US Border Dataset Random Forest.ipynb

{
 "cells": [
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   "metadata": {},
   "outputs": [],
   "source": [
    "# Created by Yash Vakilna\n",
    "# Last updated: 17 August 2020\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import OneHotEncoder, LabelEncoder\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import math\n",
    "\n",
    "plt.style.use('fivethirtyeight') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Port Name</th>\n",
       "      <th>State</th>\n",
       "      <th>Port Code</th>\n",
       "      <th>Border</th>\n",
       "      <th>Measure</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-02-01</th>\n",
       "      <td>Alcan</td>\n",
       "      <td>AK</td>\n",
       "      <td>3104</td>\n",
       "      <td>US-Canada Border</td>\n",
       "      <td>Personal Vehicle Passengers</td>\n",
       "      <td>1414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-01</th>\n",
       "      <td>Alcan</td>\n",
       "      <td>AK</td>\n",
       "      <td>3104</td>\n",
       "      <td>US-Canada Border</td>\n",
       "      <td>Personal Vehicles</td>\n",
       "      <td>763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-01</th>\n",
       "      <td>Alcan</td>\n",
       "      <td>AK</td>\n",
       "      <td>3104</td>\n",
       "      <td>US-Canada Border</td>\n",
       "      <td>Truck Containers Empty</td>\n",
       "      <td>412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-01</th>\n",
       "      <td>Alcan</td>\n",
       "      <td>AK</td>\n",
       "      <td>3104</td>\n",
       "      <td>US-Canada Border</td>\n",
       "      <td>Truck Containers Full</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-01</th>\n",
       "      <td>Alcan</td>\n",
       "      <td>AK</td>\n",
       "      <td>3104</td>\n",
       "      <td>US-Canada Border</td>\n",
       "      <td>Trucks</td>\n",
       "      <td>545</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Port Name State  Port Code            Border  \\\n",
       "Date                                                      \n",
       "2020-02-01     Alcan    AK       3104  US-Canada Border   \n",
       "2020-02-01     Alcan    AK       3104  US-Canada Border   \n",
       "2020-02-01     Alcan    AK       3104  US-Canada Border   \n",
       "2020-02-01     Alcan    AK       3104  US-Canada Border   \n",
       "2020-02-01     Alcan    AK       3104  US-Canada Border   \n",
       "\n",
       "                                Measure  Value  \n",
       "Date                                            \n",
       "2020-02-01  Personal Vehicle Passengers   1414  \n",
       "2020-02-01            Personal Vehicles    763  \n",
       "2020-02-01       Truck Containers Empty    412  \n",
       "2020-02-01        Truck Containers Full    122  \n",
       "2020-02-01                       Trucks    545  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./Border_Crossing_Entry_Data.csv', \n",
    "                 index_col='Date', parse_dates=['Date'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preprocessing\n",
    "Deleting State, Port Name, and Border since these information are uniquely represented in \"Port Code\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "del df['State']    \n",
    "del df['Port Name']\n",
    "del df['Border']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting daily sums and constructing useful measures\n",
    "df.groupby(['Date','Port Code', 'Measure']).sum()\n",
    "df.reset_index()\n",
    "df = df.reset_index()\n",
    "\n",
    "# Getting Month, Year, and Day-of-the-week as predictor\n",
    "df['Month'] = pd.DatetimeIndex(df['Date']).month\n",
    "df['Year'] = pd.DatetimeIndex(df['Date']).year\n",
    "df['day'] = pd.DatetimeIndex(df['Date']).dayofweek\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3104, 'Personal Vehicle Passengers', 2020, 2, 5],\n",
       "       [3104, 'Personal Vehicles', 2020, 2, 5],\n",
       "       [3104, 'Truck Containers Empty', 2020, 2, 5],\n",
       "       ...,\n",
       "       [115, 'Trucks', 1996, 1, 0],\n",
       "       [3421, 'Truck Containers Empty', 1996, 1, 0],\n",
       "       [3103, 'Buses', 1996, 1, 0]], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting X, and y\n",
    "X = df.loc[:,['Port Code','Measure','Year','Month','day']].to_numpy()\n",
    "y = df['Value'].to_numpy()\n",
    "X\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converting categorical variables (Measure, Port Code) into onehot encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\yashv\\Anaconda3\\envs\\PythonDataL\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:415: FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values.\n",
      "If you want the future behaviour and silence this warning, you can specify \"categories='auto'\".\n",
      "In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\yashv\\Anaconda3\\envs\\PythonDataL\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:451: DeprecationWarning: The 'categorical_features' keyword is deprecated in version 0.20 and will be removed in 0.22. You can use the ColumnTransformer instead.\n",
      "  \"use the ColumnTransformer instead.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# Encoding Measure (col 1) into one-hot encodded\n",
    "from sklearn.preprocessing import OneHotEncoder, LabelEncoder\n",
    "labelencoder_X = LabelEncoder()\n",
    "X[:, 1] = labelencoder_X.fit_transform(X[:, 1])\n",
    "onehotencoder = OneHotEncoder(categorical_features = [0])\n",
    "X = onehotencoder.fit_transform(X).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the dataset into the Training set and Test set\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training Random forest model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
       "                      max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      presort=False, random_state=0, splitter='best')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting Decision Tree Regression to the dataset\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "regressor = DecisionTreeRegressor(random_state = 0)\n",
    "regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting a new result\n",
    "y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'RMSE = 22187.02'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluating model using Root mean squared error\n",
    "f\"RMSE = {np.round(math.sqrt(mean_squared_error(y_test, y_pred)),2)}\""
   ]
  }
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