BACKGROUND
Cab booking system is the process where renting a cab is automated through an app throughout a city. Using this app, people can book a cab from one location to another location. Being a cab booking app company, exploiting the understanding of cab supply and demand could increase the efficiency of their service and enhance user experience by minimizing waiting time.
Objective of this project is to combine historical usage pattern along with the open data sources like weather data to forecast cab booking demand in a city.
PROCESS FLOW
You will be provided with hourly renting data span of two years. Data is randomly divided into train and test set. You must predict the total count of cabs booked in each hour covered by the test set, using the information available prior to the booking period. You need to append the train_label dataset to train.csv as ‘Total_booking’ column.
Please find the descriptions of the columns present in the dataset as below.
datetime - hourly date + timestamp
season - spring, summer, autumn, winter
holiday - whether the day is considered a holiday
workingday - whether the day is neither a weekend nor holiday
weather - Clear , Cloudy, Light Rain, Heavy
temp - temperature in Celsius
atemp - "feels like" temperature in Celsius
humidity - relative humidity
windspeed - wind speed Total_
booking - number of total booking
DATASET
The recommended datasets will be shared. You can download them from the LMS.
TARGET ENVIRONMENT
You can use jupyter notebook to complete this.
TASKS
Following are the tasks, which need to be developed while executing the project:
Task 1:
1. Visualize data using different visualizations to generate interesting insights.
2. Outlier Analysis
3. Missing value analysis
4. Visualizing Total_booking Vs other features to generate insights
5. Correlation Analysis
Task 2:
1. Feature Engineering
2. Grid search
3. Regression Analysis
4. Ensemble Mode
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