A symbolic regression model

Implement model regression linear simple and multiple form scratch and compare it the sklearn model

This model is designed to determine the age of a crab based on its other physical characteristics. Using this model, it is possible to determine the age of a crab through its other data!

Electronic Music Classification ML

Uber Trip Demand Analysis and Prediction using Python and Machine Learning. This project explores trip patterns, peak demand times, and uses models like Random Forest, GBRT, and XGBoost to forecast ride demand.

Cybersecurity threat detection project that analyzes AWS CloudWatch web traffic logs to identify suspicious and anomalous interactions using machine learning models like Isolation Forest, Random Forest, and Neural Networks.

A text classification library creating an easy way to interface with Sklearn and build models

Software aimed at real-time prediction of match outcomes for FIRST Tech Challenge using machine learning algorithms.

The Telco Customer Churn dataset, the project involves collecting, cleaning, and analyzing customer data to uncover key factors influencing churn.

Diabetes Prediction Dataset

Reduce the time that cars spend on the test bench. Work with a dataset representing different permutations of features in a Mercedes-Benz car to predict the time it takes to pass testing. Optimal algorithms will contribute to faster testing, resulting in lower carbon dioxide emissions without reducing Mercedes-Benz’s standards.

Machine Learning Drug Classification

Laptop Price Prediction using Machine Learning. This project analyzes laptop specifications like RAM, CPU, storage, and GPU to predict prices using data preprocessing, EDA, and a Linear Regression model.

Coca-Cola Stock Analysis & Price Prediction using Python and Machine Learning. This project performs exploratory data analysis (EDA), technical indicator creation, and uses a Random Forest model to forecast future stock prices based on historical market data.

The project “HR Analytics – Employee Attrition Prediction” aims to predict employee attrition based on various work-related factors using the IBM HR Analytics Dataset.

Titanic - Predicting Survival Using Machine Learning

Reduce the time that cars spend on the test bench. Work with a dataset representing different permutations of features in a Mercedes-Benz car to predict the time it takes to pass testing. Optimal algorithms will contribute to faster testing, resulting in lower carbon dioxide emissions without reducing Mercedes-Benz’s standards.

Comparing and evaluating various regression algorithms to accurately predict car prices using data from kaggle.

We used various techniques to train and evaluate a model based on loan risk. We used a dataset of historical lending activity from a peer-to-peer lending services company to build a model that can identify the creditworthiness of borrowers.

No description provided.

Consist of ML projects based on Python along with DataSheets

Leverages Logistic Regression to predict credit risk for customers based on the German Credit Data dataset

Playing with AI in Python. Pandas, nltk & sklearn

Create a model that helps choose the region with the highest profit margin.

Model designed to predict premier league games outcomes based on data from the year 1993 - 2023

Classification of Text from Youtube Comments using BistillBERT alanguage models from Hugginface Transformers

This group project aims to predict the arrest of different types of crime given a specific input (day/ location/etc.) using machine learning models.

Repository containing files from Cognitive Computing CIE innovative course. This includes many tools and training methods use in Machine Learning Python development

KMeans Clustering of data using Sklearn library, numpy and Pickle data

One of the main pain point that AT&T users are facing is constant exposure to SPAM messages. AT&T has been able to manually flag spam messages for a time, but they are looking for an automated way of detecting spams to protect their users.