machine learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the seed value for the notebook so the results are reproducible\n",
    "from numpy.random import seed\n",
    "seed(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dependencies\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.2.4-tf'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow\n",
    "tensorflow.keras.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"filtered_df_2017.csv\")\n",
    "data_1 = data.drop(['Stock_Ticker'], axis=1)\n",
    "data.head()\n",
    "stock_name_list_2017 = list(data.Stock_Ticker)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_2019 = pd.read_csv(\"data2019.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'Stock Ticker', 'Previous Close', 'Open', 'Bid', 'Ask',\n",
       "       'Day's Range', '52 Week Range', 'Volume', 'Avg. Volume', 'Market Cap',\n",
       "       'Beta (3Y Monthly)', 'PE Ratio (TTM)', 'EPS (TTM)', 'Forward Dividend',\n",
       "       'Forward Yeild', 'Ex-Dividend Date', 'Dividend_yield'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2019.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_2019_drop_col = data_2019.drop([\"Open\"], axis=1).drop([\"Bid\"], axis=1).drop([\"Ask\"], axis=1)\\\n",
    ".drop([\"52 Week Range\"], axis=1).drop([\"Ex-Dividend Date\"], axis=1).drop([\"Day's Range\"], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_2019_drop_col\n",
    "ticker_list_2019 = data_2019_drop_col['Stock Ticker']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_2019_drop_col = data_2019_drop_col.drop(['Unnamed: 0'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock_list_2019 = list(data_2019_drop_col[\"Stock Ticker\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64.01"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "float(data_2019_drop_col.loc[data_2019_drop_col[\"Stock Ticker\"]==\"XEL\"][\"Previous Close\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock_name_list_2017 = list(data.Stock_Ticker)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AMD not on list\n",
      "ALXN not on list\n",
      "AAPL not on list\n",
      "BIIB not on list\n",
      "CA not on list\n",
      "CELG not on list\n",
      "CHTR not on list\n",
      "CMG not on list\n",
      "CINF not on list\n",
      "DISH not on list\n",
      "DOV not on list\n",
      "DPS not on list\n",
      "EIX not on list\n",
      "EXR not on list\n",
      "FFIV not on list\n",
      "FISV not on list\n",
      "FLIR not on list\n",
      "FLR not on list\n",
      "GPC not on list\n",
      "HAS not on list\n",
      "HP not on list\n",
      "HII not on list\n",
      "JBHT not on list\n",
      "MLM not on list\n",
      "MKC not on list\n",
      "KORS not on list\n",
      "MU not on list\n",
      "MSFT not on list\n",
      "MHK not on list\n",
      "MNST not on list\n",
      "MYL not on list\n",
      "NWL not on list\n",
      "NFX not on list\n",
      "NLSN not on list\n",
      "NBL not on list\n",
      "NOC not on list\n",
      "NCLH not on list\n",
      "NVDA not on list\n",
      "ORLY not on list\n",
      "PYPL not on list\n",
      "PCG not on list\n",
      "PSA not on list\n",
      "DGX not on list\n",
      "REGN not on list\n",
      "ROK not on list\n",
      "COL not on list\n",
      "CRM not on list\n",
      "SHW not on list\n",
      "SNA not on list\n",
      "SRCL not on list\n",
      "SNPS not on list\n",
      "TWX not on list\n",
      "TSS not on list\n",
      "TDG not on list\n",
      "FOXA not on list\n",
      "FOX not on list\n",
      "ULTA not on list\n",
      "UAL not on list\n",
      "URI not on list\n",
      "VAR not on list\n",
      "VRSN not on list\n",
      "VMC not on list\n",
      "WAT not on list\n",
      "WDC not on list\n",
      "WY not on list\n",
      "WHR not on list\n",
      "WYN not on list\n"
     ]
    }
   ],
   "source": [
    "prev_close_list_2017 = []\n",
    "prev_close_list_2019 = []\n",
    "difference_list = []\n",
    "not_on_list = []\n",
    "stock_list_use = []\n",
    "for stock_name in stock_name_list_2017:\n",
    "    \n",
    "    try:\n",
    "        prev_close_list_2019.append(float(data_2019_drop_col.loc\\\n",
    "        [data_2019_drop_col[\"Stock Ticker\"]==stock_name][\"Previous Close\"]))\n",
    "        prev_close_list_2017.append(float(data.loc[data\\\n",
    "        [\"Stock_Ticker\"]==stock_name][\"Previous_Close\"]))\n",
    "        \n",
    "        \n",
    "        \n",
    "        difference_list.append(100*(float(data_2019_drop_col.loc\\\n",
    "        [data_2019_drop_col[\"Stock Ticker\"]==stock_name][\"Previous Close\"])\\\n",
    "        -float(data.loc[data\\\n",
    "        [\"Stock_Ticker\"]==stock_name][\"Previous_Close\"]))/\\\n",
    "        float(data.loc[data\\\n",
    "        [\"Stock_Ticker\"]==stock_name][\"Previous_Close\"]))\n",
    "        \n",
    "        \n",
    "        \n",
    "        stock_list_use.append(stock_name)\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "    except TypeError:\n",
    "        print(stock_name +\" not on list\")\n",
    "        not_on_list.append(stock_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "149\n",
      "149\n",
      "149\n"
     ]
    }
   ],
   "source": [
    "print(len(prev_close_list_2017))\n",
    "print(len(prev_close_list_2019))\n",
    "print(len(stock_list_use))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-19.903746992093502"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "difference_list[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "149\n",
      "149\n",
      "216\n"
     ]
    }
   ],
   "source": [
    "print(len(prev_close_list_2017))\n",
    "print(len(prev_close_list_2019))\n",
    "print(len(stock_name_list_2017))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Stock_Ticker', 'Previous_Close', 'Open', 'Market_Cap',\n",
       "       'Beta_3Y_Monthly', 'PE Ratio_TTM', 'EPS_TTM', 'one_y_Target_Est'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock_tick_list_df = []\n",
    "prev_close_list_df = []\n",
    "open_list_df = []\n",
    "market_cap_list_df = []\n",
    "beta_list_df = []\n",
    "pe_list_df =[]\n",
    "eps_list_df =[]\n",
    "one_year_list_df = []\n",
    "good_bad_list = []\n",
    "ct = 0\n",
    "for stock_nname in stock_list_use:\n",
    "    prev_close_list_df.append(float(data.loc[data\\\n",
    "        [\"Stock_Ticker\"]==stock_nname][\"Previous_Close\"]))\n",
    "    \n",
    "    open_list_df.append(float(data.loc[data\\\n",
    "        [\"Stock_Ticker\"]==stock_name][\"Open\"]))\n",
    "    \n",
    "    market_cap_list_df.append(float(data.loc[data\\\n",
    "        [\"Stock_Ticker\"]==stock_name][\"Market_Cap\"]))\n",
    "    beta_list_df.append(float(data.loc[data\\\n",
    "        [\"Stock_Ticker\"]==stock_name][\"Beta_3Y_Monthly\"]))\n",
    "    \n",
    "    pe_list_df.append(float(data.loc[data\\\n",
    "        [\"Stock_Ticker\"]==stock_name][\"PE Ratio_TTM\"]))\n",
    "    \n",
    "    eps_list_df.append(float(data.loc[data\\\n",
    "        [\"Stock_Ticker\"]==stock_name][\"EPS_TTM\"]))\n",
    "    \n",
    "    one_year_list_df.append(float(data.loc[data\\\n",
    "        [\"Stock_Ticker\"]==stock_name][\"one_y_Target_Est\"]))\n",
    "    \n",
    "    if 4 >  difference_list[ct] > -4 :\n",
    "        good_bad_list.append(\"neutral\")\n",
    "    elif difference_list[ct] >= 4:\n",
    "        good_bad_list.append(\"good\")\n",
    "    elif difference_list[ct] <= -4:\n",
    "        good_bad_list.append(\"bad\")\n",
    "\n",
    "    \n",
    "    ct = ct +1\n",
    "    \n",
    "    \n",
    "df_2017_ML = pd.DataFrame({\n",
    "  \"Stock\":stock_list_use,\n",
    "  \"prev_close\":prev_close_list_df,\n",
    "  \"open\":open_list_df,\n",
    "    \"market_cap\":market_cap_list_df,\n",
    "    \"beta\":beta_list_df,\n",
    "    \"pe\":pe_list_df,\n",
    "    \"eps\":eps_list_df,\n",
    "    \"1year_target\":one_year_list_df,\n",
    "    \"label\":good_bad_list\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64.01"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "float(data_2019_drop_col.loc[data_2019_drop_col[\"Stock Ticker\"]==\"XEL\"][\"Previous Close\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Stock</th>\n",
       "      <th>prev_close</th>\n",
       "      <th>open</th>\n",
       "      <th>market_cap</th>\n",
       "      <th>beta</th>\n",
       "      <th>pe</th>\n",
       "      <th>eps</th>\n",
       "      <th>1year_target</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MMM</td>\n",
       "      <td>203.63</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABT</td>\n",
       "      <td>63.27</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABBV</td>\n",
       "      <td>93.12</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ATVI</td>\n",
       "      <td>75.94</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A</td>\n",
       "      <td>65.65</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MO</td>\n",
       "      <td>57.79</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AAL</td>\n",
       "      <td>43.11</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AXP</td>\n",
       "      <td>99.91</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ABC</td>\n",
       "      <td>85.67</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>AMGN</td>\n",
       "      <td>191.40</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>T</td>\n",
       "      <td>34.35</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ADP</td>\n",
       "      <td>135.35</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>BAC</td>\n",
       "      <td>30.06</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>BBY</td>\n",
       "      <td>73.26</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>HRB</td>\n",
       "      <td>29.60</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>BMY</td>\n",
       "      <td>53.94</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>AVGO</td>\n",
       "      <td>261.43</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CHRW</td>\n",
       "      <td>85.62</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CPB</td>\n",
       "      <td>34.05</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>COF</td>\n",
       "      <td>88.05</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CAT</td>\n",
       "      <td>155.32</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>CBS</td>\n",
       "      <td>51.13</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>CERN</td>\n",
       "      <td>64.86</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>CVX</td>\n",
       "      <td>126.44</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>CHD</td>\n",
       "      <td>55.31</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>CFG</td>\n",
       "      <td>42.34</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>KO</td>\n",
       "      <td>43.95</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>CMCSA</td>\n",
       "      <td>32.38</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CMA</td>\n",
       "      <td>69.63</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>CVS</td>\n",
       "      <td>70.09</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>bad</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>TEL</td>\n",
       "      <td>75.14</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>TXT</td>\n",
       "      <td>69.32</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>CLX</td>\n",
       "      <td>131.76</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>HSY</td>\n",
       "      <td>91.22</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>TRV</td>\n",
       "      <td>129.56</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>DIS</td>\n",
       "      <td>104.33</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>TMO</td>\n",
       "      <td>205.57</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>TIF</td>\n",
       "      <td>136.02</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>TJX</td>\n",
       "      <td>94.73</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>TSCO</td>\n",
       "      <td>87.65</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>TSN</td>\n",
       "      <td>70.99</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>USB</td>\n",
       "      <td>52.05</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>UDR</td>\n",
       "      <td>43.75</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>UNP</td>\n",
       "      <td>150.25</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>UNH</td>\n",
       "      <td>250.68</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>UTX</td>\n",
       "      <td>124.55</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>VFC</td>\n",
       "      <td>83.66</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>VLO</td>\n",
       "      <td>116.83</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>VZ</td>\n",
       "      <td>48.81</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>VIAB</td>\n",
       "      <td>29.09</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>V</td>\n",
       "      <td>134.86</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>VNO</td>\n",
       "      <td>67.46</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>WMT</td>\n",
       "      <td>84.10</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>WBA</td>\n",
       "      <td>65.80</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>WM</td>\n",
       "      <td>83.59</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>WFC</td>\n",
       "      <td>54.48</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>WYNN</td>\n",
       "      <td>178.12</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>XRX</td>\n",
       "      <td>27.98</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>XLNX</td>\n",
       "      <td>90.54</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>YUM</td>\n",
       "      <td>83.18</td>\n",
       "      <td>83.36</td>\n",
       "      <td>26.908</td>\n",
       "      <td>0.45</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.27</td>\n",
       "      <td>88.17</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>149 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Stock  prev_close   open  market_cap  beta    pe   eps  1year_target  \\\n",
       "0      MMM      203.63  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "1      ABT       63.27  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "2     ABBV       93.12  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "3     ATVI       75.94  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "4        A       65.65  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "5       MO       57.79  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "6      AAL       43.11  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "7      AXP       99.91  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "8      ABC       85.67  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "9     AMGN      191.40  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "10       T       34.35  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "11     ADP      135.35  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "12     BAC       30.06  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "13     BBY       73.26  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "14     HRB       29.60  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "15     BMY       53.94  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "16    AVGO      261.43  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "17    CHRW       85.62  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "18     CPB       34.05  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "19     COF       88.05  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "20     CAT      155.32  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "21     CBS       51.13  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "22    CERN       64.86  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "23     CVX      126.44  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "24     CHD       55.31  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "25     CFG       42.34  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "26      KO       43.95  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "27   CMCSA       32.38  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "28     CMA       69.63  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "29     CVS       70.09  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "..     ...         ...    ...         ...   ...   ...   ...           ...   \n",
       "119    TEL       75.14  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "120    TXT       69.32  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "121    CLX      131.76  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "122    HSY       91.22  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "123    TRV      129.56  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "124    DIS      104.33  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "125    TMO      205.57  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "126    TIF      136.02  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "127    TJX       94.73  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "128   TSCO       87.65  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "129    TSN       70.99  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "130    USB       52.05  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "131    UDR       43.75  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "132    UNP      150.25  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "133    UNH      250.68  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "134    UTX      124.55  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "135    VFC       83.66  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "136    VLO      116.83  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "137     VZ       48.81  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "138   VIAB       29.09  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "139      V      134.86  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "140    VNO       67.46  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "141    WMT       84.10  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "142    WBA       65.80  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "143     WM       83.59  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "144    WFC       54.48  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "145   WYNN      178.12  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "146    XRX       27.98  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "147   XLNX       90.54  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "148    YUM       83.18  83.36      26.908  0.45  19.5  4.27         88.17   \n",
       "\n",
       "       label  \n",
       "0        bad  \n",
       "1       good  \n",
       "2        bad  \n",
       "3        bad  \n",
       "4       good  \n",
       "5        bad  \n",
       "6        bad  \n",
       "7       good  \n",
       "8       good  \n",
       "9       good  \n",
       "10      good  \n",
       "11      good  \n",
       "12   neutral  \n",
       "13   neutral  \n",
       "14       bad  \n",
       "15   neutral  \n",
       "16      good  \n",
       "17   neutral  \n",
       "18      good  \n",
       "19   neutral  \n",
       "20       bad  \n",
       "21       bad  \n",
       "22      good  \n",
       "23       bad  \n",
       "24      good  \n",
       "25       bad  \n",
       "26      good  \n",
       "27      good  \n",
       "28       bad  \n",
       "29       bad  \n",
       "..       ...  \n",
       "119     good  \n",
       "120      bad  \n",
       "121     good  \n",
       "122     good  \n",
       "123     good  \n",
       "124     good  \n",
       "125     good  \n",
       "126      bad  \n",
       "127      bad  \n",
       "128     good  \n",
       "129     good  \n",
       "130     good  \n",
       "131     good  \n",
       "132     good  \n",
       "133  neutral  \n",
       "134     good  \n",
       "135     good  \n",
       "136      bad  \n",
       "137     good  \n",
       "138      bad  \n",
       "139     good  \n",
       "140      bad  \n",
       "141     good  \n",
       "142      bad  \n",
       "143     good  \n",
       "144      bad  \n",
       "145      bad  \n",
       "146     good  \n",
       "147     good  \n",
       "148     good  \n",
       "\n",
       "[149 rows x 9 columns]"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2017_ML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(149, 7) (149,)\n"
     ]
    }
   ],
   "source": [
    "X = df_2017_ML.drop(\"label\", axis=1).drop(\"Stock\", axis=1)\n",
    "y = df_2017_ML[\"label\"]\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder, MinMaxScaler\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "\n",
    "X_train, X_test= train_test_split(X, random_state=1)\n",
    "X_scaler = MinMaxScaler().fit(X_train)\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)\n",
    "\n",
    "\n",
    "# Step 1: Label-encode data set\n",
    "label_encoder = LabelEncoder()\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "\n",
    "# Create a Neural Network model here\n",
    "# YOUR CODE HERE\n",
    "\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Dense(units=10, activation='relu', input_dim=7))\n",
    "\n",
    "model.add(Dense(units=10, activation='relu'))\n",
    "model.add(Dense(units=50, activation='relu'))\n",
    "model.add(Dense(units=30, activation='relu'))\n",
    "model.add(Dense(units=100, activation='relu'))\n",
    "\n",
    "\n",
    "\n",
    "model.add(Dense(units=3, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_26\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_165 (Dense)            (None, 10)                80        \n",
      "_________________________________________________________________\n",
      "dense_166 (Dense)            (None, 10)                110       \n",
      "_________________________________________________________________\n",
      "dense_167 (Dense)            (None, 50)                550       \n",
      "_________________________________________________________________\n",
      "dense_168 (Dense)            (None, 30)                1530      \n",
      "_________________________________________________________________\n",
      "dense_169 (Dense)            (None, 100)               3100      \n",
      "_________________________________________________________________\n",
      "dense_170 (Dense)            (None, 3)                 303       \n",
      "=================================================================\n",
      "Total params: 5,673\n",
      "Trainable params: 5,673\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "ename": "InvalidArgumentError",
     "evalue": "Expected begin[0] == 0 (got 1) and size[0] == 0 (got 1) when input.dim_size(0) == 0\n\t [[{{node loss_30/dense_170_loss/softmax_cross_entropy_with_logits/Slice_1}}]]",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-306-f86fdadf5cbd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m     \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m )\n",
      "\u001b[0;32m/anaconda3/envs/PythonData/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[1;32m    778\u001b[0m           \u001b[0mvalidation_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidation_steps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    779\u001b[0m           \u001b[0mvalidation_freq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidation_freq\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 780\u001b[0;31m           steps_name='steps_per_epoch')\n\u001b[0m\u001b[1;32m    781\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    782\u001b[0m   def evaluate(self,\n",
      "\u001b[0;32m/anaconda3/envs/PythonData/lib/python3.6/site-packages/tensorflow/python/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mmodel_iteration\u001b[0;34m(model, inputs, targets, sample_weights, batch_size, epochs, verbose, callbacks, val_inputs, val_targets, val_sample_weights, shuffle, initial_epoch, steps_per_epoch, validation_steps, validation_freq, mode, validation_in_fit, prepared_feed_values_from_dataset, steps_name, **kwargs)\u001b[0m\n\u001b[1;32m    361\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    362\u001b[0m         \u001b[0;31m# Get outputs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 363\u001b[0;31m         \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    364\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_outs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    365\u001b[0m           \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mbatch_outs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/envs/PythonData/lib/python3.6/site-packages/tensorflow/python/keras/backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   3290\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3291\u001b[0m     fetched = self._callable_fn(*array_vals,\n\u001b[0;32m-> 3292\u001b[0;31m                                 run_metadata=self.run_metadata)\n\u001b[0m\u001b[1;32m   3293\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_fetch_callbacks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3294\u001b[0m     output_structure = nest.pack_sequence_as(\n",
      "\u001b[0;32m/anaconda3/envs/PythonData/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1456\u001b[0m         ret = tf_session.TF_SessionRunCallable(self._session._session,\n\u001b[1;32m   1457\u001b[0m                                                \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1458\u001b[0;31m                                                run_metadata_ptr)\n\u001b[0m\u001b[1;32m   1459\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1460\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mInvalidArgumentError\u001b[0m: Expected begin[0] == 0 (got 1) and size[0] == 0 (got 1) when input.dim_size(0) == 0\n\t [[{{node loss_30/dense_170_loss/softmax_cross_entropy_with_logits/Slice_1}}]]"
     ]
    }
   ],
   "source": [
    "# Compile and fit the model\n",
    "# YOUR CODE HERE\n",
    "model.compile(optimizer='adam',\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(\n",
    "    X_train_scaled,\n",
    "    epochs=100,\n",
    "    shuffle=True,\n",
    "    verbose=0\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38/38 - 0s - loss: 1.0038 - acc: 0.5263\n",
      "Normal Neural Network - Loss: 1.003816256397649, Accuracy: 0.5263158082962036\n"
     ]
    }
   ],
   "source": [
    "model_loss, model_accuracy = model.evaluate(X_test_scaled, y_test_categorical, verbose=2)\n",
    "print(f\"Normal Neural Network - Loss: {model_loss}, Accuracy: {model_accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual Labels: ['bad', 'good', 'neutral', 'neutral', 'bad', 'good', 'bad', 'good', 'bad', 'good']\n"
     ]
    }
   ],
   "source": [
    "print(f\"Actual Labels: {list(y_test[:10])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock_list_2019 = list(data_2019_drop_col[\"Stock Ticker\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_2019 = data_2019_drop_col.drop([\"Stock Ticker\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1., 0.],\n",
       "       [0., 1., 0.],\n",
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     "execution_count": 300,
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