PROJECTS

Built a Regression model using various EDA techniques and Feature Selection techniques such as ExtraTreesRegressor and Mutual Information Gain. 

Deployed the model on Heroku and Azure using Azure CLI. Developed a user interface with Flask. 

Project website on Azure: https://bodyfat-estimator.azurewebsites.net/ 

Project website on Heroku: https://bodyfat-estimator-59c91bbb65f1.herokuapp.com/ 

Built a classification model using pycaret library for comparing various machine learning algorithms and choosing the best algorithm to create the best model and then tune the hyperparameters to investigate if a credit card fraud detection has been done. 

kaggle notebook: https://www.kaggle.com/code/raghuveerraothoka/credit-card-fraud-detection

Built a classification model for detecting credit card fraud with handling imbalanced data with sampling techniques and training the data on Decision Tree Model, Random Forest and plotting their scores using seaborn.  

Github: https://github.com/traghuveerrao/creditCardFraudDetection

Built an end-to-end Image Classification model using CNN techniques to classify images into healthy and diseased (cocci). 

Implemented pipeline versioning through DVC and created a Docker image. 

Deployed the model on AWS EC2 container and integrated CI/CD pipelines through GitHub workflow actions. Developed a user interface with Flask. 

GitHub repository: https://github.com/traghuveerrao/Chicken-Disease-Classification