This project focuses on forecasting the closing prices of Yes Bank's stock. Through data analysis and predictive modeling, this project provides valuable insights for investors and traders, aiding them in making informed decisions about their investments in Yes Bank's stock.

Regression analysis

Built a regression model to predict university admission using linear, polynomial, and regularized regression techniques (lasso, ridge, and elastic net) and achieved 98% accuracy.

Lasso + Bootstrap methods for predictive modeling

This project compares the different machine learning models on Walmart Weekly Sales Data and predicts the weekly sales for the test data.

Ridge, elastic net, and logistic regressions implemented without using any statistical or machine learning library. All steps are done by hand, using matrix operations as much as possible.

A project aim to predict default rate of Commercial Real Estate(CRE) Loans

Machine Learning Basics

The project encompasses the statistical analysis of a high-dimensional data using different classification, feature selection, clustering and dimension reduction techniques.

Machine learning (regression) exercise on prediction of house pricing in Melbourne with post-model analysis and recommendations for maximizing home value.

Yes-Bank-Stock-Closing-Price-Prediction refers to a type of project or task in the field of data science and machine learning that involves developing predictive models to estimate the Closing Price of stock

Machine Learning Predictions of 2023 Formula One Constructors' Championship Standings

Various Regression models including linear, polynomial, ridge, lasso and elastic net were experimented with to find which model best predicted health insurance costs. The models were evaluated using cross-validation, from which the best models were optimized using randomized search. The best model was then evaluated on the test data.

Prediction of Sales Prices of Houses

The project aims to enhance aircraft engine maintenance operations and planning using statistical learning and machine learning methods.

Data Models in R for Multiple Linear Regression and three models (Ridge, Lasso, and Elastic-Net), to predict Medicare claim costs of Type 2 diabetes patients with other diagnoses. We used Data from Entrepreneur’s Medicare Claims Synthetic Public Use Files (DE-SynPUFs) for our analysis.

Regression on BOSTON dataset from sklearn

This project focuses on forecasting the closing prices of Yes Bank's stock. Through data analysis and predictive modeling, this project provides valuable insights for investors and traders, aiding them in making informed decisions about their investments in Yes Bank's stock.

ML Project implementing ANN, SVM, Random Forest, Elastic Net regression models from scratch.