main_noel.py.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "url=\"https://raw.githubusercontent.com/johnosullivan/ml-horse-prediction/master/data/training-2016-10-01-2016-12-31.csv\"\n",
    "race_data = pd.read_csv(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "row_id                0\n",
       "entry_id              0\n",
       "morning_line        152\n",
       "post_pos              0\n",
       "offset                0\n",
       "post                  0\n",
       "speed                 0\n",
       "horse_win             0\n",
       "horse_wps             0\n",
       "horse_roi             0\n",
       "driver_win            0\n",
       "driver_wps            0\n",
       "driver_roi            0\n",
       "trainer_win           0\n",
       "trainer_wps           0\n",
       "trainer_roi           0\n",
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       "previous_break        0\n",
       "days_since            0\n",
       "same_track            0\n",
       "same_driver           0\n",
       "last_race_res         0\n",
       "last_race_wps         0\n",
       "last_three_races      0\n",
       "purse                 0\n",
       "finish_pos            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "race_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "                                              row_id  \\\n",
       "0  2016-10-01_Batavia Downs_1_Highview Conall N_F...   \n",
       "1      2016-10-01_Batavia Downs_1_Heaven Rocks_False   \n",
       "2   2016-10-01_Batavia Downs_1_Foreign Officer_False   \n",
       "3   2016-10-01_Batavia Downs_1_Knocking Around_False   \n",
       "4     2016-10-01_Batavia Downs_1_Milliron Pete_False   \n",
       "\n",
       "                           entry_id morning_line  post_pos  offset  post  \\\n",
       "0  2016-10-01_Batavia Downs_1_False          3-1         4       1     1   \n",
       "1  2016-10-01_Batavia Downs_1_False          7-2         2       1     1   \n",
       "2  2016-10-01_Batavia Downs_1_False          6-5         5       1     1   \n",
       "3  2016-10-01_Batavia Downs_1_False          2-1         3       1     1   \n",
       "4  2016-10-01_Batavia Downs_1_False          9-2         1       1     1   \n",
       "\n",
       "   speed  horse_win  horse_wps  horse_roi     ...      min_races  \\\n",
       "0      0          0          0          1     ...              1   \n",
       "1      1          0          0          0     ...              1   \n",
       "2      1          0          0          0     ...              1   \n",
       "3      1          0          0          1     ...              1   \n",
       "4      1          0          0          1     ...              1   \n",
       "\n",
       "   previous_break  days_since  same_track  same_driver  last_race_res  \\\n",
       "0               1           0           0            0              1   \n",
       "1               1           1           1            1              0   \n",
       "2               1           1           1            1              1   \n",
       "3               1           1           1            1              0   \n",
       "4               1           1           1            0              0   \n",
       "\n",
       "   last_race_wps  last_three_races  purse  finish_pos  \n",
       "0              1                 1      1           1  \n",
       "1              1                 1      0           3  \n",
       "2              1                 1     -1           4  \n",
       "3              0                 1      0           2  \n",
       "4              0                 1      0           5  \n",
       "\n",
       "[5 rows x 26 columns]"
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     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
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    "race_data.head(5)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "race_data['finish_pos'] = race_data['finish_pos'].map(lambda x: 1 if x >= 3 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
       "      <th>95509</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95510</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95511</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95512</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95513</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95514</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95515</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95516</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95517</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95518 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       horse_roi  last_three_races  finish_pos\n",
       "0              1                 1           0\n",
       "1              0                 1           1\n",
       "2              0                 1           1\n",
       "3              1                 1           0\n",
       "4              1                 1           1\n",
       "5              0                 1           0\n",
       "6              0                 1           0\n",
       "7              0                 1           1\n",
       "8              0                 1           1\n",
       "9              0                 1           1\n",
       "10             0                 1           1\n",
       "11             1                 1           1\n",
       "12             0                 1           1\n",
       "13             0                 1           1\n",
       "14             0                 1           1\n",
       "15             0                 1           0\n",
       "16             0                 1           0\n",
       "17             0                 1           1\n",
       "18             0                 1           1\n",
       "19             0                 1           1\n",
       "20             1                 1           1\n",
       "21             1                 1           0\n",
       "22             0                 1           1\n",
       "23             0                 1           1\n",
       "24             0                 1           0\n",
       "25             0                 1           1\n",
       "26             0                 1           1\n",
       "27             0                 1           1\n",
       "28             0                 1           1\n",
       "29             0                 1           1\n",
       "...          ...               ...         ...\n",
       "95488          0                 1           1\n",
       "95489          1                 1           0\n",
       "95490          0                 1           1\n",
       "95491          0                 1           1\n",
       "95492          0                 1           1\n",
       "95493          0                 1           1\n",
       "95494          1                 0           0\n",
       "95495          0                 1           1\n",
       "95496          1                 1           1\n",
       "95497          0                 1           1\n",
       "95498          0                 1           1\n",
       "95499          0                 1           1\n",
       "95500          0                 1           0\n",
       "95501          0                 1           0\n",
       "95502          0                 1           1\n",
       "95503          0                 1           1\n",
       "95504          0                 1           0\n",
       "95505          1                 1           1\n",
       "95506          0                 1           0\n",
       "95507          0                 1           1\n",
       "95508          0                 1           1\n",
       "95509          0                 1           0\n",
       "95510          0                 1           1\n",
       "95511          1                 1           1\n",
       "95512          0                 1           0\n",
       "95513          1                 1           0\n",
       "95514          0                 1           1\n",
       "95515          0                 1           0\n",
       "95516          0                 1           1\n",
       "95517          0                 1           1\n",
       "\n",
       "[95518 rows x 3 columns]"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "horse_data = race_data[['horse_roi', 'last_three_races', 'finish_pos']].copy()\n",
    "horse_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = horse_data[['finish_pos']].values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = horse_data[['horse_roi', 'last_three_races']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X, y_train, y = train_test_split(X, y, test_size=0.30, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#splitting training into 70% and test and development into 15% 15%\n",
    "X_dev, X_test, y_test, y_dev = train_test_split(X, y, test_size=0.50, random_state=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/sklearn/utils/validation.py:526: 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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVC(C=1.5, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='poly',\n",
       "  max_iter=-1, probability=False, random_state=1, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = SVC(kernel='poly',C=1.5,random_state=1)\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.729829704076\n"
     ]
    }
   ],
   "source": [
    "score1 = clf.score(X_dev,y_dev)\n",
    "\n",
    "print(score1)"
   ]
  }
 ],
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