Work as Data Analysis with R programming and Python and I started with making some hypothesis about the data without looking at it. Then I moved on to data exploration where I found out some nuances in the data which required remediation. Next, I performed data cleaning and feature engineering, where I imputed missing values and solved other irregularities, made new features and also made the data model-friendly by one-hot-coding. Finally I made regression, decision tree and random forest model and got a glimpse of how to tune them for better results.