Employed K means & Hierarchical Clustering on European jobs data

Applying Machine Learning approaches(and eventually comparing them) for classifying whether a person is suffering from heart disease or not, using one of the most used dataset — Cleveland Heart Disease dataset from the UCI Repository

Worked on Airbnb and Zillow data from Kaggle to build a data product to find zip codes that are most profitable to invest in for short term rentals.

Testing out several different supervised learning algorithms over credit data of candidates to classify if an individual will be "good loanee" or will tend to default.

Multinomial regression is implemented on FAA Data. The continuous response is converted to categorical using cutoff values.

An extensive EDA is performed for this very popular ML dataset. This helps understand the intricacies of data using different visualizations and the nuances between plots.

Here, I have tried to compare the Prediction ability of Linear Regression vs Regression Trees...

To study what factors and how they would indicate a risky or long landing of a commercial flight.

To study what factors and how they would impact the landing distance of a commercial flight using Linear Regression.