
              "      <td>AAL</td>\n",
              "      <td>36.17</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>34.02875</td>\n",
              "      <td>0.059200</td>\n",
              "      <td>29.9842</td>\n",
              "      <td>0.171020</td>\n",
              "      <td>26.0899</td>\n",
              "      <td>0.278687</td>\n",
              "      <td>21.83475</td>\n",
              "      <td>0.396330</td>\n",
              "      <td>0.014302</td>\n",
              "      <td>0.009465</td>\n",
              "      <td>-0.003964</td>\n",
              "      <td>70.478968</td>\n",
              "      <td>1841.07</td>\n",
              "      <td>1846.13</td>\n",
              "      <td>1835.60</td>\n",
              "      <td>1836.25</td>\n",
              "      <td>1848.38</td>\n",
              "      <td>-0.006563</td>\n",
              "      <td>1799.3680</td>\n",
              "      <td>0.020497</td>\n",
              "      <td>1814.3490</td>\n",
              "      <td>0.012071</td>\n",
              "      <td>1784.1778</td>\n",
              "      <td>0.029186</td>\n",
              "      <td>1721.73220</td>\n",
              "      <td>0.066513</td>\n",
              "      <td>-0.001919</td>\n",
              "      <td>0.004559</td>\n",
              "      <td>-0.002398</td>\n",
              "      <td>65.529727</td>\n",
              "      <td>++++</td>\n",
              "      <td>Bullish</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2014-02-24</td>\n",
              "      <td>36.20</td>\n",
              "      <td>36.6600</td>\n",
              "      <td>36.02</td>\n",
              "      <td>36.65</td>\n",
              "      <td>7022370.0</td>\n",
              "      <td>AAL</td>\n",
              "      <td>36.65</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>34.34125</td>\n",
              "      <td>0.062995</td>\n",
              "      <td>30.2196</td>\n",
              "      <td>0.175454</td>\n",
              "      <td>26.2668</td>\n",
              "      <td>0.283307</td>\n",
              "      <td>21.93130</td>\n",
              "      <td>0.401602</td>\n",
              "      <td>0.013271</td>\n",
              "      <td>0.009540</td>\n",
              "      <td>-0.003964</td>\n",
              "      <td>70.642936</td>\n",
              "      <td>1836.78</td>\n",
              "      <td>1858.71</td>\n",
              "      <td>1836.78</td>\n",
              "      <td>1847.61</td>\n",
              "      <td>1848.38</td>\n",
              "      <td>-0.000417</td>\n",
              "      <td>1802.2340</td>\n",
              "      <td>0.025178</td>\n",
              "      <td>1815.2488</td>\n",
              "      <td>0.017827</td>\n",
              "      <td>1785.8384</td>\n",
              "      <td>0.034590</td>\n",
              "      <td>1722.80680</td>\n",
              "      <td>0.072442</td>\n",
              "      <td>0.006187</td>\n",
              "      <td>0.005001</td>\n",
              "      <td>-0.000767</td>\n",
              "      <td>86.698898</td>\n",
              "      <td>++++</td>\n",
              "      <td>Bullish</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2014-02-25</td>\n",
              "      <td>36.63</td>\n",
              "      <td>37.1500</td>\n",
              "      <td>36.51</td>\n",
              "      <td>37.00</td>\n",
              "      <td>8991389.0</td>\n",
              "      <td>AAL</td>\n",
              "      <td>37.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>34.68225</td>\n",
              "      <td>0.062642</td>\n",
              "      <td>30.4398</td>\n",
              "      <td>0.177303</td>\n",
              "      <td>26.4399</td>\n",
              "      <td>0.285408</td>\n",
              "      <td>22.02940</td>\n",
              "      <td>0.404611</td>\n",
              "      <td>0.009550</td>\n",
              "      <td>0.010033</td>\n",
              "      <td>-0.003964</td>\n",
              "      <td>71.676575</td>\n",
              "      <td>1847.66</td>\n",
              "      <td>1852.91</td>\n",
              "      <td>1840.19</td>\n",
              "      <td>1845.12</td>\n",
              "      <td>1848.38</td>\n",
              "      <td>-0.001764</td>\n",
              "      <td>1805.4120</td>\n",
              "      <td>0.021994</td>\n",
              "      <td>1816.5068</td>\n",
              "      <td>0.015752</td>\n",
              "      <td>1787.3396</td>\n",
              "      <td>0.032328</td>\n",
              "      <td>1723.89905</td>\n",
              "      <td>0.070318</td>\n",
              "      <td>-0.001348</td>\n",
              "      <td>0.004455</td>\n",
              "      <td>-0.000863</td>\n",
              "      <td>83.764614</td>\n",
              "      <td>++++</td>\n",
              "      <td>Bullish</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014-02-26</td>\n",
              "      <td>37.12</td>\n",
              "      <td>37.2800</td>\n",
              "      <td>36.58</td>\n",
              "      <td>36.75</td>\n",
              "      <td>10516864.0</td>\n",
              "      <td>AAL</td>\n",
              "      <td>37.00</td>\n",
              "      <td>-0.006803</td>\n",
              "      <td>34.92175</td>\n",
              "      <td>0.049748</td>\n",
              "      <td>30.6658</td>\n",
              "      <td>0.165556</td>\n",
              "      <td>26.6090</td>\n",
              "      <td>0.275946</td>\n",
              "      <td>22.12435</td>\n",
              "      <td>0.397977</td>\n",
              "      <td>-0.006757</td>\n",
              "      <td>0.010033</td>\n",
              "      <td>-0.003923</td>\n",
              "      <td>71.891334</td>\n",
              "      <td>1845.79</td>\n",
              "      <td>1852.65</td>\n",
              "      <td>1840.66</td>\n",
              "      <td>1845.16</td>\n",
              "      <td>1848.38</td>\n",
              "      <td>-0.001742</td>\n",
              "      <td>1808.0450</td>\n",
              "      <td>0.020528</td>\n",
              "      <td>1817.9000</td>\n",
              "      <td>0.014995</td>\n",
              "      <td>1788.8525</td>\n",
              "      <td>0.031477</td>\n",
              "      <td>1724.95635</td>\n",
              "      <td>0.069685</td>\n",
              "      <td>0.000022</td>\n",
              "      <td>0.004456</td>\n",
              "      <td>-0.000719</td>\n",
              "      <td>86.114469</td>\n",
              "      <td>++++</td>\n",
              "      <td>Bullish</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>495658</th>\n",
              "      <td>2018-02-01</td>\n",
              "      <td>2816.45</td>\n",
              "      <td>2835.9600</td>\n",
              "      <td>2812.70</td>\n",
              "      <td>2821.98</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S&amp;P 500</td>\n",
              "      <td>2872.87</td>\n",
              "      <td>-0.017714</td>\n",
              "      <td>2799.94950</td>\n",
              "      <td>0.007868</td>\n",
              "      <td>2711.5490</td>\n",
              "      <td>0.040726</td>\n",
              "      <td>2629.4468</td>\n",
              "      <td>0.073222</td>\n",
              "      <td>2530.28750</td>\n",
              "      <td>0.115280</td>\n",
              "      <td>-0.000648</td>\n",
              "      <td>0.003123</td>\n",
              "      <td>-0.001713</td>\n",
              "      <td>64.581306</td>\n",
              "      <td>2816.45</td>\n",
              "      <td>2835.96</td>\n",
              "      <td>2812.70</td>\n",
              "      <td>2821.98</td>\n",
              "      <td>2872.87</td>\n",
              "      <td>-0.017714</td>\n",
              "      <td>2799.9495</td>\n",
              "      <td>0.007868</td>\n",
              "      <td>2711.5490</td>\n",
              "      <td>0.040726</td>\n",
              "      <td>2629.4468</td>\n",
              "      <td>0.073222</td>\n",
              "      <td>2530.28750</td>\n",
              "      <td>0.115280</td>\n",
              "      <td>-0.000648</td>\n",
              "      <td>0.003123</td>\n",
              "      <td>-0.001713</td>\n",
              "      <td>64.581306</td>\n",
              "      <td>++++</td>\n",
              "      <td>Bullish</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>495659</th>\n",
              "      <td>2018-02-02</td>\n",
              "      <td>2808.92</td>\n",
              "      <td>2808.9200</td>\n",
              "      <td>2759.97</td>\n",
              "      <td>2762.13</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S&amp;P 500</td>\n",
              "      <td>2872.87</td>\n",
              "      <td>-0.038547</td>\n",
              "      <td>2801.85650</td>\n",
              "      <td>-0.014179</td>\n",
              "      <td>2715.1488</td>\n",
              "      <td>0.017303</td>\n",
              "      <td>2632.1870</td>\n",
              "      <td>0.049367</td>\n",
              "      <td>2532.40730</td>\n",
              "      <td>0.090713</td>\n",
              "      <td>-0.021209</td>\n",
              "      <td>0.002641</td>\n",
              "      <td>-0.003228</td>\n",
              "      <td>45.001319</td>\n",
              "      <td>2808.92</td>\n",
              "      <td>2808.92</td>\n",
              "      <td>2759.97</td>\n",
              "      <td>2762.13</td>\n",
              "      <td>2872.87</td>\n",
              "      <td>-0.038547</td>\n",
              "      <td>2801.8565</td>\n",
              "      <td>-0.014179</td>\n",
              "      <td>2715.1488</td>\n",
              "      <td>0.017303</td>\n",
              "      <td>2632.1870</td>\n",
              "      <td>0.049367</td>\n",
              "      <td>2532.40730</td>\n",
              "      <td>0.090713</td>\n",
              "      <td>-0.021209</td>\n",
              "      <td>0.002641</td>\n",
              "      <td>-0.003228</td>\n",
              "      <td>45.001319</td>\n",
              "      <td>++++</td>\n",
              "      <td>Bullish</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>495660</th>\n",
              "      <td>2018-02-05</td>\n",
              "      <td>2741.06</td>\n",
              "      <td>2763.3900</td>\n",
              "      <td>2638.17</td>\n",
              "      <td>2648.94</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S&amp;P 500</td>\n",
              "      <td>2872.87</td>\n",
              "      <td>-0.077946</td>\n",
              "      <td>2797.14600</td>\n",
              "      <td>-0.052985</td>\n",
              "      <td>2716.1470</td>\n",
              "      <td>-0.024744</td>\n",
              "      <td>2633.7116</td>\n",
              "      <td>0.005782</td>\n",
              "      <td>2533.87280</td>\n",
              "      <td>0.045412</td>\n",
              "      <td>-0.040979</td>\n",
              "      <td>0.002641</td>\n",
              "      <td>-0.005903</td>\n",
              "      <td>30.910398</td>\n",
              "      <td>2741.06</td>\n",
              "      <td>2763.39</td>\n",
              "      <td>2638.17</td>\n",
              "      <td>2648.94</td>\n",
              "      <td>2872.87</td>\n",
              "      <td>-0.077946</td>\n",
              "      <td>2797.1460</td>\n",
              "      <td>-0.052985</td>\n",
              "      <td>2716.1470</td>\n",
              "      <td>-0.024744</td>\n",
              "      <td>2633.7116</td>\n",
              "      <td>0.005782</td>\n",
              "      <td>2533.87280</td>\n",
              "      <td>0.045412</td>\n",
              "      <td>-0.040979</td>\n",
              "      <td>0.002641</td>\n",
              "      <td>-0.005903</td>\n",
              "      <td>30.910398</td>\n",
              "      <td>++++</td>\n",
              "      <td>Bullish</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>495661</th>\n",
              "      <td>2018-02-06</td>\n",
              "      <td>2614.78</td>\n",
              "      <td>2701.0400</td>\n",
              "      <td>2593.07</td>\n",
              "      <td>2695.14</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S&amp;P 500</td>\n",
              "      <td>2872.87</td>\n",
              "      <td>-0.061865</td>\n",
              "      <td>2794.51750</td>\n",
              "      <td>-0.035562</td>\n",
              "      <td>2718.1082</td>\n",
              "      <td>-0.008450</td>\n",
              "      <td>2635.6793</td>\n",
              "      <td>0.022560</td>\n",
              "      <td>2535.60505</td>\n",
              "      <td>0.062918</td>\n",
              "      <td>0.017441</td>\n",
              "      <td>0.003214</td>\n",
              "      <td>-0.005903</td>\n",
              "      <td>35.254614</td>\n",
              "      <td>2614.78</td>\n",
              "      <td>2701.04</td>\n",
              "      <td>2593.07</td>\n",
              "      <td>2695.14</td>\n",
              "      <td>2872.87</td>\n",
              "      <td>-0.061865</td>\n",
              "      <td>2794.5175</td>\n",
              "      <td>-0.035562</td>\n",
              "      <td>2718.1082</td>\n",
              "      <td>-0.008450</td>\n",
              "      <td>2635.6793</td>\n",
              "      <td>0.022560</td>\n",
              "      <td>2535.60505</td>\n",
              "      <td>0.062918</td>\n",
              "      <td>0.017441</td>\n",
              "      <td>0.003214</td>\n",
              "      <td>-0.005903</td>\n",
              "      <td>35.254614</td>\n",
              "      <td>++++</td>\n",
              "      <td>Bullish</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>495662</th>\n",
              "      <td>2018-02-07</td>\n",
              "      <td>2690.95</td>\n",
              "      <td>2727.6700</td>\n",
              "      <td>2681.33</td>\n",
              "      <td>2681.66</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S&amp;P 500</td>\n",
              "      <td>2872.87</td>\n",
              "      <td>-0.066557</td>\n",
              "      <td>2791.03600</td>\n",
              "      <td>-0.039188</td>\n",
              "      <td>2719.6930</td>\n",
              "      <td>-0.013984</td>\n",
              "      <td>2637.5397</td>\n",
              "      <td>0.016728</td>\n",
              "      <td>2537.14260</td>\n",
              "      <td>0.056961</td>\n",
              "      <td>-0.005002</td>\n",
              "      <td>0.003214</td>\n",
              "      <td>-0.006145</td>\n",
              "      <td>34.343784</td>\n",
              "      <td>2690.95</td>\n",
              "      <td>2727.67</td>\n",
              "      <td>2681.33</td>\n",
              "      <td>2681.66</td>\n",
              "      <td>2872.87</td>\n",
              "      <td>-0.066557</td>\n",
              "      <td>2791.0360</td>\n",
              "      <td>-0.039188</td>\n",
              "      <td>2719.6930</td>\n",
              "      <td>-0.013984</td>\n",
              "      <td>2637.5397</td>\n",
              "      <td>0.016728</td>\n",
              "      <td>2537.14260</td>\n",
              "      <td>0.056961</td>\n",
              "      <td>-0.005002</td>\n",
              "      <td>0.003214</td>\n",
              "      <td>-0.006145</td>\n",
              "      <td>34.343784</td>\n",
              "      <td>++++</td>\n",
              "      <td>Bullish</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>495663 rows × 41 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            a_date     open       high  ...  S&P500_RSI V2  Alpha   Signal\n",
              "0       2014-02-20    34.43    35.6694  ...      62.607123   ++++  Bullish\n",
              "1       2014-02-21    35.78    36.4500  ...      65.529727   ++++  Bullish\n",
              "2       2014-02-24    36.20    36.6600  ...      86.698898   ++++  Bullish\n",
              "3       2014-02-25    36.63    37.1500  ...      83.764614   ++++  Bullish\n",
              "4       2014-02-26    37.12    37.2800  ...      86.114469   ++++  Bullish\n",
              "...            ...      ...        ...  ...            ...    ...      ...\n",
              "495658  2018-02-01  2816.45  2835.9600  ...      64.581306   ++++  Bullish\n",
              "495659  2018-02-02  2808.92  2808.9200  ...      45.001319   ++++  Bullish\n",
              "495660  2018-02-05  2741.06  2763.3900  ...      30.910398   ++++  Bullish\n",
              "495661  2018-02-06  2614.78  2701.0400  ...      35.254614   ++++  Bullish\n",
              "495662  2018-02-07  2690.95  2727.6700  ...      34.343784   ++++  Bullish\n",
              "\n",
              "[495663 rows x 41 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1uZcWxUOXKsw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5e862807-905f-46c8-cae0-462c49751238"
      },
      "source": [
        "## TRANSFORMATION OF THE DATAFRAME FOR DATA PREPARATION\r\n",
        "# Removing rows containing S&P 500 data from the bottom of the table (has been placed alongside every row to allow for comparison with every stock)\r\n",
        "df = df[df.Name != \"S&P 500\"]\r\n",
        "\r\n",
        "# Changing name of Date column from a_date to Date for ease of use\r\n",
        "df = df.rename(columns={'a_date':'Date'})\r\n",
        "#df\r\n",
        "\r\n",
        "# Changing SignalVal variable to categorical\r\n",
        "df['SignalVal'] = df['Signal'].astype('category').cat.codes\r\n",
        "#df\r\n",
        "\r\n",
        "# Removing closing stock prce\r\n",
        "df['ClosingStockPrice'] = df['close']\r\n",
        "df=df.drop(['close','Alpha','Signal'],axis=1)\r\n",
        "#df\r\n",
        "\r\n",
        "# Sorting Dataframe by name and date\r\n",
        "df = df.sort_values(by=['Date','Name'], ascending= True)\r\n",
        "#df\r\n",
        "\r\n",
        "# Turning Stock name into an integer value between 0 and 504\r\n",
        "df['Name'] = df['Name'].astype('category').cat.codes\r\n",
        "#df\r\n",
        "\r\n",
        "# Resetting the index to be in ascending order\r\n",
        "df= df.reset_index(drop=True)\r\n",
        "#df\r\n",
        "\r\n",
        "# Creating a dataframe with just the date column\r\n",
        "df2= df['Date']\r\n",
        "df2\r\n",
        "\r\n",
        "# Unused, for making Date into the index\r\n",
        "#df.index = df.Date\r\n",
        "#df.drop('Date',axis=1, inplace=True)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         2014-02-20\n",
              "1         2014-02-20\n",
              "2         2014-02-20\n",
              "3         2014-02-20\n",
              "4         2014-02-20\n",
              "             ...    \n",
              "494658    2018-02-07\n",
              "494659    2018-02-07\n",
              "494660    2018-02-07\n",
              "494661    2018-02-07\n",
              "494662    2018-02-07\n",
              "Name: Date, Length: 494663, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6VfBLn7fXDQp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "385950f3-2527-476c-9a9e-d341816a0e4e"
      },
      "source": [
        "# Creating a list of 505 lists for each of the stocks. Purpose is to remove lists with less than 1000 entries (a few stocks were added or removed from the index).\r\n",
        "# This was done to standardize the number of records for each stock, so that the final dataset could have a regularized shape \r\n",
        "\r\n",
        "df_list = []\r\n",
        "for i in range(0,505):\r\n",
        "  df_list.append(df[df.Name == i])\r\n",
        "\r\n",
        "# The following stock for example did not have 1000 rows\r\n",
        "len(df_list[49])"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "buAIyLpXYj_B",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "649fb875-b574-49bf-bd23-9434867462b2"
      },
      "source": [
        "# Removing stocks that had less than 1000 rows of data from the LIST\r\n",
        "list_drop = []\r\n",
        "for i in range(0,505):\r\n",
        "  if len(df_list[i])<1000:\r\n",
        "    list_drop.append(i)\r\n",
        "list_drop \r\n",
        "# len(list_drop)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[48,\n",
              " 67,\n",
              " 68,\n",
              " 93,\n",
              " 120,\n",
              " 136,\n",
              " 151,\n",
              " 152,\n",
              " 166,\n",
              " 172,\n",
              " 198,\n",
              " 206,\n",
              " 226,\n",
              " 231,\n",
              " 232,\n",
              " 241,\n",
              " 246,\n",
              " 267,\n",
              " 328,\n",
              " 350,\n",
              " 387,\n",
              " 389,\n",
              " 430,\n",
              " 453,\n",
              " 484,\n",
              " 488]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nOjkOscJZNIx"
      },
      "source": [
        "# Removing stocks that had less than 1000 rows of data from the DATAFRAME\r\n",
        "for i in list_drop:\r\n",
        "  df = df[df.Name != i]"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B0lc1UJAplNM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
        },
        "outputId": "2865235c-578a-40d4-f346-42e0cfd89073"
      },
      "source": [
        "# Filling NA a values with zeros \r\n",
        "df =  df.fillna(0)\r\n",
        "df.isnull().sum()\r\n",
        "\r\n",
        "# Calling the dataframe after all the tranformations have been completed\r\n",
        "df"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>volume</th>\n",
              "      <th>Name</th>\n",
              "      <th>52 week high</th>\n",
              "      <th>% Over/Under 52WKH</th>\n",
              "      <th>20 Day MA</th>\n",
              "      <th>% Over Under 20DMA</th>\n",
              "      <th>50 Day MA</th>\n",
              "      <th>% Over Under 50DMA</th>\n",
              "      <th>100 Day MA</th>\n",
              "      <th>% Over Under 100DMA</th>\n",
              "      <th>200 Day MA</th>\n",
              "      <th>% Over Under 200DMA</th>\n",
              "      <th>Average Gain/Loss</th>\n",
              "      <th>Average Gains2</th>\n",
              "      <th>Av Losses2</th>\n",
              "      <th>RSI V2</th>\n",
              "      <th>S&amp;P500_open</th>\n",
              "      <th>S&amp;P500_high</th>\n",
              "      <th>S&amp;P500_low</th>\n",
              "      <th>S&amp;P500_close</th>\n",
              "      <th>S&amp;P500_52 week high</th>\n",
              "      <th>S&amp;P500_% Over/Under 52WKH</th>\n",
              "      <th>S&amp;P500_20 Day MA</th>\n",
              "      <th>S&amp;P500_% Over Under 20DMA</th>\n",
              "      <th>S&amp;P500_50 Day MA</th>\n",
              "      <th>S&amp;P500_% Over Under 50DMA</th>\n",
              "      <th>S&amp;P500_100 Day MA</th>\n",
              "      <th>S&amp;P500_% Over Under 100DMA</th>\n",
              "      <th>S&amp;P500_200 Day MA</th>\n",
              "      <th>S&amp;P500_% Over Under 200DMA</th>\n",
              "      <th>S&amp;P500_Average Gain/Loss</th>\n",
              "      <th>S&amp;P500_Average Gains2</th>\n",
              "      <th>S&amp;P500_Av Losses2</th>\n",
              "      <th>S&amp;P500_RSI V2</th>\n",
              "      <th>SignalVal</th>\n",
              "      <th>ClosingStockPrice</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2014-02-20</td>\n",
              "      <td>56.1400</td>\n",
              "      <td>57.5350</td>\n",
              "      <td>56.0450</td>\n",
              "      <td>4629755.0</td>\n",
              "      <td>0</td>\n",
              "      <td>60.9300</td>\n",
              "      <td>-0.063536</td>\n",
              "      <td>58.02450</td>\n",
              "      <td>-0.012821</td>\n",
              "      <td>57.779800</td>\n",
              "      <td>-0.008549</td>\n",
              "      <td>54.891300</td>\n",
              "      <td>0.041869</td>\n",
              "      <td>50.454550</td>\n",
              "      <td>0.119313</td>\n",
              "      <td>0.024133</td>\n",
              "      <td>0.008602</td>\n",
              "      <td>-0.010923</td>\n",
              "      <td>44.057448</td>\n",
              "      <td>1829.24</td>\n",
              "      <td>1842.79</td>\n",
              "      <td>1824.58</td>\n",
              "      <td>1839.78</td>\n",
              "      <td>1848.38</td>\n",
              "      <td>-0.004653</td>\n",
              "      <td>1798.9785</td>\n",
              "      <td>0.022680</td>\n",
              "      <td>1813.7914</td>\n",
              "      <td>0.014328</td>\n",
              "      <td>1782.7328</td>\n",
              "      <td>0.032000</td>\n",
              "      <td>1720.68075</td>\n",
              "      <td>0.069216</td>\n",
              "      <td>0.006031</td>\n",
              "      <td>0.004559</td>\n",
              "      <td>-0.002723</td>\n",
              "      <td>62.607123</td>\n",
              "      <td>0</td>\n",
              "      <td>57.2900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2014-02-20</td>\n",
              "      <td>34.4300</td>\n",
              "      <td>35.6694</td>\n",
              "      <td>34.4300</td>\n",
              "      <td>9289448.0</td>\n",
              "      <td>1</td>\n",
              "      <td>35.6700</td>\n",
              "      <td>-0.000280</td>\n",
              "      <td>33.78775</td>\n",
              "      <td>0.052503</td>\n",
              "      <td>29.752800</td>\n",
              "      <td>0.165653</td>\n",
              "      <td>25.919000</td>\n",
              "      <td>0.273163</td>\n",
              "      <td>21.738800</td>\n",
              "      <td>0.390387</td>\n",
              "      <td>0.030934</td>\n",
              "      <td>0.008443</td>\n",
              "      <td>-0.004493</td>\n",
              "      <td>65.269321</td>\n",
              "      <td>1829.24</td>\n",
              "      <td>1842.79</td>\n",
              "      <td>1824.58</td>\n",
              "      <td>1839.78</td>\n",
              "      <td>1848.38</td>\n",
              "      <td>-0.004653</td>\n",
              "      <td>1798.9785</td>\n",
              "      <td>0.022680</td>\n",
              "      <td>1813.7914</td>\n",
              "      <td>0.014328</td>\n",
              "      <td>1782.7328</td>\n",
              "      <td>0.032000</td>\n",
              "      <td>1720.68075</td>\n",
              "      <td>0.069216</td>\n",
              "      <td>0.006031</td>\n",
              "      <td>0.004559</td>\n",
              "      <td>-0.002723</td>\n",
              "      <td>62.607123</td>\n",
              "      <td>1</td>\n",
              "      <td>35.6600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2014-02-20</td>\n",
              "      <td>125.9800</td>\n",
              "      <td>128.0600</td>\n",
              "      <td>125.7700</td>\n",
              "      <td>882740.0</td>\n",
              "      <td>2</td>\n",
              "      <td>127.5600</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>119.82800</td>\n",
              "      <td>0.060615</td>\n",
              "      <td>115.019400</td>\n",
              "      <td>0.098311</td>\n",
              "      <td>105.228700</td>\n",
              "      <td>0.175065</td>\n",
              "      <td>93.806650</td>\n",
              "      <td>0.264608</td>\n",
              "      <td>0.012863</td>\n",
              "      <td>0.013025</td>\n",
              "      <td>-0.005118</td>\n",
              "      <td>71.789496</td>\n",
              "      <td>1829.24</td>\n",
              "      <td>1842.79</td>\n",
              "      <td>1824.58</td>\n",
              "      <td>1839.78</td>\n",
              "      <td>1848.38</td>\n",
              "      <td>-0.004653</td>\n",
              "      <td>1798.9785</td>\n",
              "      <td>0.022680</td>\n",
              "      <td>1813.7914</td>\n",
              "      <td>0.014328</td>\n",
              "      <td>1782.7328</td>\n",
              "      <td>0.032000</td>\n",
              "      <td>1720.68075</td>\n",
              "      <td>0.069216</td>\n",
              "      <td>0.006031</td>\n",
              "      <td>0.004559</td>\n",
              "      <td>-0.002723</td>\n",
              "      <td>62.607123</td>\n",
              "      <td>1</td>\n",
              "      <td>127.5600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2014-02-20</td>\n",
              "      <td>76.1414</td>\n",
              "      <td>76.7142</td>\n",
              "      <td>75.5714</td>\n",
              "      <td>76529103.0</td>\n",
              "      <td>3</td>\n",
              "      <td>81.4413</td>\n",
              "      <td>-0.073312</td>\n",
              "      <td>75.13765</td>\n",
              "      <td>0.009764</td>\n",
              "      <td>77.393062</td>\n",
              "      <td>-0.019960</td>\n",
              "      <td>75.655671</td>\n",
              "      <td>0.002937</td>\n",
              "      <td>70.177389</td>\n",
              "      <td>0.075135</td>\n",
              "      <td>-0.011575</td>\n",
              "      <td>0.006434</td>\n",
              "      <td>-0.002029</td>\n",
              "      <td>76.027627</td>\n",
              "      <td>1829.24</td>\n",
              "      <td>1842.79</td>\n",
              "      <td>1824.58</td>\n",
              "      <td>1839.78</td>\n",
              "      <td>1848.38</td>\n",
              "      <td>-0.004653</td>\n",
              "      <td>1798.9785</td>\n",
              "      <td>0.022680</td>\n",
              "      <td>1813.7914</td>\n",
              "      <td>0.014328</td>\n",
              "      <td>1782.7328</td>\n",
              "      <td>0.032000</td>\n",
              "      <td>1720.68075</td>\n",
              "      <td>0.069216</td>\n",
              "      <td>0.006031</td>\n",
              "      <td>0.004559</td>\n",
              "      <td>-0.002723</td>\n",
              "      <td>62.607123</td>\n",
              "      <td>0</td>\n",
              "      <td>75.8785</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014-02-20</td>\n",
              "      <td>51.1900</td>\n",
              "      <td>52.1100</td>\n",
              "      <td>50.7300</td>\n",
              "      <td>6078933.0</td>\n",
              "      <td>4</td>\n",
              "      <td>54.3200</td>\n",
              "      <td>-0.047435</td>\n",
              "      <td>49.07700</td>\n",
              "      <td>0.053664</td>\n",
              "      <td>50.659600</td>\n",
              "      <td>0.023147</td>\n",
              "      <td>49.239200</td>\n",
              "      <td>0.050536</td>\n",
              "      <td>46.733500</td>\n",
              "      <td>0.098853</td>\n",
              "      <td>0.013088</td>\n",
              "      <td>0.008915</td>\n",
              "      <td>-0.003727</td>\n",
              "      <td>70.518376</td>\n",
              "      <td>1829.24</td>\n",
              "      <td>1842.79</td>\n",
              "      <td>1824.58</td>\n",
              "      <td>1839.78</td>\n",
              "      <td>1848.38</td>\n",
              "      <td>-0.004653</td>\n",
              "      <td>1798.9785</td>\n",
              "      <td>0.022680</td>\n",
              "      <td>1813.7914</td>\n",
              "      <td>0.014328</td>\n",
              "      <td>1782.7328</td>\n",
              "      <td>0.032000</td>\n",
              "      <td>1720.68075</td>\n",
              "      <td>0.069216</td>\n",
              "      <td>0.006031</td>\n",
              "      <td>0.004559</td>\n",
              "      <td>-0.002723</td>\n",
              "      <td>62.607123</td>\n",
              "      <td>1</td>\n",
              "      <td>51.8600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>494658</th>\n",
              "      <td>2018-02-07</td>\n",
              "      <td>71.2700</td>\n",
              "      <td>72.8600</td>\n",
              "      <td>71.1200</td>\n",
              "      <td>1748941.0</td>\n",
              "      <td>500</td>\n",
              "      <td>75.6600</td>\n",
              "      <td>-0.053907</td>\n",
              "      <td>72.22550</td>\n",
              "      <td>-0.006066</td>\n",
              "      <td>69.811800</td>\n",
              "      <td>0.027555</td>\n",
              "      <td>67.414450</td>\n",
              "      <td>0.060949</td>\n",
              "      <td>61.686075</td>\n",
              "      <td>0.140743</td>\n",
              "      <td>0.006449</td>\n",
              "      <td>0.007133</td>\n",
              "      <td>-0.006301</td>\n",
              "      <td>53.096000</td>\n",
              "      <td>2690.95</td>\n",
              "      <td>2727.67</td>\n",
              "      <td>2681.33</td>\n",
              "      <td>2681.66</td>\n",
              "      <td>2872.87</td>\n",
              "      <td>-0.066557</td>\n",
              "      <td>2791.0360</td>\n",
              "      <td>-0.039188</td>\n",
              "      <td>2719.6930</td>\n",
              "      <td>-0.013984</td>\n",
              "      <td>2637.5397</td>\n",
              "      <td>0.016728</td>\n",
              "      <td>2537.14260</td>\n",
              "      <td>0.056961</td>\n",
              "      <td>-0.005002</td>\n",
              "      <td>0.003214</td>\n",
              "      <td>-0.006145</td>\n",
              "      <td>34.343784</td>\n",
              "      <td>1</td>\n",
              "      <td>71.7900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>494659</th>\n",
              "      <td>2018-02-07</td>\n",
              "      <td>80.0700</td>\n",
              "      <td>81.9400</td>\n",
              "      <td>80.0000</td>\n",
              "      <td>3561068.0</td>\n",
              "      <td>501</td>\n",
              "      <td>86.4800</td>\n",
              "      <td>-0.079246</td>\n",
              "      <td>83.61100</td>\n",
              "      <td>-0.043442</td>\n",
              "      <td>82.888800</td>\n",
              "      <td>-0.034429</td>\n",
              "      <td>79.730900</td>\n",
              "      <td>0.004981</td>\n",
              "      <td>76.546600</td>\n",
              "      <td>0.044720</td>\n",
              "      <td>-0.005585</td>\n",
              "      <td>0.004063</td>\n",
              "      <td>-0.006569</td>\n",
              "      <td>38.212517</td>\n",
              "      <td>2690.95</td>\n",
              "      <td>2727.67</td>\n",
              "      <td>2681.33</td>\n",
              "      <td>2681.66</td>\n",
              "      <td>2872.87</td>\n",
              "      <td>-0.066557</td>\n",
              "      <td>2791.0360</td>\n",
              "      <td>-0.039188</td>\n",
              "      <td>2719.6930</td>\n",
              "      <td>-0.013984</td>\n",
              "      <td>2637.5397</td>\n",
              "      <td>0.016728</td>\n",
              "      <td>2537.14260</td>\n",
              "      <td>0.056961</td>\n",
              "      <td>-0.005002</td>\n",
              "      <td>0.003214</td>\n",
              "      <td>-0.006145</td>\n",
              "      <td>34.343784</td>\n",
              "      <td>0</td>\n",
              "      <td>80.1300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>494660</th>\n",
              "      <td>2018-02-07</td>\n",
              "      <td>121.6500</td>\n",
              "      <td>123.5200</td>\n",
              "      <td>120.7400</td>\n",
              "      <td>1145267.0</td>\n",
              "      <td>502</td>\n",
              "      <td>132.6100</td>\n",
              "      <td>-0.097947</td>\n",
              "      <td>124.20750</td>\n",
              "      <td>-0.028378</td>\n",
              "      <td>120.670200</td>\n",
              "      <td>0.000909</td>\n",
              "      <td>118.452500</td>\n",
              "      <td>0.019271</td>\n",
              "      <td>119.748000</td>\n",
              "      <td>0.008544</td>\n",
              "      <td>-0.012428</td>\n",
              "      <td>0.005400</td>\n",
              "      <td>-0.006147</td>\n",
              "      <td>46.766489</td>\n",
              "      <td>2690.95</td>\n",
              "      <td>2727.67</td>\n",
              "      <td>2681.33</td>\n",
              "      <td>2681.66</td>\n",
              "      <td>2872.87</td>\n",
              "      <td>-0.066557</td>\n",
              "      <td>2791.0360</td>\n",
              "      <td>-0.039188</td>\n",
              "      <td>2719.6930</td>\n",
              "      <td>-0.013984</td>\n",
              "      <td>2637.5397</td>\n",
              "      <td>0.016728</td>\n",
              "      <td>2537.14260</td>\n",
              "      <td>0.056961</td>\n",
              "      <td>-0.005002</td>\n",
              "      <td>0.003214</td>\n",
              "      <td>-0.006145</td>\n",
              "      <td>34.343784</td>\n",
              "      <td>1</td>\n",
              "      <td>120.7800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>494661</th>\n",
              "      <td>2018-02-07</td>\n",
              "      <td>52.2300</td>\n",
              "      <td>54.3150</td>\n",
              "      <td>52.2200</td>\n",
              "      <td>6427280.0</td>\n",
              "      <td>503</td>\n",
              "      <td>54.9800</td>\n",
              "      <td>-0.017771</td>\n",
              "      <td>53.50650</td>\n",
              "      <td>0.009506</td>\n",
              "      <td>51.634200</td>\n",
              "      <td>0.044165</td>\n",
              "      <td>48.920000</td>\n",
              "      <td>0.094409</td>\n",
              "      <td>45.887100</td>\n",
              "      <td>0.150553</td>\n",
              "      <td>0.028561</td>\n",
              "      <td>0.007964</td>\n",
              "      <td>-0.006243</td>\n",
              "      <td>56.057889</td>\n",
              "      <td>2690.95</td>\n",
              "      <td>2727.67</td>\n",
              "      <td>2681.33</td>\n",
              "      <td>2681.66</td>\n",
              "      <td>2872.87</td>\n",
              "      <td>-0.066557</td>\n",
              "      <td>2791.0360</td>\n",
              "      <td>-0.039188</td>\n",
              "      <td>2719.6930</td>\n",
              "      <td>-0.013984</td>\n",
              "      <td>2637.5397</td>\n",
              "      <td>0.016728</td>\n",
              "      <td>2537.14260</td>\n",
              "      <td>0.056961</td>\n",
              "      <td>-0.005002</td>\n",
              "      <td>0.003214</td>\n",
              "      <td>-0.006145</td>\n",
              "      <td>34.343784</td>\n",
              "      <td>1</td>\n",
              "      <td>54.0200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>494662</th>\n",
              "      <td>2018-02-07</td>\n",
              "      <td>72.7000</td>\n",
              "      <td>75.0000</td>\n",
              "      <td>72.6900</td>\n",
              "      <td>4534912.0</td>\n",
              "      <td>504</td>\n",
              "      <td>80.0900</td>\n",
              "      <td>-0.084349</td>\n",
              "      <td>76.58550</td>\n",
              "      <td>-0.036901</td>\n",
              "      <td>73.960800</td>\n",
              "      <td>-0.001365</td>\n",
              "      <td>70.031200</td>\n",
              "      <td>0.051839</td>\n",
              "      <td>65.820700</td>\n",
              "      <td>0.108845</td>\n",
              "      <td>0.008052</td>\n",
              "      <td>0.005042</td>\n",
              "      <td>-0.007278</td>\n",
              "      <td>40.923729</td>\n",
              "      <td>2690.95</td>\n",
              "      <td>2727.67</td>\n",
              "      <td>2681.33</td>\n",
              "      <td>2681.66</td>\n",
              "      <td>2872.87</td>\n",
              "      <td>-0.066557</td>\n",
              "      <td>2791.0360</td>\n",
              "      <td>-0.039188</td>\n",
              "      <td>2719.6930</td>\n",
              "      <td>-0.013984</td>\n",
              "      <td>2637.5397</td>\n",
              "      <td>0.016728</td>\n",
              "      <td>2537.14260</td>\n",
              "      <td>0.056961</td>\n",
              "      <td>-0.005002</td>\n",
              "      <td>0.003214</td>\n",
              "      <td>-0.006145</td>\n",
              "      <td>34.343784</td>\n",
              "      <td>1</td>\n",
              "      <td>73.8600</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>479000 rows × 40 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "              Date      open  ...  SignalVal  ClosingStockPrice\n",
              "0       2014-02-20   56.1400  ...          0            57.2900\n",
              "1       2014-02-20   34.4300  ...          1            35.6600\n",
              "2       2014-02-20  125.9800  ...          1           127.5600\n",
              "3       2014-02-20   76.1414  ...          0            75.8785\n",
              "4       2014-02-20   51.1900  ...          1            51.8600\n",
              "...            ...       ...  ...        ...                ...\n",
              "494658  2018-02-07   71.2700  ...          1            71.7900\n",
              "494659  2018-02-07   80.0700  ...          0            80.1300\n",
              "494660  2018-02-07  121.6500  ...          1           120.7800\n",
              "494661  2018-02-07   52.2300  ...          1            54.0200\n",
              "494662  2018-02-07   72.7000  ...          1            73.8600\n",
              "\n",
              "[479000 rows x 40 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "id": "q87CSOjg0y0G",
        "outputId": "dd51860a-d8df-4439-9218-bddb01a784b9"
      },
      "source": [
        "# Checking collinearity of variables by creating a correlation matrix\r\n",
        "corrmat = df.corr()\r\n",
        "f, ax = plt.subplots(figsize=(12, 9))\r\n",
        "sns.heatmap(corrmat, vmax=.8, square=True)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f68634b5048>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7dpntsOLy2Tw"
      },
      "source": [
        "### **Creating X-Y split**\r\n",
        "\r\n",
        "All variables apart from Closing Stock Price were placed in the set of X (independent) variables. Closing Stock Price was the Y (dependent) variable."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KRav2cPpug_a"
      },
      "source": [
        "# Creating X and Y split, and offsetting data by 479*60. 479 is the number of stocks remaining on the list, while 60 is the number of days to be offset by \n",
        "X = df.values[:(len(df)-(479*60)),0:41]\n",
        "Y = df.values[60*479:,-1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yjBeXcgOug_a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e08e9ef0-c125-46e6-beec-a4807ce10ae0"
      },
      "source": [
        "# Printing the shapes to ensure they are the appropriate size\r\n",
        "print(X.shape,\r\n",
        "      Y.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(450260, 40) (450260,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u3ao3bVNy7TG"
      },
      "source": [
        "### **Creating Train-Test sets.**\r\n",
        "\r\n",
        "For the purposes of creating a more robust model, we opted not to split the dataset into a chronologically earlier training set followed by a chronologically later test set. Instead, the training set was randomly chosen from records within the span of 2014 to 2018, and so was the test set. \r\n",
        "\r\n",
        "The train-test split was done 75-25. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TRVWitILug_a"
      },
      "source": [
        "# Creating Train-Test split from X and Y data, using randomized rows from entire dataset instead of chronological\r\n",
        "X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size = 0.25, random_state = 100)\r\n",
        "\r\n",
        "\r\n",
        "# traintest split instead of chronological, might not get as good a predictive model (do both ways start with train-test split)\r\n",
        "# Y is the next 12 months data (regardless of train- test split)\r\n",
        "# Might try to predict 1 month forward"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7LA1BU6Gug_b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "74263788-851e-4b1c-ec90-7555a55285a9"
      },
      "source": [
        "# Printing the shapes to ensure they are the appropriate size\r\n",
        "\r\n",
        "print(X_train.shape,\r\n",
        "      Y_train.shape,\r\n",
        "      \r\n",
        "      X_test.shape,\r\n",
        "      Y_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(337695, 40) (337695,) (112565, 40) (112565,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pLLav9rjMe_d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f29053c6-cd02-4381-bceb-459fe6a163c8"
      },
      "source": [
        "\r\n",
        "X_test_date = X_test[:,0]\r\n",
        "X_test_date"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['2017-05-02', '2016-12-07', '2014-08-20', ..., '2017-03-15',\n",
              "       '2015-07-23', '2015-02-02'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JDrtqxU_NR6F",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "802b391b-c2e6-4d32-f2ea-300d06988e4d"
      },
      "source": [
        "X_test = X_test[:,1:]\r\n",
        "X_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[249.9, 251.02, 248.76, ..., 63.93915415, 1, 250.84],\n",
              "       [81.25, 83.4, 80.6, ..., 75.75602724, 1, 83.15],\n",
              "       [162.19, 163.26, 161.68, ..., 71.03595853, 0, 162.78],\n",
              "       ...,\n",
              "       [121.74, 122.73, 121.02, ..., 59.24559951, 0, 122.12],\n",
              "       [558.19, 559.78, 553.14, ..., 57.85643971, 1, 556.61],\n",
              "       [33.24, 33.81, 32.88, ..., 48.79504779999999, 0, 33.77]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8rVaUtxyNkyX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a0af206d-c299-4bc5-a7d2-c3bbdfed091c"
      },
      "source": [
        "X_train = X_train[:,1:]\r\n",
        "X_train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[77.33, 77.88, 76.65, ..., 67.48817556, 1, 76.78],\n",
              "       [67.58, 68.65, 67.32, ..., 41.25669323, 1, 68.6],\n",
              "       [109.78, 110.44, 108.93, ..., 55.86416212, 0, 109.31],\n",
              "       ...,\n",
              "       [35.8, 36.685, 35.74, ..., 46.89373178, 1, 36.6],\n",
              "       [107.48, 107.75, 106.0, ..., 22.92593789, 1, 107.12],\n",
              "       [36.63, 36.7, 36.01, ..., 56.65291473, 0, 36.19]], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N_ZpLXrlP7_Y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "66b4b42a-9fe9-4deb-d18f-13768cc58a7a"
      },
      "source": [
        "print(X_train.shape,\r\n",
        "      Y_train.shape,\r\n",
        "      \r\n",
        "      X_test.shape,\r\n",
        "      Y_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(337695, 39) (337695,) (112565, 39) (112565,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "REASAFCrnRuS"
      },
      "source": [
        "### **RandomForest Regressor**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fs6s19wGnRua",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f7cdd67a-b1be-4d98-b9d8-d5e5b7d349b0"
      },
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\r\n",
        "\r\n",
        "RF_Model= RandomForestRegressor(max_depth=3, random_state=0)\r\n",
        "RF_Model.fit(X_train, Y_train)\r\n",
        "RF_Model.score(X_train, Y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9540581916489024"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MQ6iBEnVnRub",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "52522edb-3b4c-4dd5-bbe7-15f9ec32ffef"
      },
      "source": [
        "Y_test_RF = RF_Model.predict(X_test)\r\n",
        "print (\"RF accuracy is \", RF_Model.score(X_test,Y_test)*100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RF accuracy is  95.28908183499983\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x3yc4PDMgNOf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8175f122-e2d3-4019-b281-a7f1192e0faa"
      },
      "source": [
        "Y_test_RF"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([288.70656733,  85.99752344, 155.08011625, ..., 146.05468683,\n",
              "       578.62109233,  40.53820719])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FAQAEggngKKh"
      },
      "source": [
        "sample = X_test[100:105,:]\r\n",
        "sampley = Y_test[100:105]\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CXWDxl774uok",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f37be22b-53f0-4044-dffb-416f1cbe667a"
      },
      "source": [
        "Y_predict1 = RF_Model.predict(sample)\r\n",
        "# prof's suggested method of checking how good the results are\r\n",
        "RF_Model.score(sample,sampley)*100"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "99.11061083594491"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "97kOGxBTh-O4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5bcfa3f4-43b0-4d79-f687-b16b0530b1bd"
      },
      "source": [
        "Y_predict1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 40.53820719, 155.08011625,  40.53820719,  85.99752344,\n",
              "        40.53820719])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IHWymYQu5Hi6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 160
        },
        "outputId": "7161286e-8f75-43f2-a3aa-cfaba10f6172"
      },
      "source": [
        "pd.DataFrame(data=[Y_test,Y_test_RF])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
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              "      <th>6</th>\n",
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              "      <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",
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              "      <th>32</th>\n",
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              "      <th>37</th>\n",
              "      <th>38</th>\n",
              "      <th>39</th>\n",
              "      <th>...</th>\n",
              "      <th>112525</th>\n",
              "      <th>112526</th>\n",
              "      <th>112527</th>\n",
              "      <th>112528</th>\n",
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              "      <th>112530</th>\n",
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              "      <th>112532</th>\n",
              "      <th>112533</th>\n",
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              "      <th>112539</th>\n",
              "      <th>112540</th>\n",
              "      <th>112541</th>\n",
              "      <th>112542</th>\n",
              "      <th>112543</th>\n",
              "      <th>112544</th>\n",
              "      <th>112545</th>\n",
              "      <th>112546</th>\n",
              "      <th>112547</th>\n",
              "      <th>112548</th>\n",
              "      <th>112549</th>\n",
              "      <th>112550</th>\n",
              "      <th>112551</th>\n",
              "      <th>112552</th>\n",
              "      <th>112553</th>\n",
              "      <th>112554</th>\n",
              "      <th>112555</th>\n",
              "      <th>112556</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>282.490000</td>\n",
              "      <td>94.540000</td>\n",
              "      <td>170.780000</td>\n",
              "      <td>25.150000</td>\n",
              "      <td>95.400000</td>\n",
              "      <td>37.480000</td>\n",
              "      <td>61.650000</td>\n",
              "      <td>54.666700</td>\n",
              "      <td>32.735000</td>\n",
              "      <td>26.200000</td>\n",
              "      <td>121.090000</td>\n",
              "      <td>106.570000</td>\n",
              "      <td>78.830000</td>\n",
              "      <td>67.960000</td>\n",
              "      <td>36.800000</td>\n",
              "      <td>69.810000</td>\n",
              "      <td>65.830000</td>\n",
              "      <td>290.890000</td>\n",
              "      <td>79.960000</td>\n",
              "      <td>68.340000</td>\n",
              "      <td>40.520000</td>\n",
              "      <td>154.010000</td>\n",
              "      <td>42.940000</td>\n",
              "      <td>61.220000</td>\n",
              "      <td>71.550000</td>\n",
              "      <td>63.390000</td>\n",
              "      <td>191.550000</td>\n",
              "      <td>55.780000</td>\n",
              "      <td>103.660000</td>\n",
              "      <td>14.190000</td>\n",
              "      <td>81.220000</td>\n",
              "      <td>15.410000</td>\n",
              "      <td>47.510000</td>\n",
              "      <td>96.080000</td>\n",
              "      <td>59.590000</td>\n",
              "      <td>156.680000</td>\n",
              "      <td>82.870000</td>\n",
              "      <td>59.470000</td>\n",
              "      <td>217.960000</td>\n",
              "      <td>77.660000</td>\n",
              "      <td>...</td>\n",
              "      <td>71.170000</td>\n",
              "      <td>154.920000</td>\n",
              "      <td>125.500000</td>\n",
              "      <td>42.830000</td>\n",
              "      <td>82.260000</td>\n",
              "      <td>147.070000</td>\n",
              "      <td>130.310000</td>\n",
              "      <td>198.170000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>72.910000</td>\n",
              "      <td>46.130000</td>\n",
              "      <td>358.980000</td>\n",
              "      <td>190.360000</td>\n",
              "      <td>132.190000</td>\n",
              "      <td>191.200000</td>\n",
              "      <td>184.640000</td>\n",
              "      <td>45.130000</td>\n",
              "      <td>62.385000</td>\n",
              "      <td>114.000000</td>\n",
              "      <td>105.600000</td>\n",
              "      <td>85.590000</td>\n",
              "      <td>63.640000</td>\n",
              "      <td>67.850000</td>\n",
              "      <td>72.570000</td>\n",
              "      <td>44.300000</td>\n",
              "      <td>83.570000</td>\n",
              "      <td>44.420000</td>\n",
              "      <td>57.320000</td>\n",
              "      <td>447.900000</td>\n",
              "      <td>34.870000</td>\n",
              "      <td>47.280000</td>\n",
              "      <td>39.650000</td>\n",
              "      <td>652.480000</td>\n",
              "      <td>54.350000</td>\n",
              "      <td>137.460000</td>\n",
              "      <td>52.200000</td>\n",
              "      <td>16.260000</td>\n",
              "      <td>105.380000</td>\n",
              "      <td>514.590000</td>\n",
              "      <td>34.620000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>288.706567</td>\n",
              "      <td>85.997523</td>\n",
              "      <td>155.080116</td>\n",
              "      <td>40.538207</td>\n",
              "      <td>85.997523</td>\n",
              "      <td>40.538207</td>\n",
              "      <td>40.538207</td>\n",
              "      <td>40.538207</td>\n",
              "      <td>40.538207</td>\n",
              "      <td>40.538207</td>\n",
              "      <td>87.276923</td>\n",
              "      <td>85.997523</td>\n",
              "      <td>85.997523</td>\n",
              "      <td>85.997523</td>\n",
              "      <td>40.538207</td>\n",
              "      <td>52.013237</td>\n",
              "      <td>80.236454</td>\n",
              "      <td>288.706567</td>\n",
              "      <td>85.997523</td>\n",
              "      <td>40.538207</td>\n",
              "      <td>77.492105</td>\n",
              "      <td>155.080116</td>\n",
              "      <td>40.538207</td>\n",
              "      <td>40.538207</td>\n",
              "      <td>85.997523</td>\n",
              "      <td>85.997523</td>\n",
              "      <td>155.080116</td>\n",
              "      <td>40.538207</td>\n",
              "      <td>85.997523</td>\n",
              "      <td>40.538207</td>\n",
              "      <td>85.997523</td>\n",
              "      <td>40.538207</td>\n",
              "      <td>40.538207</td>\n",
              "      <td>85.997523</td>\n",
              "      <td>40.538207</td>\n",
              "      <td>155.080116</td>\n",
              "      <td>85.997523</td>\n",
              "      <td>85.997523</td>\n",
              "      <td>217.768244</td>\n",
              "      <td>85.997523</td>\n",
              "      <td>...</td>\n",
              "      <td>85.508969</td>\n",
              "      <td>155.080116</td>\n",
              "      <td>121.681742</td>\n",
              "      <td>40.538207</td>\n",
              "      <td>85.997523</td>\n",
              "      <td>155.080116</td>\n",
              "      <td>146.054687</td>\n",
              "      <td>155.080116</td>\n",
              "      <td>40.538207</td>\n",
              "      <td>85.997523</td>\n",
              "      <td>40.538207</td>\n",
              "      <td>288.706567</td>\n",
              "      <td>155.080116</td>\n",
              "      <td>85.997523</td>\n",
              "      <td>155.080116</td>\n",
              "      <td>155.080116</td>\n",
              "      <td>40.538207</td>\n",
              "      <td>85.997523</td>\n",
              "      <td>85.997523</td>\n",
              "      <td>100.958269</td>\n",
              "      <td>85.997523</td>\n",
              "      <td>85.997523</td>\n",
              "      <td>59.704457</td>\n",
              "      <td>85.508969</td>\n",
              "      <td>40.538207</td>\n",
              "      <td>85.997523</td>\n",
              "      <td>40.538207</td>\n",
              "      <td>40.538207</td>\n",
              "      <td>422.921975</td>\n",
              "      <td>40.538207</td>\n",
              "      <td>80.236454</td>\n",
              "      <td>40.538207</td>\n",
              "      <td>578.621092</td>\n",
              "      <td>80.236454</td>\n",
              "      <td>85.997523</td>\n",
              "      <td>40.538207</td>\n",
              "      <td>40.538207</td>\n",
              "      <td>146.054687</td>\n",
              "      <td>578.621092</td>\n",
              "      <td>40.538207</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 112565 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       0          1           2       ...      112562      112563     112564\n",
              "0  282.490000  94.540000  170.780000  ...  105.380000  514.590000  34.620000\n",
              "1  288.706567  85.997523  155.080116  ...  146.054687  578.621092  40.538207\n",
              "\n",
              "[2 rows x 112565 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VBW1G0OhIpl8"
      },
      "source": [
        "### **Graphing RandomForest Regressor**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RmwpPbDUPf81"
      },
      "source": [
        "# merging the arrays for graphing purposes\r\n",
        "Chart1 = pd.DataFrame(data = X_test_date, columns=[\"Date\"])\r\n",
        "Chart2 = pd.DataFrame(data= Y_test, columns= [\"Y_test\"])\r\n",
        "Chart3 = pd.DataFrame(data= Y_test_RF, columns= [\"Y_test_RF\"])\r\n",
        "Chart4 = pd.DataFrame(data = X_test[:,4], columns=[\"Name\"])\r\n",
        "\r\n",
        "Chart = pd.concat([Chart1, Chart4, Chart2, Chart3], axis = 1)\r\n",
        "\r\n",
        "Chart2 = Chart.sort_values(by=['Date', \"Name\"], ascending= True)\r\n",
        "Chart2[\"Date\"] = pd.to_datetime(Chart2.Date,format='%Y-%m-%d')\r\n",
        "Chart2.index = Chart2[\"Date\"]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YH2iqSyTIozT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "9197f47e-20a2-4453-a119-cd3af8484c34"
      },
      "source": [
        "rcParams['figure.figsize'] = 20,10\r\n",
        "\r\n",
        "#plots\r\n",
        "for i in [3, 38, 154, 207]:\r\n",
        "  Chart3 = Chart2[Chart2.Name == i]\r\n",
        "  plt.figure(figsize=(16,8))\r\n",
        "  plt.plot(Chart3['Y_test'], label='Close Price history')\r\n",
        "  plt.plot(Chart3['Y_test_RF'], label='Close Price history')\r\n",
        "# 3, 38, 154, 207 Corresponds to the 4 sample stock names: AAPL, AMZN, EBAY, GOOGL"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Y2oABAICXEVYBjDrr99bp+ClFktRVWU3anNVoWE1WZbW/sGoJqwAAwLsIqwBGldrmDu0+3KpFUwolSX5fMsNqq3MSsOSEVtf3rFrRBgwAANIVYRXAqFJxyBknM7s0X5K6TgNOSljtaHEOV5KkjOwknQbMAUsAACA9EVYBjCr76pzAOLkoR9KRymo4WXNWM5z7KJAzjG3AjK4BAADeR1gFMKpU1TuHHJX1CKvJbwPOdv+ApVhtwNFqK5VVAADgYYRVAKPK3rpW5WX6VZDjVB99kbDamfQ24FwpHJRCLgZIG+67DdgYyfiprAIAAE8jrAIYVfbVtWpyUY5MpH02umc1+W3A2c6108XqaqzRNZLTCkxlFQAAeBhhFcCoUlXf1rVfVZICyWwD7mjp1gYcuaerhyzFOGBJIqwCAADPI6wCGFWcymp219fRNuBwsvasRtuAk1JZDavPPatSJKzSBgwAALyLsApg1GgLhnSwqUOTC49UVv3J3LMabO6jsupmWFU/bcA+KqsAAMDTAiO9AADo7skN+3Xfa7vU3hnSNSdN06WLy1x776r6o8fWSJIvOmfV7T2rnR1OWIyG1GSE1YHagC2VVQAA4F1UVgGklLtf3KE1u2q182Cz/vNvm9Te6V7gqqpzgmJfe1ZdbwMONjvXXm3ALu5ZteHYz7FnFQAAeBxhFUDKCIWtNu5r0GVLpuh7VxyvmsZ2PbG+Ku6f3bC3vt+DkvbWHT1jVUrinNVoBTWpbcADnQZMZRUAAHgXYRVAythe06TWYEiLygp1xpwSzR6fr3tW7pQdoEW3ozOsT9+3Rhf/ZIXO++/n9cdXd6kt2Duo7atzqpoTCrO6vtfVBux2WO1oca4ZyQyrMeasSpLPT2UVAAB4GmEVQMp4c0+9JOm4KYUyxuhD7yjXhr0NWlVZG/NnWjo6ddPvVumJN6v0wdPKNSY7oC8+8qaWf/dZ/ezZbapvCXa9tqq+VaVjspQV8Hd9r6uy6vae1a424EhYDUTCqpunATsnLPX9lPFTWQUAAJ7GAUsAUsabe+uVm+nXzNJ8SdJli6foe09u1j0rduqk8uJer69vCepD//ua1u6u0/cuP05XnTRV1lq9vOOQfvX8Dn3/H5v1s2e36f3Lpunfls/Q3rrWo/arSsPZBhzZs+rmnFXLnFUAAJC+CKsAUsabe+u1YFJBV4DMyfTr/cum6a4Xtmv34RZNLc7tem11Q5tuuOc17ahp1s+vW6J3LZwkSTLG6LRZJTptVok2VTXorhd26N6XKnTvSxXy+4zOnT/+qHtG7xV2u7La0fOApSRUVm14gD2rhFUAAOBdtAEDSAmhsNVb+xq0sKzwqO/fcOp0GWP0u5crur63+3CLrvzVy9p1uEX3fPCkrqDa0/xJBfrR1Sfo+c+drRtPK5ffZ7SorOio1/gjYa8z5HZlNbpnNcmja2K1AftoAwYAAN5GZRVASogernTclKPD6uSiHF24cKL+9Ppu3XreXO2pbdX1v3lV7Z1h/eGmk7V42tgB37usKEdfuXiBvvTu+fL5jg53vsg/2blfWY2E1cxIZbUrrA5XGzAHLAEAAG8jrAJICWt310mSFvWorErSvy2fob+ur9Jn/7xOL+84pOwMnx742KmaO2HMoO7RM6hKUiCSVkP9jCwdkmCP04D9Gc6hR8PZBmyprAIAAO+Kqw3YGFNkjHnQGPO2MWaTMeZUY0yxMeafxpitkevA5Q0A6ENHZ1i/fH67ysfldh2u1N2SaWO1eFqRnty4X0W5GXrwY6cNOqjGEq2sun8acI824OhjNyur/bYBs2cVAAB4W7yV1R9LetJae4UxJlNSrqQvSnrGWvsdY8znJX1e0h1JWieANPa7lyu0o6ZZ93xwadeBRz39+oalevbtap19zHiV5Gf1+ZqhiO5ZDbldWu3ZBixFwmqLe/cY8DRgKqsAAMC7BqysGmMKJZ0h6TeSZK3tsNbWSXqvpHsjL7tX0qXJWiSA9FXd2KY7n96qc44Zr3OOmRDzdSX5Wbpy6VRXg6rUfc6qq2/rhFJfhtP+GxXIkTrd3rPa3wFLVFYBAIB3xdMGPENSjaTfGmPWGGPuNsbkSZpgra2KvGa/pNifMgEghu89uVntnSF95eIFI3L/rtE1rs9ZbTkyYzUqI9v904BjhVVDWAUAAN4WT1gNSFoi6RfW2sWSmuW0/Hax1lo5m6d6McZ8xBizyhizqqamJtH1Akgja3bV6sHVe/Th5TM1oyRv4B9IgiOV1STMWc3oEVYD2S5XVsPqf88qbcAAAMC74gmreyTtsda+Gvn6QTnh9YAxZpIkRa7Vff2wtfYua+1Sa+3S0tJSN9YMIA2Ew1Zff2yjxo/J0ifPmT1i6/BF96wmo7LaM6xm5Lq3ZzXUKbU1HL0ntjvCKgAA8LgBD1iy1u43xuw2xsyz1m6WdK6ktyK/bpT0ncj10aSuFIDnNbQF9dHfrdYpM8dpfEGW1u2p14+uPl75WSM3Raursup6WG3tuw24w6Wwun+dFGyWpi7r+3n2rAIAAI+L9xPipyT9IXIS8A5JH5JTlf2zMebDkiolXZWcJQJIF9978m29vOOQXt5xSJK0ZFqRLj2hbETX5E9WZbXPNuAcqeWQO+9fscK5Tl/e9/OMrgEAAB4XV1i11q6VtLSPp851dzkAvKKjM6y7Xtiu606errF5mQO+/vWKw/q/V3bpw8tnaGFZge56Yaf+49KFMrEOCBomPp+RMVI4GXNWM3vMjM3Idm/OasVKadwcaUyMs+2orAIAAI8bud47AJ62YluNfvDUFlUeatH3rzy+39e2BUP6/EPrNWVsjj57wVzlZgb0vsVThmmlA/Mbo85ktAHn9wiSGbnunAYcDkm7XpYWXh77Nb6AZNmzCgAAvCueA5YAoJdVFbWSpAdW79G63XX9vvbnz27T9ppm/ef7Fik3M/X+jcznM+6PruloljJyjv5eIFvqdCGs7l8vtTdI5TFagKVIZZWwCgAAvIuwCnhETWO77BBaVetbgvr23zfpQIOLI1PkhNV5E8aoJD9L33h8Y8y1bd7fqJ8/t12XLS7TmXNT80TwgM8M02nAOe60AXftV31H7NewZxUAAHhc6pU4APTymxU79R9/fUsLywp0/SnTtbCsUO2dYXV0hrtdQ0d9nZ8V0HtPmKwnN1bpV8/v0F/W7NVvbjxJC8sKE15Pe2dIa/fU6YZTpmvuxDH63IPr9di6fXpvj8OSQmGrOx5ar4KcDH354gUJ3zdZ/MYkYc5qS++xMhk5TmXVWimRvboVK6XiWVLBpNivIawCAACPI6wCKe7JDfv1rSfe0skzilXb0qE7Hnoz7p/dcqBRPp9RwGcU8Pl0xS9f0o+uOkEXLuon5MRhw94GdXSGtbS8WBcsmKDfv1ypb//tbZ03f4Lyuo2h+f3LFVq7u04/vuYEFcdxCNNI8bldWbW278pqIFuyYSnUIQWyhvbe4ZBU+ZJ07KX9v87QBgwAALyNsAqksLW763Tr/Wt0wtQi3ftvy5QV8OmNXXU62NSuzIBPWZFfmX6/sjJ8yvT7uq7//c8t+tULOzS5MFvTinN1/0dP1Ud+v0of/8Mbuv2Cubrl7NlDPol3VcVhSdKJ08fK5zP6+iULdMUvX9Y3H39L373iOEnS3rpWfe8fm3XWvFJdcvxk135PksHvdlgNdTiHG/Xcsxr9Otg69LB6YIPUXt//flUpUlklrAIAAO8irAIpavfhFt107+sqHZOlX9+wVNkZfklOQIzHV9+zQBv2NWjd7jqdNa9UpWOydN/Np+jzD63XD57aom3VTfrO5cd1ve9grKqs1YySPJWOyYqsqVi3nDVbP312m06eWazLlkzRV/6yQZL0rRQYTzMQv8+4O7qmo9m59mwDDmQ7184E9q1WrHSu/e1XlRhdAwAAPI8DloAUVN8S1If+93UFQ1a//eAyleQPvgqXFfDrF9ctUUl+lhZOdvapZmf49aOrT9DtF8zVX9bu07W/fkU1je2Del9rrVZVHNbSHqH51vPmaNmMYn3pkQ16aftB/evtan30jFmaMjY3xjulDr9xubIabHGuvQ5YinydyPiaihXS2BlSYVn/r2PPKgAA8DjCKpBirLW65Y9vqPJQs351/YmaPT5/yO81uShHL37ubN12/tyu7xlj9Mlz5ujn1y3RW1UNuvRnK7WpqiHu99xe06zalqCWlh8dVgN+n37y/sXKzfTr5ntXSZJOmz1uyGsfTn6fy3NWo2G0V1jNPvr5wQqHpcqVUvkAVVWJyioAAPA8wioQ0dgW1OW/eElPv3VgRNexp7ZVK7Yd1G3nz9MpMxMPezmZfvl8vdtw371okh746GnqDId1xSD+u1dXOvtVl5YX93puQkG2/vvqE9TcEZLPSMdOLkhs8cPE55O7c1a72oB7HrAU2bM61Fmr1Rultjqp/PSBX+sLSLJOwAUAAPAgwioQsb2mWasra3XT71bp7hd36FDT4Npj3bKtpkmSelUuk2HRlEI99snlmlmar5t/v0p3vbB9wFmur1fUqjgvUzNL8vp8/sy5pfriu4/R5UumKDfTG9viAz6fQm5OronZBhytrA5xz2q8+1Ulp7IqOQc9AQAAeBBhFYiIhtPMgE/femKTTvn2M3pyw/5Bv084bPXQ6j1q7xxaSNhe7YTVWaVDb/8djAkF2frzR0/VhQsn6r/+9rbueGi9OjpjV+NWVRzWidPH9nto0kfOmKXvX3l8MpabFD7jcmU1GlZ7zVmNhNehVlYrXpSKpktFUwd+rS/yDwW0AgMAAI/yRtkDGAaHmjskSc/cdqaaOzp1x4PrdfsD63TMxDEqj1FF7Mua3XX67APr1NTeqRtPKx/0OnYcbNbY3IxhnUuak+nXT9+/RHeWbtH//GubtlY36b3HT9a4/CxtOdCot/c3asrYHH3irNmqONSia0+eNmxrGw7OnlUX22U7opXVHqNrAgnsWQ2Hnfmq8y6M7/WEVQAA4HGEVSDiUJMTVsflZ2pqZq5+/oETddH/vKhP/OENPfyJ0+Ie8XKgwWnxfHTt3iGF1e3VTcNWVe3O5zO67YJ5mjU+X9/+29v6+uNvSXKCXG6GX+2hsE6e4exTPXF67/2qXuYzRiE3t3bGbAOOzlkdQhtwzSap9fDA81WjTOTPK2EVAAB4FG3AQMShpnblZvq79lmWFeXov686Xm9VNegbj2+M+30ORtqJ39hVp12HWga9ju01zZpZGn8l123vPaFMr3zxXL3yhXP1t0+fro3feKduOWe2OjrDen7LQWUFfFpY5o2Dk+IV8A/3nNUhVFYHs19V6lZZ5YAlAADgTYRVIOJQc4fG5R/denvOMRP08bNm6b7XduvhN/bE9T41je2Kbud8bN3eQa2hviWog03tI1JZ7WliYbYWTC5QdoZfxbnO78vTmw7o+KlFygrEV2X2CvfnrEZH1/RoA+6aszqEymrFi1LhNGns9Phe76OyCgAAvI2wCsiZbVp5qFnFeVm9nvvs+XO1bEaxvvTIBm050Djge9U0tqs0P0vLyov1l7X7Bjxdt7voScAzUyCsdjc2sn+2prFdS6cn/5Ti4ebzuR1Wo23APQ9Yyj76+XhZG/981Sj2rAIAAI8jrAKSHli9R2/sqtOFCyf2ei7g9+kn71+svCy/PvGHN9Tc3v+H/5rGdpWOydIlJ0zWtuomvVXVEPc61u2ukyQtKisc3H9AkhXnZXQ9PqmP+ape53pltaNZ8mdK/h7HAnTNWR1kZbXmbanlUPz7VSXCKgAA8DzCKka97TVN+tqjG3XKzGLdfPrMPl8zoSBbP75msbbXNOlLj7zZb7W0pqldJflZumjRJAV8Rr9dWRF3dXV1Za3KinI0sTB7SP8tyTI20gZsjLRkWvpVVv0+o5Cbe1aDrb0PV5Ikn88JsYM9DbhihXONd7+qxJxVAADgeYRVjGrtnSF9+r41ysrw6c6rF8vviz079B2zS/Tv583VX9bu032v7Y75umhldWxepq4/dboeXL0nroqstVarKp0ZpqkmGlbnjh+jwtyMAV7tPX6fcXnOanPfYVVy9rEOtrJauVIqKJPGlsf/M12VVcIqAADwJsIqRrXvP7lZG/c16HuXHxdXNfOTZ8/W6SQPt/kAACAASURBVHNK9PXHN2rD3vpez1trdbDJCauS9NWLF+hL756vf2zcr8t/8ZJ2H469V3FvXasONLRraXnqhdXCnAxl+n0puTY3uF5Z7WiRMmOE1UDO4PasWutUVsuXq+vkrnhwwBIAAPA4wuooVNvcoU1VDYM6+CcdPbe5Wnev2KnrT5muC47tvVe1Lz6f0Z1Xn6Di3Ezd8sc31NAWPOr5+taggiGr0nwnrBpjdPMZM/XbDy3TvrpWXfLTFXpp28E+33t1Za2k1Gyz9fmM7vngSfrMeXNGeilJ4XP9NOCWfiqr2YM7DfjgVqm5ZnAtwBJ7VgEAgOcRVkeZYCisq+96WRf++EXd8dB6dz+ge0hNY7tuf2Cd5k0Yoy9dNH9QPzsuP0s/vXax9tS26nMPrD8q9Nc0OjNWo5XVqDPnluqxTy5XSX6Wrr/nNf125c5e/1iwurJWeZl+HTNxzBD/q5Jr+ZwSjR+TWntp3RJIxmnAscJqIGdwc1YrXnSugzlcSZIMlVUAAOBthNVR5p4VO7XlQJMWTCrQn1ft0f97YN2oC6zhsNXtD6xTY1un/uf9i5WdMfiZoUvLi3XHu+bpyY37dc/Kiq7vxwqrklRekqdHbnmHzjlmvL7x+Fv63IPr1d55ZD/hqopaLZ42VgE//1sON9dH1/TXBpyRM7jKauVKacwkqbjvw79i6qqshgf3cwAAACmCT8WjSFV9q378zFadN3+8/vaZ03Xb+XP18Jq9uvX+teoMjZ4PtPes3Knnt9ToyxfN17wEqpg3nz5TZ8wt1f88s7WrSlrT5ITVkvzeYVWS8rMC+tUHTtSnz52jB1bv0dW/ekUHGtrU1N6pt/c3aEkKHq40GviNUdjV04D7awPOif804KHuV5XYswoAADyPsDqKfOuvmxQKW33tPcdKkj597hzd8a5j9Pi6ffr0n9YoOAoC64a99fruk2/r/AUT9IFTpif0XsYYnTd/vOpbg6qOVFT7q6xG+XxGt50/V7+4bom2HGjUe36yQve+VKGwlZYSVkeE32fU6XYbcGZe388FsuNvAz60XWo6MPj9qhJ7VgEAgOcFBn4J0sGLW2v0xJtVuu38uZpafKTi8/GzZinDb/StJzapM/SGfnrtEmUG0uvfMJrbO/XS9kN6dnO1ntywX+PysvS9y4+TGWylqg+zx+dLkrYeaNKEgmzVNLYrM+BTQfbA/2tduGiSZpTm6ebfrdL3/7FZxkgnTCtKeE0YPNdH13S0OBXUvmTkSI3743ufrv2qpw9+DYRVAADgcYTVUaC9M6SvPbpR08fl6iNn9N73dtPpM5UZ8Omrj27Ux/5vtX7xgSXKCgx+H+dIstZqy4EmTSrKVkF2hnbUNOnZzTV6bnO1Xt1xWB2hsPKzAlo+u0SfPGe2xuZlunLfOeOdNuKt1Y1aPqfEmbGanxV3ED5mYoEeu2W5bvvzWvl9RgXZ6TfD1AtcH10TbJEyYlRWMwZxwFLlSil/gjRu1uDXQBswAADwOMLqKHD3izu142Cz/vdDJ8U8TOiGU8vl9xl96ZENuu/VXfrgO2YkdM+Xth3Utpom3XBqeULv05/dh1tUcahZz2yq1j/fOqC9da0qH+dUjSsOOXMsZ4/P142nTdfZ88ZraXmx61XjkvxMFeVmaGt1kyRnz2p/LcB9GZuXqd9+aJmr68Lg+Ixx7xwia6WO5n7mrGbHt2c1ul91+jsGv19VOlJZtenf3g8AANITYTXN7alt0U/+tVXvPHaCzpo3vt/XXnfydP3x1V16eM3ehMJqY1tQn/7TWh1ubte7jp2o8QXujzupqm/VOT98TsGQVVbAp9PnlOqyJWX6w6u7dPyUQv3b8hk6e974o1qek8EYoznj87XtQCSsNrYn/Z5wn98ndbqVVjvbJdn+24DjCauHd0iNVYMfWRNFZRUAAHgcYTXN/cdf35KR0VcjhyoN5LIlU/Qff31LWw80as6EoZ2U+9Nnt+lg5FTcR9fu0819tB4n6p9vHVAwZPXli+br2pOnKTfT+aP82QvmuX6vgcweP0Z/31Ala60ONrVr8TQOSfIav88n184XCzpV/ZhtwIFsqTOO0TWVK53rkMMqe1YBAIC3pddJOjjKs5ur9Y+NB/Spc2errChGlaeHS46fLL/P6OE1e4d0z50Hm3XPip264sQpOn5q0ZDfpz/BUFh/eGWXZo/P102nz+wKqiNlzvh81bUEtbeuVYeaOwbdBoyR5/fJvdE1Hc3ONeac1VwnrA5Uya1YIeWVSiVzh7YOwioAAPA4wmqaaguG9PXHNmpmaZ5uWh5/ZbN0TJbOnFuqv6zZO6TTUf/zibeU6ffpc++cp8sWl2lTVYM2VTUM+n36c8+Kndp8oFGfe+fwV1H7smxGsSSnimxt/2NrkJr8xijk1mnAXZXVWGE10hbfX3XVWqli5dD3q0qSibYBh4b28wAAACOMsJqm7nphhyoPteiblywc9KFCly0pU1V9m17ZcWhQP/f8lho9valanzp3jsYXZOs9x09WwGf0iIvV1T21Lbrz6a06f8EEXXDsRNfeNxHHTi5Q6ZgsPbR6jySpNJ+w6jU+3zCG1UCky6G/sFpbITXsGXoLsNRtzyphFQAAeBNhNQ1tq27Sz57dpouOm6Tlc0oG/fPnzZ+gMVkBPfRG/CEzGArrm49vVPm4XH3oHeWSpOK8TJ01b7z+smavK0HAWquvPbpRxkhfvyS+PbjDwRijM+eWasdBp/2Tyqr3BNwMqx2RsBqzDThSWe3vkKVE96tKtAEDAADPI6ymkcpDzfrCw+t14Y9fUKbfp69ctGBI75Od4ddFx03S3zdUqaUjvg+6v3u5UttrmvXlixYcNaP1siVlqm5s18ptB4e0lu7+sXG/nnm7Wv9+3ty49+AOl7PmlXY9Hk9Y9Ryfm3NWBzpgKVpx7a+yWrFCyh0nlR4z9HUQVgEAgMcRVj2mLRjSVb98Wf/1t01d39tyoFG3/mmNzv7Bc3rojb26+qSp+vutp2ti4dBHxly2ZIpaOkJ6auOBAV97qKlddz69RafPKdG5848ej3POMeNVkB3Qw2/sGfJaJKmpvVNff+wtzZ9U0FW5TSWnzy6VL7K1sIQ2YM/xGzOkPdp96gqrMf5BJZB99Ov6kuh+VYmwCgAAPI/RNR7zvSc367WKw3qt4rCWTBurKWNzdMlPVygr4NeHl8/QzafPdGWu6dLpYzW1OEcPvbFHly4u6/e1P/znFrV0hPTVixfI9Phw7VRpJ+sva/aqqb1T+VlD+yP3w6c260Bjm37xgSUK+FPv31gKczO0eNpYbd7fqJxM/8A/gJTi9xl1DlsbcCTEBmNUVmsrpfpd0mmfSmwd0T2r1q2ZPAAAAMMr9T71I6YVWw/qnpU7dd3J07SwrEBfeHi9c2qvlf552xn60kULXAmqktMW+b7FU7Ry20Htr4/drrhxX73ue22Xbjh1esy5rJctKVNrMKSnNu4f0lo27K3XvS9V6LqTp6X0DNNPnTNbt5w9e6SXgSHwR8rirlRXg5HRNf3NWZWkzhh7Vrv2q74jsXV0HbBEZRUAAHgTYdUj6luDuv2BdZpVmqcvX7RAd169WK3BkO5esVPjx2RpytgYVZwEvG9xmcJWenRt3wctWWv1jcffUlFOhm49N/YsyKXTxyoz4NPmA42DXkMobPXFR97UuPws/b93JrB/bxicNW+8Pn7WrJFeBobAH+kIcGXf6oCV1cj3Y1VWK1ZIOWOl0vmJrYM2YAAA4HGEVY945I092t/Qpu9febxyMv2aPT5fX4ocoDR/UkFS7jmjJE9LphXp4Tf2yvbxIf6JN6v02s7Duv2d81SYmxHzfYwxKs3PUk1D+6DX8PuXK7R+T72+cvECFebEvgeQCF+ksurKicDRU34HmrMaa89qxQpnv6ovwb+eCasAAMDjCKvdrN1dp5e2J35qbTL8dX2Vjpk4Rku6tcF+4ORp+uiZM/WBU6Yn7b6XLZmizQca9VZVw1Hfb+0I6dt/e1vzJxXompOmDfg+pWOyVN04uLC6v75NP3jKObjpPcdNGtTPAoMRcDWsNkv+rCNtuL1uFm0D7qOyWrdbqqtMbGRNlKENGAAAeBthtZvbH1ina3/9qn789NY+K4kjZW9dq1ZV1uriHoHNGKMvXDhf5y+YkLR7X3zcJGX6fXq4x8zVX72wXXvrWvW19yzo2u/Xn/FjslQzyLD6zb9uVDAU1rcuXdjr4CbATdE/w661AcdqAZa6tQH3sWfVjfmqUV2VVQ5YAgAA3kRYjahubNO26iZNGZujHz29RZ+6b43agqGRXpYk6Yn1+yRJFx83edjvXZSbqcXTirRud13X9/bWteqXz2/XRYsm6ZSZ4+J6n/EFWapu7GeuZA/Pvl2tv725X586Z7amj4txUA3gEp9x84Cl1tgtwNKRNuC+KqsVK6TsImn8sYmvw+eTZKisAgAAzyKsRryy47Ak6WfXLtHnLzxGT7xZpat+9bIONMQfsJLl8XVVOm5KocpLRia0lYzJUm1LR9fX3/n727JW+sK74z/wqDQ/W7UtQXV0Dlzlae0I6SuPbtDs8fn6yBkcWITk87vdBtxfWA1ER9f0sWfVrf2qUb4AYRUAAHgWYTViWXmx/ut9i3Ts5AJ97MxZuuv6pdpW3aQrf/nyiFZYdx5s1pt76/WeEaiqRhXnZqq2JShJem3nYT2+bp8+euasQZ1APKnIqSZV1ccY19HNj5/Zqj21rfrPSxcqM8AfUSSfq2F1oDbgQJYk0/s04Pq9Uu3OxEfWdEdYBQAAHkYSiJhYmK1rT56mgN/5LTl/wQT9/Lol2nW4RQ+s2j1i6/rrOqcF+KIRPGBobF6m6lo6FAyF9Y3HN2pSYbY+fubgKp7lkVbenQeb+33d5v2NuvvFHbryxCk6Oc4WYyBRru5ZDbbEnrEqScZIGTm956y6uV81yueXwqmxnQEAAGCwCKv9OHNuqZZOH6ufP7dd7Z0j84Hvr+urdFL5WE0uyhmR+0tScW6Gwlb69Ys7tHFfg77w7vnKyYxx0mkM5SVOpamin7AajsxUHZMd0BfeneCMSWAQuuasutIG3OKE0f4EsntXVitWSFmF0oSFia8hyueXLGEVAAB4E2G1H8YYfea8Oaqqb9OfV+0Z9vtv3t+ozQca9Z7jR64FWHIqq5J059NbdVL52CGNkSnNz1Jepl8Vh2LMlpR0/6rdWl1Zqy++e76KI/cEhkN0zqorB+cO1AYsOWG252nAFSuk6afFHnkzFLQBAwAADyOsDmD57BKdOH2sfv7stmGvrv51/T75jHThwpGdMRoNjsFQWF97z7FDGiNjjFF5SV7MNuDWjpC+8/e3dfKMYl1x4pSE1gsMVnTOaqcbaTXY3H8bsORUVru3ATdUSYe3u7tfVSKsAgAATyOsDsAYo8+c61RXH3Cxurq9pkk33PNazPBmrdXj6/bp1FnjVDomy7X7DsW4POf+15w0VQvLCof8PuUleao41Pu/d1t1k/7waqXqW4O65ezZzFTFsOuqrLqyZ7V14DbgjNyj24Cj+1WnE1YBAACiCKtxOH1OiZZMK3KtutoZCuuTf1yjF7bU6IdPbe7zNWt216niUMuIngIcdczEMfrWpQsT3kc6syRPe2pbFQwdXb26+Xer9K0nNik306+TZxYndA9gKI7sWXXhzTpapMwBKqsZPSqrFSukrAJp4nEuLKAbwwFLAADAuwircTDG6Nbz5mpffZseXJ14dfWuF3doU1WD8rMCen5LjWy3ak5Vfau+/thGXXPXKxqTFdC7Fk5M+H6J8vmMPnDKdBVkZyT0PuXj8hQKW+0+fGTfaktHp3YebNYZc0v1gyuPV1bAxf16QJwih4AnfsCStZEDlgbYsxrIPnrPasUKadopkj+Q2P174jRgAADgYXF9MjLGVEhqlBSS1GmtXWqMKZZ0v6RySRWSrrLW1iZnmSPv9DklWjytSD98aoseX7dP04pz9d3Ljxt0y+qWA426859bdeHCiTp11jh99dGN2t/QprCVfvHcNv359T0KW6vLl0zRJ86epaLc9DloqLzEqTZVHGrWzNJ8SdKOGqct+JqTpurdi0Z2by5GL7/PSasJh9VgqyQb3wFLTdXO48YD0qGt0pLrE7t3X2gDBgAAHjaYf8Y/21p7sNvXn5f0jLX2O8aYz0e+vsPV1aUQY4y+cOF8feHh9TrY1KFXdhzW+5dN0+JpY+N+j85QWLc/sE752QH9x6ULtb26SZJ02/3rtKrysCTpyqVT9fEzZ2lq8QAfdj1oRkl01uqRyuq2yO/B7PH5I7ImQOpWWU10z2q0WjpQZTUjR+qM7FmtXOFcp7s4XzWKsAoAADwskZ6z90o6K/L4XknPKY3DqiQtm1GsZz57lpraO3XKfz2j379cOaiw+rNnt2v9nnr97NolKsnPUsBn5PcZra6s1fuXTdPHzpw1ovNUk21sboYKsgNHzVrdVt0kv8+ofNwAe/yAJPK5NWc1GPmzPWAbcLfRNRUrpcx8adLxid27L74AbcAAAMCz4g2rVtJTxhgr6VfW2rskTbDWVkWe3y9pQjIWmIryswK6bEmZ/vTabn354gUDzgS11uqHT23RT5/dpveeMFkXReaUFuVm6sGPnapJhTmaWJg9HEsfUcYYzehxIvDW6kZNH5erzADbpzFy/G6dBtwR6RoYsA24257VypXJ2a8qST6fZAmrAADAm+JNCMuttUskXSjpFmPMGd2ftM4JQX1+yjPGfMQYs8oYs6qmpiax1aaQD5wyXR2hsP68ane/r2vvDOkzf1qrnz67TdecNFU/uPLo6sniaWNHRVCNKi/J69qnKjmV1dmltACjD6//Rtr+7LDcKhpWO0PDWFntbJOaaqSat6XyJLQAS7QBAwAAT4srrFpr90au1ZIekbRM0gFjzCRJilyrY/zsXdbapdbapaWlpe6sOgXMnTBGJ88o1v+9UhmzdbC2uUPX3/2aHlu3T5971zx9+7JFyvCP7gpi+bg87atvVVswpI7OsCoPtbBfFX179r+k1+8elltFR9ckXFkdzJ7VYGu3+aqEVQAAgJ4GTE7GmDxjzJjoY0kXSNog6TFJN0ZedqOkR5O1yFR1w6nl2lPbque39M7pFQebddkvXtLaPXX6yfsX6xNnzR70ycHpaEZJnqyVdh9uUeWhZnWGreZMIKyih1Cn1HJIatg7LLeLVlYT3rPa1QY80JzVHCkclHY+L2XkSZNPSOy+sbBnFQAAeFg8m6QmSHokErQCkv5orX3SGPO6pD8bYz4sqVLSVclbZmq64NgJKh2Tpd+/XKlzjjmyZXd15WHd/LvVstbqjzedrKXlxSO4ytRS3nUicHNXMJhdOmYkl4RU1HJQkpUa9g3L7XzRsJpwZTXeNuBI6//Wp6VpJ0v+xGYYx2R8VFYBAIBnDRhWrbU7JPU6ptJae0jSuclYlFdk+H16/7Jp+sm/tmrXoRZNG5erJ9ZX6d//vFaTC7P12w8t6xrXAseMcUdmrbYHw5KkWeP5PUIPTQci12qps0MKJHfecCAaVhPesxptAx7gVO/o8/W7pBNv7P+1ifAFpM725L0/AABAEiXh+MnR5dpl0/SzZ7fp7hU7lJsZ0C+f366l08fqrhuWDnhK8GhUmJuhsbkZ2nmwRS0dnSorylFuJn8M0UNTtLXeSk37paJpSb1d1+iahE8DjlRW42kDjio/PbF79oc9qwAAwMNICQmaWJitCxZM0O9erpQkXbV0ir753oXKzvCP8MpSV3lJnioONqu+NcjhSuhbtLIqSQ1VSQ+rXaNrEp6zGtmzGm8bcCBHmrw4sXv2h7AKAAA8jLDqgs+cN0fWSjedPoP9qXGYMS5PK7cfVH1rUKfOGjfSy0EqOiqsJv+QJb9be1ajByzF2wY87eTktjj7/BywBAAAPGt0z1FxyTETC/TL608kqMapvCRPBxra1RYMU1lF35pqnKqgNCyHLLl2GnCwxama+gborIhWVpM1sibK56eyCgAAPIuwimHX/dApwir61HRAKprujHUZjrBqXAyrA7UAS1LxDCmrQJp3YWL3G4gvIFkqqwAAwJtoA8awOyqslhJW0Yemail/gjN6ZTjbgN2YszrQ4UqSVDxT+vwuKdmzl9mzCgAAPIzKKoZddNZqSX6mxnJiMvrSdEDKHy8VTBqWymp0zmo44TmrLQPvV41KdlCVImGVyioAAPAmwiqGXX5WQCX5WZpFVRWxRCurBWXDElajc1Y7h6sNeLiwZxUAAHgYbcAYEbdfMFfjC7JGehlIRcE2qb3eqaxm5TtzVsOhgQ8tSkB0zmrCo2vibQMeLobTgAEAgHcRVjEirlmW3LmZ8LDmaueaP0EKtTuVweYaaczEpN3SvdOAm6XcFBrHxJ5VAADgYbQBA0gtTd3CakGZ8zjJhyx1nQacYFZVsDXF2oDZswoAALyLsAogtTQdcK75pVLBZOdxkvet+v3Rymo4sTdKtTZg9qwCAAAPow0YQGrpCqsTJH/ktOhkh9WuOasJvlGwOQUrq4RVAADgTYRVAKkl2gacV+qELX9m0tuAfZEek8RH17TGP7pmOPj8kqUNGAAAeBNtwABSS1O1c0iRP8OZRTom+bNWj1RWEwir4bAzuial2oAjldVEQzgAAMAIIKwCSC0tB6XckiNfD8OsVb8bc1Y7W51rqrUBS5JNtL8ZAABg+BFWAaSWjmZnvmpUweSkh1VjjHwmwTmrHS3ONaXCamQ2LftWAQCABxFWAaSWjuajW2mjYTXJrax+n1EokXsEI2E1M4XCqiGsAgAA7yKsAkgtHU1SZvfKapkUapdaDif1tj5jEqusBlOxshppA2bWKgAA8CDCKoDU0ldlVUr6icABn0lsz2pKtgFHwyqVVQAA4D2EVQCppVdYLXOuSd636vOZxE4DTsU24K49q1RWAQCA9xBWAaSWjuYebcDDU1n1+0xic1a72oBTbHSNRGUVAAB4EmEVQOoIh3tXVvPHOwcFDcOs1YQqqx3NzjUlK6uEVQAA4D2EVQCpo7NVkj06rPr80piJwzJr1ZU24Iwcdxbkhq45q7QBAwAA7yGsAkgdXdXJHq20BZOlxlQPq63ONSXbgAmrAADAewirAFJHR5Nz7b5nVToyazWJfCbBOau0AQMAALiKsAogdcSsrJZJ9XulRMLkAPw+l+asBlKwDZiwCgAAPIiwCiB19NcGHGyW2huSduvE56w2O0HVl0J/rRpG1wAAAO9KoU9VAEa9/tqApaS2AvvcGF2TSi3AEpVVAADgaYRVAKmjvzZgKamzVhMeXXPgLalwqnsLcgMHLAEAAA8jrAJIHf21AUtJr6yGwkP84dY6ac/r0uzzXF1TwjhgCQAAeBhhFUDq6AqrPdqA8yc614aqpN064DMKhYeYVnc+78wyJawCAAC4hrAKIHV07VntUVkNZEp545PaBuzzGYWG2gW87Wkpq0CastTVNSUs2gZsaQMGAADeQ1gFkDo6miXjkwLZvZ9L8qxVv9HQRtdYK217Rpp5puTPcH9hieCAJQAA4GGEVQCpo6PZaQE2pvdzBWXJDau+IR6wVLPZqfimWguw1K0NmMoqAADwHsIqgNTR0dS7BTiqYHJyTwMealjd9rRznXWuuwtyA5VVAADgYYRVAKmjo7n/sNpWd+QQJpf5fUahocxZ3fa0VHqMVJRiY2skyXDAEgAA8C7CKoDU0W9Yjc5aTc6JwL6hzFntaJEqX0rNqqrEnFUAAOBphFUAqSO6Z7UvXbNWk9MK7PcZhQdbWa1cKYXapdmEVQAAkFy7D7eooS040ssYVoRVAKljoD2rUtLCasBn1DnY2TXbnpYCOdL0dyRlTQljzioAAGnja49t1OU/f2mklzGsCKsAUkd/bcCFU6WsQmnnC0m5tc8MobK67WmpfLmU0ceonVTAAUsAAKSFcNjqjV21WjJt7EgvZVgRVgGkjv7CaiBTWnCJtOlxKdjq+q0HfRpwbYV0aFtqjqyJilZWLW3AAAB42Y6DTaprCerE6YRVABgZHU2x96xK0qIrnddsedL1W/sGexrwtmeca0qHVSqrAACkg8fXOQdMnjpr3AivZHgRVgGkBmv7r6xKTstt/kTpzQddv31gsJXVbc9IRdOkcbNcX4trOGAJAADPawuG9PtXKnXuMeM1tTh3pJczrAirAFJDqMOpAPYXVn1+aeHl0tanpNZaV2/vH8zoms4OaefzTlXVGFfX4SoOWAIAwPMeemOPDjd36KbTZ470UoYdYRVAauhodq79tQFL0qIrnGC76XFXb+/zGYXjDau7X3XakVO5BViiDRgAAI8Lh61+8+JOLSor1Ckzi0d6OcOOsAogNXQ0Odf+KquSNHmxVDxLevMBV2/vN4PYs7r9GScIzjjD1TW4zkQrq7QBAwDgRf96u1o7DjbrptNnyKRyN1eSBEZ6AZ5SWyFVrJROuDa1W/8AL+qqrA4QVo1xDlp6/rtSQ5VUMMmV2/v9RhNC+6Vnvy3ZcP8v3vCQNO1UKWuMK/dOGvasAgDgab9+cYcmF2br3Yvc+bzjNYTVeIRD0qu/lP71LSnYIhVNTf2KCuA18bYBS04r8PPfkTY+LJ16iyu3D8jqu6EfSM/vlDTAP0YZn3T67a7cN6nYswoAgGe9uader+48rC+9e74y/KOzIZawOpD9G6THPiXte0Oa806p4kVp418Iq4Db4m0DlqSSOdKkE5xWYJfC6gl1T2m+dkqX3S0dd6Ur7znijHFagQmrAAB4zq9f3KH8rICuXjZ1pJcyYkZnRI9HZ7tTSb3rTKlul3TFPdK190tzLpA2PUZbHeC2aGU1I84j2RddKe1bIx3clvi9g606d9+vtMHOck4bTic+wioAAF6zt65VT7xZpWtOmqqC7IyRXs6IIaz2Zdcr0i+XSy983/lA/MnXnQ+wxkjHXio110iVK0d6lUB6GUwb1SohgQAAIABJREFUsCQtvEySkTa4MHP1lZ+rMFit74avk3xp9teiLyBZ/nENAAAv+e2KnZKkDy2fMcIrGVm0AUd1tEiNVdIrP5dev1sqnCZ94KHeoynmXCAFcqR190vFo2/WEZA09XucazxtwJJUMFkqX+60Ai/+wNDv294ovfgjbRl7hl6tXjD090lVvoDUWnfk9xcAAKS0xvZOPff6Gl13TInKzCGpvtuT/kwpf/yIrW24xR1WjTF+Sask7bXWXmyMmSHpT5LGSVot6XprbUdyljkMNv9NeujDkox08selc74sZfVR4cnMk+a+U1r7f84vAO4xvsGdsLvoSunxT0s/OjbB+/r1wvRPKnQgDSuQGTnSmt87vwAAQMobI+lpI2mHpB/1eHLyYukjzw33kkbMYCqrn5G0SVJB5OvvSvqRtfZPxphfSvqwpF+4vL7hU7ZEuuQn0qTjnV/9ufC7kYprnDMZAcSncErf/0gUywnXOmGssy2x+044Vg0b8xUKb5O1Nr3mmF39f1LN2yO9CgAAEIfOsNX3nnxbxXmZ+tiZs3q/IHfc8C9qBMUVVo0xUyRdJOk/Jd1mnE9y50i6NvKSeyV9XV4Oq8Uz42/rHTNRWnJ9ctcDYGD+DOm4q9x5q01bJUlhK/nTKKtq6jLnFwAASHlPrN2ru5pKdPdlS6UFE0Z6OSMu3pNE7pT0OUnhyNfjJNVZa6NHTO6RVOby2gBg2ETHl4XCdEwAAIDh19gW1E//tU0zS/N0zjGjZ19qfwYMq8aYiyVVW2tXD+UGxpiPGGNWGWNW1dTUDOUtACDpfD6nnBq2hFUAADC8Wjo69W//+7p2HmzWVy5a0PW5ZLSLp7L6DkmXGGMq5ByodI6kH0sqMsZE24inSNrb1w9ba++y1i611i4tLS11YckA4D5/ZJ8qlVUAADCc2oIh3XTvKq2urNWd15ygs6mqdhkwrFprv2CtnWKtLZd0jaR/WWuvk/SspCsiL7tR0qNJWyUAJJk/8i+YISqrAABgmLR3hvTR36/WyzsO6YdXHa+Lj5s80ktKKfHuWe3LHXIOW9omZw/rb9xZEgAMv66wGiKsAgCA5OvoDOuWP7yh57fU6NvvW6T3LZ4y0ktKOYMZXSNr7XOSnos83iGJIyYBpAUqqwAAYLh0hsK69f41enpTtb753mN1zbJpI72klJRIZRUA0oYvsmc1zJ5VAACQRKGw1WcfWKe/vblfX75ovm44tXykl5SyCKsAIClAZRUAgJRhrdVf1uzVxn31I70UV4XDVp9/aL0eXbtP/++d83TT6TNHekkpjbAKADoyuqaTPasAAIwoa62+++Rm3Xr/Wt107yo1tXeO9JJcYa3V/2/vvsOjqvI/jr/PzKT3HloIgRAIvfcizd527WUV21rW7q5uUddd3Z+ube2ra+8duygiIiC9QwIEElpIg0B6n/v7I0OTBBKYJJPk83qeeRLmnrnne3O843zntHs/X8dHy3dyy+REbjqpR0uH5PGUrIqIcHDrGu2zKiIi0nKcTosHvkzhv3O3MDU5huzCch6duaGlwzphlmXxj69SeGfxdn4/IYHbpyS2dEitgpJVEREOWWBJc1ZFRESahNNpHbWXtMZp8ZcZa3n9l61cM7YbL10+hCtGxfPmom0s35bfjJG61/6e4tcWbOXK0fHcc0ovjOtLcjm6Rq0GLCLSVu1PVtWzKiIicuKqa5xsySth/a4C1mUWsm5XAam7CimqqObmST24fUrPA1Nw9pf/48drmLEyk5sn9eCOqT0xxvDHk5OYlZLD3Z+s5etbxuLjsLfgVR1u5fa9BPg46BkTdNRyT81O479zt3DJiDjuPzNZiWojKFkVEeFgslqtnlUREZFGsyyLL9dksSRjD+syC0nNKqSi2gmAr5eN3h2COXtQRwrKqnnmx81szi3m8QsG4O/toLK6dhuXb9Zmc9e0nvxh0sEhsgE+Dh48ty/TX1vKCz9t4bYpPVvqEg9TXePk2jeXYVnw9S3jiA3xrbPc8z9t5j8/pHHekM48eHZfJaqNpGRVRISDW9doGLCIiEjj/bghl1veW0mQj4PkjsFcNrIrfTsF06djCAmRATjstbMPLctiQOcQ/vVNKtv/W8pzlwzmwa9T+CE1l7+d3rvO1XFPSormnIEdeW7OZk7v14HEY/RkNoclW/PZXVwJwM3vreDda0fiZT98huXL89L598yNnDWgI4/8tv9hPcnSMEpWRUQ4ZBiws4UDERERaYW+W59NkI+D5fdOxdtR/7I4xhiuGZdA96hAbn5vJZMe/wmnBQ+e05fLRnat93X3npHM3E153P3JGj66fvSB/2+3lG/WZuHnZee+M5P586dr6XP/d3QO8yPQx4Gvw47NBovS8zm1byxPXDCgxeNtrbTAkogI2mdVRETkeNU4LWan5jKxV/RRE9VDndQrmhk3jmZYfDhPXDDgqIkqQESgD/edmcyK7ft4e9G2esttzC4iZVdho+JvrKoaJzPX5XBSryguHh7Hi5cP4crR8fSODSY8wBubDcqrnFwyIo6nLhp0oFdZGk89qyIiHNxntUZdqyIiIo2yasde9pRUMqV3dKNelxgTxAe/H9Xg8ucM7MSMlbv498wNTEmOoVOo32HH84oquPClhRhg7p9OItjXq1HxNNSHy3awu7iC84d0AeDkPrGc3Ce2Sepq75Tmi4hwcJ/VGuWqIiIijTIrJReHzTAxqXHJamMZY3jonL44LfjbjLVYvxoN9fcv1lNSUc3e0ir+93N6k8Twzdos/v7FeobFhzExKapJ6pCDlKyKiAA217uhFlgSEZHWrKK6hmdmp5FbWN5sdc5KyWZEQjghfk3Tk3moLuH+3HVyEnM25vHlmqwDz89cl83Xa7O4dXIiZw3oyMvzMtz+N/hk+U7+8O4K+ncO5ZUrh2ll32agZFVEBHC4slXtsyoiIq3ZCz9t4fFZm3j+py3NUl96XjFb8kqY2jumWeoDuHJ0PAM6h/DAF+vZW1JJQWkV936+jt4dgvn9hO7cOa0nVTVOnpqd5rY631q0jTs/Ws2o7hG8dfXwJhtiLIdTsioiAuxf+0D7rIqISGuVllPEc3M242U3fLJ8J6WV1U1e5w+pOQBMSW6+ZNVuMzz82/4UlFVx1RtLufClheSXVPLoef3xstvoGhHApSPieH/pDtLzik+4vnWZBdz72Tom9YrmlSuG4e+tZX+ai5JVEREO7rPqVLIqIiKtkNNpcc+nawnwcfDMxYMpqqjm81W7mrzeH1Jy6d0hmM5h/k1e16F6dwjmlsmJpOwqxM/bzsO/6UffTiEHjt88ORFfh43Hvt94wnV9szYLu83wxAUD8PWyn/D5pOGUrIqIcHCfVc1ZFRGR1uidxdtYvm0v956ezMl9YugVG8Tbi7YdsQjRr5VX1Rx3nfkllSzbls/URq4C7C63TE5kwz9PYcaNYzh/aJfDjkUG+nDt+AS+WZvNyu17T6ie71NyGNEtnFB/7xM6jzSeklUREQ5JVjVnVUREWpld+8p4ZOZGxiVG8pvBnTDGcPmorqzfVchzczbX+7oZK3fS696ZbN9Telz1/rghF6cFU5NbbtuWoy1ydM24BCIDvXn42w3HTNrrk55XzObcYqY14zBnOUjJqogI6lkVEZHWybIs7v1sHTVOi3+d2+9A8nbh0C6cO6gTj32/ice/31hnsvbkrNoFiD5dufO46v4hJYfYYF/6dgo+/gtoQoE+Dm6ZnMjijHx+2pR3XOeYldL8c3LlICWrIiIcus+qklUREWk9fkjNZfaGXO6c1pMu4QfnjTrsNh47fwAXDevCMz9u5v9+1bu4dmcB2/Nre1RnrMxsdM+jZVksTN/D+J6RHr2Fy0XD4ogL9+eRbzcc17oU36fk0Kdj88/JlVpKVkVEAJurZ1Vb14iISGthWRZPz06ja4Q/V46OP+K43Wb417n9+N2orrz0czr3f7GezbnF7NpXxjM/phHk6+DvZyazbU8pKxo5rzNjdwkFZVUMjgtz09U0DW+HjbtOTmJDdhGfr85s1GvziipYsX0v01pwmHN7p2RVRARwaBiwiIi0MnM35bE2s4AbJ3bHYa/7Y73NZnjgrD5cNz6BNxduY8oTcxn98I98n5LDlaPjOW9oF/y87HyyonGJ3NKt+QAMjAs94etoamf060DfTsE89t0mKqprWLVjH8u35R/zdbNTc7AsmKohwC1GmwSJiHBw6xrtsyoiIq2BZVk88+NmOoX6ce6gzkcta4zhz6f2YlKvaHIKyymtrKHGafGbwZ3w93ZwSt9Yvlq9i/vOSG7Q1iyWZfHu4u0kRAbQMzrIXZfUZGw2wz2n9OayVxbz1sJtvP7LVrIKynnm4kGc1q9Dva/7PiWHzmF+9O7g+dfYVilZFRHh4AJL2mdVRERag0Xp+Szftpd/nt0Hb8exB0saYxiZEFHnsd8M7sSMlZn8uCGXqckxvPRzOvPTdvPyFUMJ8DkyXVixfR+rdxbwz7P7HJhG4+nGJkYyLjGSB79OBSDU34ub31uJZcHp/Y9MWEsqqpm/eTeXjejq0XNy2zoNAxYRQVvXiIhI6/LMj2lEBfkcsb/o8RjdPZKYYB9e+jmds55dwKPfbWRh+h4WbtlTZ/lXF2QQ5OvgN4OP3qPrae4+pdeB3z+7cQyD40K55f2VfLl61xFlf96UR2W1U0OAW5iSVRER1LMqIiKeYdWOfWzKKTpqmbU7C/hlyx5+Pz6hQcN2j8VuM5wzsBOrduxjT3EFz1w8CB+HjV/qSFYz95Uxc102Fw+Pq7PX1ZP17RTCy78byg93TCA+MoDXpw9nSFwYt76/ks9XHT5n9/uUHEL9vRgW79kLSLV1reu/MBGRJmLXnFUREWkC2/eU4uNlIybY95hlN+cWccGLC6msdnJ6/w7cNjmRxJgj50uu21UAwKlHmW/ZWNdP6E50sC/nDelMiJ8X7y/dzsL0g8nqY99tZFH6HnpEB2JZFr8b1dVtdTenQ/dLDfBx8Nr0YVz1+lJu/2AVAGcP7ERVjZMfN+QypXdMvQtXSfNQsioiwsGta7QasIiIuNMVry0hr6iCf/2mH2cN6FhvuRqnxV0frSHA285VY7rx1sKtfLM2izP7d+SWyYn0iA48UHbXvjLsNkNMkI/b4gwL8Obqsd0O/Ht090ge/W4j+SWVLMnI59k5mwFYtm0vZw7o2Gb2Hf11wuq0LGKCfCkoq9IQYA+grwpERDhkGLDmrIqIiJvsLakkY3cJBrjlvZXc88kayipr6iz7yvx0Vu3YxwNn9+WeU3sx7+5JXD+hOz+k5jDtybnc8cEqMnaXAJC5t4zYYN8m7fUb1b12MaZPlu/kTx+vpn/nEJ6/dDATekZx/5nJTVZvS/D3dvDalcMZmRDBnR+u5qFvUvFx2BjfM7KlQ2v31LMqIsKh+6y2cCAiIuJ2lmWRua+MFdv3EeBtZ3Lv5ukxW5tZO1z3mUsGsTgjnxd+2sKK7Xt57pLBhw3v3ZJXzOPfb2JacgxnulamDQ/w5u5TenHN2G689HM6byzcyuerd3HuoE6kZBXSMfTYw4pPRP9OIQT6OPjXt6kEeDt45uJBdI0IOOpWL62Zn7edV64YxjVvLmXB5j1M6R2Dv7dSpZamFhAR4eA+qzVOZasiIm3BnuIKPlmxk+Xb9rJy+z5yiyoOHPvjyUncdFKPJqu7sLyKNxZs5eX5Gfh52RnUJYyJSdGMTIjgjg9Wceaz87lrWhJD48PpGOrLnz5eg6+XnQfP7XvENikRgT78+bTeXDMugRfnbuGtRduoqHZy9sD6hxS7g8NuY3i3cH7ckMv//aYfXSMCmrQ+T7A/YX3su42ceZQh29J8lKyKiHDI1jXKVUVE3GJjdhFfr82itKKaksoaSiurKamo/VleVcOwbuFcOLQLCVGBxz5ZI5RV1vDqggxe+GkLxRXVdI3wZ3T3CAZ3DWNgl1BenZ/Bo99txGEz/H5Cd7fWXVBWxWsLMnh1fgaF5dVM7hXN7VN7EuLvBcCEnlF8e+s4bvtg1YH9Pvd78sIBRAfV31saFeTD385I5rrxCbyzeDsTkqLcGntdbpuSWNvb244SN18vO387o20Nc27NlKyKiAD79zTXPqsiIicuY3cJF760kH2lVQR42/H3cdT+9HYQ4GPHYbPx8rwMXpybzmn9YnnywoH4OE5sC5Yap8Uny3fy+KyN5BRWMC05hj+dkkSP6MNX033s/AHUWPB/327AbjNcMy7hhOoF2FdayasLtvLaggyKyquZmhzDrZMT6dsp5Iiy0cG+vH31CNJyi9mRX8rOvaV4OWycM7BTg+qKDvbl9qk9TzjmhujfOZT+nUObpS6RuihZFREBjDHYbUb7rIqInKB9pZVc/fpSDDD3jxPrHT6aW1jOW4u28cyPmzFmNU9fNOjAKJfGsCyLORtzefjbDWzKKWZQXCjPXjKYYfHhdZZ32G08ecEAapxOHvw6FYfNcOWYbnWWPZqK6hoWpeczKyWbz1buoriimlP6xHLz5B706Xhkknoom82QFBtEUuyR29KIyEFKVkVEXOzGaJ9VEZETUFnt5Ia3V7BzbxnvXDviqPMco4N9uXNaEkG+Dv71zQZC/bx48Jwj52zWx7IslmTk8+QPm1iUnk98hD8vXDqYU/rGHvMcDruNpy4aRI1zBX//MgW73cblI4+9b+i+0krmbMzlh5Rc5m7Ko7iiGj8vO1OSY7hxYnd6dwhuUOwi0jBKVkVEXGw2bV0jInK8LMvi3s/WsTB9D09eOKDens1fu258d/JLqvjv3C1EBHhzx7Sko5avrHby9dpdvDI/g3WZhUQEePOPs/tw8fA4vBqxlYuX3cYzFw/mxneWc+9n63DYDBcPjzui3LY9JcxKyWFWSg7Ltu2lxmkRHeTDmQM6Mi05hlHdI/D1OrEhzCJSNyWrIiIudmOoUc+qiMhxeenndD5YtoObJ/Xg3EGdG/Xau09JYl9pJU//uJlQf2+uGnvksNyCsireXrSNNxduJaewgu5RATx0bl9+M6gzft7Hlyx6O2w8d+lgrn9rOX/+dC12Y7hgWBfS84r5cnUWX6/dxaacYgB6xQZx48TuTOkdQ79OIdiOY8iyiDSOklURERe7TcmqiMjx+G59Ng/P3MDp/Tpw+5TGL/5jjOGhc/tRUFbFP75KwRiY7ppHWlBaxSsLMg4sXjQuMZKHf9ufCYlRbkkYfRx2XrhsCNe+uYy7P13Dqwsy2JBdhDEwLD6c+85IZmpyDF3C/U+4LhFpHCWrIiIuSlZFRBpvXWYBt72/iv6dQ3n8ggHHnUDabYanLhqE9d5KHvgyhcKyaqpqnLz+y9YDixf9YVKPOlfYPVG+Xnb+97uh3PzeSnKLKvjb6b05o39HYkPq30pGRJqeklURERe7zWjrGhGRRsguKOfqN5YS5u/F/3435ITnbno7bDx7ySDu/Gg1T/6wCYDT+sVy86TEJl+8aH/CKiKeQ8mqiIiLzWjrGhE5XOa+MuZsyKW8qoZBcaEM6dqwRYPag9LKaq55cynF5dV8fMNoooPc0wvpsNt44oKBjOkeyYAuodreRaQdU7IqIuLi0DBgkTapvKqGT1dk8sr8dIZ2DeeR8/o36HVrdu5j+mtL2VNSeeC5a8Z2466Tk9r96q9Op8Vt768iZVchL18x1O29nnZb7UJHItK+KVkVEXGxKVkVaVOKyqt4Z/F2XpmfQV5RBd4OG8UVuQ167by0PK5/azlhAd58ffVYYoJ9+c8Pm3h5fgbz0nbz5IUDSe7YfvfUfOS7DXyfksN9ZyQzqVdMS4cjIm1UwzejEhFp4zRnVaTt+CElh9EP/8jD326gV2wQ71wzgtun9CSnsIKCsqqjvvaL1bu46vWldAn355MbRtOnYwiRgT48eE4/Xps+jPzSSs55bgEvzt3SLr/g+mDpdl6cm85lI+OYPia+pcMRkTZMPasiIi7aZ1WkbSivquHez9fRMcSPx64ZQL/OIQeeB3j8+434edspLKumsLyKwrIqapwWd05LYlNOEX/+dC3Du4Xzv98NJcTP67Bzn5QUzXe3jefPn67h/77dwOwNufznwoF0DPVr9utsCQu37OGvM9YxLjGS+8/sgzHaa1REmo6SVRERF7vN4FTPqkir9/ovW8kqKOfxCw4mqgD9OoXg7bDx5sJteNttBPt5EeznINjXi6yCMq58dQkYGJkQzuvTh9c7LzU8wJv/XjaET1Zkcv/n6/jbZ+t49cphzXV5LSZjdwnXv72c+MgAnr1kMF52DdATkaalZFVExMVuM1TXKFkVac0+X5XJv2duYErvGEZ3jzzsWHSwL6vvm4YxHJGI7tpXxgUvLmTn3jKuG59wzAWUjDGcN6Qzq3bsZcaKTKprnDjaePL2yvx0KqudvHrFsCN6nEVEmkLbflcVEWkEm1HPqkhrNmPlTm7/YBXDu4Xz9MUD6yzj522vMxHtGOrH+9eN5P4zk5nQM7rBdQ6LD6eksoYN2UXHHXdrkV9SSecwP+Ii/Fs6FBFpJ5Ssioi42LUasEir9fHyndzx4WpGJkTw2pXD8fdu/OCxzmH+TB/TDbut4fMwh8XX7ru6JCO/0fW1NgVlVQSrR1VEmpGGAYuIuNSuBtzSUYi0L9U1Tv7w7kpqLIvE6EASYwJJjA4iMSYQH0fD9jL9cOkO7v50DWO6R/K/3w3Fz7v59kDtGOpHp1A/lm7N56qx3Zqt3pZQWFZNZKB3S4chIu2IklUREZfanlVnS4ch0q6k5RYzc3020UE+zNmQS7VrdIO3w8bALqEMjw9nWLdwhnQNI9DnyI8t7y/Zzj2frmVcYm2ieqy5pk1heLdw5qXlYVlWm14dt7C8ioSogJYOQ0TaESWrIiIu2rpGpPmlZhUC8M41I+gaEcC2PSWk5RazcvtelmTk88LcLTw7ZzM2A306hjAtOYYLh3chOsiXdxZv468z1jExKYr/XjakRRJVqB0KPGNlJhm7S0iICmyRGJpDQVmVFlYSkWalZFVExMVmA3WsijSv1KxCvB02ukUG4LDbSIwJIjEmiNP6dQCgpKKaldv3sSRjDwvT9/D4rE08/WMaIxMimJe2m0m9onnhssENHjLcFIZ3CwNg6db8NpWs3vjOcorKq3nqokGE+XtRWFZFsK+SVRFpPkpWRURcHDYbZTU1LR2GSLuSmlVEUkxQvdu+BPg4GJsYydjE2m1o0vOKeWfxdj5atoNpyTE8c8mgFk1UAbpHBRIe4M2SjL1cOCyuRWNxl7U7C/hmbTYA5z6/gKcvGoTTgmA/fXQUkeZzzHccY4wv8DPg4yr/sWVZ9xtjugHvAxHAcuByy7IqmzJYEZGmZLOZA/PlRKTpWZZFalYhk3s3fKuYhKhA7j0jmb+c1hubwSPmiBpjGNo1jGXbPHtF4IbMqV21Yx/vLNrGptxiAn0cPHfpYO74YBUXvrQQQD2rItKsGrJ1TQUwybKsAcBA4BRjzEjgEeBJy7J6AHuBq5suTBGRpmc34FSyKtJs8ooq2FNSSe8OwY1+rd1mPCJR3W9YfDjb9pSSW1je0qEcwem0eHLWJoY99ANLt9adUGcXlHPHB6s457kFfLUmi9U79nHpiDgm9Izis5vGEBdeu7dqqL+SVRFpPsfsWbUsywKKXf/0cj0sYBJwiev5N4C/Ay+4P0QRkeahfVZFmleKa3Gl40lWPc2wbq79Vrfmc0b/js1at9NpsWRrPgO7hB6xyFRJRTV3friameuzCfC2c+2by/jkhtF0d82tLa+q4aWf03nhpy3UOC1umNidm07qQXlVDaGuxZS6hPvz8Q2j+WDJDsb0iGzWaxOR9q1BEw+MMXZqh/r2AJ4DtgD7LMuqdhXZCXSq57XXAdcBxMW1jXkcItI22W0Gp6VkVaS5pGYVAdA7tvUnq306BuPnZWdpRvMnq9+tz+aGd1YQ6u/FhUO7cOmIrsRF+LMjv5Rr31zGppwi/nZ6b6Ymx/Cb539h+mtL+eSG0SxM38PD36Syq6CcU/vG8udTexMXUduD+uttgoJ9vbh2fEKzXpeISIOSVcuyaoCBxphQYAbQq6EVWJb1EvASwNChQ/UpUEQ8ll1zVkWaVWpWIZ1C/QhpA0NLvew2BncNZcnWvc1e95rMAhw2w6iECF6en8FL89LpERXI7uIKqp0Wr00fzoSeUQC8fMVQLv7fIiY+OoeSyhqSOwTz+AUDGdU9otnjFhE5lobMWT3Asqx9wBxgFBBqjNmf7HYGMt0cm4hIs7IZozmrIs0oNauQ3h2CWjoMtxkWH86G7EIKyqqatd7UrEJ6RAfywmVDWHD3JC4b0ZW03GICfR18ftOYA4kqwKC4MJ65eDBdwv15+Df9+PLmsUpURcRjHTNZNcZEuXpUMcb4AVOBVGqT1vNcxa4APm+qIEVEmoPdZqhpx8OALcvink/WMDs157jPUVXj5MNlOyiv0hZAcnTlVTWk7y4huQ3MV91veHw4lgUrtjVv7+qGrKID835jQ3z5x9l9eO3KYXx9y7g6932dmhzDzNvGc9HwOOw2z1mkSkTk1xrSs9oBmGOMWQMsBWZZlvUVcDdwhzFmM7Xb17zSdGGKiDS99r7A0s9pu3l/6Q6ufmPZcfcwv7t4O3/6eA2vzM9wc3TS1qTlFFPjtNrE4kr7DYwLxWEz9a642xT2llSSXVhOr9iDPdTGGE7qFa1tZkSk1TtmsmpZ1hrLsgZZltXfsqy+lmX9w/V8umVZwy3L6mFZ1vmWZVU0fbgiIk3Hbtp3svryvPQDv9/7+TqsRvYyl1RU89yczQC8Oj+Dskr1rkr9UtvQSsD7+Xs76NMphDkb8ygqb56hwKnZbe/vKCKyX4MWWBIRaQ/ac8/qhuxC5qXt5o8nJ1FUXs1/527Bz8vOX0/vfcReliu27yU1q5Cde8vI3FvGzr2lZO4rI7eoAsuCO6b25IlZm/hg6XauHNNqZzQ/AAAdKElEQVStha5IPF1KViEB3vYD+3e2FZcM78I9n65l2pM/89C5fZnUK6ZRr88tKmfB5t2cPaATtgYM0d3gWlG5Vxua+ysisp+SVRERF1s73rrm5XkZ+HnZuXREHCF+XpRX1fDy/Az8ve3cMS0JgDU79/HIzA0s2LwHAIfN0CHUl86h/oxLjKJzmB8Du4QyMSmaeWl5vPRzOpeM6Iq3o1Fr+Uk7kZJVSFJsUIMSstbkwmFxJMYEcc8na7jq9WWcNaAj95+ZTESgT72vsSzrwJdCf/l0HT+k5jBzXTZPXjgQf++jf1TbkldMiJ8X0UG+br0OERFPoGRVRMTFcYye1cpqJ2szC1i/q4BxiVF0iwxoxuiaTm5hOZ+vyuSS4XGE+nsDcN8ZyZRX1fD0j5upqHGyM7+Mr9dmER7gzb1nJHNq31hign3rXZzlxok9mP76Uv4yYy3D48O5YFgXLMvilfkZjOkRqSGL7ZxlWaRmFXLWgObdj7S5DI4L46ubx/H8T5t5bs5m5qXlcd+ZyZwzsNMRIxXyiiqY+uRcOof5MalXDD+k5jA8PpzvU3K48MVFvHzFUGKC609Et+eX0jWibfVOi4jsp2RVRMTFbjOUVdVQXFFNoI+DkopqVmzfy9KMfJZszWfVjn2UVzkBCPHz4pUrhjI0PryFoz4+S7fmkxAZQESgD28s3Eq10+KqsQeH7NpshofO7Ud5VQ0vzk3H39vOLZMTuXZcN4IasGjLxKQoesUG8fHynXy8fCdTkmPI2F3Mg1+nEubvxYe/H0VijIYttleZ+8ooKq9u019aeDts3DalJ6f168Ddn6zh9g9W89nKXTx6fv/DekFfXZBBQVkVscG+PD07rfa95cqhLMnI5+b3VnL2swt4+Yqh9O0UUmc92/aUMqBLaHNdlohIs1KyKiLickqfWN5cuI2LXlqIzRjW7yqkxmlhM9CnYwgXD49jeHw4ncL8uPX9VVz68mKevWQwU5MbNyetJeUUlvPwtxuYsTKTYfFhvD59OG8v2s7JybF0jTi8p9huMzx2/gBO6hXN6O6RRAXVP4zx14wxXDc+gTs+XA3Apyt2MndTHn5edhx2G5e/soSPrh9FlzY2X1EaJtU1z7ItJ6v79YwJ4uPrR/PWwq08MnMjV7y6lHtO7cW4HpEUV1bz9sJtnNavA89ePIh1mYV4O2wE+XoxuXcMH18/mqvfWMoFLy7k6YsGMeVX7zVVNU4y95W12R5qERHT2NUeT8TQoUOtZcuWNVt9IiKN9eGyHfzzqxSSOwQzvFs4w+LDGdw1jECfw7/b21NcwVWvL2VtZgH/OrcfFw2Pa6GIG25x+h4ufGkRAJN7RTN7Qy4jE8JZlJ7Px9ePcnsvsWVZrN5ZwJ8+Xk1abjGWBQ+d25fBcWFc+OJCwgO8+ej60Y1KgqVteHp2Gk/+sIl1fz+ZAJ/28735nA253PnRavJLKukc5kf3qEDmbsrjq5vH1ttzmltYzjVvLmNtZgF/Pa03V4/thjGGnXtLufPD1SzOyOfFy4dwcp/YZr4aEZG6GWOWW5Y11C3nUrIqInJ8SiqqueGdFfy8KY87p/bkD5N6HDEfzZN8uGwHf/p4Df/73VAm94rm/BcXsnzbXgZ2CWXGjaObLPZX52fw4NcpPPLb/pw/tAsAy7ft5bKXFxMfGcD7140kxE/7QbYnN7y9nNSsQn7640ktHUqzq6iu4fv1Oby3ZDu/bNnDSUlRvDZ9+FFfU1ZZw+0frGLm+mwuHh7H4LhQ/vFlChZw/5nJnDeks0e/94hI+6JkVUTEQ1TVOLn74zV8ujKTq8Z0474zk1s6pHq9tiCDB75MYcW9UwkP8GZDdiHnv7CQ/1w0kMm9m24os2VZ7CmpJPJXq6H+vCmPq99YyoDOobx19Qj8vO1NFoN4lomPzqF3h2BeuGxIS4fSonbtKyPYz+uIkRt1cTotHv1+Iy/8tAWA4fHhPH7BAA2lFxGP485ktf2MvRERaQJedhuPnT+AIF8Hry7IYFT3CI+dw1paWQNAgE9tUtgrNpg1f5/W5D0yxpgjElWA8T2jeOqiQfzh3RWc/+IvXDgsjlP6xGpYcBtXUlHNtvxSfju4c0uH0uI6hvo1uKzNZrj7lF707RhCfkkFl4zoWu9q3CIibYU2vxMROUE2m+GvpyfTKzaIv85YS0FpVUuHVKfiimocNoO3/eBbf0sPHTytXwf+c9EgSitruPezdYz41w/c//m6Fo1JmtaG7CIsq30srtQUTu/fgctHxStRFZF2QcmqiIgbeDtqe1j3lFTyj69SWjqcOpVWVBPg42jxBPXXzhrQkdl3TGDmbeMY0jWM79bntHRI0oRSswoB6N1RyaqIiBydklURETfp2ymE6yck8MmKnczZmHvUsuVVNc0U1UHFFTUEeOi8UGMMvWKDGRofzu7iCpzO5ltPQZpXalYhwb4OOob4HruwiIi0a0pWRUTc6JbJiSRGB/LnT9ZSWF73cOBPV+ykz/3fceM7y8nYXdJssZVWVnv8NiFRgT5UOy0KyjxzKLWcuHW7CundIdjjevhFRMTzKFkVEXEjH4edR88fQG5ROQ99lXrE8Xlpefzp4zX0iArkp415TH1iLn/7bC15RRVNHltxRTX+Hp6sRroWV8orbvq/hzS/1Tv2sXrHPiYkRbV0KCIi0gooWRURcbOBXUK5dnwCHyzbwc+b8g48n7KrkBveXkGP6EA+umEUP/1xIhcPj+P9JTuY8Ogcnpy1ieKK6iaLq7SyhkAfzxwGvF+Ua9Xg3Y1I3pdvy+fJWZuaKiRxo6dnpxHi58XlI7u2dCgiItIKKFkVEWkCt0/pSUJUAH/+dC3FFdVk7itj+utLCPJ18Pr04QT7ehEd5Ms/z+nLrDsmMDEpiqdmpzHx0Tm8tXArVTVOt8dUUlGNv7dn96xGBXkDDe9ZzSoo49o3l/PU7DT2lVY2ZWhygtbuLGD2hlyuGduNIF+vlg5HRERaASWrIiJNwNfLzqPn9WdXQRn3fbaOK19dQmllDa9PH07srxaW6RYZwPOXDmHGjaPpHhXIvZ+vZ+oTc/l6TRaW5b6Fhkoqqwn08GHAMcG+GAPpeceey1tV4+Smd1aQX1KbpKY34/xfabynZqcR7OvgijHxLR2KiIi0EkpWRUSayJCu4Vw1phufrsxk255SXrp8KEmxQfWWHxQXxvvXjeTVK4fi47Bz07srOOf5X1i4ZY9b4imtqMHfQ1cD3i/I14uBXUKZvSHnmIn6w99uYMX2fdwxtScAGQ1IcKVlrMss4IfUHK4em0CwelVFRKSBlKyKiDShu6YlcUb/Djx98SBGdY84ZnljDJN6xfDNreN49Lz+5BaWc/H/FjH9tSVsyC48oViKKzx/NWCAM/p3ZF1mIXd8uJrSyrrn8M5cl8Ur8zO4cnQ810/ojt1mSN9d3MyRSkM97epVvVK9qiIi0gie/6lFRKQV8/O28+wlgxv9OrvNcP7QLpw5oCOv/7KV5+ds5tSn5vHbwZ25fWpPOoX6Nep81TVOKqqdBHj4nFWA6aPjKS6v5j+zN5Gyq5AnLhxARIAPldVOKmuc7C6u4I8frWFgl1D+clpvvB024sL9GzR0WJpfyq5Cvk/J4bYpiYT4qVdVREQazvM/tYiItGO+Xnaun9Cdi4Z14fmftvD6L1v5YvUupo+OZ2T3CP42Yx3vXDOC+MiAo56npLIGgAAPXw0YwGYz3DolkUFxodz6/kpOf3r+EWVC/b147tLBeDtqBwglRAY065610nBPz04jyNfB9DHdWjoUERFpZZSsioi0AqH+3vzltN5cMTqeJ77fxEvz0nnx53QAXluQwQNn9z3q6/cPp20Nw4D3G98zim9vHc+s1BzsxuDtsNU+7IZ+nUMP613uFhnA/M27cTotbDbTglEf3f55uMZ4bozulJpVyMz12dwyWb2qIiLSeK3nU4uIiNAp1I/HLxjANeO68fxPW/hy9S4+X72LP5/WG1+v+ntNS1z7t3r6Aku/Fhvi26A9OROiAqmodrKroIzOYf7NENnx+XJNFnd9tJrLR3blhondiXTtK9tWPT07jSAfB1erV1VERI6DFlgSEWmFencI5pmLB/HONSPYV1rFzHXZRy1fUlE7DNjTt645Xt1cw6A9fd7q/LQ8LMvitQUZjP/3HB77biMFZVUtHVaT2JBdyLfrsrlyTDwh/upVFRGRxlOyKiLSio1KiCA+wp93F28/armSyv09q20zWe0eVZusevq81dSsIkZ0i2DWHROY1CuaZ+dsZtwjP/Laggyqa5wtHZ5bPTN7M4E+Dq4eq15VERE5PkpWRURaMZvNcNHwOJZszSctp6jecm29ZzUqyIcAbzvpeZ67fU11jZONOUUkdwyme1Qgz14ymK9vGcuALqE88GUKpz8932176jalgtIqPl2xk8rq+pPrTTlFfLMuiytGdyXU37sZoxMRkbZEyaqISCt33pDOeNkN7y6pv3d1/wJL/q1gNeDjYYwhISqQdA/uWU3fXUJltZPeHYIOPNenYwhvXjWc/142hJLKai7+3yJuencFu/aVtWCkR3f3J2u448PVnP/iQrbvKa2zzFdrsjDA1WMTmjc4ERFpU9rmV+wiIu1IZKAPJ/eJ5cOlOyipqKaksgabMdgM2IxhWHw4FdWurWva6DBgqJ23unzb3pYOo14puwoBSO4QctjzxhhO6RvLxKQoXpybzvM/bebH1FxuOqk714xLOOrCWc3tu/XZzFyfzen9OvBzWh6nPz2P//ttP87o3/Gwcmt37iMxOojwAPWqiojI8Wu7n1pERNqRS0bE8dWaLD5ctpO4cH8cNoPTsiirqmHGyswD5VrDPqvHKyEqgC/X7KK8qua4E7yK6hoy95ax0/XYsbeUnXvLCPJ1cOvkRGKCfY87vpSsQrztNhKi6t4T19fLzq1TEvntkE489HUqj32/iQ+X7eRPpyRxWt8ObtuSJ6ewnEXpe+jfOZT4CP8Gb6NTVF7F/Z+vp1dsEP+5aCDZBeXc/N5K/vDuShZs3sN9ZyTj523HsizW7CxgUq9ot8QrIiLtl5JVEZE2YFRCxIHff7xzAg577SwPy7L4ZcseLn15MdB2F1iC2u1rLAu27imhV2xwg15jWRZPz97MvLQ8duwtJaew4rDjXnZDx1A/sgrK+WLVLu6c1pPfjYrHXkfiWFXjZOvuEuIjA/CyHznLJjWrkJ6xgXUeO1TnMH9euGwI89N284+v1vOHd1eS3GELd53ck5OSok94j9a/f7Geb12rR0cEeNOrQxB+Xg58vWz4etmZmBRVZ3L86HcbySkq57+XD8HLbqNLuD8fXT+Kx7/fxH/nbmH5tnyeu2Qwft529pRU0r9zSF3Vi4iINFjb/dQiItKOGGOYeds4KqqcBxLV/c+P6RHJ9RO6Myslu84kq61IOGT7moYmqz9tzOPJHzYxoHMI4xOj6BzmT5dwPzqH+dM5zI+YYF/sNsPW3SXc98V6HvgyhU9W7OShc/oxoEvogfOk5xVz6/urWJtZgL+3naHx4YxKiGBkQjj9OoVgtxlSdhU2qrdxbGIk3946ni9WZ/LkrDSuen0Zg+NCuevkJEZ3j2zcH8fll827mZWSw3lDOjM4Lozl2/aSvruY/JIqKqprKCit4uPlO0mK2cztUxOZlhyLzWZYvm0vby3axpWj4xl4yHV72W3cc2ovRnWP4I4PVnHms/OZlhwLQP/OofWFISIi0iDGsqxmq2zo0KHWsmXLmq0+ERFpP0oqqulz/3f88eQkbjqpxzHL1zgtTn96HmVVNcy6fQLejqP3eFqWxTdrs3ngy/XkFVdw2Yiu3HVyEt+uzeKBL1Pw8bLxh5N6sD2/lEXpe9iUU7sycYC3nUFxYczfvJv7z0xm+pjGb+VSVePko2U7eXp2GtmF5YzpEcFd05IYFBfWoNdblsXrv2zlwa9TSYgM4J1rRhBdx5DmGqfF12uz+M8Pm0jPKyG5QzC3TE7kiVkbKS6v5vs7JtS7onRuYTm3f7iKBZv34GU3rHvgZHwcbXfYuYiI1M0Ys9yyrKHuOJd6VkVEpE0I8HEQE+zDlgZuXzNjZSYbsot49pJBx0xUobaX+vT+HRjfM5InZm3ijV+28tHyHZRXORnTI4InLhh42JzW3cUVLE7PZ2H6bhal5+OwGUZ0izhKDfXzstu4ZEQcvxnciXcWb+f5OZs59/lfsNsMkYHexAb7EhPsS3iAN+l5JezYW8rgrmFM7BnFyX1jefCrFD5ctpOpyTE8eeHAehNOu81w1oCOnN6vA5+vyuSp2Wlc//ZyAF65YuhRtz6KDvblzatG8Or8DCprnEpURUTkhKlnVURE2oyLX1pEeXUNM24cc9Ry5VU1THrsJyKDfPjsxjHHtXjRuswCHv9+I2N6RHLVmG7HPEeN03LbMOySimreX7qD3KJy8osryS4sJ6ewnD3FlcRF+NMx1I8lGfnkFR2cg3vLpB7cNqVno661usbJjJWZFJVXc9XYxvcIi4hI+6OeVRERkTp0iwrgy1W7yCuqICrIp95yn6/KZFdBOY+dP+C4V9nt2ymE16YPb3B5d84XDvBxcPUxkkfLsvjdq0uYl7abq8Z0445pSY2ux2G3cf7QLscbpoiIyAk59rgnERGRVuK3gztR5XTy2xd+IbeovN5yX63JomuEP6O6H9+w3NbAGMMfT04iKSaIa8erV1RERFofJasiItJmDOkazrvXjiSnsJw/frSGuqa67C2p5Jctezi1b4cT3gbG0/XvHMp3t4+nQ4hfS4ciIiLSaEpWRUSkTRkcF8ZfT+/N3E15vPHL1iOOz0rJqV0JuF+H5g9OREREGkzJqoiItDmXj+zKpF7R/OvbDWzMLjrs2IItu4kO8qFvp4btxSoiIiItQ8mqiIi0OcYY/n1ef4J9Hdz6/krKq2oOHFu9Yx8Du4S2+SHAIiIirZ1WAxYRkTYpMtCHR88fwPTXlnLu878Q4uegusZi655SrXArIiLSCqhnVURE2qyTkqL52+m98fOy4bTA22FjSu9ozuiv+aoiIiKeTj2rIiLSpl0zLoFrxiW0dBgiIiLSSOpZFREREREREY+jZFVEREREREQ8jpJVERERERER8ThKVkVERERERMTjKFkVERERERERj6NkVURERERERDyOklURERERERHxOEpWRURERERExOMoWRURERERERGPo2RVREREREREPM4xk1VjTBdjzBxjTIoxZr0x5lbX8+HGmFnGmDTXz7CmD1dERERERETag4b0rFYDd1qWlQyMBG4yxiQD9wCzLctKBGa7/i0iIiIiIiJywo6ZrFqWlWVZ1grX70VAKtAJOBt4w1XsDeCcpgpSRERERERE2pdGzVk1xsQDg4DFQIxlWVmuQ9lATD2vuc4Ys8wYsywvL+8EQhUREREREZH2osHJqjEmEPgEuM2yrMJDj1mWZQFWXa+zLOsly7KGWpY1NCoq6oSCFRERERERkfahQcmqMcaL2kT1HcuyPnU9nWOM6eA63gHIbZoQRUREREREpL1pyGrABngFSLUs64lDDn0BXOH6/Qrgc/eHJyIiIiIiIu2RowFlxgCXA2uNMatcz/0FeBj40BhzNbANuKBpQhQREREREZH2xtRON22myozJozaxbYhIYHcThiPupfZqndRurYvaq3VSu7U+arPWSe3Wuqi9WqeGtFtXy7LcslhRsyarjWGMWWZZ1tCWjkMaRu3VOqndWhe1V+ukdmt91Gatk9qtdVF7tU7N3W6N2rpGREREREREpDkoWRURERERERGP48nJ6kstHYA0itqrdVK7tS5qr9ZJ7db6qM1aJ7Vb66L2ap2atd08ds6qiIiIiIiItF+e3LMqIiIiIiIi7ZTbklVjTBdjzBxjTIoxZr0x5lbX8+HGmFnGmDTXzzDX872MMQuNMRXGmLvqOJ/dGLPSGPPVUeq8wnXeNGPMFYc8/5AxZocxpthd19fWeEp7GWOCjDGrDnnsNsb8p6muu7VzZ7sZY7YaY9a6/u7LjlLnKcaYjcaYzcaYew55/g+u5yxjTGRTXXNr5mHtNe+Q+2yXMeazprru1s7N7RZqjPnYGLPBGJNqjBlVT526z06Ah7WZ7rUGcle7GWOSfvVZotAYc1s9depeO04e1l66zxrIze+Pt7vOsc4Y854xxreeOt2Xo1mW5ZYH0AEY7Po9CNgEJAP/Bu5xPX8P8Ijr92hgGPAQcFcd57sDeBf4qp76woF0188w1+9hrmMjXfEUu+v62trDk9rrV+WWA+Nb+u/jqQ93thuwFYg8Rn12YAuQAHgDq4Fk17FBQHxDztNeH57UXr8q9wnwu5b++3jqw83t9gZwjet3byC0Me2m+6z1tdmvyulea6Z2+1XbZFO7z2OD2033Wutqr1+V033WDO0GdAIyAD/Xvz8ErqyjPrfmaG7rWbUsK8uyrBWu34uAVNdFnU3tGz+un+e4yuRalrUUqPr1uYwxnYHTgZePUuXJwCzLsvIty9oLzAJOcZ17kWVZWW65sDbKk9rrkPP0pPYGmXcCl9amubPdGmg4sNmyrHTLsiqB9111YVnWSsuyth7vtbQHntRe+xljgoFJgL6Froe72s0YEwKMB15xlau0LGtfHVXqPjtBntRmh5xL99oxNNF75GRgi2VZ2+o4pnvtBHhSe+2n++zY3NxuDsDPGOMA/IFddZRxa47WJHNWjTHx1H5DtRiIOSSobCCmAaf4D/AnwHmUMp2AHYf8e6frOWkkD2qvi4APLNdXL3J0bmg3C/jeGLPcGHNdPWV0n7mJB7XXOcBsy7IKGxh6u3aC7dYNyANeM7XTJF42xgTUUU73mRt5UJvpXmsEN7xH7ncR8F49x3SvuYkHtZfus0Y4kXazLCsTeAzYDmQBBZZlfV9HUbfeZ25PVo0xgdR2x9/26/9wXEnIURMRY8wZQK5lWcvdHZscycPa62hvWHKIE203l7GWZQ0GTgVuMsaMd3+kAh7XXhej+6xB3NBuDmAw8IJlWYOAEmqHWkkT8bA2073WQG56j8QY4w2cBXzk9iDlAA9rL91nDeSGz/xh1PbGdgM6AgHGmMuaKNwD3JqsGmO8qP0jvGNZ1qeup3OMMR1cxzsAucc4zRjgLGPMVmq7+ycZY942xow4ZCL1WUAm0OWQ13V2PScN5EntZYwZADj0JcWxuand9n9DhmVZucAMYLhrEv7+drse3WcnzJPay9QuGjIc+PrEr6xtc1O77QR2Wpa12PXvj4HBus+ahie1me61hnPXe6TLqcAKy7JyXK/VveZmntReus8azk3tNgXIsCwrz7KsKuBTYHRT52juXA3YUDvHI9WyrCcOOfQFsH8VqCuAz492Hsuy/mxZVmfLsuKp7Wn70bKsyyzLWmxZ1kDX4wvgO2CaMSbMlelPcz0nDeCB7aVvxhrAXe1mjAkwxgTt/53a9lhnWdaOQ9rtv8BSINEY0831DehFrrqkATywvc6jdhG0cndcX1vlxvfHbGCHMSbJ9dRkIEX3mft5YJvpXmsAd7XbIQ77LKF7zb08sL10nzWAG9ttOzDSGOPvOudk1zmbNkez3LfS1Fhqu4/XAKtcj9OACGA2kAb8AIS7ysdS+w1mIbDP9Xvwr845kXpWl3UdvwrY7HpMP+T5f7vO53T9/Lu7rrOtPDypvVzH0oFeLf138fSHu9qN2pX1Vrse64G/HqXO06hdOW7LoeWAW1znq6Z2gv3LLf338bSHJ7WX69hPwCkt/Xfx9Ic73x+BgcAy17k+o45V0I/WbrrPWl+buY7pXmv+dgsA9gAhx6hT91obaC/XMd1nzd9uDwAbgHXAW4BPPXW6LUczrheKiIiIiIiIeIwmWQ1YRERERERE5EQoWRURERERERGPo2RVREREREREPI6SVREREREREfE4SlZFRERERETE4yhZFREREREREY+jZFVEREREREQ8jpJVERERERER8Tj/D9lRTrKMuddSAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_j_wQPAxB2IK"
      },
      "source": [
        "### **AdaBoostRegressor**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9y8K9N7UB_w5"
      },
      "source": [
        "from sklearn.ensemble import AdaBoostRegressor\r\n",
        "from sklearn.metrics import accuracy_score\r\n",
        "\r\n",
        "clf_ADA = AdaBoostRegressor(n_estimators=50, learning_rate=1, random_state=0)\r\n",
        "clf_ADA.fit(X_train, Y_train)\r\n",
        "clf_ADA.score(X_train, Y_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ezrIqIGfCI1-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ea735a6f-13d0-4463-85d7-fed819c4fcb6"
      },
      "source": [
        "Y_test_ADA = clf_ADA.predict(X_test)\r\n",
        "Y_test_ADA"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([242.57535652,  83.46893932, 149.55058524, ...,  87.9514142 ,\n",
              "       545.15774581,  69.20318958])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 349
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IMRYEdiaCKLz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4986cac2-2350-4a93-a51f-4b67812e73a7"
      },
      "source": [
        "print (\"ADA boost accuracy is \", clf_ADA.score(X_test,Y_test)*100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ADA boost accuracy is  91.94564714612808\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WCROdTwik0OJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 160
        },
        "outputId": "703d3444-9d50-461c-c4bb-ba40eec22bfc"
      },
      "source": [
        "pd.DataFrame(data=[Y_test,Y_test_ADA])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>0</th>\n",
              "      <th>1</th>\n",
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              "      <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>...</th>\n",
              "      <th>112525</th>\n",
              "      <th>112526</th>\n",
              "      <th>112527</th>\n",
              "      <th>112528</th>\n",
              "      <th>112529</th>\n",
              "      <th>112530</th>\n",
              "      <th>112531</th>\n",
              "      <th>112532</th>\n",
              "      <th>112533</th>\n",
              "      <th>112534</th>\n",
              "      <th>112535</th>\n",
              "      <th>112536</th>\n",
              "      <th>112537</th>\n",
              "      <th>112538</th>\n",
              "      <th>112539</th>\n",
              "      <th>112540</th>\n",
              "      <th>112541</th>\n",
              "      <th>112542</th>\n",
              "      <th>112543</th>\n",
              "      <th>112544</th>\n",
              "      <th>112545</th>\n",
              "      <th>112546</th>\n",
              "      <th>112547</th>\n",
              "      <th>112548</th>\n",
              "      <th>112549</th>\n",
              "      <th>112550</th>\n",
              "      <th>112551</th>\n",
              "      <th>112552</th>\n",
              "      <th>112553</th>\n",
              "      <th>112554</th>\n",
              "      <th>112555</th>\n",
              "      <th>112556</th>\n",
              "      <th>112557</th>\n",
              "      <th>112558</th>\n",
              "      <th>112559</th>\n",
              "      <th>112560</th>\n",
              "      <th>112561</th>\n",
              "      <th>112562</th>\n",
              "      <th>112563</th>\n",
              "      <th>112564</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>282.490000</td>\n",
              "      <td>94.540000</td>\n",
              "      <td>170.780000</td>\n",
              "      <td>25.15000</td>\n",
              "      <td>95.400000</td>\n",
              "      <td>37.48000</td>\n",
              "      <td>61.65000</td>\n",
              "      <td>54.66670</td>\n",
              "      <td>32.73500</td>\n",
              "      <td>26.20000</td>\n",
              "      <td>121.09000</td>\n",
              "      <td>106.57000</td>\n",
              "      <td>78.830000</td>\n",
              "      <td>67.960000</td>\n",
              "      <td>36.80000</td>\n",
              "      <td>69.810000</td>\n",
              "      <td>65.830000</td>\n",
              "      <td>290.890000</td>\n",
              "      <td>79.96000</td>\n",
              "      <td>68.34000</td>\n",
              "      <td>40.520000</td>\n",
              "      <td>154.010000</td>\n",
              "      <td>42.94000</td>\n",
              "      <td>61.22000</td>\n",
              "      <td>71.550000</td>\n",
              "      <td>63.390000</td>\n",
              "      <td>191.55000</td>\n",
              "      <td>55.78000</td>\n",
              "      <td>103.660000</td>\n",
              "      <td>14.19000</td>\n",
              "      <td>81.22000</td>\n",
              "      <td>15.41000</td>\n",
              "      <td>47.51000</td>\n",
              "      <td>96.080000</td>\n",
              "      <td>59.59000</td>\n",
              "      <td>156.680000</td>\n",
              "      <td>82.87000</td>\n",
              "      <td>59.470000</td>\n",
              "      <td>217.960000</td>\n",
              "      <td>77.66000</td>\n",
              "      <td>...</td>\n",
              "      <td>71.170000</td>\n",
              "      <td>154.920000</td>\n",
              "      <td>125.500000</td>\n",
              "      <td>42.83000</td>\n",
              "      <td>82.26000</td>\n",
              "      <td>147.070000</td>\n",
              "      <td>130.310000</td>\n",
              "      <td>198.17000</td>\n",
              "      <td>45.00000</td>\n",
              "      <td>72.910000</td>\n",
              "      <td>46.13000</td>\n",
              "      <td>358.980000</td>\n",
              "      <td>190.360000</td>\n",
              "      <td>132.19000</td>\n",
              "      <td>191.20000</td>\n",
              "      <td>184.640000</td>\n",
              "      <td>45.13000</td>\n",
              "      <td>62.385000</td>\n",
              "      <td>114.00000</td>\n",
              "      <td>105.600000</td>\n",
              "      <td>85.590000</td>\n",
              "      <td>63.640000</td>\n",
              "      <td>67.850000</td>\n",
              "      <td>72.570000</td>\n",
              "      <td>44.30000</td>\n",
              "      <td>83.57000</td>\n",
              "      <td>44.42000</td>\n",
              "      <td>57.32000</td>\n",
              "      <td>447.900000</td>\n",
              "      <td>34.87000</td>\n",
              "      <td>47.280000</td>\n",
              "      <td>39.65000</td>\n",
              "      <td>652.480000</td>\n",
              "      <td>54.350000</td>\n",
              "      <td>137.46000</td>\n",
              "      <td>52.20000</td>\n",
              "      <td>16.26000</td>\n",
              "      <td>105.380000</td>\n",
              "      <td>514.590000</td>\n",
              "      <td>34.62000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>242.575357</td>\n",
              "      <td>83.468939</td>\n",
              "      <td>149.550585</td>\n",
              "      <td>69.20319</td>\n",
              "      <td>83.468939</td>\n",
              "      <td>69.20319</td>\n",
              "      <td>69.20319</td>\n",
              "      <td>69.20319</td>\n",
              "      <td>69.20319</td>\n",
              "      <td>69.20319</td>\n",
              "      <td>84.01235</td>\n",
              "      <td>84.01235</td>\n",
              "      <td>83.468939</td>\n",
              "      <td>72.216901</td>\n",
              "      <td>69.20319</td>\n",
              "      <td>72.216901</td>\n",
              "      <td>72.216901</td>\n",
              "      <td>287.167886</td>\n",
              "      <td>75.49687</td>\n",
              "      <td>69.20319</td>\n",
              "      <td>72.216901</td>\n",
              "      <td>145.301912</td>\n",
              "      <td>69.20319</td>\n",
              "      <td>69.20319</td>\n",
              "      <td>72.216901</td>\n",
              "      <td>72.216901</td>\n",
              "      <td>192.72926</td>\n",
              "      <td>69.20319</td>\n",
              "      <td>83.468939</td>\n",
              "      <td>69.20319</td>\n",
              "      <td>75.49687</td>\n",
              "      <td>69.20319</td>\n",
              "      <td>69.20319</td>\n",
              "      <td>83.468939</td>\n",
              "      <td>69.20319</td>\n",
              "      <td>103.292067</td>\n",
              "      <td>75.49687</td>\n",
              "      <td>72.216901</td>\n",
              "      <td>216.355815</td>\n",
              "      <td>75.49687</td>\n",
              "      <td>...</td>\n",
              "      <td>72.216901</td>\n",
              "      <td>108.040206</td>\n",
              "      <td>87.951414</td>\n",
              "      <td>69.20319</td>\n",
              "      <td>75.49687</td>\n",
              "      <td>103.292067</td>\n",
              "      <td>87.951414</td>\n",
              "      <td>192.72926</td>\n",
              "      <td>69.20319</td>\n",
              "      <td>83.468939</td>\n",
              "      <td>69.20319</td>\n",
              "      <td>320.206616</td>\n",
              "      <td>149.550585</td>\n",
              "      <td>84.01235</td>\n",
              "      <td>192.72926</td>\n",
              "      <td>149.550585</td>\n",
              "      <td>69.20319</td>\n",
              "      <td>72.216901</td>\n",
              "      <td>84.01235</td>\n",
              "      <td>87.951414</td>\n",
              "      <td>83.468939</td>\n",
              "      <td>72.216901</td>\n",
              "      <td>72.216901</td>\n",
              "      <td>72.216901</td>\n",
              "      <td>69.20319</td>\n",
              "      <td>75.49687</td>\n",
              "      <td>69.20319</td>\n",
              "      <td>69.20319</td>\n",
              "      <td>419.430426</td>\n",
              "      <td>69.20319</td>\n",
              "      <td>72.216901</td>\n",
              "      <td>69.20319</td>\n",
              "      <td>545.157746</td>\n",
              "      <td>72.216901</td>\n",
              "      <td>84.01235</td>\n",
              "      <td>69.20319</td>\n",
              "      <td>69.20319</td>\n",
              "      <td>87.951414</td>\n",
              "      <td>545.157746</td>\n",
              "      <td>69.20319</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 112565 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       0          1           2       ...      112562      112563    112564\n",
              "0  282.490000  94.540000  170.780000  ...  105.380000  514.590000  34.62000\n",
              "1  242.575357  83.468939  149.550585  ...   87.951414  545.157746  69.20319\n",
              "\n",
              "[2 rows x 112565 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 352
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ro1wEhXRvHiR"
      },
      "source": [
        "### **Graphing ADABOOST**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T9xKqe_Nq4l4"
      },
      "source": [
        "# merging the arrays for graphing purposes\r\n",
        "Chart1 = pd.DataFrame(data = X_test_date, columns=[\"Date\"])\r\n",
        "Chart2 = pd.DataFrame(data= Y_test, columns= [\"Y_test\"])\r\n",
        "ChartADA = pd.DataFrame(data= Y_test_ADA, columns= [\"Y_test_ADA\"])\r\n",
        "Chart4 = pd.DataFrame(data = X_test[:,4], columns=[\"Name\"])\r\n",
        "\r\n",
        "Chart = pd.concat([Chart1, Chart2, ChartADA, Chart4], axis = 1)\r\n",
        "Chart2 = Chart.sort_values(by=['Date', \"Name\"], ascending= True)\r\n",
        "Chart2[\"Date\"] = pd.to_datetime(Chart2.Date,format='%Y-%m-%d')\r\n",
        "Chart2.index = Chart2[\"Date\"]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sF4m0RfFv37Y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "outputId": "65c073f4-afd9-471a-88ab-9750ab2bccdc"
      },
      "source": [
        "import matplotlib.pyplot as plt\r\n",
        "%matplotlib inline\r\n",
        "\r\n",
        "from matplotlib.pylab import rcParams\r\n",
        "rcParams['figure.figsize'] = 20,10\r\n",
        "\r\n",
        "#plots\r\n",
        "for i in [3, 38, 154, 207]:\r\n",
        "  Chart3 = Chart2[Chart2.Name == i]\r\n",
        "  plt.figure(figsize=(16,8))\r\n",
        "  plt.plot(Chart3['Y_test'], label='Close Price history')\r\n",
        "  plt.plot(Chart3['Y_test_RF'], label='Close Price history')\r\n",
        "# 3, 38, 154, 207 Corresponds to the 4 sample stock names: AAPL, AMZN, EBAY, GOOGL"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fb83f502978>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 354
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9S4pGSfNHokU"
      },
      "source": [
        "### **Gradient Boosting**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "anKg_D5zHoKG"
      },
      "source": [
        "from sklearn.ensemble import GradientBoostingRegressor\r\n",
        "GBR = GradientBoostingRegressor(random_state = 100)\r\n",
        "GBR.fit(X_train, Y_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GdhMvBZ2lrOn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "da89c57f-d7f5-4775-98da-dacc4d299c12"
      },
      "source": [
        "GBR.score(X_train, Y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9890863897491784"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 355
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lYIlbzi_crv1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 182
        },
        "outputId": "0c025f14-bbcd-4f41-865a-24772d2108c8"
      },
      "source": [
        "# Calculating Feature importance\r\n",
        "Importance =GBR.feature_importances_\r\n",
        "Importance_cols = df.columns[0:39]\r\n",
        "Importance_table = pd.DataFrame(data=[Importance_cols,np.around(Importance,2)])\r\n",
        "Importance_table"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Date</td>\n",
              "      <td>open</td>\n",
              "      <td>high</td>\n",
              "      <td>low</td>\n",
              "      <td>volume</td>\n",
              "      <td>Name</td>\n",
              "      <td>52 week high</td>\n",
              "      <td>% Over/Under 52WKH</td>\n",
              "      <td>20 Day MA</td>\n",
              "      <td>% Over Under 20DMA</td>\n",
              "      <td>50 Day MA</td>\n",
              "      <td>% Over Under 50DMA</td>\n",
              "      <td>100 Day MA</td>\n",
              "      <td>% Over Under 100DMA</td>\n",
              "      <td>200 Day MA</td>\n",
              "      <td>% Over Under 200DMA</td>\n",
              "      <td>Average Gain/Loss</td>\n",
              "      <td>Average Gains2</td>\n",
              "      <td>Av Losses2</td>\n",
              "      <td>RSI V2</td>\n",
              "      <td>S&amp;P500_open</td>\n",
              "      <td>S&amp;P500_high</td>\n",
              "      <td>S&amp;P500_low</td>\n",
              "      <td>S&amp;P500_close</td>\n",
              "      <td>S&amp;P500_52 week high</td>\n",
              "      <td>S&amp;P500_% Over/Under 52WKH</td>\n",
              "      <td>S&amp;P500_20 Day MA</td>\n",
              "      <td>S&amp;P500_% Over Under 20DMA</td>\n",
              "      <td>S&amp;P500_50 Day MA</td>\n",
              "      <td>S&amp;P500_% Over Under 50DMA</td>\n",
              "      <td>S&amp;P500_100 Day MA</td>\n",
              "      <td>S&amp;P500_% Over Under 100DMA</td>\n",
              "      <td>S&amp;P500_200 Day MA</td>\n",
              "      <td>S&amp;P500_% Over Under 200DMA</td>\n",
              "      <td>S&amp;P500_Average Gain/Loss</td>\n",
              "      <td>S&amp;P500_Average Gains2</td>\n",
              "      <td>S&amp;P500_Av Losses2</td>\n",
              "      <td>S&amp;P500_RSI V2</td>\n",
              "      <td>SignalVal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.26</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.26</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     0     1     2   ...                 36             37         38\n",
              "0  Date  open  high  ...  S&P500_Av Losses2  S&P500_RSI V2  SignalVal\n",
              "1     0  0.21  0.26  ...                  0              0       0.26\n",
              "\n",
              "[2 rows x 39 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 356
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1GH5zfXyoBIm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c4722e1c-3b65-4145-b1f4-0a7beb287854"
      },
      "source": [
        "Y_test_GBR = GBR.predict(X_test)\r\n",
        "Y_test_GBR\r\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([249.79529241,  86.3919338 , 169.95449828, ..., 126.14542322,\n",
              "       544.71236634,  35.87870462])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 357
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FlzO2rUaiyc9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3599d021-e30b-4f63-a76a-61518615e7a7"
      },
      "source": [
        "print (\"GBR accuracy is \", GBR.score(X_test,Y_test)*100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "GBR accuracy is  98.82941938737864\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tCGoKGxuogeg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 160
        },
        "outputId": "10af84d5-070f-44f5-d16d-48a2cbd85a92"
      },
      "source": [
        "pd.DataFrame(data=[Y_test,Y_test_GBR])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <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",
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              "      <th>37</th>\n",
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              "      <th>39</th>\n",
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              "      <th>112526</th>\n",
              "      <th>112527</th>\n",
              "      <th>112528</th>\n",
              "      <th>112529</th>\n",
              "      <th>112530</th>\n",
              "      <th>112531</th>\n",
              "      <th>112532</th>\n",
              "      <th>112533</th>\n",
              "      <th>112534</th>\n",
              "      <th>112535</th>\n",
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              "      <th>112537</th>\n",
              "      <th>112538</th>\n",
              "      <th>112539</th>\n",
              "      <th>112540</th>\n",
              "      <th>112541</th>\n",
              "      <th>112542</th>\n",
              "      <th>112543</th>\n",
              "      <th>112544</th>\n",
              "      <th>112545</th>\n",
              "      <th>112546</th>\n",
              "      <th>112547</th>\n",
              "      <th>112548</th>\n",
              "      <th>112549</th>\n",
              "      <th>112550</th>\n",
              "      <th>112551</th>\n",
              "      <th>112552</th>\n",
              "      <th>112553</th>\n",
              "      <th>112554</th>\n",
              "      <th>112555</th>\n",
              "      <th>112556</th>\n",
              "      <th>112557</th>\n",
              "      <th>112558</th>\n",
              "      <th>112559</th>\n",
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              "      <th>112561</th>\n",
              "      <th>112562</th>\n",
              "      <th>112563</th>\n",
              "      <th>112564</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>282.490000</td>\n",
              "      <td>94.540000</td>\n",
              "      <td>170.780000</td>\n",
              "      <td>25.150000</td>\n",
              "      <td>95.400000</td>\n",
              "      <td>37.480000</td>\n",
              "      <td>61.650000</td>\n",
              "      <td>54.666700</td>\n",
              "      <td>32.735000</td>\n",
              "      <td>26.200000</td>\n",
              "      <td>121.090000</td>\n",
              "      <td>106.570000</td>\n",
              "      <td>78.830000</td>\n",
              "      <td>67.960000</td>\n",
              "      <td>36.800000</td>\n",
              "      <td>69.810000</td>\n",
              "      <td>65.830000</td>\n",
              "      <td>290.890000</td>\n",
              "      <td>79.960000</td>\n",
              "      <td>68.34000</td>\n",
              "      <td>40.520000</td>\n",
              "      <td>154.010000</td>\n",
              "      <td>42.940000</td>\n",
              "      <td>61.220000</td>\n",
              "      <td>71.550000</td>\n",
              "      <td>63.390000</td>\n",
              "      <td>191.550000</td>\n",
              "      <td>55.780000</td>\n",
              "      <td>103.660000</td>\n",
              "      <td>14.190000</td>\n",
              "      <td>81.220000</td>\n",
              "      <td>15.41000</td>\n",
              "      <td>47.51000</td>\n",
              "      <td>96.08000</td>\n",
              "      <td>59.590000</td>\n",
              "      <td>156.680000</td>\n",
              "      <td>82.870000</td>\n",
              "      <td>59.470000</td>\n",
              "      <td>217.960000</td>\n",
              "      <td>77.660000</td>\n",
              "      <td>...</td>\n",
              "      <td>71.170000</td>\n",
              "      <td>154.920000</td>\n",
              "      <td>125.500000</td>\n",
              "      <td>42.830000</td>\n",
              "      <td>82.260000</td>\n",
              "      <td>147.070000</td>\n",
              "      <td>130.310000</td>\n",
              "      <td>198.170000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>72.910000</td>\n",
              "      <td>46.130000</td>\n",
              "      <td>358.980000</td>\n",
              "      <td>190.360000</td>\n",
              "      <td>132.190000</td>\n",
              "      <td>191.200000</td>\n",
              "      <td>184.640000</td>\n",
              "      <td>45.130000</td>\n",
              "      <td>62.385000</td>\n",
              "      <td>114.000000</td>\n",
              "      <td>105.600000</td>\n",
              "      <td>85.590000</td>\n",
              "      <td>63.640000</td>\n",
              "      <td>67.850000</td>\n",
              "      <td>72.570000</td>\n",
              "      <td>44.300000</td>\n",
              "      <td>83.570000</td>\n",
              "      <td>44.420000</td>\n",
              "      <td>57.320000</td>\n",
              "      <td>447.900000</td>\n",
              "      <td>34.87000</td>\n",
              "      <td>47.280000</td>\n",
              "      <td>39.650000</td>\n",
              "      <td>652.480000</td>\n",
              "      <td>54.350000</td>\n",
              "      <td>137.460000</td>\n",
              "      <td>52.200000</td>\n",
              "      <td>16.260000</td>\n",
              "      <td>105.380000</td>\n",
              "      <td>514.590000</td>\n",
              "      <td>34.620000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>249.795292</td>\n",
              "      <td>86.391934</td>\n",
              "      <td>169.954498</td>\n",
              "      <td>27.527518</td>\n",
              "      <td>99.494869</td>\n",
              "      <td>34.734349</td>\n",
              "      <td>61.944363</td>\n",
              "      <td>54.350108</td>\n",
              "      <td>32.857402</td>\n",
              "      <td>29.427872</td>\n",
              "      <td>115.582386</td>\n",
              "      <td>104.222794</td>\n",
              "      <td>85.140827</td>\n",
              "      <td>71.027318</td>\n",
              "      <td>38.230474</td>\n",
              "      <td>63.807105</td>\n",
              "      <td>66.292811</td>\n",
              "      <td>307.560474</td>\n",
              "      <td>78.100793</td>\n",
              "      <td>58.67276</td>\n",
              "      <td>62.807134</td>\n",
              "      <td>161.721965</td>\n",
              "      <td>43.841288</td>\n",
              "      <td>56.663025</td>\n",
              "      <td>74.436795</td>\n",
              "      <td>63.670522</td>\n",
              "      <td>184.960429</td>\n",
              "      <td>54.765028</td>\n",
              "      <td>93.427979</td>\n",
              "      <td>15.834337</td>\n",
              "      <td>79.349056</td>\n",
              "      <td>18.43986</td>\n",
              "      <td>42.41173</td>\n",
              "      <td>95.00703</td>\n",
              "      <td>54.524773</td>\n",
              "      <td>150.456162</td>\n",
              "      <td>77.018728</td>\n",
              "      <td>69.180331</td>\n",
              "      <td>213.018753</td>\n",
              "      <td>78.599577</td>\n",
              "      <td>...</td>\n",
              "      <td>63.129246</td>\n",
              "      <td>151.050694</td>\n",
              "      <td>119.378681</td>\n",
              "      <td>43.331036</td>\n",
              "      <td>76.270794</td>\n",
              "      <td>139.779383</td>\n",
              "      <td>131.231977</td>\n",
              "      <td>197.001679</td>\n",
              "      <td>45.614087</td>\n",
              "      <td>83.093142</td>\n",
              "      <td>42.461427</td>\n",
              "      <td>321.185361</td>\n",
              "      <td>164.282555</td>\n",
              "      <td>117.077544</td>\n",
              "      <td>186.702877</td>\n",
              "      <td>175.660422</td>\n",
              "      <td>43.348542</td>\n",
              "      <td>67.749143</td>\n",
              "      <td>112.524122</td>\n",
              "      <td>118.955324</td>\n",
              "      <td>84.167179</td>\n",
              "      <td>70.445125</td>\n",
              "      <td>66.495651</td>\n",
              "      <td>69.434366</td>\n",
              "      <td>38.626201</td>\n",
              "      <td>82.412764</td>\n",
              "      <td>43.699844</td>\n",
              "      <td>52.453919</td>\n",
              "      <td>453.987251</td>\n",
              "      <td>34.68511</td>\n",
              "      <td>64.933543</td>\n",
              "      <td>42.893281</td>\n",
              "      <td>603.268762</td>\n",
              "      <td>66.898983</td>\n",
              "      <td>103.494215</td>\n",
              "      <td>48.385861</td>\n",
              "      <td>23.205444</td>\n",
              "      <td>126.145423</td>\n",
              "      <td>544.712366</td>\n",
              "      <td>35.878705</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 112565 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       0          1           2       ...      112562      112563     112564\n",
              "0  282.490000  94.540000  170.780000  ...  105.380000  514.590000  34.620000\n",
              "1  249.795292  86.391934  169.954498  ...  126.145423  544.712366  35.878705\n",
              "\n",
              "[2 rows x 112565 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 359
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YO4beQw87fQh"
      },
      "source": [
        "### Graphing GBR"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7N5CdJ22xZ5n"
      },
      "source": [
        "# merging the arrays for graphing purposes\r\n",
        "Chart1 = pd.DataFrame(data = X_test_date, columns=[\"Date\"])\r\n",
        "Chart2 = pd.DataFrame(data= Y_test, columns= [\"Y_test\"])\r\n",
        "ChartGBR = pd.DataFrame(data= Y_test_GBR, columns= [\"Y_test_GBR\"])\r\n",
        "Chart4 = pd.DataFrame(data = X_test[:,4], columns=[\"Name\"])\r\n",
        "\r\n",
        "Chart = pd.concat([Chart1, Chart2, ChartGBR, Chart4], axis = 1)\r\n",
        "Chart2 = Chart.sort_values(by=['Date', \"Name\"], ascending= True)\r\n",
        "Chart2[\"Date\"] = pd.to_datetime(Chart2.Date,format='%Y-%m-%d')\r\n",
        "Chart2.index = Chart2[\"Date\"]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "id": "_-pJ-bSd1bK5",
        "outputId": "e5e7c8c3-9d8d-4dee-eb24-9c5950326ca3"
      },
      "source": [
        "import matplotlib.pyplot as plt\r\n",
        "%matplotlib inline\r\n",
        "\r\n",
        "from matplotlib.pylab import rcParams\r\n",
        "rcParams['figure.figsize'] = 20,10\r\n",
        "\r\n",
        "#plots\r\n",
        "for i in [3, 38, 154, 207]:\r\n",
        "  Chart3 = Chart2[Chart2.Name == i]\r\n",
        "  plt.figure(figsize=(16,8))\r\n",
        "  plt.plot(Chart3['Y_test'], label='Close Price history')\r\n",
        "  plt.plot(Chart3['Y_test_RF'], label='Close Price history')\r\n",
        "# 3, 38, 154, 207 Corresponds to the 4 sample stock names: AAPL, AMZN, EBAY, GOOGL"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fb8790eee80>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 361
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "rDsvDK2h6c-Z",
        "outputId": "0d822a52-99f2-41c9-af31-b5b83e7a50ac"
      },
      "source": [
        "# Creating a final chart showing the Actual Y values when compared with RandomForest, AdaBoost, and GradientBoost Machine Learning algorithms\r\n",
        "Chart_Final = pd.concat([Chart, ChartADA, ChartGBR], axis=1)\r\n",
        "Chart_Final"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Name</th>\n",
              "      <th>Y_test</th>\n",
              "      <th>Y_test_RF</th>\n",
              "      <th>Y_test_ADA</th>\n",
              "      <th>Y_test_GBR</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2017-05-02</td>\n",
              "      <td>436</td>\n",
              "      <td>282.49</td>\n",
              "      <td>179.840654</td>\n",
              "      <td>242.575357</td>\n",
              "      <td>249.795292</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2016-12-07</td>\n",
              "      <td>390</td>\n",
              "      <td>94.54</td>\n",
              "      <td>60.616302</td>\n",
              "      <td>83.468939</td>\n",
              "      <td>86.391934</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2014-08-20</td>\n",
              "      <td>391</td>\n",
              "      <td>170.78</td>\n",
              "      <td>179.840654</td>\n",
              "      <td>149.550585</td>\n",
              "      <td>169.954498</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2014-05-30</td>\n",
              "      <td>213</td>\n",
              "      <td>25.15</td>\n",
              "      <td>60.616302</td>\n",
              "      <td>69.203190</td>\n",
              "      <td>27.527518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2016-12-20</td>\n",
              "      <td>82</td>\n",
              "      <td>95.4</td>\n",
              "      <td>60.616302</td>\n",
              "      <td>83.468939</td>\n",
              "      <td>99.494869</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112560</th>\n",
              "      <td>2015-02-25</td>\n",
              "      <td>337</td>\n",
              "      <td>52.2</td>\n",
              "      <td>60.616302</td>\n",
              "      <td>69.203190</td>\n",
              "      <td>48.385861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112561</th>\n",
              "      <td>2017-08-15</td>\n",
              "      <td>123</td>\n",
              "      <td>16.26</td>\n",
              "      <td>60.616302</td>\n",
              "      <td>69.203190</td>\n",
              "      <td>23.205444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112562</th>\n",
              "      <td>2017-03-15</td>\n",
              "      <td>30</td>\n",
              "      <td>105.38</td>\n",
              "      <td>164.493215</td>\n",
              "      <td>87.951414</td>\n",
              "      <td>126.145423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112563</th>\n",
              "      <td>2015-07-23</td>\n",
              "      <td>393</td>\n",
              "      <td>514.59</td>\n",
              "      <td>659.374500</td>\n",
              "      <td>545.157746</td>\n",
              "      <td>544.712366</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112564</th>\n",
              "      <td>2015-02-02</td>\n",
              "      <td>195</td>\n",
              "      <td>34.62</td>\n",
              "      <td>60.616302</td>\n",
              "      <td>69.203190</td>\n",
              "      <td>35.878705</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>112565 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "              Date Name  Y_test   Y_test_RF  Y_test_ADA  Y_test_GBR\n",
              "0       2017-05-02  436  282.49  179.840654  242.575357  249.795292\n",
              "1       2016-12-07  390   94.54   60.616302   83.468939   86.391934\n",
              "2       2014-08-20  391  170.78  179.840654  149.550585  169.954498\n",
              "3       2014-05-30  213   25.15   60.616302   69.203190   27.527518\n",
              "4       2016-12-20   82    95.4   60.616302   83.468939   99.494869\n",
              "...            ...  ...     ...         ...         ...         ...\n",
              "112560  2015-02-25  337    52.2   60.616302   69.203190   48.385861\n",
              "112561  2017-08-15  123   16.26   60.616302   69.203190   23.205444\n",
              "112562  2017-03-15   30  105.38  164.493215   87.951414  126.145423\n",
              "112563  2015-07-23  393  514.59  659.374500  545.157746  544.712366\n",
              "112564  2015-02-02  195   34.62   60.616302   69.203190   35.878705\n",
              "\n",
              "[112565 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 180
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nMwRuTFn-HkR"
      },
      "source": [
        "# Writing Y output to csv\r\n",
        "\r\n",
        "# Chart_Final.to_csv(r'/content/drive/MyDrive/Colab Notebooks/ChartFinal.csv', index = False)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}