Prediction of House prices
Machine learning model created to prerdict house prices, used Multiple linear Regression and compared performance of multiple models using test data. Further optimized the model using Ridge and Lasso regression techniques. Total 21612 datapoints each with 21 attributed were used and important features were found to be squarefeet living, numebr of bedrooms, number of bathrooms, number of floors.
Prediction of Credit Risk
Created the risk model which aims at minimizing the credit risk incurred in the bank. The dataset contained the details about the 65535 customers with each row having 23 attributes of their loan details. Used Decision tree classifier to train the model, implementation was done using the scikit-learn library.
Clustering of countries for expansion of a firm in foreign markets
Built clustering model using food and beverages sales dataset of countries, which makes easier for a frim to take business decisions. Used Hierarchical clustering and K-means to create the clusters.
