This contains DataScience, MachineLearning, NLP, NeuralNet & RL related Micro Projects & Tasks completed during academic & self-learning period. Contents Machine Learning ML - CO2 Emission Prediction: This project tries to identify the macroeconomic factors that are biggest predictors of global warming i.e. global carbon dioxide emissions using Lasso Regularization Regression. Read More ML - SMS Classifier: This project classifies the SMS as spam vs non-spam through naive bayes.

Abstract : This project tries to identify customers at high risk of churn and identify the main indicators of churn through logistic regression. Github Repo WIP…

Abstract : This project tries to predict the bike demand after the ongoing quarantine ends using multiple linear regression model. Business Objective : A bike-sharing system is a service in which bikes are made available for shared use to individuals on a short term basis for a price or free. Many bike share systems allow people to borrow a bike from a “dock” which is usually computer-controlled wherein the user enters the payment information, and the system unlocks it.

Abstract : The Credit Default Analysis is performed on customer data of LendingClub to identify the driver features of loan defaulters. Business Objective : For lending companies, lending loans to risky applicants is the largest source of financial loss. The credit loss is the amount of money lost by the lender when the borrower defaults to a loan payment. In such a case, the concerned person is labelled as charged-off and defaulters.

Abstract : The CEO of Spark Funds, an asset management company wants to understand the global trends in investments so that she can take the investment decisions effectively. Business Objective : The objective is to identify the best sectors, countries, and a suitable investment/funding type for making investments. Investment Constraints : Strategy is to invest where most other investors are investing with below 2 minor restrictions for investments Invest between 5 to 15 million USD per round of investment Invest only in English speaking countries for the ease of communication with the companies it would invest in.