Cab Booking System

• 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.
• Predict the total count of cabs booked in each hour covered by the test set, using the information available prior to the booking period

Dataset Description

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

Performed

1. Feature Engineering
2. Outlier Analysis
3. Correlation Analysis
4. Data Visualization
5. Regression analysis using various models (Random Forest Regressor, Ada Boost Regressor, Bagging Regressor,SVR, and K-Neighbors Regressor)
6. Hyperparameter tuning using GridCV

Run on Jupyter

git clone https://github.com/mahithabsl/Cab-Booking-System
cd Cab-Booking-System
jupyter notebook