PySVM : A NumPy implementation of SVM based on SMO algorithm. Numpy构建SVM分类、回归与单分类，支持缓存机制与随机傅里叶特征

IoT and ML to assuage the uncertainty in city bus schedules. Track live running status and avail tentative schedule of buses. Minimal IoT setup with a central ML-driven web-backend.

The objective of this project is to study the COVID-19 outbreak using basic statistical techniques and make short term predictions using ML regression methods.

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…

A Jupyter Notebook / Google Colab based Machine Learning Notebook for training a model to predict the first inning score of an IPL match using data from matches played between 2008 to 2017.

Find the best algorithm to analyze and predict the demand for cash withdrawals

Predicting Fetal Health, and Birth-Weight of fetus using Machine Learning

Repository of my master’s thesis "Development and evaluation of a model for predicting the state of health of traction batteries based on artificial neural networks"

A collection of machine learning models for predicting laptop prices

Machine learning models to predict a patient's diagnosis based on features selected from a dataset

Using Machine learning to predict a student final grade

- Notebook for car price prediction using regression models.

Ranking Reviews Based on their utility using Advanced NLP Techniques

This repository contains implementations of popular machine learning algorithms including Support Vector Machine (SVM), Decision Tree, and Naive Bayes. Each algorithm is implemented separately, providing clear and concise examples of their usage for classification tasks.

This is my ML projects repository

Helping our cousin from La Mancha to make stimations in the sales of iberian ham the next months. Applying feature engineer we help our SVM and Random Forest models to make the best predictions we can.

Boston house price prediction using Linear Regression.

This project aims to develop a machine learning model to predict bike-sharing demand based on various factors such as weather conditions, time of day, and historical usage patterns. The dataset used for this project consists of 8760 records and 14 attributes.