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bike-sharing-demand-prediction

Built a regression model to predict bike demand on data from Seoul, South Korea. and employed one hot encoding to create dummy variables Benchmarked Cat Boost against Linear regression, Lasso and Ridge regression, Gradient Boost and performed feature engineering and tuned the hyperparameters for the optimum performance

cardiovascular-risk-prediction

Built hyper-parameter tuned RF to classify patients on the basis of Coronary heart risk .Applied One Hot Encoding to label categorical features. Benchmarked Random Forest against KNN , SVM , Decision Tree and Linear Regression and performed missing value imputation using KNN and simple imputer

cifar10-neural-network

Built a simple neural network with the help of ANN and CNN to classify images into their respective classes. used Matplotlib to plot images.

decision-tree-implementation

eda---capstone-project

Analyzed Hotel Booking data to get the useful insights which will help the industry to come up with the strategies to increase and retain future customers

eda-on-uber-data

insurance-decisioning

ml-deployment-sentimental-analysis-case-study

retail-customer-segmentation

Implemented K - means and Hierarchical clustering to cluster the retail customers into different segments, based on their spending habits. Employed RFM metrics and assigned labels to each customer based on RFM score.

sales-trend-analysis

sentimental-analysis-using-nlp