RF_Modele2_Conducteur_62.ipynb

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    "<div id = \"monlogo\"><center> <img src=\"https://datascientest.fr/train/assets/logo_datascientest.png\", style=\"height:150px\"></center></div>\n",
    "\n",
    "<hr style=\"border-width:2px;border-color:#75DFC1\"><br>\n",
    "<center> <h1>Challenge Sorbonne</h1> </center> <br>\n",
    "<center> <h3>Exemple Modélisation</h3> </center> <br>\n",
    "<hr style=\"border-width:2px;border-color:#75DFC1\"> \n",
    "\n",
    "### Modélisation Machine Learning\n",
    "\n",
    ">  L'objectif est de créer un modèle permettant d'**identifier pour chaque course de la saison 2021, le vainqueur ainsi que le podium**. Il faut donc créer un modèle de classification binaire, ainsi qu'un modèle de classification multiclasse. Pour rappel, chacun de ces 2 modèles servira pour la moitié de la note de modélisation.\n",
    ">\n",
    "> Il est nécessaire de garder ces éléments en tête :\n",
    "> - **Données futures** : Les Grand Prix ont lieu les uns après les autres, et vous avez dans les différentes tables accès à des données qui n'existent pas encore au moment du départ d'un Grand Prix. Créer un modèle reposant sur des informations futures serait un non-sens mathématiques et une grosse erreur de méthodologie.\n",
    "> - **Preprocessing** : Il y a bon nombre de données manquantes, incomplètes ou inutiles dans le jeu de données, faites le tri. \n",
    "> - **Feature Engineering** : Vous avez un grand nombre de tables, certaines contiennent des features directement exploitables pour le machine learning, d'autres sont intéressantes mais nécessitent des modifications afin d'être utilisées à bon escient. Vous avez également la possibilité d'augmenter vos données à l'aide de librairies comme [FastF1](https://theoehrly.github.io/Fast-F1/), ou en scrapant sur le web.\n",
    "> - **Métriques et interprétabilités** : Vos modèles seront évaluées selon la métrique bien connue du [f1-score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html), prenez cela en compte lors de l'optimisation de vos modèles.\n",
    "\n",
    "### Exemple\n",
    "\n",
    "> Vous trouverez ci-dessous un exemple de modèle naïf de classification binaire réalisé à partir des tables `results.csv`, `races.csv`, `circuits.csv` et `constructor_standings.csv`, se basant uniquement sur **la position de départ de chaque pilote sur la grille**, **le pays du circuit**, **le classement et le nombre de points constructeur au moment du départ**.\n",
    ">\n",
    "> Attention, ce travail de modélisation a uniquement vocation à vous donner une piste pour démarrer, celui-ci n'est pas parfaitement optimisé et ne constitue pas un bon exemple à suivre."
   ]
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  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "d97088d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "a5f37892",
   "metadata": {},
   "outputs": [],
   "source": [
    "#chargemnt des données\n",
    "drivers = pd.read_csv(r\"data\\driver_standings.csv\", sep=\",\") #on peut prendre le raceID, driverID, Points, Position, wins\n",
    "driversINFO = pd.read_csv(r\"data\\drivers.csv\",sep=\",\") #on prend la date de naisse dob[:4]\n",
    "races = pd.read_csv(r\"data\\races.csv\",sep=\",\") #raceID, year, circuitID, date, time\n",
    "results = pd.read_csv(r\"data\\results.csv\", sep=\",\") #driverID, raceID, positionORder, grid\n",
    "\n",
    "results = results[['raceId','driverId','grid','positionOrder']] \n",
    "driversINFO = driversINFO[['driverId','dob']]\n",
    "drivers = drivers[['raceId','driverId','points','wins']]\n",
    "races = races[['raceId','year','circuitId','date','time']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "9fa02115",
   "metadata": {},
   "outputs": [],
   "source": [
    "#transformation pour avoir la date de naissance des conducteurs\n",
    "driversINFO.loc[:,'dob'] = pd.to_datetime(driversINFO['dob']).dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "7f9f4571",
   "metadata": {},
   "outputs": [],
   "source": [
    "#transformation de la date des courses pour avoir le mois \n",
    "races.loc[:,'date'] = pd.to_datetime(races['date']).dt.month\n",
    "races = races.rename(columns={'date' : 'month'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "0d3041b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "C:\\Users\\luang\\AppData\\Local\\Temp\\ipykernel_8988\\4191630194.py:3: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  races['hour'] = pd.to_datetime(races['time']).dt.hour\n"
     ]
    }
   ],
   "source": [
    "#retirer une erreur dans les dates et garder uniquement l'heure de la course\n",
    "races[\"time\"] = races['time'].replace({'\\\\N' : '12:00:00'})\n",
    "races['hour'] = pd.to_datetime(races['time']).dt.hour\n",
    "races = races.drop(columns = [\"time\"])"
   ]
  },
  {
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   "execution_count": 131,
   "id": "5d075c10",
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       "   raceId  driverId  grid  positionOrder\n",
       "0      18         1     1            1.0\n",
       "1      18         2     5            2.0\n",
       "2      18         3     7            3.0\n",
       "3      18         4    11            4.0\n",
       "4      18         5     3            5.0"
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    "results.head()"
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       "   driverId   dob\n",
       "0         1  1985\n",
       "1         2  1977\n",
       "2         3  1985\n",
       "3         4  1981\n",
       "4         5  1981"
      ]
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    "driversINFO.head()"
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       "   raceId  driverId  points  wins\n",
       "0      18         1    10.0     1\n",
       "1      18         2     8.0     0\n",
       "2      18         3     6.0     0\n",
       "3      18         4     5.0     0\n",
       "4      18         5     4.0     0"
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    "drivers.head()"
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  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "fabf31e9",
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   "outputs": [
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       "   raceId  year  circuitId month  hour\n",
       "0       1  2009          1     3     6\n",
       "1       2  2009          2     4     9\n",
       "2       3  2009         17     4     7\n",
       "3       4  2009          3     4    12\n",
       "4       5  2009          4     5    12"
      ]
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   ],
   "source": [
    "races.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "8eae3f35",
   "metadata": {},
   "outputs": [],
   "source": [
    "# on joint resultat et races sur la base de raceID\n",
    "df1 = pd.merge(results[['raceId','driverId','grid','positionOrder']], races[['raceId','year','circuitId','month','hour']], on = 'raceId')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "000fca3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# on joint le précédent avec drivers avec drivers info sur driverID\n",
    "df2 = pd.merge(drivers, driversINFO, on = 'driverId')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "82fb9191",
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       "      <td>1999</td>\n",
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       "    <tr>\n",
       "      <th>25367</th>\n",
       "      <td>1096</td>\n",
       "      <td>825</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022</td>\n",
       "      <td>24</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25368</th>\n",
       "      <td>1096</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022</td>\n",
       "      <td>24</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>240.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25369</th>\n",
       "      <td>1096</td>\n",
       "      <td>849</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022</td>\n",
       "      <td>24</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25370</th>\n",
       "      <td>1096</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022</td>\n",
       "      <td>24</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1981</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25371 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       raceId  driverId  grid  positionOrder  year  circuitId month  hour  \\\n",
       "0          18         1     1            1.0  2008          1     3     4   \n",
       "1          18         2     5            2.0  2008          1     3     4   \n",
       "2          18         3     7            3.0  2008          1     3     4   \n",
       "3          18         4    11            4.0  2008          1     3     4   \n",
       "4          18         5     3            5.0  2008          1     3     4   \n",
       "...       ...       ...   ...            ...   ...        ...   ...   ...   \n",
       "25366    1096       854    12            NaN  2022         24    11    13   \n",
       "25367    1096       825    16            NaN  2022         24    11    13   \n",
       "25368    1096         1     5            NaN  2022         24    11    13   \n",
       "25369    1096       849    20            NaN  2022         24    11    13   \n",
       "25370    1096         4    10            NaN  2022         24    11    13   \n",
       "\n",
       "       points  wins   dob  \n",
       "0        10.0     1  1985  \n",
       "1         8.0     0  1977  \n",
       "2         6.0     0  1985  \n",
       "3         5.0     0  1981  \n",
       "4         4.0     0  1981  \n",
       "...       ...   ...   ...  \n",
       "25366    12.0     0  1999  \n",
       "25367    25.0     0  1992  \n",
       "25368   240.0     0  1985  \n",
       "25369     2.0     0  1995  \n",
       "25370    81.0     0  1981  \n",
       "\n",
       "[25371 rows x 11 columns]"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = pd.merge(df1,df2, on = ['raceId', 'driverId'])\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "f723469d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#on utilise positionOrder pour faire le winner\n",
    "df3['winner'] = df3['positionOrder'].apply(lambda x: 1 if x==1 else 0)\n",
    "df3[\"AgeAtRace\"] = df3['year'] - df3['dob']\n",
    "df4 = df3.drop(['positionOrder','circuitId', 'dob'], axis = 1).set_index(['raceId', 'driverId'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "8d4a5105",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th rowspan=\"5\" valign=\"top\">18</th>\n",
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       "      <td>11</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>825</th>\n",
       "      <td>16</td>\n",
       "      <td>2022</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
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       "    <tr>\n",
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       "      <td>5</td>\n",
       "      <td>2022</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>240.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
       "      <td>27</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>2022</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25371 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 grid  year month  hour  points  wins  winner AgeAtRace\n",
       "raceId driverId                                                        \n",
       "18     1            1  2008     3     4    10.0     1       1        23\n",
       "       2            5  2008     3     4     8.0     0       0        31\n",
       "       3            7  2008     3     4     6.0     0       0        23\n",
       "       4           11  2008     3     4     5.0     0       0        27\n",
       "       5            3  2008     3     4     4.0     0       0        27\n",
       "...               ...   ...   ...   ...     ...   ...     ...       ...\n",
       "1096   854         12  2022    11    13    12.0     0       0        23\n",
       "       825         16  2022    11    13    25.0     0       0        30\n",
       "       1            5  2022    11    13   240.0     0       0        37\n",
       "       849         20  2022    11    13     2.0     0       0        27\n",
       "       4           10  2022    11    13    81.0     0       0        41\n",
       "\n",
       "[25371 rows x 8 columns]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "f609f7ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "#on retire qui a gagné la course à chaque course\n",
    "df4[\"wins\"] = df4[\"wins\"] - df4[\"winner\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "46362b7d",
   "metadata": {},
   "outputs": [
    {
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       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
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       "      <th>825</th>\n",
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       "      <td>13</td>\n",
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       "      <td>30</td>\n",
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       "      <td>5</td>\n",
       "      <td>2022</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>240.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
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       "    <tr>\n",
       "      <th>849</th>\n",
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       "      <td>2022</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>2022</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25371 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 grid  year month  hour  points  wins  winner AgeAtRace\n",
       "raceId driverId                                                        \n",
       "18     1            1  2008     3     4    10.0     0       1        23\n",
       "       2            5  2008     3     4     8.0     0       0        31\n",
       "       3            7  2008     3     4     6.0     0       0        23\n",
       "       4           11  2008     3     4     5.0     0       0        27\n",
       "       5            3  2008     3     4     4.0     0       0        27\n",
       "...               ...   ...   ...   ...     ...   ...     ...       ...\n",
       "1096   854         12  2022    11    13    12.0     0       0        23\n",
       "       825         16  2022    11    13    25.0     0       0        30\n",
       "       1            5  2022    11    13   240.0     0       0        37\n",
       "       849         20  2022    11    13     2.0     0       0        27\n",
       "       4           10  2022    11    13    81.0     0       0        41\n",
       "\n",
       "[25371 rows x 8 columns]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0adb18ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "#'grid','year','month','hour','points','wins','AgeAtRace'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "d840376e-0805-48a7-9934-f47a563d48c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#On effectue notre train_test_split selon les dates puis on sépare X et y\n",
    "df_train = df4[df4['year']<=2020]\n",
    "df_test = df4[df4['year']==2021]\n",
    "\n",
    "X_train = df_train[['grid','year','month','hour','points','wins','AgeAtRace']]\n",
    "X_test = df_test[['grid','year','month','hour','points','wins','AgeAtRace']]\n",
    "\n",
    "y_train = df_train[['winner']]\n",
    "#y_test ne contient pas d'informations, puisque c'est ce qu'il faut prédire et soumettre sur la plateforme :)\n",
    "y_test = df_test[['winner']]\n",
    "y_pred = df_test[['winner']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "443869f7-dee4-4256-b4b6-62eb35745e4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_depth': 20, 'n_estimators': 75}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Définition du modèle\n",
    "random_forest = RandomForestClassifier(random_state = 42)\n",
    "\n",
    "# Définition de la grille de paramètres et du GridSearchCV\n",
    "param_grid = {\n",
    "    'n_estimators': [25, 50, 75, 100,150, 200],\n",
    "    'max_depth': [5, 10, 20, 30, 40, 50]\n",
    "}\n",
    "\n",
    "grid_search = GridSearchCV(random_forest, param_grid, scoring='recall')\n",
    "\n",
    "# Entraînement du modèle\n",
    "grid_search.fit(X_train, y_train.values.ravel())\n",
    "print(grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "1fcfb9cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9990366088631984\n"
     ]
    }
   ],
   "source": [
    "print(grid_search.score(X_train,y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "edbf9fa3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\luang\\AppData\\Local\\Temp\\ipykernel_8988\\3762631047.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  y_pred.loc[:,'PREpositionOrder'] = probabilite\n"
     ]
    }
   ],
   "source": [
    "probabilite = grid_search.predict_proba(X_test)\n",
    "probabilite = pd.DataFrame(probabilite)\n",
    "probabilite = probabilite.drop(columns=(0))\n",
    "probabilite = np.array(probabilite)\n",
    "y_pred.loc[:,'PREpositionOrder'] = probabilite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "a94c2a5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>winner</th>\n",
       "      <th>PREpositionOrder</th>\n",
       "    </tr>\n",
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       "      <th>raceId</th>\n",
       "      <th>driverId</th>\n",
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       "      <th rowspan=\"5\" valign=\"top\">1052</th>\n",
       "      <th>1</th>\n",
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       "</table>\n",
       "<p>440 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 winner  PREpositionOrder\n",
       "raceId driverId                          \n",
       "1052   1              0          0.480000\n",
       "       830            0          0.360000\n",
       "       822            0          0.213333\n",
       "       846            0          0.093333\n",
       "       815            0          0.360000\n",
       "...                 ...               ...\n",
       "1073   849            0          0.000000\n",
       "       841            0          0.000000\n",
       "       847            0          0.013333\n",
       "       8              0          0.000000\n",
       "       853            0          0.000000\n",
       "\n",
       "[440 rows x 2 columns]"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "a7e0cddc",
   "metadata": {},
   "outputs": [],
   "source": [
    "Taille = y_pred.index\n",
    "raceID = Taille.get_level_values('raceId').unique()\n",
    "PodiumOr0 = []\n",
    "Top1 = []\n",
    "for RACE in raceID:\n",
    "    driverID = y_pred[\"PREpositionOrder\"][RACE].index.get_level_values('driverId')\n",
    "    array = np.array(y_pred[\"PREpositionOrder\"][RACE])\n",
    "    ranks = array.argsort()\n",
    "    ranks = 20 - ranks.argsort()\n",
    "    count = 0\n",
    "    for DRIVER in driverID:\n",
    "        rang = ranks[count]\n",
    "        if rang <= 3:\n",
    "            PodiumOr0.append(ranks[count])\n",
    "            if rang == 1:\n",
    "                Top1.append(1)\n",
    "            else:\n",
    "                Top1.append(0)\n",
    "        else:\n",
    "            PodiumOr0.append(0)\n",
    "            Top1.append(0)\n",
    "        count += 1 \n",
    "    count = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "1eaefde9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "440 440\n"
     ]
    }
   ],
   "source": [
    "print(len(PodiumOr0),len(Top1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "121ee896",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\luang\\AppData\\Local\\Temp\\ipykernel_8988\\1890261867.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  y_pred.loc[:,'positionOrder'] = PodiumOr0\n"
     ]
    }
   ],
   "source": [
    "y_pred.loc[:,'winner'] = Top1\n",
    "y_pred.loc[:,'positionOrder'] = PodiumOr0\n",
    "y_pred = y_pred.drop(columns=(\"PREpositionOrder\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "b6de7963",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
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       "  <thead>\n",
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       "      <th>winner</th>\n",
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       "      <th>849</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>440 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 winner  positionOrder\n",
       "raceId driverId                       \n",
       "1052   1              1              1\n",
       "       830            0              2\n",
       "       822            0              0\n",
       "       846            0              0\n",
       "       815            0              3\n",
       "...                 ...            ...\n",
       "1073   849            0              0\n",
       "       841            0              0\n",
       "       847            0              0\n",
       "       8              0              0\n",
       "       853            0              0\n",
       "\n",
       "[440 rows x 2 columns]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "1b1b8ca4",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred.to_csv('model2OnlyDriver.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "611ed864",
   "metadata": {},
   "source": [
    "score : 62.61"
   ]
  }
 ],
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