Machine learning regression algorithm on cryptocurrency stock price for the next 30 days.

Using Tensorflow and a Support Vector Machine to Create an Image Classifications Engine

NASA Project; Plastic Marine Debris Classification-Machine Learning Software

I developed Machine Learning Software with multiple models that predict and classify AID362 biology lab data. Accuracy values are 99% and above, and F1, Recall and Precision scores are average (average of 3) 78.33%. The purpose of this study is to prove that we can establish an artificial intelligence (machine learning) system in health. With my…

India is one of the countries with the highest air pollution country. Generally, air pollution is assessed by PM value or air quality index value. For my further analysis, I have selected PM-2.5 value to determine the air quality prediction and the India-Bangalore region. Also, the data was collected through web scraping with the help of Beautif…

Machine Learning Software Predicting Germany's Economic Development from 2015 to 2019

Machine Learning Prediction Software Based on Classification and Regression Based on Processor [CPU] Specifications

Multi-label classification of various tree types

Machine Learning and Data Mining based project to make a predictive model for real time vehicular traffic

Classifying Forest Cover type

It contains the code for the deployed streamlit app which helps to determine importance of features for classification datasets using Random Forest and Extra Trees Classifiers

CS5228 Kaggle Inclass Competition: Predicting if Income > 50K

Predicting wildfire risk using classification and regression tools. My final project as part of the Metis data science bootcamp.

A prediction model that uses genetic data for disease classification. Data is extracted from a DNA microarray which measures the expression levels of large numbers of genes simultaneously.

A Comparative analysis of machine learning models on Quora Question Pair dataset

Iris Species Classification usin various ML models.

This repo describes a supervised approach to text classification using different features and classifiers. This, obviously, is good to use if there is labelled data available.

Applied numerous algorithm models to solve a binary classification problem of predicting if any given prospective customer converts to a sale, through the company’s online sales channel.

Implementation of different types of machine learning algorithm and there performance comparison on a same dataset

It contains different types of machine learning models like Linear Regression, Logistic Regression, Clustering techniques, Time Series Forecasting etc