The purpose of this project is to leverage machine learning and find out fraudulent transactions of the credit cards. The idea is to prevent fraudulent activity only by analyzing credit card transaction data. The transaction data that has been used is highly imbalanced, having only 0.2% fraud cases. The overall challenge is to make a supervised model that can detect fraud transactions from normal transactions. The data can be found in the following link:

The objective of this project is to predict if a customer will get a loan given applicant income, loan amount, loan amount term, credit history, education status, self-employment status, property area, etc. A model is trained using the training data on previous customers’ loan approval history. A web service is created, which runs the trained model in the background. The service presents an interface through which any user can request to get an automated decision/prediction (i.

The purpose of this project is to predict if it will rain tomorrow. The data includes weather information (i.e., temperature, evaporation, wind speed, humidity, pressure and cloud status) of different locations in Australia. The data is quite imbalanced having a few instances of rain information. The objective is to train a neural network to predict rain tomorrow from the given information. Pytorch is used for training. Feature selection is performed using Pearson’s correlation.