RandomForest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train_set = pd.read_csv('train_aggregated.csv', low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>ride_id</th>\n",
       "      <th>travel_date</th>\n",
       "      <th>travel_time</th>\n",
       "      <th>travel_from</th>\n",
       "      <th>car_type</th>\n",
       "      <th>max_capacity</th>\n",
       "      <th>number_of_tickets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1442</td>\n",
       "      <td>17-10-17</td>\n",
       "      <td>7:15</td>\n",
       "      <td>Migori</td>\n",
       "      <td>Bus</td>\n",
       "      <td>49</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5437</td>\n",
       "      <td>19-11-17</td>\n",
       "      <td>7:12</td>\n",
       "      <td>Migori</td>\n",
       "      <td>Bus</td>\n",
       "      <td>49</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5710</td>\n",
       "      <td>26-11-17</td>\n",
       "      <td>7:05</td>\n",
       "      <td>Keroka</td>\n",
       "      <td>Bus</td>\n",
       "      <td>49</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5777</td>\n",
       "      <td>27-11-17</td>\n",
       "      <td>7:10</td>\n",
       "      <td>Homa Bay</td>\n",
       "      <td>Bus</td>\n",
       "      <td>49</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5778</td>\n",
       "      <td>27-11-17</td>\n",
       "      <td>7:12</td>\n",
       "      <td>Migori</td>\n",
       "      <td>Bus</td>\n",
       "      <td>49</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ride_id travel_date travel_time travel_from car_type  max_capacity  \\\n",
       "0     1442    17-10-17        7:15      Migori      Bus            49   \n",
       "1     5437    19-11-17        7:12      Migori      Bus            49   \n",
       "2     5710    26-11-17        7:05      Keroka      Bus            49   \n",
       "3     5777    27-11-17        7:10    Homa Bay      Bus            49   \n",
       "4     5778    27-11-17        7:12      Migori      Bus            49   \n",
       "\n",
       "   number_of_tickets  \n",
       "0                1.0  \n",
       "1                1.0  \n",
       "2                1.0  \n",
       "3                5.0  \n",
       "4               31.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train_set.drop(['ride_id'], axis=1, inplace=True) #ride_id is unnecessary in training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train_set[\"travel_date\"] = pd.to_datetime(df_train_set[\"travel_date\"],infer_datetime_format=True)\n",
    "df_train_set[\"travel_date\"] = df_train_set[\"travel_date\"].dt.dayofweek #change the full date to day of week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train_set[\"car_type\"] = pd.Categorical(df_train_set[\"car_type\"])\n",
    "car_type_categories = df_train_set.car_type.cat.categories\n",
    "df_train_set[\"car_type\"] = df_train_set.car_type.cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train_set[\"travel_from\"] = pd.Categorical(df_train_set[\"travel_from\"])\n",
    "travel_from_categories = df_train_set.travel_from.cat.categories\n",
    "df_train_set[\"travel_from\"] = df_train_set.travel_from.cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#express travel time in minutes\n",
    "df_train_set[\"travel_time\"] = df_train_set[\"travel_time\"].str.split(':').apply(lambda x: int(x[0]) * 60 + int(x[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>travel_date</th>\n",
       "      <th>travel_time</th>\n",
       "      <th>travel_from</th>\n",
       "      <th>car_type</th>\n",
       "      <th>max_capacity</th>\n",
       "      <th>number_of_tickets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>435</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>432</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>425</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>430</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>432</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   travel_date  travel_time  travel_from  car_type  max_capacity  \\\n",
       "0            1          435            9         0            49   \n",
       "1            6          432            9         0            49   \n",
       "2            6          425            4         0            49   \n",
       "3            0          430            1         0            49   \n",
       "4            0          432            9         0            49   \n",
       "\n",
       "   number_of_tickets  \n",
       "0                1.0  \n",
       "1                1.0  \n",
       "2                1.0  \n",
       "3                5.0  \n",
       "4               31.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_set.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random forest model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_train_set.drop([\"number_of_tickets\"], axis=1)\n",
    "y = df_train_set.number_of_tickets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = RandomForestRegressor(n_estimators=100, criterion=\"mae\", n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mae', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=-1,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds_train_set = model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.1712946071371415\n"
     ]
    }
   ],
   "source": [
    "print (mean_absolute_error(preds_train_set,y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predictions for test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test_set = pd.read_csv('test_questions.csv', low_memory=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first format the data as we did for the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test_set.drop(['travel_to'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test_set[\"travel_date\"] = pd.to_datetime(df_test_set[\"travel_date\"],infer_datetime_format=True)\n",
    "df_test_set[\"travel_date\"] = df_test_set[\"travel_date\"].dt.dayofweek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test_set[\"car_type\"] = pd.Categorical(df_test_set[\"car_type\"], categories=car_type_categories)\n",
    "df_test_set[\"car_type\"] = df_test_set.car_type.cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test_set[\"travel_from\"] = pd.Categorical(df_test_set[\"travel_from\"], categories=travel_from_categories)\n",
    "df_test_set[\"travel_from\"] = df_test_set.travel_from.cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test_set[\"travel_time\"] = df_test_set[\"travel_time\"].str.split(':').apply(lambda x: int(x[0]) * 60 + int(x[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's calculate predictions using the random forest model we trained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = df_test_set.drop(['ride_id'], axis=1)\n",
    "test_set_predictions = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And finally let's create a csv file with predictions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {'ride_id': df_test_set[\"ride_id\"], 'number_of_ticket': test_set_predictions}\n",
    "df_predictions = pd.DataFrame(data=d)\n",
    "df_predictions = df_predictions[['ride_id','number_of_ticket']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ride_id</th>\n",
       "      <th>number_of_ticket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>247</td>\n",
       "      <td>5.935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>256</td>\n",
       "      <td>5.960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>275</td>\n",
       "      <td>1.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>285</td>\n",
       "      <td>9.085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>286</td>\n",
       "      <td>10.495</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   ride_id  number_of_ticket\n",
       "0      247             5.935\n",
       "1      256             5.960\n",
       "2      275             1.010\n",
       "3      285             9.085\n",
       "4      286            10.495"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_predictions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_predictions.to_csv('preds_test_set.csv', index=False) #save to csv file"
   ]
  }
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