Will_Bill_Solve_It

Hacker Earth Machine Learning Competition

Data Preparation

• Merge user's submission log with user and problem details
• Created new features like num_attempts at a problem, only took 200,000 examples from around 400,000 available.

Feature Engineering

• Created some features related to user's problem solving capability, how hard a problem is and used rest of the features specified in the examples
• One hot encoding of user's skills
• Label Encoding categorical Features

Modelling

• Trained Logistic Regression Model, Random Forest Classifier, Extra Trees Classifier, SGD and XGBoost Model
• But final model was an Extreme Gradient Boosting Model with parameters that produced the best accuracy on the cv set