Download - Predict cancellation based on past cab booking data

Predict cancellation based on past cab booking data

Neeraj Tiwary Sep-16 of 17


Author: Neeraj Tiwary

Abstract Based on past cab booking data, we need to predict the probability of cancellation of any booking.

Data source: Training dataset contains 9999 records whereas test dataset contains 10000 records.

The main attributes in the training/test dataset were

driver_id

customer_id

booking_date

booking_from_long

booking_from_lat

booking_to_long

booking_to_lat

booking_status

original_rate_cost

amount_cust_discount

is_rush_hour_price

Developed a model with the help of R statistical language.

Keywords: Booking, Predict, Cancellation

Introduction, Motivation and Questions Loaded the data into R



Data Cleansing

Converted some features into categorical variables

From booking_date feature, derived below new attributes

o Hour (hr)

o Day (day)

Since training dataset contains just 2 days of data, so no other new features could derive here.

Dropped booking_date feature from the data frame

Removed duplicates

Verified the initial summary of the data

Output



Feature Engineering

This is the man step of any model development activity. We need to enhance our features to have a

better predictability.

At the above step developed two new features (hr and day).

In this step, initially developed a feature termed as “Rate_Cost” which is the difference between

“original_rate_cost” and “amount_cust_discount”.

Checked the histogram of that feature and observed that if I do a logarithmic transformation

over this new feature, it would be good.

We have latitude/longitude details for source and destination of booking.

From these details, I derived 3 new features.

o Dist_KM: This is the total distance in kilometers between source and destination of

booking. This may not be complete accurate as this attribute is derived via Euclidean

distance.

o Price_KM: This is the ratio of Rate_Cost divided by Dist_KM.

o Log_Price_KM: Logarithmic transformation of above derived attribute.



Exploratory data analysis

Here I did an exploratory data analysis of the attributes

Boxplot

train_data$Log_Rate_Cost train_data$Log_Price_KM

train_data$Dist_KM

Histograms



Qplot

Plot of cancellation over source location Plot of cancellation over destination location

Plot of cancellation against rate vs peak hour Plot of cancellation against Price_KM vs Dist_KM



Plot of cancellation against Price_KM vs hour

Plot of cancellation on different hours Chart of cancellation on different hours

Dropped unnecessary variables



Supervised Model Information Gain

Output

Decision Tree

Developed a decision tree model

Confusion matrix of the model

Model Structure



Naïve Bayes

Developed a Naïve Bayes model


Model Structure



Naïve Bayes

Developed another version of Naïve Bayes model


Model Structure



Random Forest

Developed a random forest model


ROC Curve

Importance of variable in the model



Support Vector Machines

Developed a linear SVM model with cost = 500 and gamma = 1


Model Structure

Developed a nonlinear SVM model with cost = 500 and gamma = 1


Model Structure



Support Vector Machines – Non Linear

Methodology Supervised machine learning models

Results Observation:

Models developed with Support vector machine algorithm are good models out of all the

models that I developed.

In SVM too, if we develop the model in non-linear space, then it has good accuracy.

Conclusions and Next Steps After a thoroughly understanding of the problem, below are my further recommendations to proceed

ahead

From the longitude / latitude details, we can derive the geo locations / locality and then group

them or see the relationship between them. That information might be helpful for the model.

If we have further details of customer / driver, then it would be helpful in improving the

accuracy of the model

Constraints I tried to find out the locality from reverse geocoding, but all APIs have some kind of daily usage

limit.



Code R Code:

Download - Predict cancellation based on past cab booking data

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