Cab Cancellation Prediction using Data Science and Machine Learning!

Introduction:

For me personally, there were numerous occasions of not getting my cab or being cancelled and many reasons when I booked it from many services and I felt that there may be some kind of functionality which tells us about the cab cancellation rate so that we may not wait for the cab which may get cancelled or due to no cabs availability. Disclaimer: There is a booking option available to book in advance but for some people who want to travel urgently and not getting a cab on time, will go through a hell lot of struggle. So from many incidents that happened, I believe that this functionality in all the leading service providers will be a game changer and will help many people across the world who depend on this kind of transportation.

Our Strategy:

Let’s follow the CRISP-DM process to answer our questions!

CRISP-DM is a common process used to find many solutions in Data Science.

The phases of this CRIPS-DM process includes:

- Business Understanding

- Data Understanding

- Data Preparation

- Data Modelling

- Evaluation

- Deployment

Quick Glance about the data:

data_frame.head() to view the first 5 rows

How many book a cab from the city and from airport?

How many people got their cabs and how many got cancelled?

What are the total requests that come in for every hour in the day?

What will be my Cab cancellation rate If I want to book the cab at 6:30?

For this question, we will be using machine learning algorithms to predict us the cab cancellation rate but before this we need to clean the data which takes most of the time and calculate the percentages for each and every hour.

Conclusion:

We answered all the questions except some part of the last question which needed more data to consider the cutoff rate.

There’s a lot that can be done in the future such as studying the cancellation rates and getting the cab ratio.

This is my first data science blog, hope you enjoy it, thank you for your attention and any kind of advice or any thing that should be improved from further blogs are welcomed as it will really help me to improve and learn!

To view the code:

To Connect with me via Linkedin :

https://www.linkedin.com/in/kaushik-tummalapalli/

I would love to connect :) See You in the next blog until then keep Hustling and stay safe!

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Kaushik Tummalapalli

Kaushik Tummalapalli

Junior Machine Learning Engineer-Omdena | Microsoft Learn Student Ambassador-Beta | Exploring Data Science | Pythoneer | I’m curious about tech and Love chess|