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Jayashree HV's Projects

cl icon cl

Course material for a workshop in OpenCL

cvfiles icon cvfiles

Computer Vision project files repository

data_analytics_projects icon data_analytics_projects

The end to end development of web app for Telecom Data Analytics is presented in this project. The project involves, data cleaning and preprocessing. Data visualization using dash and plotly . End product is a web app accessible by the client.

dsnd_term2 icon dsnd_term2

Contains files related to content and project of DSND Term 2

logistic_heroku icon logistic_heroku

This project is about predicting whether a married woman has extra marital affair or not. It is a logistic regression based Binary classification model based application. To eliminate features recursive feature elimination technique is explored. It is observed that with the reduced number of features model's f1-score and AUC-ROC score have improved.

ml-foundations icon ml-foundations

Machine Learning Foundations: Linear Algebra, Calculus, Statistics & Computer Science

people-analytics icon people-analytics

This repository includes 3 projects 1. Employee Satisfaction Analysis 2. Training Effectiveness Analysis 3. Employee Turnover (attrition) Analysis

product_review icon product_review

This project demonstrates sentiment analysis of product reviews for an e-commerce website jewellery products. The end product is a web app with an option for user interface. User can enter the comments to see the sentiment instantly. It also displays distribution of sentiments.

randomforest_boston_heroku icon randomforest_boston_heroku

This project is for predicting Boston house prediction using Random Forest Regressor. This project involves Flask Framework based web application for user interface. The project is deployed on Heroku platform

regression_projects icon regression_projects

This repository is created to showcase regression models, development from data collection to deployment

titanic_survivor_heroku icon titanic_survivor_heroku

This project is a very common topic for any data science learner, here the imputing technique used is from RandomForestRegressor and PCA technique is used to reduce dimensionality reduction. Basic model is built using decision tree then hyper parameter tuning is carried out. Random Forest classification is explored.

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