Analyzing Driver Signup and First trip rates for a ride sharing service
Analyzing Driver Signup and First trip rates for a ride sharing service

This is a synthetic dataset for ridesharing that shows the details of driver sign-up data. The data consists of when the drivers signed up, the city, sign-up OS, sign-up channel (paid, referred, etc.). Once they signed up for the driving, they are requested to be available for background check, add their vehicle and once those stages are cleared, they are then allowed to start picking up passengers.

Here, the company wants to maximize the trips taken by the drivers. There may be several hurdles before first trip is taken, such as, the background check may be negative, or the drivers may fail to add their vehicle, and so on. This data analysis will try to find the important factors that is involved in the first trip taken by the drivers. And consequently, some recommendations are suggested based on the analysis.

Click here for the code and dataset.

What is your future car? Predictive Shiny App
What is your future car? Predictive Shiny App

I created this Shiny App in 2014 based on my research on consumer behavior and infrastructure analysis. The backend of this model is based on a multinomial logistic regression algorithm, which takes in consumer characteristics, vehicle attributes, and predicts the probability of the consumer purchasing a vehicle technology. The App gives the top 3 optimal choices for the consumer.

Click here for the Shiny App link. For more details about this project, see the blog post.

Conversion rate of a website
Conversion rate of a website

This is a simple random forest classification done in R, to identify the conversion of a website. 

Analyzing Driver Signup and First trip rates for a ride sharing service
What is your future car? Predictive Shiny App
Conversion rate of a website
Analyzing Driver Signup and First trip rates for a ride sharing service

This is a synthetic dataset for ridesharing that shows the details of driver sign-up data. The data consists of when the drivers signed up, the city, sign-up OS, sign-up channel (paid, referred, etc.). Once they signed up for the driving, they are requested to be available for background check, add their vehicle and once those stages are cleared, they are then allowed to start picking up passengers.

Here, the company wants to maximize the trips taken by the drivers. There may be several hurdles before first trip is taken, such as, the background check may be negative, or the drivers may fail to add their vehicle, and so on. This data analysis will try to find the important factors that is involved in the first trip taken by the drivers. And consequently, some recommendations are suggested based on the analysis.

Click here for the code and dataset.

What is your future car? Predictive Shiny App

I created this Shiny App in 2014 based on my research on consumer behavior and infrastructure analysis. The backend of this model is based on a multinomial logistic regression algorithm, which takes in consumer characteristics, vehicle attributes, and predicts the probability of the consumer purchasing a vehicle technology. The App gives the top 3 optimal choices for the consumer.

Click here for the Shiny App link. For more details about this project, see the blog post.

Conversion rate of a website

This is a simple random forest classification done in R, to identify the conversion of a website. 

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