In my last article : Logistic Regression: The Theoretical Way , I have talked to you about the important key concepts of logistic regression, one should know before starting to implement practically. I hope you have gone through the concepts. If not, prior reading this, I would request you to kindly go through my previous article.
This article will not just cover logistic regression , the main aim of this is to talk about key approach to address a business problem in brief. Building model fascinates us, but in reality an Analyst only spend 20% of his time. Rest 80% is spent on data exploration and processing. It is imperative to understand your data before you work on and create your model.
In this article, I will be talking about the following :
Defining : Problem Statement
Glimpse of how the data looks
Road-Map or Approach
Logistic Regression Model
In this article, I have worked on Mobility Analytics Dataset. Source is AnakyticsVidhya Jantahack. Note : Kindly contact AnalyticsVidhya before using this dataset for commercial purpose!

1) Problem Statement:

XYZ is a cab aggregator service. Their customers can download their app on smartphones and book a cab from any where in the cities they operate in. They search for cabs from various service providers and provide the best option to their client across available options. They have captured surge_pricing_type from the service providers, which is the key variable of this study!
The objective is to build a predictive model, which could help in predicting the surge_pricing_type pro-actively. This would in turn help them in matching the right cabs with the right customers quickly and efficiently.

#gradient-descent #classification-algorithms #machine-learning #machine-learning-python #logistic-regression