FinalProject_group103.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COGS 108 - Final Project "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview\n",
    "Our project predicts whether the following day’s weather in Australia will have rain or not. After taking into account various weather features(wind direction, range of temperatures, rainfall, etc.), we worked with the data to determine which feature(s) would have the most effect on weather prediction accuracy of the following day. We found humidity to be the most significant feature in weather prediction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Names and IDs\n",
    "Achint Singh: XXXXXXXXX\n",
    "\n",
    "Jonathan Choi: XXXXXXXXX\n",
    "\n",
    "Xuanyu Wu: XXXXXXXXX\n",
    "\n",
    "Iris Chan: XXXXXXXXX\n",
    "\n",
    "Elliot Pourmand: XXXXXXXXX"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Research Question\n",
    "Which weather feature (or combination of features) has the greatest influence over the overall accuracy when predicting the weather of the next day (rain or not) in Australia?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background and Prior Work\n",
    "The dataset we will be looking at has compiled weather information from various weather stations in Australia. It notes general logistical information about the weather as well as whether or not it rained the following day. The objective of the dataset was to predict whether it would rain the next day (given the weather patterns in Australia). \n",
    "\n",
    "Our question aims to further delve into how weather is predicted on a daily basis. Weather predictions are currently predicted using forecasting models as well as data collected from satellites. This information is generally recorded in historical datasets by location recording the weather history of each location. Given this knowledge, we know that we do have the scientific and statistical advancements to predict rain based on past and present data. However, it is also important to recognize the limitations of this analysis. Compared to the power of satellites, purely using machine learning and data will be restrictive. This is one thing we will keep in mind in pursuing analysis. We want to further investigate all the recorded values and what elements contribute the greatest to the weather predictions. \n",
    "\n",
    "There were several smaller scale weather prediction products online that could be used as a reference for further approach in this project. Taking a study at Stanford, “Machine Learning Applied to Weather Forecasting”, they were able to create both linear and functional regression models that outperformed professional datasets that were used. They noted that their linear regression model thrived under large amounts of data, which is one that we can take from their study. We intend to use a large amount of data.\n",
    "\n",
    "Another article online was able to use the Stanford study to corroborate their findings and create a fairly accurate linear regression model to analyze and predict the weather data. \n",
    "\n",
    "In doing research, we were able to find a Medium article discussing the weather prediction algorithm. They noticed that using a limited range of elements may decrease the accuracy. If that is the case, it is valuable to increase the historical component of the weather data. This association of the number of elements and time is one that our group will be considering in our analysis. Also noted from research was that weather trends have derailed due to climate change. This is also important to note when looking at time as a major component in the prediction process.\n",
    "\n",
    "For our own project, we initially considered using a linear regression model for general weather trends. However, because the nature of our project was to note specific features that impacted weather predictions, we chose to train models instead. More specifically, we trained models isolating different features and identifying which ones most clearly indicated prediction of rain. We did notice a general decline in precision of accuracy as we limited the range of elements. This was corroborated by the Medium article.\n",
    "\n",
    "Sources:\n",
    "https://pdfs.semanticscholar.org/2761/8afb77c5081d942640333528943149a66edd.pdf\n",
    "\n",
    "https://stackabuse.com/using-machine-learning-to-predict-the-weather-part-2/\n",
    "\n",
    "https://medium.com/@shivamtrivedi25/what-is-the-weather-prediction-algorithm-how-it-works-what-is-the-future-a159040dd269\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis\n",
    "Since rain is formed by the condensation of warm and humid air as it rises and cools, it is possible that the more humid the atmosphere is, the more likely it will rain the next day. Therefore, we hypothesize that humidity will be the most significant feature in predicting the weather of the next day. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset(s)\n",
    "Our dataset is composed of weather metrics and observations from multiple Australian weather stations. \n",
    "\n",
    "The observations are taken between the dates,  2008-12-01 and  2016-11-26. The dataset is composed of 23 columns and 142,000 data points. \n",
    "\n",
    "What makes this dataset great at tackling our question is that this dataset has a large number of features. This gives us a large amount of data to work with in determining which features are the most significant. \n",
    "\n",
    "The dataset was found on Kaggle.com and is located at the url: https://www.kaggle.com/jsphyg/weather-dataset-rattle-package"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from keras.callbacks import History "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Location</th>\n",
       "      <th>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>Evaporation</th>\n",
       "      <th>Sunshine</th>\n",
       "      <th>WindGustDir</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindDir9am</th>\n",
       "      <th>...</th>\n",
       "      <th>Humidity3pm</th>\n",
       "      <th>Pressure9am</th>\n",
       "      <th>Pressure3pm</th>\n",
       "      <th>Cloud9am</th>\n",
       "      <th>Cloud3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Temp3pm</th>\n",
       "      <th>RainToday</th>\n",
       "      <th>RISK_MM</th>\n",
       "      <th>RainTomorrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-12-01</td>\n",
       "      <td>Albury</td>\n",
       "      <td>13.4</td>\n",
       "      <td>22.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>44.0</td>\n",
       "      <td>W</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1007.7</td>\n",
       "      <td>1007.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.9</td>\n",
       "      <td>21.8</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-12-02</td>\n",
       "      <td>Albury</td>\n",
       "      <td>7.4</td>\n",
       "      <td>25.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WNW</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1010.6</td>\n",
       "      <td>1007.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.2</td>\n",
       "      <td>24.3</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-12-03</td>\n",
       "      <td>Albury</td>\n",
       "      <td>12.9</td>\n",
       "      <td>25.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WSW</td>\n",
       "      <td>46.0</td>\n",
       "      <td>W</td>\n",
       "      <td>...</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1007.6</td>\n",
       "      <td>1008.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-12-04</td>\n",
       "      <td>Albury</td>\n",
       "      <td>9.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NE</td>\n",
       "      <td>24.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>...</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1017.6</td>\n",
       "      <td>1012.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.1</td>\n",
       "      <td>26.5</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-12-05</td>\n",
       "      <td>Albury</td>\n",
       "      <td>17.5</td>\n",
       "      <td>32.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>ENE</td>\n",
       "      <td>...</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1010.8</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>29.7</td>\n",
       "      <td>No</td>\n",
       "      <td>0.2</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date Location  MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine  \\\n",
       "0  2008-12-01   Albury     13.4     22.9       0.6          NaN       NaN   \n",
       "1  2008-12-02   Albury      7.4     25.1       0.0          NaN       NaN   \n",
       "2  2008-12-03   Albury     12.9     25.7       0.0          NaN       NaN   \n",
       "3  2008-12-04   Albury      9.2     28.0       0.0          NaN       NaN   \n",
       "4  2008-12-05   Albury     17.5     32.3       1.0          NaN       NaN   \n",
       "\n",
       "  WindGustDir  WindGustSpeed WindDir9am  ... Humidity3pm  Pressure9am  \\\n",
       "0           W           44.0          W  ...        22.0       1007.7   \n",
       "1         WNW           44.0        NNW  ...        25.0       1010.6   \n",
       "2         WSW           46.0          W  ...        30.0       1007.6   \n",
       "3          NE           24.0         SE  ...        16.0       1017.6   \n",
       "4           W           41.0        ENE  ...        33.0       1010.8   \n",
       "\n",
       "   Pressure3pm  Cloud9am  Cloud3pm  Temp9am  Temp3pm  RainToday  RISK_MM  \\\n",
       "0       1007.1       8.0       NaN     16.9     21.8         No      0.0   \n",
       "1       1007.8       NaN       NaN     17.2     24.3         No      0.0   \n",
       "2       1008.7       NaN       2.0     21.0     23.2         No      0.0   \n",
       "3       1012.8       NaN       NaN     18.1     26.5         No      1.0   \n",
       "4       1006.0       7.0       8.0     17.8     29.7         No      0.2   \n",
       "\n",
       "   RainTomorrow  \n",
       "0            No  \n",
       "1            No  \n",
       "2            No  \n",
       "3            No  \n",
       "4            No  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/weatherAUS.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning\n",
    "\n",
    "In order to clean the dataset we first made note of any columns that high numbers of NaN values. We chose to drop those columns completely when we found that over half the values in those columns were NaN values. We then further dropped rows that contained any NaN values. There were several columns in general that needed to be dropped, because they objectively did not contribute to the question at hand including date and wind direction. Lastly, we changed one categorical variable (Rain Tomorrow) into a boolean value of 0 or 1 in order to be easily accessible for computation. After doing this, our data was clean to begin being used for analysis and begin looking at the data distribution. Overall, the data was relatively clean to begin with and we were simply able to filter through the information to extract exactly what was needed.\n",
    "\n",
    "After cleaning the data into a usable format, we noticed some inconsistencies with the data. There was not an equal weighting between the days in which it rained and did not. This lack of weighting would create an inconsistency and weight the predictions favoring one side over the other. In order to resolve this we discussed both downsampling and upsampling. Ultimately, we felt that downsampling would be the best decision, because upsampling required us to replicate data. This felt staged and also gave us far more data than computing power would allow us to deal with. We therefore, chose to randomly remove particular datasets in order to even out and make the data of whether it rained or not an equal split. \n",
    "\n",
    "Additionally, in plotting the values we used the z-score to create a more consistent scaling on the visualizations. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop Columns with Large Amounts of Null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                 0\n",
       "Location             0\n",
       "MinTemp            637\n",
       "MaxTemp            322\n",
       "Rainfall          1406\n",
       "Evaporation      60843\n",
       "Sunshine         67816\n",
       "WindGustDir       9330\n",
       "WindGustSpeed     9270\n",
       "WindDir9am       10013\n",
       "WindDir3pm        3778\n",
       "WindSpeed9am      1348\n",
       "WindSpeed3pm      2630\n",
       "Humidity9am       1774\n",
       "Humidity3pm       3610\n",
       "Pressure9am      14014\n",
       "Pressure3pm      13981\n",
       "Cloud9am         53657\n",
       "Cloud3pm         57094\n",
       "Temp9am            904\n",
       "Temp3pm           2726\n",
       "RainToday         1406\n",
       "RISK_MM              0\n",
       "RainTomorrow         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindSpeed9am</th>\n",
       "      <th>WindSpeed3pm</th>\n",
       "      <th>Humidity9am</th>\n",
       "      <th>Humidity3pm</th>\n",
       "      <th>Pressure9am</th>\n",
       "      <th>Pressure3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Temp3pm</th>\n",
       "      <th>RainToday</th>\n",
       "      <th>RainTomorrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13.4</td>\n",
       "      <td>22.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>44.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1007.7</td>\n",
       "      <td>1007.1</td>\n",
       "      <td>16.9</td>\n",
       "      <td>21.8</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.4</td>\n",
       "      <td>25.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1010.6</td>\n",
       "      <td>1007.8</td>\n",
       "      <td>17.2</td>\n",
       "      <td>24.3</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12.9</td>\n",
       "      <td>25.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1007.6</td>\n",
       "      <td>1008.7</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1017.6</td>\n",
       "      <td>1012.8</td>\n",
       "      <td>18.1</td>\n",
       "      <td>26.5</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.5</td>\n",
       "      <td>32.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1010.8</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>29.7</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MinTemp  MaxTemp  Rainfall  WindGustSpeed  WindSpeed9am  WindSpeed3pm  \\\n",
       "0     13.4     22.9       0.6           44.0          20.0          24.0   \n",
       "1      7.4     25.1       0.0           44.0           4.0          22.0   \n",
       "2     12.9     25.7       0.0           46.0          19.0          26.0   \n",
       "3      9.2     28.0       0.0           24.0          11.0           9.0   \n",
       "4     17.5     32.3       1.0           41.0           7.0          20.0   \n",
       "\n",
       "   Humidity9am  Humidity3pm  Pressure9am  Pressure3pm  Temp9am  Temp3pm  \\\n",
       "0         71.0         22.0       1007.7       1007.1     16.9     21.8   \n",
       "1         44.0         25.0       1010.6       1007.8     17.2     24.3   \n",
       "2         38.0         30.0       1007.6       1008.7     21.0     23.2   \n",
       "3         45.0         16.0       1017.6       1012.8     18.1     26.5   \n",
       "4         82.0         33.0       1010.8       1006.0     17.8     29.7   \n",
       "\n",
       "  RainToday RainTomorrow  \n",
       "0        No           No  \n",
       "1        No           No  \n",
       "2        No           No  \n",
       "3        No           No  \n",
       "4        No           No  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Date is irrelevant\n",
    "# Sunshine and Evaporation have over 60k nans\n",
    "# RISK_MM is too correlated\n",
    "# Cloud 9am, Cloud 3pm 50k nans\n",
    "# WindDir, we are measuring form different locations\n",
    "df = df.drop(columns=['Date', 'Sunshine', 'Evaporation', \\\n",
    "                      'RISK_MM', 'Cloud9am', 'Cloud3pm', \\\n",
    "                      'WindDir9am', 'WindDir3pm', 'WindGustDir', 'Location'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MinTemp            637\n",
       "MaxTemp            322\n",
       "Rainfall          1406\n",
       "WindGustSpeed     9270\n",
       "WindSpeed9am      1348\n",
       "WindSpeed3pm      2630\n",
       "Humidity9am       1774\n",
       "Humidity3pm       3610\n",
       "Pressure9am      14014\n",
       "Pressure3pm      13981\n",
       "Temp9am            904\n",
       "Temp3pm           2726\n",
       "RainToday         1406\n",
       "RainTomorrow         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(119590, 14)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.dropna()\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "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>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindSpeed9am</th>\n",
       "      <th>WindSpeed3pm</th>\n",
       "      <th>Humidity9am</th>\n",
       "      <th>Humidity3pm</th>\n",
       "      <th>Pressure9am</th>\n",
       "      <th>Pressure3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Temp3pm</th>\n",
       "      <th>RainToday</th>\n",
       "      <th>RainTomorrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13.4</td>\n",
       "      <td>22.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>44.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1007.7</td>\n",
       "      <td>1007.1</td>\n",
       "      <td>16.9</td>\n",
       "      <td>21.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.4</td>\n",
       "      <td>25.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1010.6</td>\n",
       "      <td>1007.8</td>\n",
       "      <td>17.2</td>\n",
       "      <td>24.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12.9</td>\n",
       "      <td>25.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1007.6</td>\n",
       "      <td>1008.7</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1017.6</td>\n",
       "      <td>1012.8</td>\n",
       "      <td>18.1</td>\n",
       "      <td>26.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.5</td>\n",
       "      <td>32.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1010.8</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MinTemp  MaxTemp  Rainfall  WindGustSpeed  WindSpeed9am  WindSpeed3pm  \\\n",
       "0     13.4     22.9       0.6           44.0          20.0          24.0   \n",
       "1      7.4     25.1       0.0           44.0           4.0          22.0   \n",
       "2     12.9     25.7       0.0           46.0          19.0          26.0   \n",
       "3      9.2     28.0       0.0           24.0          11.0           9.0   \n",
       "4     17.5     32.3       1.0           41.0           7.0          20.0   \n",
       "\n",
       "   Humidity9am  Humidity3pm  Pressure9am  Pressure3pm  Temp9am  Temp3pm  \\\n",
       "0         71.0         22.0       1007.7       1007.1     16.9     21.8   \n",
       "1         44.0         25.0       1010.6       1007.8     17.2     24.3   \n",
       "2         38.0         30.0       1007.6       1008.7     21.0     23.2   \n",
       "3         45.0         16.0       1017.6       1012.8     18.1     26.5   \n",
       "4         82.0         33.0       1010.8       1006.0     17.8     29.7   \n",
       "\n",
       "   RainToday  RainTomorrow  \n",
       "0          0             0  \n",
       "1          0             0  \n",
       "2          0             0  \n",
       "3          0             0  \n",
       "4          0             0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Encode Binary Labels for True/False Values\n",
    "df['RainToday'] = df['RainToday'].apply(lambda x: 0 if x == 'No' else 1)\n",
    "df['RainTomorrow'] = df['RainTomorrow'].apply(lambda x: 0 if x == 'No' else 1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Identify Outliers\n",
    "By plotting histograms of our features we are able to visualize the distribution and identify outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "n_bins = 12\n",
    "grid = plt.GridSpec(4, 4)#, wspace=0.2, hspace=0.2)\n",
    "fig = plt.figure(figsize=(18, 12))\n",
    "ax1 = fig.add_subplot(grid[0,0])\n",
    "ax2 = fig.add_subplot(grid[0,1])\n",
    "ax3 = fig.add_subplot(grid[1,0])\n",
    "ax4 = fig.add_subplot(grid[1,1])\n",
    "\n",
    "ax1.hist(df['Humidity9am'].values,  bins=n_bins, edgecolor='black', linewidth=1.2)\n",
    "ax1.set_xlabel('Humidity at 9 am')\n",
    "ax1.set_ylabel('Frequency')\n",
    "\n",
    "ax2.hist(df['Humidity3pm'].values,  bins=n_bins, edgecolor='black', linewidth=1.2)\n",
    "ax2.set_xlabel('Humidity at 3 pm')\n",
    "ax2.set_ylabel('Frequency')\n",
    "\n",
    "ax3.hist(df['Temp9am'].values,  bins=n_bins, edgecolor='black', linewidth=1.2)\n",
    "ax3.set_xlabel('Tempurature at 9 am')\n",
    "ax3.set_ylabel('Frequency')\n",
    "\n",
    "ax4.hist(df['Temp3pm'].values,  bins=n_bins, edgecolor='black', linewidth=1.2)\n",
    "ax4.set_xlabel('Tempurature at 3 pm')\n",
    "ax4.set_ylabel('Frequency')\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see there are some clear outliers in humidity and tempurature at 9 am and 3 pm. Below we trim off some outliers for some of our features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropping 2265 rows where (Temperature at 9 am) is less than 6 degrees\n",
      "Dropping 150 rows where (Temperature at 3 pm) is greater than 42 degrees\n",
      "Dropping 251 rows where (Humidity at 9 am) is less than 10\n",
      "(116855, 14)\n"
     ]
    }
   ],
   "source": [
    "print(f\"Dropping {len(df[df['Temp9am'] <= 5])} rows where (Temperature at 9 am) is less than 6 degrees\")\n",
    "df = df[df['Temp9am'] > 5]\n",
    "print(f\"Dropping {len(df[df['Temp3pm'] > 42])} rows where (Temperature at 3 pm) is greater than 42 degrees\")\n",
    "df = df[df['Temp3pm'] <= 42]\n",
    "print(f\"Dropping {len(df[df['Humidity9am'] <= 9])} rows where (Humidity at 9 am) is less than 10\")\n",
    "df = df[df['Humidity9am'] > 10]\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis & Results\n",
    "\n",
    "We primarily studied the distributions of 4 variables: Humidity at 9 am, Humidity at 3 pm, Temperature at 9 am, and Temperature at 3 pm. Both the temperature distributions were fairly similar to each other, while the humidity distributions varied. More notably, we also saw outliers in the humidity distributions and they were removed before proceeding.\n",
    "\n",
    "In order to note relationships between variables, we implemented a correlation matrix. There were no features in particular that were correlated more than expected. Humidity at different timings of the day as well as temperature at different timings of the day were correlated with each, as expected. However, if we do compare features that were correlated with rainfall, we can see that the highest correlation is between Humidity and Rainfall. This would further be corroborated by other models.\n",
    "\n",
    "We trained our model based on the different features, including Humidity, Temperature, and Rainfall. Our model could predict tomorrow’s rainfall with 87.28% accuracy. We then tested the significance of the 5 most highly correlated features by dropping each feature and seeing the change in the prediction’s accuracy. The one that decreases the prediction accuracy the most would be the feature that is most significant in predicting whether or not it would rain the next day. We expected the most highly correlated feature to RainTomorrow would also most significantly contribute to the accuracy of our model. From our findings, we confirmed that Humidity, the most highly correlated feature, was indeed most significant to predicting RainTomorrow.\n",
    "\n",
    "\n",
    "### Find correlations\n",
    "\n",
    "We first plot a correlation heatmap to see the all the correlations between each features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1124591588>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(12,10))\n",
    "colormap = plt.cm.Spectral\n",
    "plt.title('Correlations between each features', y=1.05, size=15)\n",
    "corrs = df.corr()\n",
    "sns.heatmap(corrs, cmap = colormap, linecolor = 'white', annot = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we extract the correlations with 'RainTomorrow' to have a closer look"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Correlation to RainTomorrow')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fETEbOAy4HLgb+FVEzJB0vKSd88vOAJaXNBM4EpinW66ZmTWnxysNSYsBSwArSFqWOd1sRwGvLxEeEZcCl3bZd2zb4xeAvUpkmZlZ//V2T+NjwBGkCmIqcyqNp0njK8zMbJjp7Z7G94DvSfpkRJzSYJnMzKxDVRmncYqk9Umjthdr2//TOgtmZmadp8oa4V8C3kmqNC4lTftxA+BKw8xsmKkyjciewIbAtIg4UNJKwOn1FsvMrIzuBuPZgqsyYeHzEfEqMFvSKOAx+j+wz8zMBqEqVxpTJC0D/ITUi+o/QPl5BszMrONVuRH+8fzwR5ImAqMi4vZ6i2VmZp2ot8F9G/X2XETcWk+RzMysU/V2pfGtXp4LYNvCZTEzsw7X2+C+bZosiJmZdb4qExYuIekYSRPy9tqSdqq/aGZm1mmqdLk9C3gJ2CJvzwK+WluJzMysY1WpNNaKiJOAlwEi4nnmTF5oZmbDSJVK4yVJi5NX75O0FvBiraUyM7OOVGVw35eAicDqkn4GbAkcUGehzMysM/VaaUgScA+wO7A5qVnq8Ih4ooGymZlZh+m10oiIkPTbiNgY+ENDZTIzsw5V5Z7GTZI2qb0kZmbW8apUGtsAN0q6X9Ltku6Q1K+5pyQtJ+lKSffl78v28LqJkv4l6ff9yTMzszKq3AjfsYbco4CrI+IESUfl7c9387qTgSVI65WbmdkA6/VKQ9II4A8R8XDXr37m7gKckx+fA+za3Ysi4mrgmX5mmZlZIb1WGnnxpdskjSmcu1JEPJozHgVWLHx8MzOrQZXmqVWAGZJuAZ5t7YyInXv7IUlXASt389TR81XCCiQdAhwCMGZM6frNzMxaqlQaxy3IgSPiXT09J+kfklaJiEclrUJaQnaBRcQEYALA+PHjoz/HMjOznvXZeyoiriMN8Fsqf92d9/XHJcD++fH+wMX9PJ6ZmTWgytToHyCtCb4X8AHgZkl79jP3BGB7SfcB2+dtJI2XdHpb9p+AC4DtJM2S9J5+5pqZWT9UaZ46GtgkIh4DkDQauAr49YKGRsSTwHbd7J8CHNy2/fYFzTAzs/KqDO4b0aowsicr/pyZmQ0xVa40Jkq6HPhF3v4gcFl9RTIzs07VZ6UREZ+VtDuwFWmW2wkRcVHtJTMzs47TY6Uh6Y2kQXiTIuJC4MK8f2tJa0XE/U0V0szMOkNv9ya+S/dTeDyXnzMzs2Gmt0pjbETMM5tt7uE0trYSmZlZx+qt0lisl+cWL10QMzPrfL1VGpMlfbTrTkkHAVPrK5KZmXWq3npPHQFcJOlDzKkkxgOLALvVXTAzM+s8PVYaEfEPYAtJ2wDr591/iIg/NlIyMzPrOFXGaVwDXNNAWczMrMN5OhAzM6vMlYaZmVXmSsPMzCqrMmGh9WG1w84c6CKYmTXCVxpmZlaZKw0zM6vMlYaZmVXmSsPMzCpzpWFmZpUNSKUhaTlJV0q6L39ftpvXvEXSjZJmSLpd0gcHoqxmZjbHQF1pHAVcHRFrA1fn7a6eA/47ItYDdgC+K2mZBstoZmZdDFSlsQtwTn58DrBr1xdExF8i4r78+G/AY8DoxkpoZmbzGKhKY6WIeBQgf1+xtxdL2pQ0JXu365JLOkTSFElTHn/88eKFNTOzpLYR4ZKuAlbu5qmj5/M4qwDnAvtHxKvdvSYiJgATAMaPHx/zWVQzs35Z5XOrDHQRGlNbpRER7+rpOUn/kLRKRDyaK4XHenjdKOAPwDERcVNNRTUzs4oGqnnqEmD//Hh/4OKuL5C0CHAR8NOIuKDBspmZWQ8GqtI4Adhe0n3A9nkbSeMlnZ5f8wFga+AASdPz11sGprhmZgYDNMttRDwJbNfN/inAwfnxecB5DRfNzMx64RHhZmZWmSsNMzOrzIswmdmAOHK37nrkW6fzlYaZmVXmSsPMzCpzpWFmZpW50jAzs8p8I9zM5vKbPTYd6CJYB/OVhpmZVeZKw8zMKnOlYWZmlbnSMDOzylxpmJlZZa40zMysMlcaZmZWmSsNMzOrzJWGmZlVpogY6DIUJelx4OH5/LEVgCdqKI5zBkeGczo3wznNZawREaP7etGQqzQWhKQpETHeOZ2XM5Tey1DLGUrvZajl1Jnh5ikzM6vMlYaZmVXmSiOZ4JyOzRlK72Wo5Qyl9zLUcmrL8D0NMzOrzFcaZmZWmSsNMzOrzCv3mdmwJWkcMJa2c2FEXDJYc5owrCsNSaOBTYAApkTE4zVmLQFERDxfV4aZVSfpJ8B44C7g1bw7gKIn8wZz3hwRd5c8Zrc5w/VGuKQDgeOB6wABWwHHRsQ5hXM2As4ARuecvwMHR8S0Qse/g/QLOM9TpEpqgwIZz/SQASlkVH8zuuTtBHwFWIP0wab1XorlNJHRlrUm8Enm/aS582DLGWLv5W5gXNR8Emww5ybS3+lZwC8i4pk6cobzlcZRwEatqwtJKwCTgKKVBuk/8IiIuCbnvBM4G9iw0PF3KnScHkXEUgCSjidVeueSTrIfApaqIfK7wO7AHTX+oTWR0fJb0geH3zHnk+ZgzRlK7+VmYB3g3pqO32hORGwu6c3AR4DpkiYBZ7XOPaUM5yuNPwLviYiX8/bCwMSI2K5wzp8jYosu+yZFxJYlc5og6eaI2KyvfQVyrgG2i4jaTkpNZLRlFf83GqicIfZe3k6qlP4KvMicq82NBmNOW94IYBfgVOA54GXgfyPi4iLHH8aVxtnA+qRPNAHsCkwG7gGIiO8XyvkOsAjwi5zzQeBp4Fc55/Z+Hr+npqM6mnT+DJwGnJ8z9wE+0bVSLJCzCanp6DrSHxkAEfHtwZTRlrUvsDZwRZesWwdbzhB7L/cBnwfuoO1qJiLuL5XRcM444EBgZ+Ba4IyIuEXS6sANEbFGiZzh3Dz1SP5aNG9PzN/7nOVxPrUmDet6b+EdpBPv1v05eKvpqCH7At/LX0Fqztu3hpyvAf8BFiNVuHVoIqPlv4APA9sy943QbQdhzlB6L49ExIUFjzfQOT8BTge+FBHPtXZGxCOSvlQqZNheaQxVklYknQgBiIj/G8DiLJDBPgtoN1n3ABtExEuDPWeIvZdTgVGkpqP2q5nSvZoayWnKsL3SyL2ajmLe3hml2zNHAft1k3Nk4ZydgW8BrwceI/UKuhtYr2DGOsAPgZUiYn1JGwA7R8RXS2VkV0l6d0RcUfi4TWe03AYsQ/p/Gew5Q+m9LJ2/t/fIKt4VtqkcSWuRrqDHMfcHx3WK5gzXK438SeYL1N/OeANwazc5ZxTOuY106X5VRLxV0jbAPhFxSMGM64DPAj+OiLfmfXdGxPqlMvIxnwGWJH0qe5l67s+0Ml7KGZTOaMu6ltQ8OZm5P2mW7qZae85Qei9DjaQ/AV8Fvkm6R3sg8GpEHFsyZ9heaQBPNtTOuEREfKqBnJcj4klJIySNiIhrJJ1YOGOJfGOtfd/swhmN3Kdp+F5QsfbkDsgZMu9F0qLAAaSr8fZP5sU+aOWcscB3gLflXZOA/4mIh0rmkP4+L5f0zfzh95hckRQ1nCuN4yT9GLiKetsZf54HEv6+S87ThXP+Jel1wPXAzyQ9RvkT+hP5EjgAJO0JPFo4g3zsZUm9Z9r/mK8vnLE7aVBnAH+KiN+WPH5LRFxXx3G7y5G0MrAp6T1Njoi/l84oebzecup+L8BPgQdIY52+RurUMaNwBqSekxNIPSfJOb9gTiVSyotKn+jul3QoqYvvioUzhnXz1Dmky9+5hvZHxH8XzjkUOBFo7xobETGm0PEXjYgXJS0JvMCcQXdLAz+LiCdL5OSsN5B++bcAngIeBPYr/YlJ0sHA4cBqwHRgc+DGiCjWc0bSD4A3kv54If1B3x8RnyiV0Za1OXAK8GZST62RwLM1jKQ/GDgW+CPp9+AdwPERcWbBjKH0XqblptzbI2KDPFbr8pK/ZzmnqfFNm5HOZ8uSKsGlgRMjYlLJHCJiWH4BdzaUcz+wYo3HvzV/P7fBf7slgaVqPP4dpCuM6Xn7TcAvC2fMIH9oytsjgBk1vZ8ppApqGukkeyDw9Rpy7gWWb9teHrjX76XHjFvy9+tJleBypA8Opd/LN4DPkD4ErQocCRxD6lE1qo7fuTq/hnPz1M2S1o2IuqcQuIs0mK8ui0jaH9giN7fMJQret5G0EvB14PURsWMeTPS2KHxTH3ghIl6Q1LqSukfSuoUz7gXGAA/n7dWBfg207E1EzJQ0MiJeAc7KAyVLm0W6om15hjQWqagh9F7OyM2gXwIuB5YgXd2Utl/+fniX/R8jtT70q9VB0kX0PjfcPOeF/hjOlcamwO2SZlLv0P6XgGl52pL2exqlutweSmqOWgZ4f5fnAih5s/9s0lxaR+ftvwC/JM0RVNIsScuQRutfKekp4G+FM5YH7pZ0S97eBLhR0iVQvJfOc5IWIc0HdBLpPtCSBY/f8lfSh6GLSf/3uwC3SDoSio12HzLvJSJ+nB9eQz9P3H3krF7XsbNT8/ddSF3uf5a39yG1dBQ1nO9prNXd/ijf5fagHnJKd7k9qIZP/F0zJkfEJq224LxvekS8pcbMd5DaZidGwYFe+bg9ioI3fCWtQRpvsDDwadL7+UFEzCyVkXN67XEUEccVyBgS7yW/j91IV5izgfuA8yPiP/05bjc5G5E+jE7LV8vvAe6OiCtL5uSs6yNi67ZtAde17yuSM1wrDXjtpt46EfFTScsDS0YNI6jzJ7Mxpf+wusnZgnkHEf604PGvBfYAroyIjfK/34kR0esJeAFyRpPaf2cDD5b+Q+4mb7mI+GedGdY5JH2C9Ht8PbAjaRzV06TBd4dERJFuqpKOIX36X4jUe3Ir4E/AdsDvIuKEEjltefcAO0TumJIrxokR8eaSOQN+U2Wgvkg3oi4D/pK3VyVN6lU6532k9vMH8/ZbgItqyDkX+DPwA1LvllOA7xfO2IjUx/zf+ftfSFM9lDr+OFIX6JmkZr2bST20zgaWLpSxJWmk/AxgM+BKUrfLR0j3Z0r+e72OtGbLjPxv9jhwE7B/4ZyVSSP1TyM1u32ZdH/mV8Aqfi/zZNwBLJQfLwlcmx+PJXcsKZRzJ6mzwJKkSmnpvH8J4PaS/275uO/Lv8dX5a+HgR2L55Q+4GD5InXlFDCtbV8d/5FTSfcb2nPuqCHnbtp6A9X477YQaTDU+sDChY99E7BufrwpcE5+/FHg14UybiFNhvc24Algq7x/I2BS4fdzMWnw2GqkHjNfJI09OYeCPY5Ik21+kjQtzu2kGVXH5H0X+73Mk3EnsEh+vAwwtf25gu/l1rbH07o8N61UTpfjLg5snL8WryWjjoMOhi/g5vb/2Bpr/5u6/pLUlHMBhT6J9ZKxWD5hXAj8BjgCWKzg8W/rst3+R3dXoYz2/4e7e8qr6f1Mzt9HAPcUzGl/T//X5bnpfi/zZBxJ6jL8A1Lvxo/m/aNLfnAgXSkvnh8v3LZ/VB2VBukD3cdJSxecT+oks1DpnOHce+pCSacBS+cR2wcBxQYOtblb0geAEUpLWB5O+kRd2grAXbk3UF1z9fyU1PXxlLy9D6lZbK9Cx79f0heBq0mr6k2H1xbIKvW7OqLt8f92ea70FOnPStoqIm6Q9H7gnwAR8aq6zMXST+3vqes9rBGUMWTeS0R8W9LVpLEZp0XEjLz/cVLzZSnbkAbcEnmxt6w1fUlpp5Gawlrnsf1IV9BFp0UZtpVGRJwoaUdS2/mGwNci4rIaog4j9f1+lfQJ/XLSRImlfbmGY3a1bkS0L1N7TZ4osZSPkP5tvkCa5bTVr30JoNRI/S9KWiIinou2aUNyb7pinQayQ4HTc6+ZO0gfTFo3+k8rmHOxpNdFxH8i4pjWTklvJN13KqH1XtYhNe8M5vdCRNwG3CZpA0kfJ68PE/1cFK1LxmtrWijNCN2asmZSzi9t8y5/n1cU/vsEhmHvKUlXRMS7G8j5ekTUUTkMGKXVDn8UETfl7c1IN0I/PqAF63CSFoqI4hM7dpPTGnBnFUj6AmmMU+vDwy6kqXe+UTjnaNJ8U3XnTAN2izm9p8aSOt28tWjOMKw0ppX+R+wh59aoaQ3gLjk3RMRWmnfZ1zqmE78bWBdodUseQ7oB/2rO6ro64fwe/3f0PrK1301tTWR0k/kg8GunfZULAAASEklEQVTgrIi4q/Txm8xpalaAJnLy7/PGrSsCSUuQbooX7aLaYM72pIG2fyH9/b8ROCgiriqZMxybp5bubrqNlig37cbIPEVBt+29UWhcQERslb83MdX3DjUf/5v5++6krpfn5e19gIcGUUZXGwB7k5p3RpDanM+P8jMdN5FzNs3MCtBEzsPMfQ5ciNT9urRacyRtHhE3RcSVuSn0zaTzzl0R8XypnNfyhuGVxpOk7oPdncwjIj5SKOdF0lQIPeW8oURON7nFl3vNn4xebt3My7+Y7wUeLljJtufNNbK1p32dntFD7takmXWXIV0VfCVqGPRZV05TswI0kSPpQtL0MZeTrj7fDdwA/APKTfVTd05TrRotw/FK4+FSFUMf7mqiGaxF9S73OpF04/O+fEPyRtL8NjtJ2iQiuvZC6q/Rkt4QEQ8A5F5nowdhBvnYI0kDrw4kDSD7Funf7+3ApUCR5Tgbynk2z54QOXNz0mC/0prI+UP+aqmjV2OTOY0YjpVGye6BneQrpHUn5lrutdCxl42I+/Lj/YFfRMQn8/QoU5m362p/fRq4VlLrEn4saUbQwZbRch9pUryTI6J9Rthf5yuCwZRzJGlt67UkTSJVtHsWOnajOaXvwwxgzhuUJ9rsIb/ofbrhWGl8uKGc7wFIWiwiXmh/QtIKEfFE4bw6l3ttb8PcFjgZICJekvRq9z/Sj7CIiZLWJq2jAWnw2Iu9/UwnZrT574i4oX2HpC0jYlKUXQq41px8n2Qx0oJI65I+gN3bZQxCvzWYswPpw9YapHNhq/PIcoMs53HSVWUjht09jZZ8M/xE0nKIoobeRjnnDtKI01Y31T2Ab0REkSaJtpyrSIvJf4M00O8xYJOI2KLAsc8D/k6anvzzwJoR8ZzS9OXXdekb3m/5HsqRwBoR8dF8cl83In4/mDLasuZpc66jHbqJHEk3RkTpZUoHJEdpWYQPkMbQvPbhp3S35bpzfE+jOScB74+Iu2vO2Rc4U2mG2NeTJmErupxktgvwPKnZpbXc6/GFjv1R0kC7McC72wYtjWNOb6SSziI1e7VOGrNI06SUPKHXniHpbaSlcUcrrwORjSJNZDeocrIr8gefC6PeT5xN5MwiTU1S/Gq54ZyHajput4ZzpfGPBioMIuIOSV8jTbfxDLB1RMyqIefZ/PBV4Jx8U3Rv5izI0p9jPw+cIOnwaBvJGhF/lrRJf4/fjbUi4oOS9mnlS0WnqmgqYxHS7LALAe1dop+mbPt8UzmQrs6WBGZLaq1JX/wKvaGczwG/yx/o2qfe+X7BjNpzom1lPtW8PAIM70pjiqRfkkZptv9HFu1CKukMYC1SH/p1SL88p0ZEkakXJI0CPkGa2v0S0lTfnwA+S5q7qd+VRpv9yfdq2hzQzb7+eknS4szpObMWbf9HgyUj0kJO10k6OyIe7vMHOjwnZzUxHqipnOOAl0ndkuu82mgkR9K5pHPNdKDV9BUUnh5nOFcao4DnSH2mW0ovjwppnp6D8yX2g7nrYIllN1vOBZ4idYM9mFRZLALsEhHTSwTkT+P7Amt26aUxCniyREYXXyJ1811d0s9Ik8gdMNgyJH03Io4ATpU0TxNLqV4tTeXkrG57YUXE9aUyGsxZMSI2Lni8gc4ZD4yrudlw+N4IHyok3RER/5UfjyStETEmIp4pmLEGsCbpJvtRbU89Q5rmvfi8SrmP/uakZombauhtVnuGpI0jYqp6WFo2Ci0p21ROzvpd2+ZipHVPpkZE0ft0TeQorXE+MSL+WOqYA5xzAfCpiHi01pzhVmlI+lxEnCTpFLqZg6hwF0hyr5xvkG4at4/ULjIivGvPiTp7UkhaEng+0nTY65C6q15WQ1dIkW7mvyEijpc0Blg5Im4ZTBnDgaTVgZMiotSYoMZyJD1F6jDyHGm267q63DaVcw1pZdA6l0cYls1TrZvfUxrKO4vUFPId0vz6B1J2gOGGklpzCwlYPG/XcePweuDtSnNqXU36N/wg6eRb0g9Ibb/bknqAPUNa9KnkTffaM3J3694mR+zXBI9N5/RgFmkVx7rVkbNC4eMNdM6XmwgZdpVGRPwufz+nocjFI+JqSco3Kb8s6U+kiqTfIqJ0l8reKI/POAg4JV+xTashZ7OI2Kh17Ih4Smn0+WDL2Cl//0T+fm7+/iHSp87BlkOXK/QRpE+25ddsaCAnIl6RtDfpavPrklYDViJ1xR6MOcWaIXsz7CoN9TLcHmqZGvuFPML1PkmHkSYxXLFwRlOUxwR8iLwID/X8Dr2c78+0ejaNpnyvk9ozWj2ZlEZlt68Id5TS1BhFxtE0lZO1X6HPJk0pM6ng8RvLkXQqsDCwNWka9ueAH1H2irb2HDW4PAIMw0qDNJjrEdIMoDdT/1xUR5BWnvsUaSqBbUldVwejI0jzTF0UETMkvYE011Fp3wcuAlbKY1z2BI7p/Uc6MqNlSeWlUuG1vvRLDsac9iv03Ey5esnjN5yzRZerzX/WcLVZe040uzzCsLwRPhLYnjSZ3wak2Sd/EXmdYOsMkt4EbJc3/1jHQMwmMnLOxqS1LZbOu/4FfCQibh1sOXmA2s6kD5zTSfMeXReFphFvMkfSzaQPkVPySX158oSfpTKazGnLK748Qrthd6URab6XicBESYuSKo9rJR0fEaeUyhmAZrDa5d4Z3fU4q2NalCVIU2AEsHgNx28qg4iYSuqwMIr0Qa2OqcSbylk6Ip6WdDBphcAvSSq2rnYTOZqz/O5ppM4PoyUdR5of6rgSGU3mtOXVuTzCa4ZdpQGQK4v3kSqMsaSmitKD+ppuBmvCZ9oeLwbsQWpvLkrSscBepD80AWdJuiAivjrIMvaLiPM093xQKM9WEhFFBnk2lZMtJGkV0onv6L5e3KE5twAbRcRPJU0F3kX6HdgrIu4chDktdS6P8JphV2lIOofUde8y4Lia/vMgLSXaagbblyHQDJY/ybabJKmOHhv7AG+NPKW8pBOAW4FiJ/SGMlr3E+pua24qB9JN9cuBGyJicr6vdV8fP9NpOa99gMt/j3X9TTaV01Ln8givGY73NF4FWpP71d7TIGe2msFOBoo2gzVJUvtgpBHAxsD3I2LdwjmXAftExL/y9jLAeRGxU+8/2VkZ1pkkzaKXqXwKXgE2ktOWV9vyCO2G3ZVGRIxoKquhZrAmTSVVtCI1Sz3InK63Jb0IzJB0Zc7bHrhB0veh2Kj9JjIAWkvJfpJ5Zx8tel+riRylKTG+SpqGfyKwIXBERJxXKqOBnJGkWYHrbjJuKqelzuURXjPsrjSa0qUZ7Pwam8GGHEm9dkkuMTCziYy2rNuAM5h3EZ6iTXtN5EiaHhFvkbQb6VPtp4FrovxCXLXlqKFFi5rK6SV/JLB3RJSc6Xr4XWk06MOkZrB1gE9pzlINtTWD1SX3xlkp8jrhkvZiTm+jyyPiHyXzWidsSQuTKt6/RsRjgy2jzQtRfo2GgcpZOH9/L+ke3T9VfBmS2nOa+uTfSI6aXR7BVxrWN0kTgD9HxNl5eybpCmpxYHZEHFoo50ek6UlmSFqaNN37K8BywGci4heDIaObzH2BtYErmHsiudLjNGrPyR0GdiU1g2xKWiPi9xGxWamMunMkLRcR/+zvcToo52LmLI+wHbAsaXmEw6PQ8ghz5bnSsL7kkawbRf5lkTStNTCpNYVBoZwZEbFefnwE8M6I2FXSyqTZdPs9GKqJjG4yv0G68ryfOc1GUXp8S4M5ywJPR5pTaUlgqYj4e8mMJnMGOzWwPEI7N09ZFQvF3J8uPtz2eJmCOS+1Pd6etGY3EfH3gk0TTWR0tRtpsrqX+nxlh+dIWoLU9DEGOIQ0kGxdyq7f3ljOEPHa0gS5gn2wrgoDUrdJs768mj+JA9C6qS9pVcpO8vcvSTtJeitpJb2JOWchyo3YbiKjq9soW7kOZM5ZpIq31Y1zFmXHtjSdMxRsKOnp/PUMsEHrseYsm1CMrzSsipNJa5v/D9CaCn0j4Jv5uVI+RuqWvDKpe2WrKWI70uDIwZLR1UrAPZImU+PiOA3lrBURH1RaApiIeF71XKI1lTPoRbPLI7jSsL7lKSqeIH3Sa81jcydwbERcVjDnL8AO3ey/nDQ6eFBkdKPI2ikdkvOSpMWZM6X8WrRVUIMwx+aTb4Rbx1APS/C2lBhw10TGUCZpe9IU8uNIvbS2BA6IiGsHY47NP19pWJ8aPNG2Ft7ZknSy+GXe3otyq5w1kTEXzb04ziKkMQjPlh6rU3dObh66B9idNDGeSN06nyhx/KZzbMH4SsP61DZ6utsTbUR8unDeNcC7I+LlvL0wcEVEbDOYMnrJ3hXYNCK+MNhyJE2NiI1LHW+gc2z+ufeU9SkizskjqNcGtomIU/Kki9uR1m4u7fXMPWPr6/K+wZbRrYj4LWkFx8GYc5OkosuhDnCOzSc3T9n8aJ1oW6Nc6zrRngBMy1cDAO8AvjwIMwCQtHvb5ghgPL0093V4zjbAoZIeIk2T05oWZ4NBmmPzyc1TVpmkA0kn1rlOtCUn92vLWhloTRlxc00jjmvPyDlntW3OBh4CflJ6rqsmciSt0d3+iHi4VEaTOTb/XGnYfGnwRLsqabnK9im+rx9sGUOFpMWAQ4E3kmbRPSPSUqaDMscWnCsNmy8NncxPBD5IWumsfQ6lkutCNJHRSK+zhroq/5I0XcWfgB2BhyPi8P4ed6BybMH5noZV1tOJFij96XxXYN2IqHMwVxMZU9oeH0d9g++ayBnXNineGaT1r+vQVI4tIFcaNj+aONECPEAaY1BnTu0Z7fd6JB1Rx72fBnPaJ8WbXeOMHk3l2AJypWHzo4mTOcBzwHRJVzP3HEolR2s3kdGuqXbgunI2bJv8TsDiebv0omJN5dgCcqVh86OpE+0l+atOTWQMGU1Nitf05Hs2/3wj3CpTD+tq19XsMth1mdZjCVKlC4U/NTeVYwauNKyDSPpVRHxA0h1008xSYmBXExlmQ5mbp6xPDZ5op+WpI3aj7YZoYU1kmA1ZrjSsiqZOtMsD3wPeBNwO/BmYBNwYEf/s7Qc7LMNsyHLzlPVJ0jdJy242cqKVtAhp3qQtgLflr39FxLjBlGE2FPlKw/oUEZ+BeU60HwF+IqmOE+3iwChg6fz1N9KUEoMtw2zIcaVh86PWE62kCaTlZJ8BbiZd0Xw7Ip4aTBlmQ5krDetTgyfaMcCiwH3AX4FZwL8GYYbZkOV7GtYnSROBFYA7SRXGjcCdUcMvT17qcz1SE9gWwPqk9TtujIgicyo1kWE2VLnSsEqaPtFKWo20vOwWwE7A8hGxzGDLMBtqXGnYfKnzRCvpU/m4W5K69k4iXdVMAu6IiFd7+fGOyTAbylxpWJ+aOtFK+ja5O29EPFrimAORYTaUudKwPvlEa2YtrjTMzKyyEQNdADMzGzxcaZiZWWWuNMx6IOkVSdPbvsYuwDGWkfTx8qUzGxi+p2HWA0n/iYjX9fMYY4HfR8T68/lzIyPilf5km9XBVxpm80HSSEknS5os6XZJH8v7Xyfpakm3SrpD0i75R04A1spXKidLeqek37cd71RJB+THD0k6VtINwF6S1pI0UdJUSX+S9Kb8ur0k3SnpNknXN/svYMOd554y69nikqbnxw9GxG7AQcC/I2ITSYsCkyRdATwC7BYRT0taAbhJ0iXAUcD6EfEWAEnv7CPzhYjYKr/2auDQiLhP0mbAD4BtgWOB90TEXyV5BLs1ypWGWc+eb53s27wb2EDSnnl7aWBt0sSHX5e0NfAqsCqw0gJk/hLSlQtpQOUFaQYXIE20CGlQ5dmSfgVcuAAZZgvMlYbZ/BHwyYi4fK6dqYlpNLBxRLws6SFgsW5+fjZzNwt3fc2z+fsI0qJQXSstIuLQfOXxPmC6pLdExJML8mbM5pfvaZjNn8uB/ydpYQBJ60haknTF8ViuMLYB1sivfwZYqu3nHwbGSVpU0tLAdt2FRMTTwIOS9so5krRhfrxWRNwcEccCTwCrl3+bZt3zlYbZ/DkdGAvcmmf+fRzYFfgZ8DtJU4DpwD0AEfGkpEmS7gQui4jP5mal20lrekzrJetDwA8lHQMsDJwP3AacLGlt0lXP1XmfWSPc5dbMzCpz85SZmVXmSsPMzCpzpWFmZpW50jAzs8pcaZiZWWWuNMzMrDJXGmZmVpkrDTMzq+z/A5UW4KthCxAzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "raintomorrow_corrs = corrs[['RainTomorrow']].drop(['RainTomorrow'], axis = 0).reset_index()\n",
    "sns.barplot(x = 'index', y = 'RainTomorrow', data = raintomorrow_corrs)\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('Correlation to RainTomorrow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>index</th>\n",
       "      <th>RainTomorrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Humidity3pm</td>\n",
       "      <td>0.456052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>RainToday</td>\n",
       "      <td>0.313550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Humidity9am</td>\n",
       "      <td>0.271905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Pressure9am</td>\n",
       "      <td>0.244576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rainfall</td>\n",
       "      <td>0.237617</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          index  RainTomorrow\n",
       "7   Humidity3pm      0.456052\n",
       "12    RainToday      0.313550\n",
       "6   Humidity9am      0.271905\n",
       "8   Pressure9am      0.244576\n",
       "2      Rainfall      0.237617"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A closer look into the numbers\n",
    "raintomorrow_corrs['RainTomorrow'] = [abs(i) for i in raintomorrow_corrs['RainTomorrow']]\n",
    "raintomorrow_corrs = raintomorrow_corrs.sort_values(by = 'RainTomorrow', ascending = False)\n",
    "raintomorrow_corrs[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the bar plot above, we can see that the top 5 most correlated features are 'Humidity3pm','RainToday','Humidity9am','Pressure9am',and 'Rainfall'. Those are probably the most significant features in our prediction, so we trained several models below to confirm our theory.\n",
    "\n",
    "Note: \n",
    "Here, we rank the features based on the absolute value of their correlations, which represent the strength of correlation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Helper Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_model_results(history):\n",
    "    val_loss = history.history['val_loss']\n",
    "    train_loss = history.history['loss']\n",
    "    val_acc = history.history['val_acc']\n",
    "    train_acc = history.history['acc']\n",
    "\n",
    "\n",
    "    plt.rcParams['figure.figsize'] = [12, 5]\n",
    "\n",
    "    ax1 = plt.subplot(211)\n",
    "    ax2 = plt.subplot(212)\n",
    "    x_range = len(train_loss)\n",
    "    \n",
    "    # Loss Axis\n",
    "    ax1.plot(list(range(x_range)), train_loss)\n",
    "    ax1.plot(list(range(x_range)), val_loss)\n",
    "    ax1.set_xlabel('Epoch')\n",
    "    ax1.legend(['training loss', 'validation loss'])\n",
    "    ax1.set_ylabel('Binary Cross Entropy Loss')\n",
    "\n",
    "    # Accuracy Axis\n",
    "    ax2.plot(list(range(x_range)), train_acc)\n",
    "    ax2.plot(list(range(x_range)), val_acc)\n",
    "    ax2.legend(['training accuracy', 'validation accuracy'])\n",
    "    ax2.set_xlabel('Epoch')\n",
    "    ax2.set_ylabel('Accuracy')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def split_data(df, val_pct=.2, test_pct=.1):\n",
    "    val_size   = int(val_pct * df.shape[0])\n",
    "    test_size  = int(test_pct * df.shape[0])\n",
    "    train_size = int((1.0 - (val_pct + test_pct)) * df.shape[0])\n",
    "    df_train = df[:train_size]\n",
    "    df_val = df[train_size : train_size + val_size]\n",
    "    df_test = df[train_size + val_size : train_size + val_size + test_size]\n",
    "    \n",
    "    print(f\"Training data length:   {len(df_train)}\")\n",
    "    print(f\"Validation data length: {len(df_val)}\")\n",
    "    print(f\"Test data length:       {len(df_test)}\")\n",
    "\n",
    "    y_train = df_train['RainTomorrow'].values\n",
    "    y_test = df_test['RainTomorrow'].values\n",
    "    y_val = df_val['RainTomorrow'].values\n",
    "    X_train = df_train.drop(columns=['RainTomorrow']).values\n",
    "\n",
    "    X_test = df_test.drop(columns=['RainTomorrow']).values\n",
    "    X_val = df_val.drop(columns=['RainTomorrow']).values\n",
    "\n",
    "    return X_train, X_val, X_test, y_train, y_val, y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splitting Into Targets and Labels\n",
    "Below the train, test, and validation data sets are split into labels and targets then converted to numpy arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data length:   81798\n",
      "Validation data length: 23371\n",
      "Test data length:       11685\n"
     ]
    },
    {
     "data": {
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       "\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>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindSpeed9am</th>\n",
       "      <th>WindSpeed3pm</th>\n",
       "      <th>Humidity9am</th>\n",
       "      <th>Humidity3pm</th>\n",
       "      <th>Pressure9am</th>\n",
       "      <th>Pressure3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Temp3pm</th>\n",
       "      <th>RainToday</th>\n",
       "      <th>RainTomorrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.128913</td>\n",
       "      <td>-0.106483</td>\n",
       "      <td>-0.206059</td>\n",
       "      <td>0.275396</td>\n",
       "      <td>0.629094</td>\n",
       "      <td>0.555250</td>\n",
       "      <td>0.158151</td>\n",
       "      <td>-1.400501</td>\n",
       "      <td>-1.401285</td>\n",
       "      <td>-1.158545</td>\n",
       "      <td>-0.081504</td>\n",
       "      <td>-0.045970</td>\n",
       "      <td>-0.537262</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.846313</td>\n",
       "      <td>0.215952</td>\n",
       "      <td>-0.276203</td>\n",
       "      <td>0.275396</td>\n",
       "      <td>-1.199439</td>\n",
       "      <td>0.324134</td>\n",
       "      <td>-1.286403</td>\n",
       "      <td>-1.255039</td>\n",
       "      <td>-0.987789</td>\n",
       "      <td>-1.057633</td>\n",
       "      <td>-0.032851</td>\n",
       "      <td>0.328283</td>\n",
       "      <td>-0.537262</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.047644</td>\n",
       "      <td>0.303889</td>\n",
       "      <td>-0.276203</td>\n",
       "      <td>0.424875</td>\n",
       "      <td>0.514810</td>\n",
       "      <td>0.786367</td>\n",
       "      <td>-1.607415</td>\n",
       "      <td>-1.012603</td>\n",
       "      <td>-1.415544</td>\n",
       "      <td>-0.927888</td>\n",
       "      <td>0.583419</td>\n",
       "      <td>0.163612</td>\n",
       "      <td>-0.537262</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.553745</td>\n",
       "      <td>0.640980</td>\n",
       "      <td>-0.276203</td>\n",
       "      <td>-1.219390</td>\n",
       "      <td>-0.399456</td>\n",
       "      <td>-1.178124</td>\n",
       "      <td>-1.232901</td>\n",
       "      <td>-1.691425</td>\n",
       "      <td>0.010307</td>\n",
       "      <td>-0.336831</td>\n",
       "      <td>0.113108</td>\n",
       "      <td>0.657626</td>\n",
       "      <td>-0.537262</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.795317</td>\n",
       "      <td>1.271194</td>\n",
       "      <td>-0.159297</td>\n",
       "      <td>0.051178</td>\n",
       "      <td>-0.856589</td>\n",
       "      <td>0.093017</td>\n",
       "      <td>0.746673</td>\n",
       "      <td>-0.867141</td>\n",
       "      <td>-0.959271</td>\n",
       "      <td>-1.317121</td>\n",
       "      <td>0.064455</td>\n",
       "      <td>1.136671</td>\n",
       "      <td>-0.537262</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    MinTemp   MaxTemp  Rainfall  WindGustSpeed  WindSpeed9am  WindSpeed3pm  \\\n",
       "0  0.128913 -0.106483 -0.206059       0.275396      0.629094      0.555250   \n",
       "1 -0.846313  0.215952 -0.276203       0.275396     -1.199439      0.324134   \n",
       "2  0.047644  0.303889 -0.276203       0.424875      0.514810      0.786367   \n",
       "3 -0.553745  0.640980 -0.276203      -1.219390     -0.399456     -1.178124   \n",
       "4  0.795317  1.271194 -0.159297       0.051178     -0.856589      0.093017   \n",
       "\n",
       "   Humidity9am  Humidity3pm  Pressure9am  Pressure3pm   Temp9am   Temp3pm  \\\n",
       "0     0.158151    -1.400501    -1.401285    -1.158545 -0.081504 -0.045970   \n",
       "1    -1.286403    -1.255039    -0.987789    -1.057633 -0.032851  0.328283   \n",
       "2    -1.607415    -1.012603    -1.415544    -0.927888  0.583419  0.163612   \n",
       "3    -1.232901    -1.691425     0.010307    -0.336831  0.113108  0.657626   \n",
       "4     0.746673    -0.867141    -0.959271    -1.317121  0.064455  1.136671   \n",
       "\n",
       "   RainToday  RainTomorrow  \n",
       "0  -0.537262             0  \n",
       "1  -0.537262             0  \n",
       "2  -0.537262             0  \n",
       "3  -0.537262             0  \n",
       "4  -0.537262             0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rain_tomorrow = df['RainTomorrow']\n",
    "df = (df - df.mean()) / df.std()\n",
    "df['RainTomorrow'] = rain_tomorrow\n",
    "\n",
    "X_train, X_val, X_test, y_train, y_val, y_test = split_data(df, val_pct=.2, test_pct=.1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tensorflow Feed Forward Neural Network\n",
    "Below we define a feed forward neural network. The network takes as input the features of a single day. The output is a sigmoid layer meaning our output layer is essentially a logistic regression layer. We use binary cross entropy loss and accuracy to evaluate model performace. The model is evaluated on validation data on every epoch."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 1 (Baseline)\n",
    "Here we fit the network onto the training data set for 10 epochs. We use our validation data to prevent overfitting. We use all of the features of our dataset to establish a baseline of performance. We will compare our reduced feature performace to this model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 32)                448       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 128)               4224      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 32)                4128      \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 33        \n",
      "=================================================================\n",
      "Total params: 8,833\n",
      "Trainable params: 8,833\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "INPUT_DIM = X_train.shape[1]\n",
    "OUTPUT_DIM = 1\n",
    "EPOCHS = 10\n",
    "BATCH_SIZE = 128\n",
    "\n",
    "model = keras.Sequential([\n",
    "    keras.layers.Dense(32, input_dim=INPUT_DIM, \n",
    "                       activation=tf.nn.relu),\n",
    "    keras.layers.Dense(128, activation=tf.nn.tanh),\n",
    "    keras.layers.Dense(32, activation=tf.nn.tanh),\n",
    "    keras.layers.Dense(OUTPUT_DIM, activation=tf.nn.sigmoid)\n",
    "])\n",
    "model.compile(optimizer='adam', \n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy', 'binary_crossentropy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 81798 samples, validate on 23371 samples\n",
      "Epoch 1/10\n",
      "81798/81798 [==============================] - 2s 24us/step - loss: 0.3744 - acc: 0.8385 - binary_crossentropy: 0.3744 - val_loss: 0.3252 - val_acc: 0.8640 - val_binary_crossentropy: 0.3252\n",
      "Epoch 2/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3610 - acc: 0.8451 - binary_crossentropy: 0.3610 - val_loss: 0.3206 - val_acc: 0.8661 - val_binary_crossentropy: 0.3206\n",
      "Epoch 3/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3576 - acc: 0.8471 - binary_crossentropy: 0.3576 - val_loss: 0.3191 - val_acc: 0.8669 - val_binary_crossentropy: 0.3191\n",
      "Epoch 4/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3554 - acc: 0.8476 - binary_crossentropy: 0.3554 - val_loss: 0.3266 - val_acc: 0.8611 - val_binary_crossentropy: 0.3266\n",
      "Epoch 5/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3544 - acc: 0.8482 - binary_crossentropy: 0.3544 - val_loss: 0.3222 - val_acc: 0.8628 - val_binary_crossentropy: 0.3222\n",
      "Epoch 6/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3527 - acc: 0.8487 - binary_crossentropy: 0.3527 - val_loss: 0.3216 - val_acc: 0.8631 - val_binary_crossentropy: 0.3216\n",
      "Epoch 7/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3516 - acc: 0.8481 - binary_crossentropy: 0.3516 - val_loss: 0.3288 - val_acc: 0.8596 - val_binary_crossentropy: 0.3288\n",
      "Epoch 8/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3509 - acc: 0.8495 - binary_crossentropy: 0.3509 - val_loss: 0.3241 - val_acc: 0.8599 - val_binary_crossentropy: 0.3241\n",
      "Epoch 9/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3499 - acc: 0.8493 - binary_crossentropy: 0.3499 - val_loss: 0.3214 - val_acc: 0.8642 - val_binary_crossentropy: 0.3214\n",
      "Epoch 10/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3495 - acc: 0.8501 - binary_crossentropy: 0.3495 - val_loss: 0.3167 - val_acc: 0.8666 - val_binary_crossentropy: 0.3167\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f112293ccf8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history = History()\n",
    "model.fit(X_train, y_train, epochs=EPOCHS, \n",
    "          batch_size=BATCH_SIZE, validation_data=(X_val, y_val), callbacks=[history])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model_results(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11685/11685 [==============================] - 0s 17us/step\n",
      "Model 1\n",
      "Test Loss: 0.2954863436242735\n",
      "Test Accuracy: 0.8715447154471545\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc, t = model.evaluate(X_test, y_test)\n",
    "print(f\"Model 1\\nTest Loss: {test_loss}\\nTest Accuracy: {test_acc}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now train models with different features dropped\n",
    "In this section, We drop the top 5 most correlated features that we picked in the previous section one by one. The one that affects test accuracy the most would be the most representative feature in our prediction.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Humidity 3pm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data length:   81798\n",
      "Validation data length: 23371\n",
      "Test data length:       11685\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 32)                416       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 128)               4224      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 32)                4128      \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 33        \n",
      "=================================================================\n",
      "Total params: 8,801\n",
      "Trainable params: 8,801\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "df_h = df.drop(columns=['Humidity3pm'])\n",
    "X_train_h3pm, X_val_h3pm, X_test_h3pm, y_train_h3pm, \\\n",
    "    y_val_h3pm, y_test_h3pm = split_data(df_h, val_pct=.2, test_pct=.1)\n",
    "\n",
    "\n",
    "INPUT_DIM = X_train_h3pm.shape[1]\n",
    "\n",
    "tf.keras.backend.clear_session()\n",
    "model = keras.Sequential([\n",
    "    keras.layers.Dense(32, input_dim=INPUT_DIM, \n",
    "                       activation=tf.nn.relu),\n",
    "    keras.layers.Dense(128, activation=tf.nn.tanh),\n",
    "    keras.layers.Dense(32, activation=tf.nn.tanh),\n",
    "    keras.layers.Dense(OUTPUT_DIM, activation=tf.nn.sigmoid)\n",
    "])\n",
    "model.compile(optimizer='adam', \n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy', 'binary_crossentropy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 81798 samples, validate on 23371 samples\n",
      "Epoch 1/10\n",
      "81798/81798 [==============================] - 1s 17us/step - loss: 0.3983 - acc: 0.8241 - binary_crossentropy: 0.3983 - val_loss: 0.3390 - val_acc: 0.8580 - val_binary_crossentropy: 0.3390\n",
      "Epoch 2/10\n",
      "81798/81798 [==============================] - 1s 13us/step - loss: 0.3783 - acc: 0.8361 - binary_crossentropy: 0.3783 - val_loss: 0.3439 - val_acc: 0.8549 - val_binary_crossentropy: 0.3439\n",
      "Epoch 3/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3760 - acc: 0.8377 - binary_crossentropy: 0.3760 - val_loss: 0.3523 - val_acc: 0.8470 - val_binary_crossentropy: 0.3523\n",
      "Epoch 4/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3741 - acc: 0.8377 - binary_crossentropy: 0.3741 - val_loss: 0.3408 - val_acc: 0.8563 - val_binary_crossentropy: 0.3408\n",
      "Epoch 5/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3733 - acc: 0.8387 - binary_crossentropy: 0.3733 - val_loss: 0.3397 - val_acc: 0.8561 - val_binary_crossentropy: 0.3397\n",
      "Epoch 6/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3720 - acc: 0.8390 - binary_crossentropy: 0.3720 - val_loss: 0.3457 - val_acc: 0.8513 - val_binary_crossentropy: 0.3457\n",
      "Epoch 7/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3708 - acc: 0.8396 - binary_crossentropy: 0.3708 - val_loss: 0.3373 - val_acc: 0.8564 - val_binary_crossentropy: 0.3373\n",
      "Epoch 8/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3702 - acc: 0.8405 - binary_crossentropy: 0.3702 - val_loss: 0.3427 - val_acc: 0.8533 - val_binary_crossentropy: 0.3427\n",
      "Epoch 9/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3694 - acc: 0.8409 - binary_crossentropy: 0.3694 - val_loss: 0.3392 - val_acc: 0.8578 - val_binary_crossentropy: 0.3392\n",
      "Epoch 10/10\n",
      "81798/81798 [==============================] - 1s 15us/step - loss: 0.3683 - acc: 0.8395 - binary_crossentropy: 0.3683 - val_loss: 0.3398 - val_acc: 0.8587 - val_binary_crossentropy: 0.3398\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f10d06caa58>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history = History()\n",
    "model.fit(X_train_h3pm, y_train_h3pm, epochs=EPOCHS, \n",
    "          batch_size=BATCH_SIZE, validation_data=(X_val_h3pm, y_val_h3pm), callbacks=[history])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model_results(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11685/11685 [==============================] - 0s 20us/step\n",
      "Test Loss: 0.3026596692097834\n",
      "Test Accuracy: 0.8690629011553274\n"
     ]
    }
   ],
   "source": [
    "test_loss_h3pm, test_acc_h3pm, t = model.evaluate(X_test_h3pm, y_test_h3pm)\n",
    "print(f\"Test Loss: {test_loss_h3pm}\\nTest Accuracy: {test_acc_h3pm}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Rain Today"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data length:   81798\n",
      "Validation data length: 23371\n",
      "Test data length:       11685\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 32)                416       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 128)               4224      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 32)                4128      \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 33        \n",
      "=================================================================\n",
      "Total params: 8,801\n",
      "Trainable params: 8,801\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "df_rt = df.drop(columns=['RainToday'])\n",
    "X_train_rt, X_val_rt, X_test_rt, y_train_rt, \\\n",
    "    y_val_rt, y_test_rt = split_data(df_rt, val_pct=.2, test_pct=.1)\n",
    "\n",
    "\n",
    "INPUT_DIM = X_train_rt.shape[1]\n",
    "\n",
    "tf.keras.backend.clear_session()\n",
    "model = keras.Sequential([\n",
    "    keras.layers.Dense(32, input_dim=INPUT_DIM, \n",
    "                       activation=tf.nn.relu),\n",
    "    keras.layers.Dense(128, activation=tf.nn.tanh),\n",
    "    keras.layers.Dense(32, activation=tf.nn.tanh),\n",
    "    keras.layers.Dense(OUTPUT_DIM, activation=tf.nn.sigmoid)\n",
    "])\n",
    "model.compile(optimizer='adam', \n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy', 'binary_crossentropy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 81798 samples, validate on 23371 samples\n",
      "Epoch 1/10\n",
      "81798/81798 [==============================] - 1s 18us/step - loss: 0.3757 - acc: 0.8398 - binary_crossentropy: 0.3757 - val_loss: 0.3341 - val_acc: 0.8611 - val_binary_crossentropy: 0.3341\n",
      "Epoch 2/10\n",
      "81798/81798 [==============================] - 1s 15us/step - loss: 0.3618 - acc: 0.8451 - binary_crossentropy: 0.3618 - val_loss: 0.3252 - val_acc: 0.8634 - val_binary_crossentropy: 0.3252\n",
      "Epoch 3/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3585 - acc: 0.8460 - binary_crossentropy: 0.3585 - val_loss: 0.3260 - val_acc: 0.8620 - val_binary_crossentropy: 0.3260\n",
      "Epoch 4/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3560 - acc: 0.8475 - binary_crossentropy: 0.3560 - val_loss: 0.3211 - val_acc: 0.8646 - val_binary_crossentropy: 0.3211\n",
      "Epoch 5/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3540 - acc: 0.8476 - binary_crossentropy: 0.3540 - val_loss: 0.3305 - val_acc: 0.8586 - val_binary_crossentropy: 0.3305\n",
      "Epoch 6/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3533 - acc: 0.8480 - binary_crossentropy: 0.3533 - val_loss: 0.3232 - val_acc: 0.8640 - val_binary_crossentropy: 0.3232\n",
      "Epoch 7/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3521 - acc: 0.8481 - binary_crossentropy: 0.3521 - val_loss: 0.3252 - val_acc: 0.8627 - val_binary_crossentropy: 0.3252\n",
      "Epoch 8/10\n",
      "81798/81798 [==============================] - 1s 13us/step - loss: 0.3515 - acc: 0.8489 - binary_crossentropy: 0.3515 - val_loss: 0.3226 - val_acc: 0.8598 - val_binary_crossentropy: 0.3226\n",
      "Epoch 9/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3509 - acc: 0.8491 - binary_crossentropy: 0.3509 - val_loss: 0.3168 - val_acc: 0.8642 - val_binary_crossentropy: 0.3168\n",
      "Epoch 10/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3498 - acc: 0.8500 - binary_crossentropy: 0.3498 - val_loss: 0.3220 - val_acc: 0.8632 - val_binary_crossentropy: 0.3220\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f11229cd358>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history = History()\n",
    "model.fit(X_train_rt, y_train_rt, epochs=EPOCHS, \n",
    "          batch_size=BATCH_SIZE, validation_data=(X_val_rt, y_val_rt), callbacks=[history])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model_results(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11685/11685 [==============================] - 0s 17us/step\n",
      "Test Loss: 0.2964290714126509\n",
      "Test Accuracy: 0.8738553701326487\n"
     ]
    }
   ],
   "source": [
    "test_loss_rt, test_acc_rt, t = model.evaluate(X_test_rt, y_test_rt)\n",
    "print(f\"Test Loss: {test_loss_rt}\\nTest Accuracy: {test_acc_rt}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Humidity 9am"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data length:   81798\n",
      "Validation data length: 23371\n",
      "Test data length:       11685\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 32)                416       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 128)               4224      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 32)                4128      \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 33        \n",
      "=================================================================\n",
      "Total params: 8,801\n",
      "Trainable params: 8,801\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "df_h9 = df.drop(columns=['Humidity9am'])\n",
    "X_train_h9am, X_val_h9am, X_test_h9am, y_train_h9am, \\\n",
    "    y_val_h9am, y_test_h9am = split_data(df_h9, val_pct=.2, test_pct=.1)\n",
    "\n",
    "\n",
    "INPUT_DIM = X_train_h9am.shape[1]\n",
    "\n",
    "tf.keras.backend.clear_session()\n",
    "model = keras.Sequential([\n",
    "    keras.layers.Dense(32, input_dim=INPUT_DIM, \n",
    "                       activation=tf.nn.relu),\n",
    "    keras.layers.Dense(128, activation=tf.nn.tanh),\n",
    "    keras.layers.Dense(32, activation=tf.nn.tanh),\n",
    "    keras.layers.Dense(OUTPUT_DIM, activation=tf.nn.sigmoid)\n",
    "])\n",
    "model.compile(optimizer='adam', \n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy', 'binary_crossentropy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 81798 samples, validate on 23371 samples\n",
      "Epoch 1/10\n",
      "81798/81798 [==============================] - 1s 17us/step - loss: 0.3777 - acc: 0.8383 - binary_crossentropy: 0.3777 - val_loss: 0.3258 - val_acc: 0.8630 - val_binary_crossentropy: 0.3258\n",
      "Epoch 2/10\n",
      "81798/81798 [==============================] - 1s 15us/step - loss: 0.3629 - acc: 0.8447 - binary_crossentropy: 0.3629 - val_loss: 0.3289 - val_acc: 0.8626 - val_binary_crossentropy: 0.3289\n",
      "Epoch 3/10\n",
      "81798/81798 [==============================] - 1s 15us/step - loss: 0.3594 - acc: 0.8461 - binary_crossentropy: 0.3594 - val_loss: 0.3360 - val_acc: 0.8579 - val_binary_crossentropy: 0.3360\n",
      "Epoch 4/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3573 - acc: 0.8461 - binary_crossentropy: 0.3573 - val_loss: 0.3262 - val_acc: 0.8624 - val_binary_crossentropy: 0.3262\n",
      "Epoch 5/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3553 - acc: 0.8469 - binary_crossentropy: 0.3553 - val_loss: 0.3291 - val_acc: 0.8603 - val_binary_crossentropy: 0.3291\n",
      "Epoch 6/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3546 - acc: 0.8472 - binary_crossentropy: 0.3546 - val_loss: 0.3250 - val_acc: 0.8625 - val_binary_crossentropy: 0.3250\n",
      "Epoch 7/10\n",
      "81798/81798 [==============================] - 1s 13us/step - loss: 0.3533 - acc: 0.8481 - binary_crossentropy: 0.3533 - val_loss: 0.3267 - val_acc: 0.8593 - val_binary_crossentropy: 0.3267\n",
      "Epoch 8/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3525 - acc: 0.8481 - binary_crossentropy: 0.3525 - val_loss: 0.3335 - val_acc: 0.8570 - val_binary_crossentropy: 0.3335\n",
      "Epoch 9/10\n",
      "81798/81798 [==============================] - 1s 13us/step - loss: 0.3519 - acc: 0.8482 - binary_crossentropy: 0.3519 - val_loss: 0.3232 - val_acc: 0.8616 - val_binary_crossentropy: 0.3232\n",
      "Epoch 10/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3513 - acc: 0.8482 - binary_crossentropy: 0.3513 - val_loss: 0.3215 - val_acc: 0.8642 - val_binary_crossentropy: 0.3215\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f10a061bd68>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history = History()\n",
    "model.fit(X_train_h9am, y_train_h9am, epochs=EPOCHS, \n",
    "          batch_size=BATCH_SIZE, validation_data=(X_val_h9am, y_val_h9am), callbacks=[history])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model_results(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11685/11685 [==============================] - 0s 18us/step\n",
      "Test Loss: 0.30400921451537577\n",
      "Test Accuracy: 0.8729139922978177\n"
     ]
    }
   ],
   "source": [
    "test_loss_h9am, test_acc_h9am, t = model.evaluate(X_test_h9am, y_test_h9am)\n",
    "print(f\"Test Loss: {test_loss_h9am}\\nTest Accuracy: {test_acc_h9am}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Rainfall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data length:   81798\n",
      "Validation data length: 23371\n",
      "Test data length:       11685\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 32)                416       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 128)               4224      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 32)                4128      \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 33        \n",
      "=================================================================\n",
      "Total params: 8,801\n",
      "Trainable params: 8,801\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "df_rf = df.drop(columns=['Rainfall'])\n",
    "X_train_rf, X_val_rf, X_test_rf, y_train_rf, \\\n",
    "    y_val_rf, y_test_rf = split_data(df_rf, val_pct=.2, test_pct=.1)\n",
    "\n",
    "\n",
    "INPUT_DIM = X_train_rf.shape[1]\n",
    "\n",
    "tf.keras.backend.clear_session()\n",
    "model = keras.Sequential([\n",
    "    keras.layers.Dense(32, input_dim=INPUT_DIM, \n",
    "                       activation=tf.nn.relu),\n",
    "    keras.layers.Dense(128, activation=tf.nn.tanh),\n",
    "    keras.layers.Dense(32, activation=tf.nn.tanh),\n",
    "    keras.layers.Dense(OUTPUT_DIM, activation=tf.nn.sigmoid)\n",
    "])\n",
    "model.compile(optimizer='adam', \n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy', 'binary_crossentropy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 81798 samples, validate on 23371 samples\n",
      "Epoch 1/10\n",
      "81798/81798 [==============================] - 1s 16us/step - loss: 0.3740 - acc: 0.8398 - binary_crossentropy: 0.3740 - val_loss: 0.3303 - val_acc: 0.8629 - val_binary_crossentropy: 0.3303\n",
      "Epoch 2/10\n",
      "81798/81798 [==============================] - 1s 13us/step - loss: 0.3610 - acc: 0.8456 - binary_crossentropy: 0.3610 - val_loss: 0.3236 - val_acc: 0.8633 - val_binary_crossentropy: 0.3236\n",
      "Epoch 3/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3583 - acc: 0.8470 - binary_crossentropy: 0.3583 - val_loss: 0.3165 - val_acc: 0.8667 - val_binary_crossentropy: 0.3165\n",
      "Epoch 4/10\n",
      "81798/81798 [==============================] - 1s 13us/step - loss: 0.3570 - acc: 0.8477 - binary_crossentropy: 0.3570 - val_loss: 0.3291 - val_acc: 0.8585 - val_binary_crossentropy: 0.3291\n",
      "Epoch 5/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3556 - acc: 0.8472 - binary_crossentropy: 0.3556 - val_loss: 0.3325 - val_acc: 0.8591 - val_binary_crossentropy: 0.3325\n",
      "Epoch 6/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3540 - acc: 0.8480 - binary_crossentropy: 0.3540 - val_loss: 0.3207 - val_acc: 0.8635 - val_binary_crossentropy: 0.3207\n",
      "Epoch 7/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3532 - acc: 0.8482 - binary_crossentropy: 0.3532 - val_loss: 0.3176 - val_acc: 0.8665 - val_binary_crossentropy: 0.3176\n",
      "Epoch 8/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3523 - acc: 0.8485 - binary_crossentropy: 0.3523 - val_loss: 0.3351 - val_acc: 0.8542 - val_binary_crossentropy: 0.3351\n",
      "Epoch 9/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3515 - acc: 0.8492 - binary_crossentropy: 0.3515 - val_loss: 0.3336 - val_acc: 0.8552 - val_binary_crossentropy: 0.3336\n",
      "Epoch 10/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3507 - acc: 0.8493 - binary_crossentropy: 0.3507 - val_loss: 0.3302 - val_acc: 0.8577 - val_binary_crossentropy: 0.3302\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f107868eb38>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history = History()\n",
    "model.fit(X_train_rf, y_train_rf, epochs=EPOCHS, \n",
    "          batch_size=BATCH_SIZE, validation_data=(X_val_rf, y_val_rf), callbacks=[history])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model_results(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11685/11685 [==============================] - 0s 19us/step\n",
      "Test Loss: 0.28995992080314476\n",
      "Test Accuracy: 0.8716302952503209\n"
     ]
    }
   ],
   "source": [
    "test_loss_rf, test_acc_rf, t = model.evaluate(X_test_rf, y_test_rf)\n",
    "print(f\"Test Loss: {test_loss_rf}\\nTest Accuracy: {test_acc_rf}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pressure 9 am"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data length:   81798\n",
      "Validation data length: 23371\n",
      "Test data length:       11685\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 32)                416       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 128)               4224      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 32)                4128      \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 33        \n",
      "=================================================================\n",
      "Total params: 8,801\n",
      "Trainable params: 8,801\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "df_p9am = df.drop(columns=['Rainfall'])\n",
    "X_train_p9am, X_val_p9am, X_test_p9am, y_train_p9am, \\\n",
    "    y_val_p9am, y_test_p9am = split_data(df_p9am, val_pct=.2, test_pct=.1)\n",
    "\n",
    "\n",
    "INPUT_DIM = X_train_p9am.shape[1]\n",
    "\n",
    "tf.keras.backend.clear_session()\n",
    "model = keras.Sequential([\n",
    "    keras.layers.Dense(32, input_dim=INPUT_DIM, activation=tf.nn.relu),\n",
    "    keras.layers.Dense(128, activation=tf.nn.tanh),\n",
    "    keras.layers.Dense(32, activation=tf.nn.tanh),\n",
    "    keras.layers.Dense(OUTPUT_DIM, activation=tf.nn.sigmoid)\n",
    "])\n",
    "model.compile(optimizer='adam', \n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy', 'binary_crossentropy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 81798 samples, validate on 23371 samples\n",
      "Epoch 1/10\n",
      "81798/81798 [==============================] - 1s 17us/step - loss: 0.3775 - acc: 0.8383 - binary_crossentropy: 0.3775 - val_loss: 0.3308 - val_acc: 0.8615 - val_binary_crossentropy: 0.3308\n",
      "Epoch 2/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3627 - acc: 0.8451 - binary_crossentropy: 0.3627 - val_loss: 0.3275 - val_acc: 0.8635 - val_binary_crossentropy: 0.3275\n",
      "Epoch 3/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3592 - acc: 0.8465 - binary_crossentropy: 0.3592 - val_loss: 0.3233 - val_acc: 0.8646 - val_binary_crossentropy: 0.3233\n",
      "Epoch 4/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3572 - acc: 0.8468 - binary_crossentropy: 0.3572 - val_loss: 0.3226 - val_acc: 0.8636 - val_binary_crossentropy: 0.3226\n",
      "Epoch 5/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3547 - acc: 0.8481 - binary_crossentropy: 0.3547 - val_loss: 0.3337 - val_acc: 0.8543 - val_binary_crossentropy: 0.3337\n",
      "Epoch 6/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3535 - acc: 0.8480 - binary_crossentropy: 0.3535 - val_loss: 0.3288 - val_acc: 0.8591 - val_binary_crossentropy: 0.3288\n",
      "Epoch 7/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3521 - acc: 0.8491 - binary_crossentropy: 0.3521 - val_loss: 0.3213 - val_acc: 0.8647 - val_binary_crossentropy: 0.3213\n",
      "Epoch 8/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3513 - acc: 0.8486 - binary_crossentropy: 0.3513 - val_loss: 0.3281 - val_acc: 0.8606 - val_binary_crossentropy: 0.3281\n",
      "Epoch 9/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3507 - acc: 0.8487 - binary_crossentropy: 0.3507 - val_loss: 0.3327 - val_acc: 0.8572 - val_binary_crossentropy: 0.3327\n",
      "Epoch 10/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.3498 - acc: 0.8494 - binary_crossentropy: 0.3498 - val_loss: 0.3223 - val_acc: 0.8628 - val_binary_crossentropy: 0.3223\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f107816d828>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history = History()\n",
    "model.fit(X_train_p9am, y_train, epochs=EPOCHS, \n",
    "          batch_size=BATCH_SIZE, validation_data=(X_val_p9am, y_val), callbacks=[history])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model_results(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11685/11685 [==============================] - 0s 18us/step\n",
      "Test Loss: 0.29610997142591405\n",
      "Test Accuracy: 0.8751390671801454\n"
     ]
    }
   ],
   "source": [
    "test_loss_p9am, test_acc_p9am, t = model.evaluate(X_test_p9am, y_test)\n",
    "print(f\"Test Loss: {test_loss_p9am}\\nTest Accuracy: {test_acc_p9am}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Rank Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>Test Accuracy</th>\n",
       "      <th>Difference from Base line</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Models</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Without Humidity 3pm</th>\n",
       "      <td>0.869063</td>\n",
       "      <td>0.002482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Base line</th>\n",
       "      <td>0.871545</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Without Rainfall</th>\n",
       "      <td>0.871630</td>\n",
       "      <td>-0.000086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Without Humidity 9am</th>\n",
       "      <td>0.872914</td>\n",
       "      <td>-0.001369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Without Rain Today</th>\n",
       "      <td>0.873855</td>\n",
       "      <td>-0.002311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Without Pressure 9am</th>\n",
       "      <td>0.875139</td>\n",
       "      <td>-0.003594</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Test Accuracy  Difference from Base line\n",
       "Models                                                        \n",
       "Without Humidity 3pm       0.869063                   0.002482\n",
       "Base line                  0.871545                   0.000000\n",
       "Without Rainfall           0.871630                  -0.000086\n",
       "Without Humidity 9am       0.872914                  -0.001369\n",
       "Without Rain Today         0.873855                  -0.002311\n",
       "Without Pressure 9am       0.875139                  -0.003594"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = {'Models':['Base line','Without Humidity 3pm',\n",
    "                    \"Without Rain Today\", 'Without Humidity 9am',\n",
    "                    'Without Rainfall','Without Pressure 9am'], \n",
    "          'Test Accuracy':[test_acc, test_acc_h3pm, test_acc_rt, \n",
    "                           test_acc_h9am,test_acc_rf, test_acc_p9am]}\n",
    "result_df = pd.DataFrame(data = result)\n",
    "result_df['Difference from Base line'] = [\n",
    "    test_acc - i for i in result_df['Test Accuracy']\n",
    "]\n",
    "result_df.sort_values(by = 'Difference from Base line', \n",
    "                      ascending=False).set_index('Models')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Differences')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x = 'Models', \n",
    "            y = 'Difference from Base line', \n",
    "            data = result_df, palette=sns.color_palette(\"RdBu\", n_colors=7))\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel('Models')\n",
    "plt.ylabel('Differences')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Result\n",
    "From the plot above, we can see that the most significant features in our prediction are Rain Today and Humidity (the furthest to baseline). Since RainToday also positively correlated with humidity based on our heatmap above, and it is also a fact that does not need furture proof. We think this result confirmed our hypothesis that humidity is more influencial on the weather next day."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model 3\n",
    "Here we train a neural network with the same architecture as the baseline on a reduced dataset. We drop all columns other than humidity at 9 am and 3 pm to further prove our hypothesis. We then train the network in the same fashion as before, utilizing a validation dataset to prevent overfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data length:   81798\n",
      "Validation data length: 23371\n",
      "Test data length:       11685\n"
     ]
    },
    {
     "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>Humidity9am</th>\n",
       "      <th>Humidity3pm</th>\n",
       "      <th>RainTomorrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.158151</td>\n",
       "      <td>-1.400501</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.286403</td>\n",
       "      <td>-1.255039</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.607415</td>\n",
       "      <td>-1.012603</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.232901</td>\n",
       "      <td>-1.691425</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.746673</td>\n",
       "      <td>-0.867141</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Humidity9am  Humidity3pm  RainTomorrow\n",
       "0     0.158151    -1.400501             0\n",
       "1    -1.286403    -1.255039             0\n",
       "2    -1.607415    -1.012603             0\n",
       "3    -1.232901    -1.691425             0\n",
       "4     0.746673    -0.867141             0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[['Humidity9am', 'Humidity3pm', 'RainTomorrow']]\n",
    "X_train, X_val, X_test, y_train, y_val, y_test = split_data(\n",
    "    df, val_pct=.2, test_pct=.1\n",
    ")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 32)                96        \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 128)               4224      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 32)                4128      \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 33        \n",
      "=================================================================\n",
      "Total params: 8,481\n",
      "Trainable params: 8,481\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "INPUT_DIM = X_train.shape[1]\n",
    "\n",
    "tf.keras.backend.clear_session()\n",
    "model = keras.Sequential([\n",
    "    keras.layers.Dense(32, input_dim=INPUT_DIM, activation=tf.nn.relu),\n",
    "    keras.layers.Dense(128, activation=tf.nn.tanh),\n",
    "    keras.layers.Dense(32, activation=tf.nn.tanh),\n",
    "    keras.layers.Dense(OUTPUT_DIM, activation=tf.nn.sigmoid)\n",
    "])\n",
    "model.compile(optimizer='adam', \n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy', 'binary_crossentropy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 81798 samples, validate on 23371 samples\n",
      "Epoch 1/10\n",
      "81798/81798 [==============================] - 1s 17us/step - loss: 0.4254 - acc: 0.8211 - binary_crossentropy: 0.4254 - val_loss: 0.3971 - val_acc: 0.8312 - val_binary_crossentropy: 0.3971\n",
      "Epoch 2/10\n",
      "81798/81798 [==============================] - 1s 13us/step - loss: 0.4175 - acc: 0.8253 - binary_crossentropy: 0.4175 - val_loss: 0.4041 - val_acc: 0.8284 - val_binary_crossentropy: 0.4041\n",
      "Epoch 3/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.4166 - acc: 0.8260 - binary_crossentropy: 0.4166 - val_loss: 0.4012 - val_acc: 0.8282 - val_binary_crossentropy: 0.4012\n",
      "Epoch 4/10\n",
      "81798/81798 [==============================] - 1s 13us/step - loss: 0.4158 - acc: 0.8257 - binary_crossentropy: 0.4158 - val_loss: 0.4047 - val_acc: 0.8270 - val_binary_crossentropy: 0.4047\n",
      "Epoch 5/10\n",
      "81798/81798 [==============================] - 1s 13us/step - loss: 0.4159 - acc: 0.8269 - binary_crossentropy: 0.4159 - val_loss: 0.3980 - val_acc: 0.8295 - val_binary_crossentropy: 0.3980\n",
      "Epoch 6/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.4156 - acc: 0.8261 - binary_crossentropy: 0.4156 - val_loss: 0.4005 - val_acc: 0.8282 - val_binary_crossentropy: 0.4005\n",
      "Epoch 7/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.4152 - acc: 0.8267 - binary_crossentropy: 0.4152 - val_loss: 0.4016 - val_acc: 0.8296 - val_binary_crossentropy: 0.4016\n",
      "Epoch 8/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.4149 - acc: 0.8269 - binary_crossentropy: 0.4149 - val_loss: 0.3973 - val_acc: 0.8302 - val_binary_crossentropy: 0.3973\n",
      "Epoch 9/10\n",
      "81798/81798 [==============================] - 1s 13us/step - loss: 0.4148 - acc: 0.8266 - binary_crossentropy: 0.4148 - val_loss: 0.4073 - val_acc: 0.8239 - val_binary_crossentropy: 0.4073\n",
      "Epoch 10/10\n",
      "81798/81798 [==============================] - 1s 14us/step - loss: 0.4148 - acc: 0.8262 - binary_crossentropy: 0.4148 - val_loss: 0.3981 - val_acc: 0.8293 - val_binary_crossentropy: 0.3981\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f107014e5c0>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "history = History()\n",
    "model.fit(X_train, y_train, epochs=EPOCHS, batch_size=BATCH_SIZE, \n",
    "          validation_data=(X_val, y_val), callbacks=[history])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model_results(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11685/11685 [==============================] - 0s 18us/step\n",
      "Model 3\n",
      "Test Loss: 0.35072151773669397\n",
      "Test Accuracy: 0.8550278134360291\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc, t = model.evaluate(X_test, y_test)\n",
    "print(f\"Model 3\\nTest Loss: {test_loss}\\nTest Accuracy: {test_acc}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the result above, we can see humidity alone gives us over 85% accuracy on prediction, thus concluding that humidity has the greatest influence over the weather prediction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ethics & Privacy\n",
    "We did have permission to use the data we used for training models to predict rain in Australia according to the data given. The following is a link to the terms of use from Kaggle, the website we used: https://www.kaggle.com/terms. There were no privacy concerns regarding our datasets that we needed to deal with, because the data is publicly disclosed and is not about people but about weather. We have included a link to the terms of use above, and we have complied with those terms.\n",
    "\n",
    "There are limited potential biases in our dataset, as it was collected on weather features, which are objective, are not easily affected by bias, and are unlikely to inspire bias (due to lack of invested/controversial opinions about it). The data was collected from the Australian government’s Bureau of Metereology, which is likely inclined to be accurate about collecting data. Our data did not exclude particular populations and was collected from multiple different areas in Australia. It is unlikely that the data we used reflected any particular human biases in a way that could be a problem.\n",
    "\n",
    "We address the areas that commonly are considered potentially problematic in terms of data privacy and equitable impact in the following sections, most of which were mostly irrelevant to our research question, data collection, data usage/analysis, and conclusion.\n",
    "\n",
    "Question: The result(s) of the question do matter, because they can help people plan for the following day– if the analysis we do allows us to make accurate predictions about the weather. The scope of people who could be affected by the results of this study is large, potentially covering everyone in Australia, if they choose to use the results of the study to plan out their days. It could also include anyone who intends to visit Australia, as whether it rains or does not rain on those days could influence their decision to visit. However, the potential to harm people through our findings is limited, as only extreme situations of inaccuracy would harm people. One example of this situation is inaccurately predicting no rain causing people to go out the following day in less clothing and the weather ends up actually stormy or more extreme, causing people to catch colds or hypothermia. Another way the outcome of this question could harm people is if the model created more accurately predicts rain than professional weather stations or channels. If so, it can negatively impact the livelihood of meteorologists who predict the weather if they are not needed for weather prediction. This is unlikely because they probably have better prediction models, and they are also predicting far more than just rain or no rain. The tourism industry in Australia may also be affected if people decided not to visit Australia because they thought it would rain on the days of their visit. \n",
    "\n",
    "Implications: There are no shareholders (we were not being paid to do this study). We were not pushed to do something we are not morally comfortable doing. This information is unlikely to be used for nefarious purposes. It cannot be used to judge people based on illegitimate criteria or anything along those lines as the data and the question being asked is not about people, but about weather.\n",
    "\n",
    "Data: There is data available about observations of Australian weather, which is related to the question being asked. They are from multiple weather stations, which increases the legitimacy and accuracy of the data provided. We were permitted to use the data we used. The terms of use were included above. \n",
    "\n",
    "Informed consent: There were no human subjects that we studied for this question, merely observations on weather.\n",
    "\n",
    "Privacy: There were no human subjects that we studied for this question, so there is no personally identifiable information that we collected. There was then no need to de-identify or identify data.\n",
    "\n",
    "Evaluation: Our metric for success is separate from the goal. The goal is to identify what feature in weather has the greatest influence on predicting the weather for the next day. \n",
    "\n",
    "Analysis: We strove to make sure that any correlations that we found were not assumed to be causal. For example, had we found that an increased number of people visiting a location correlated to sunny weather, we did not conclude that people visiting a location caused sunny weather in that location. \n",
    "\n",
    "Transparency: It does not matter if the model is a black box or not because the decisions that the model generates is not judging or making statements about the character or anything about people. The results may affect people, but only so far as it affects how people prepare for weather. The primary problem with black box models is that they are hard for people to appeal, but this is not a problem for the question we asked.\n",
    "\n",
    "Continuous monitoring: None of the data collected should be able to be used to harm people. We tried to make sure that we filtered out the data that is not needed and used the data that we did need.\n",
    "\n"
   ]
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   "source": [
    "# Conclusion & Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our question asked which weather feature(s) would have the most influence over the accuracy when predicting the following day’s weather in Australia. We hypothesized that humidity would be the most influential in predicting the weather. After data analysis, we were able to see that the most significant feature in our prediction is Humidity at 3pm, which aligns with what we stated in our hypothesis. Therefore, we conclude that humidity is a significant factor in predicting future weather. \n",
    "\n",
    "Limitations on our project are that our data was only collected from the years of 2008-2016 and only focuses on Australia, so the weather prediction feature we are able to conclude from our data cannot be generalized to other locations in the world or cannot be generalized to current weather patterns(from 2017-2019) in Australia. This work can be followed similarly if one has enough current data and wants to predict more current weather patterns of other locations in the world.\n"
   ]
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