RahulGupta16/Zindi-Mobile-Banking-Prediction-Challenge

Banks and financial service providers value knowing what habits their clients follow. This allows them to tailor products and services. This challenge asks you to build a machine learning model to predict if individuals across Africa and around the world use mobile or internet banking. # Zindi-Mobile-Banking-Prediction-Challenge Banks and financial service providers value knowing what habits their clients follow. This allows them to tailor products and services. This challenge asks you to build a machine learning model to predict if individuals across Africa and around the world use mobile or internet banking. This solution will provide insight into people’s financial behavior, which can help financial services providers, including insurance companies and banks, tailor the services they provide their clients. ## Data Description The train set contains ~100 000 and the test contains ~45 000 survey responses from around Africa and the world. The objective of this challenge is to build a machine learning model to predict which individuals across Africa and around the world use mobile or internet banking. ## Files available for download: Train.csv - contains the target. This is the dataset that you will use to train your model. Test.csv- resembles Train.csv but without the target-related columns. This is the dataset on which you will apply your model to. SampleSubmission.csv - shows the submission format for this competition, with the ‘ID’ column mirroring that of Test.csv and the ‘target’ column containing your predictions. The order of the rows does not matter, but the names of the ID must be correct. VariableDefinitions.csv - A file that contains the definitions of each column in the dataset. For columns(FQ1 - FQ37), Value 1 - Yes, 2 - No, 3 - Don’t Know 4 - refused to answer ## Rules:- Zindi maintains a public leaderboard and a private leaderboard for each competition. The Public Leaderboard includes approximately 20% of the test dataset. While the competition is open, the Public Leaderboard will rank the submitted solutions by the accuracy score they achieve. Upon close of the competition, the Private Leaderboard, which covers the other 80% of the test dataset, will be made public and will constitute the final ranking for the competiti …