FinalProject_KentuckyDerby.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install scikit-plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dependencies \n",
    "import tensorflow as tf\n",
    "from keras.models import Sequential\n",
    "from keras.utils import to_categorical\n",
    "from keras import utils as np_utils\n",
    "from keras.layers import Dense\n",
    "from keras.callbacks import EarlyStopping\n",
    "\n",
    "# Clean and Preprocess Data for Machine Learning\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "#Graphing dependencies \n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import scikitplot as skplt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import and create DataFrames\n",
    "kderby_stats = pd.read_csv('K_Derby_Stats_2019-2008.csv')\n",
    "kderby_placing_order = pd.read_csv('Kentucky_Derby_Placing_Order2008-2019.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "kderby_stats_df = pd.DataFrame(kderby_stats) \n",
    "kderby_placing_order_df = pd.DataFrame(kderby_placing_order) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "scrolled": true
   },
   "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>Year</th>\n",
       "      <th>Post_Position</th>\n",
       "      <th>WinnersatPost_last49yrs</th>\n",
       "      <th>ITM</th>\n",
       "      <th>Starters</th>\n",
       "      <th>ITM%atPost_last49yrs</th>\n",
       "      <th>Horse</th>\n",
       "      <th>Morning _Line</th>\n",
       "      <th>Derby_Points</th>\n",
       "      <th>Trainer</th>\n",
       "      <th>...</th>\n",
       "      <th>Dam</th>\n",
       "      <th>Dams_Sire</th>\n",
       "      <th>Tomlinson_DST</th>\n",
       "      <th>Tomlinson_Wet</th>\n",
       "      <th>BRIS_Pace</th>\n",
       "      <th>AWD_Sire</th>\n",
       "      <th>AWD_Dams_Sire</th>\n",
       "      <th>LastPrep_Finish</th>\n",
       "      <th>Dosage_Index</th>\n",
       "      <th>Finish_Position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>13%</td>\n",
       "      <td>War of Will</td>\n",
       "      <td>15-1</td>\n",
       "      <td>60</td>\n",
       "      <td>Casse</td>\n",
       "      <td>...</td>\n",
       "      <td>Visions of Clarity</td>\n",
       "      <td>Sadler's Wells</td>\n",
       "      <td>370.0</td>\n",
       "      <td>329.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>10.7</td>\n",
       "      <td>9th, Louisiana Derby</td>\n",
       "      <td>1.72</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>12.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>24%</td>\n",
       "      <td>Tax</td>\n",
       "      <td>20-1</td>\n",
       "      <td>52</td>\n",
       "      <td>Gargan</td>\n",
       "      <td>...</td>\n",
       "      <td>Toll</td>\n",
       "      <td>Giant's Causeway</td>\n",
       "      <td>338.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>8.1</td>\n",
       "      <td>2nd, Wood Memorial</td>\n",
       "      <td>1.56</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>11.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>22%</td>\n",
       "      <td>By My Standards</td>\n",
       "      <td>15-1</td>\n",
       "      <td>100</td>\n",
       "      <td>Calhoun</td>\n",
       "      <td>...</td>\n",
       "      <td>A Jealous Woman</td>\n",
       "      <td>Muqtarib</td>\n",
       "      <td>252.0</td>\n",
       "      <td>292.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1st, Louisiana Derby</td>\n",
       "      <td>3.00</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>9.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>18%</td>\n",
       "      <td>Gray Magician</td>\n",
       "      <td>50-1</td>\n",
       "      <td>41</td>\n",
       "      <td>Miller</td>\n",
       "      <td>...</td>\n",
       "      <td>Burg Berg</td>\n",
       "      <td>Johannesburg</td>\n",
       "      <td>227.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>7</td>\n",
       "      <td>2nd, UAE Derby</td>\n",
       "      <td>5.00</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>13.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>27%</td>\n",
       "      <td>Improbable</td>\n",
       "      <td>1-May</td>\n",
       "      <td>65</td>\n",
       "      <td>Baffert</td>\n",
       "      <td>...</td>\n",
       "      <td>Rare Event</td>\n",
       "      <td>A.P. Indy</td>\n",
       "      <td>308.0</td>\n",
       "      <td>443.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>8.2</td>\n",
       "      <td>2nd, Arkansas Derby</td>\n",
       "      <td>4.23</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Post_Position WinnersatPost_last49yrs   ITM  Starters  \\\n",
       "0  2019              1                       1   6.0      47.0   \n",
       "1  2019              2                       4  12.0      49.0   \n",
       "2  2019              3                       4  11.0      49.0   \n",
       "3  2019              4                       6   9.0      49.0   \n",
       "4  2019              5                       6  13.0      49.0   \n",
       "\n",
       "  ITM%atPost_last49yrs            Horse Morning _Line Derby_Points  Trainer  \\\n",
       "0                  13%      War of Will          15-1           60    Casse   \n",
       "1                  24%              Tax          20-1           52   Gargan   \n",
       "2                  22%  By My Standards          15-1          100  Calhoun   \n",
       "3                  18%    Gray Magician          50-1           41   Miller   \n",
       "4                  27%       Improbable         1-May           65  Baffert   \n",
       "\n",
       "   ...                 Dam         Dams_Sire Tomlinson_DST Tomlinson_Wet  \\\n",
       "0  ...  Visions of Clarity    Sadler's Wells         370.0         329.0   \n",
       "1  ...                Toll  Giant's Causeway         338.0         405.0   \n",
       "2  ...     A Jealous Woman          Muqtarib         252.0         292.0   \n",
       "3  ...           Burg Berg      Johannesburg         227.0         393.0   \n",
       "4  ...          Rare Event         A.P. Indy         308.0         443.0   \n",
       "\n",
       "  BRIS_Pace  AWD_Sire  AWD_Dams_Sire       LastPrep_Finish  Dosage_Index  \\\n",
       "0     117.0       7.3           10.7  9th, Louisiana Derby          1.72   \n",
       "1     116.0       7.9            8.1    2nd, Wood Memorial          1.56   \n",
       "2     104.0       6.3            5.8  1st, Louisiana Derby          3.00   \n",
       "3     105.0       6.7              7        2nd, UAE Derby          5.00   \n",
       "4     112.0       6.5            8.2   2nd, Arkansas Derby          4.23   \n",
       "\n",
       "   Finish_Position  \n",
       "0              7.0  \n",
       "1             14.0  \n",
       "2             11.0  \n",
       "3             19.0  \n",
       "4              4.0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge CSV's to create one dataframe \n",
    "merge_tables = pd.merge(kderby_stats_df, kderby_placing_order_df, how='left', left_on=['Horse','Year'], right_on = ['Horse','Year'])\n",
    "merge_tables.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Post_Position</th>\n",
       "      <th>WinnersatPost_last49yrs</th>\n",
       "      <th>ITM</th>\n",
       "      <th>Starters</th>\n",
       "      <th>ITM%atPost_last49yrs</th>\n",
       "      <th>Horse</th>\n",
       "      <th>Morning _Line</th>\n",
       "      <th>Derby_Points</th>\n",
       "      <th>Trainer</th>\n",
       "      <th>...</th>\n",
       "      <th>Dams_Sire</th>\n",
       "      <th>Tomlinson_DST</th>\n",
       "      <th>Tomlinson_Wet</th>\n",
       "      <th>BRIS_Pace</th>\n",
       "      <th>AWD_Sire</th>\n",
       "      <th>AWD_Dams_Sire</th>\n",
       "      <th>LastPrep_Finish</th>\n",
       "      <th>Dosage_Index</th>\n",
       "      <th>Finish_Position</th>\n",
       "      <th>Place_Bins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>13%</td>\n",
       "      <td>War of Will</td>\n",
       "      <td>15-1</td>\n",
       "      <td>60</td>\n",
       "      <td>Casse</td>\n",
       "      <td>...</td>\n",
       "      <td>Sadler's Wells</td>\n",
       "      <td>370.0</td>\n",
       "      <td>329.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>10.7</td>\n",
       "      <td>9th, Louisiana Derby</td>\n",
       "      <td>1.72</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>12.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>24%</td>\n",
       "      <td>Tax</td>\n",
       "      <td>20-1</td>\n",
       "      <td>52</td>\n",
       "      <td>Gargan</td>\n",
       "      <td>...</td>\n",
       "      <td>Giant's Causeway</td>\n",
       "      <td>338.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>8.1</td>\n",
       "      <td>2nd, Wood Memorial</td>\n",
       "      <td>1.56</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>11.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>22%</td>\n",
       "      <td>By My Standards</td>\n",
       "      <td>15-1</td>\n",
       "      <td>100</td>\n",
       "      <td>Calhoun</td>\n",
       "      <td>...</td>\n",
       "      <td>Muqtarib</td>\n",
       "      <td>252.0</td>\n",
       "      <td>292.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1st, Louisiana Derby</td>\n",
       "      <td>3.00</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>9.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>18%</td>\n",
       "      <td>Gray Magician</td>\n",
       "      <td>50-1</td>\n",
       "      <td>41</td>\n",
       "      <td>Miller</td>\n",
       "      <td>...</td>\n",
       "      <td>Johannesburg</td>\n",
       "      <td>227.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>7</td>\n",
       "      <td>2nd, UAE Derby</td>\n",
       "      <td>5.00</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>13.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>27%</td>\n",
       "      <td>Improbable</td>\n",
       "      <td>1-May</td>\n",
       "      <td>65</td>\n",
       "      <td>Baffert</td>\n",
       "      <td>...</td>\n",
       "      <td>A.P. Indy</td>\n",
       "      <td>308.0</td>\n",
       "      <td>443.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>8.2</td>\n",
       "      <td>2nd, Arkansas Derby</td>\n",
       "      <td>4.23</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Post_Position WinnersatPost_last49yrs   ITM  Starters  \\\n",
       "0  2019              1                       1   6.0      47.0   \n",
       "1  2019              2                       4  12.0      49.0   \n",
       "2  2019              3                       4  11.0      49.0   \n",
       "3  2019              4                       6   9.0      49.0   \n",
       "4  2019              5                       6  13.0      49.0   \n",
       "\n",
       "  ITM%atPost_last49yrs            Horse Morning _Line Derby_Points  Trainer  \\\n",
       "0                  13%      War of Will          15-1           60    Casse   \n",
       "1                  24%              Tax          20-1           52   Gargan   \n",
       "2                  22%  By My Standards          15-1          100  Calhoun   \n",
       "3                  18%    Gray Magician          50-1           41   Miller   \n",
       "4                  27%       Improbable         1-May           65  Baffert   \n",
       "\n",
       "   ...         Dams_Sire Tomlinson_DST Tomlinson_Wet BRIS_Pace  AWD_Sire  \\\n",
       "0  ...    Sadler's Wells         370.0         329.0     117.0       7.3   \n",
       "1  ...  Giant's Causeway         338.0         405.0     116.0       7.9   \n",
       "2  ...          Muqtarib         252.0         292.0     104.0       6.3   \n",
       "3  ...      Johannesburg         227.0         393.0     105.0       6.7   \n",
       "4  ...         A.P. Indy         308.0         443.0     112.0       6.5   \n",
       "\n",
       "   AWD_Dams_Sire       LastPrep_Finish  Dosage_Index  Finish_Position  \\\n",
       "0           10.7  9th, Louisiana Derby          1.72              7.0   \n",
       "1            8.1    2nd, Wood Memorial          1.56             14.0   \n",
       "2            5.8  1st, Louisiana Derby          3.00             11.0   \n",
       "3              7        2nd, UAE Derby          5.00             19.0   \n",
       "4            8.2   2nd, Arkansas Derby          4.23              4.0   \n",
       "\n",
       "   Place_Bins  \n",
       "0           2  \n",
       "1           4  \n",
       "2           3  \n",
       "3           5  \n",
       "4           2  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create new dataframe with only the desired columns, create a bin, and drop na  \n",
    "bins = [1, 3, 9, 12, 18, 20]\n",
    "labels = [\"1\",\"2\",\"3\", \"4\", \"5\"]\n",
    "merge_tables['Place_Bins'] = pd.cut(merge_tables['Finish_Position'], bins=bins, labels=labels)\n",
    "merge_tables.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_table = merge_tables[['Starters', 'Dosage_Index', 'Place_Bins']].dropna()\n",
    "new_table.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_table['Place_Bins'] = pd.to_numeric(new_table['Place_Bins'],errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Starters        float64\n",
       "Dosage_Index    float64\n",
       "Place_Bins        int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_table.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = new_table[[\"Starters\",\"Dosage_Index\"]]\n",
    "y = new_table[[\"Place_Bins\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(178, 2) (178, 1)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "classifier = LogisticRegression()\n",
    "classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/envs/PythonData/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "//anaconda3/envs/PythonData/lib/python3.6/site-packages/sklearn/utils/validation.py:724: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "//anaconda3/envs/PythonData/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data Score: 0.39849624060150374\n",
      "Testing Data Score: 0.37777777777777777\n"
     ]
    }
   ],
   "source": [
    "#Validate the model using the test data\n",
    "print(f\"Training Data Score: {classifier.score(X_train, y_train)}\")\n",
    "print(f\"Testing Data Score: {classifier.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 10 Predictions: [4 4 2 2 2 2 2 4 2 4]\n",
      "First 10 Actual labels: [[2], [4], [5], [2], [2], [4], [2], [4], [1], [4]]\n"
     ]
    }
   ],
   "source": [
    "#Make predictions using Logistic Regression Model\n",
    "predictions = classifier.predict(X_test)\n",
    "print(f\"First 10 Predictions: {predictions[:10]}\")\n",
    "print(f\"First 10 Actual labels: {y_test[:10].values.tolist()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "#p_r = predictions.reshape((-1, 1))\n",
    "y_r = y_test.values.reshape((45))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "scrolled": true
   },
   "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>Prediction</th>\n",
       "      <th>Actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Prediction  Actual\n",
       "0           4       2\n",
       "1           4       4\n",
       "2           2       5\n",
       "3           2       2\n",
       "4           2       2"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pd.DataFrame.from_dict({\"Prediction\": predictions, \"Actual\": y_r})\n",
    "prediction_df = pd.DataFrame({\"Prediction\": predictions, \"Actual\": y_r})\n",
    "prediction_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 2.2, R2: 0.37777777777777777\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "MSE = mean_squared_error(y_test, predictions)\n",
    "r2 = classifier.score(X_test, y_test)\n",
    "\n",
    "print(f\"MSE: {MSE}, R2: {r2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 3 Predictions: [[0.12234642 0.25882648 0.14978008 0.3525593  0.11648773]\n",
      " [0.08369297 0.29848268 0.15844491 0.38669153 0.07268791]\n",
      " [0.0551158  0.37463131 0.17740007 0.36000111 0.03285171]\n",
      " [0.18492758 0.36441891 0.20558296 0.18419316 0.06087739]\n",
      " [0.16030203 0.35465884 0.19737335 0.22296514 0.06470064]\n",
      " [0.14085799 0.38194232 0.20493505 0.22115698 0.05110766]\n",
      " [0.14264284 0.33630727 0.18655132 0.26271362 0.07178495]\n",
      " [0.09299303 0.30126866 0.16186812 0.36817405 0.07569613]\n",
      " [0.10321835 0.36612568 0.19021125 0.28944846 0.05099626]\n",
      " [0.06471773 0.22798248 0.12494781 0.47681308 0.1055389 ]]\n",
      "First 10 Actual labels: [[2], [4], [5], [2], [2], [4], [2], [4], [1], [4]]\n"
     ]
    }
   ],
   "source": [
    "#Predict_Proba\n",
    "probability_class_1 = classifier.predict_proba(X_test)\n",
    "print(f\"First 3 Predictions: {probability_class_1[:10]}\")\n",
    "print(f\"First 10 Actual labels: {y_test[:10].values.tolist()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/envs/PythonData/lib/python3.6/site-packages/sklearn/utils/deprecation.py:85: DeprecationWarning: Function plot_precision_recall_curve is deprecated; This will be removed in v0.5.0. Please use scikitplot.metrics.plot_precision_recall instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot.\n",
    "skplt.metrics.plot_precision_recall_curve(y_test, probability_class_1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the model\n",
    "import pickle\n",
    "filename = 'finalized_winning_horsemodel.sav'\n",
    "pickle.dump(classifier, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.37777777777777777\n"
     ]
    }
   ],
   "source": [
    "# How to load the model from disk (perhaps at a later date when needed):   \n",
    " loaded_model = pickle.load(open(filename, 'rb'))\n",
    " result = loaded_model.score(X_test, y_test)\n",
    " print(result)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}