This project was done as a part of hackathon on analytics vidhya.

Here I have mainly focused on data visualisation and data cleaning With the upcoming cab aggregators and demand for mobility solutions, the past decade has seen immense growth in data collected from commercial vehicles with major contributors such as Uber, Lyft and Ola to name a few.

There are loads of innovative data science and machine learning solutions being implemented using such data and that has led to tremendous business value for such organizations. Calculating the feature importance shows that the output is highly dependent on one feature and that feature have nearly equal distribution among different classes and we so cannot use statistical parameters to fill up the missing values for that column. We have to use imputation here for filling up the missing values. The model uses KNN for this purpose.

Random Forest Classifier is used for prediction and private leaderboard score on submission was 0.688 which is very much near to the top private score of 0.706.