#!/usr/bin/env python
# coding: utf-8

# # Title
# Finding Heavy Traffic Indicators on I-94

# # Project Description
# I'm going to analyze a dataset about the westbound traffic on the I-94 Interstate highway.
# 
# My goal is to find out what factors affect heavy traffic on I-94. These factors can be weather type, time of the day, time of the week, etc. 

# ## Importation
# Here is the section to import all the packages/libraries that will be used through this notebook.

# In[2]:


# Data handling
import pandas as pd
import numpy as np
from datetime import datetime

# Vizualisation (Matplotlib, Plotly, Seaborn, etc. )
import seaborn as sns
import matplotlib.pyplot as plt

# EDA (pandas-profiling, etc. )
...

# Feature Processing (Scikit-learn processing, etc. )
...

# Machine Learning (Scikit-learn Estimators, Catboost, LightGBM, etc. )
...

# Hyperparameters Fine-tuning (Scikit-learn hp search, cross-validation, etc. )
...

# Other packages


# # Data Loading
# Here is the section to load the datasets (train, eval, test) and the additional files

# In[3]:


traffic=pd.read_csv("Metro_Interstate_Traffic_Volume.csv")


# # Exploratory Data Analysis: EDA
# Here is the section to **inspect** the datasets in depth, **present** it, make **hypotheses** and **think** the *cleaning, processing and features creation*.

# ## Dataset overview
# 
# Have a look at the loaded datsets using the following methods: `.head(), .info()`

# In[4]:


traffic.shape


# In[5]:


traffic.head(20)


# In[6]:


traffic['holiday'].unique()


# ## Hypothesis
# #### Null Hypothesis, HO
# Time is the number one factor that affects traffic the most.
# 
# #### AlternativeHypothesis, H1
# Time is NOT the number one factor that affects traffic the most.

# ## Questions
# 1. Which holidays have the most traffic?
# 2. Which weekdays have the most traffic?
# 3. What time of day has the most traffic?(Is it morning, afternoon, evening, midnight or midday?)
# 
# MORNING
# This is the time from midnight to midday.
# 
# AFTERNOON
# This is the time from midday (noon) to evening.
# From 12:00 hours to approximately 18:00 hours.
# 
# EVENING
# This is the time from the end of the afternoon to midnight.
# From approximately 18:00 hours to 00:00 hours.
# 
# MIDNIGHT
# This is the middle of the night (00:00 hours).
# 
# MIDDAY
# This is the middle of the day, also called "NOON" (12:00 hours).
# 
# 4. Which factor affects traffic the most?
# 5. Compare rain, snow and temparature based on traffic_volume
# 6. What's the highest recorded traffic?
# 

# In[7]:


traffic.info()


# ## Data Cleaning

# In[8]:


#splitting the date_time column into date and time
traffic[['date','time']] = traffic['date_time'].str.split(' ',expand=True)
traffic.head(1)


# In[9]:


#creating weekday column from date_time column
traffic['weekday'] =pd.to_datetime(traffic['date_time']).dt.day_name()
#.dt.dayofweek
traffic.drop(columns=['date_time'], axis=1, inplace = True)

traffic['weekday'].unique()


# In[10]:


def stringToTime(timeString):
    return datetime.strptime(timeString, '%H:%M:%S').time()
midnight=stringToTime('00:00:00')
midday=stringToTime('12:00:00')
sixpm=stringToTime('18:00:00')

#creating time_of_day column from time column
traffic['time_of_day'] = traffic['time'].apply(
    lambda x: 'morning' if midnight<stringToTime(x)<midday 
    else ('afternoon' if midday<stringToTime(x)<=sixpm
    else ('evening' if sixpm<=stringToTime(x)>midnight 
    else ('midday' if stringToTime(x)==midday
    else ('midnight' if stringToTime(x)==midnight
    else x)))) 
)

#dropping time column
#traffic.drop(columns=['time'], axis=1, inplace = True)
traffic['time'] = traffic['time'].apply(lambda x: int(stringToTime(x).strftime("%H%M%S")))
# int(current_date.strftime("%Y%m%d%H%M%S")))

traffic['time_of_day'].unique()


# In[11]:


traffic.head(60)


# ## Analysis
# 
# 1. Which holidays have the most traffic?

# In[12]:


top_holidays=traffic.groupby("holiday")["traffic_volume"].sum().reset_index().sort_values(by="traffic_volume",ascending=False)
top_holidays=top_holidays[top_holidays.holiday != 'None']
top_holidays


# In[25]:


fig = plt.figure(figsize=(12,5))
plt.title("Top Holidays by Traffic_Volume")
sns.barplot(data=top_holidays.head(-7), y="holiday", x="traffic_volume", palette='Blues_d')

fig.show()


# 2. Which weekdays have the most traffic?

# In[14]:


top_weekdays=traffic.groupby('weekday')["traffic_volume"].sum().reset_index().sort_values(by="traffic_volume",ascending=False)
top_weekdays


# In[43]:


fig = plt.figure(figsize=(5,5))
plt.title("Top Weekdays by Traffic_Volume")
sns.barplot(data=top_weekdays.head(-3), x="weekday", y="traffic_volume")

fig.show()


# 3. What time of day has the most traffic?(Is it morning, afternoon, evening, midnight or midday?)

# In[15]:


top_hours=traffic.groupby('time_of_day')["traffic_volume"].sum().reset_index().sort_values(by="traffic_volume",ascending=False)
top_hours


# 4. Which factor affects traffic the most?

# In[16]:


corr_matrix=traffic.corr()
corr_matrix['traffic_volume'].sort_values(ascending=False)


# No variable has a strong linear correlation with traffic_volume.
# 
# Although traffic is independent of all other variables in our data, time has the strongest linear correlation with traffic volume.
# 
# Our null hypothesis is therefore true, since time affects traffic_volume the most.

# 5. Compare rain, snow and temparature based on traffic_volume

# 6. What's the highest recorded traffic?

# In[17]:


top_traffic = traffic.sort_values('traffic_volume', ascending=False)
top_traffic.head(1)


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