Market Basket Optimization

Apriori is a popular algorithm for extracting frequent itemsets with applications in association rule learning. The apriori algorithm has been designed to operate on databases containing transactions, such as purchases by customers of a store. An itemset is considered as "frequent" if it meets a user-specified support threshold. For instance, if the support threshold is set to 0.5 (50%), a frequent item set is defined as a set of items that occur together in at least 50% of all transactions in the database.

More details can be seen here. 

House Price Prediction

This is a Kaggle Competition Project.
Various Techniques like Target Guided Encoding, logarithmic transformation technique, Standard Scaler. Hyperparameter-tuning etc. have been used.

Algorithms like Random Forest Regressor, Linear Regression, SVR, Gradient Boosting Regressor and ANN have been applied.

More details can be seen here.