ML Project implementing ANN, SVM, Random Forest, Elastic Net regression models from scratch.
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Updated
May 29, 2024 - Jupyter Notebook
ML Project implementing ANN, SVM, Random Forest, Elastic Net regression models from scratch.
Deep reinforcement learning for smart calibration of radio telescopes. Automatic hyper-parameter tuning.
High Throughput Light Weight Regularized Regression Modeling for Molecular Data
snpnet - Efficient Lasso Solver for Large-scale genetic variant data
Prediction of Sales Prices of Houses
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
Built a regression model to predict university admission using linear, polynomial, and regularized regression techniques (lasso, ridge, and elastic net) and achieved 98% accuracy.
A project aim to predict default rate of Commercial Real Estate(CRE) Loans
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.
Predicting 2023 Formula One Constructors' Championship Standings
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.
The project aims to enhance aircraft engine maintenance operations and planning using statistical learning and machine learning methods.
Regression analysis
A demonstration of the basic Machine Learning Algorithms
Regression on BOSTON dataset from sklearn
Lasso + Bootstrap methods for predictive modeling
Machine learning (regression) exercise on prediction of house pricing in Melbourne with post-model analysis and recommendations for maximizing home value.
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.
Logistic Regression technique in machine learning both theory and code in Python. Includes topics from Assumptions, Multi Class Classifications, Regularization (l1 and l2), Weight of Evidence and Information Value
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.
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