📈 Quant Market Predictor is a Python tool designed to predict stock prices from the NASDAQ

Forecasting hourly bike rental demand by combining historical usage patterns with weather data using Linear Regression Algorithm.

Developed a customer segmentation system to analyze customer behaviors and spending patterns. Utilized Python for clustering analysis, applied K-Means to segment customers, and visualized insights in Power BI to identify key customer groups and support targeted marketing strategies.

🚢 Ce projet utilise les données du Titanic pour prédire la survie des passagers en fonction de caractéristiques comme l'âge, le sexe et la classe. À travers des modèles de machine learning et des visualisations, il explore les facteurs clés de survie dans l'un des naufrages les plus célèbres de l'histoire.

This project focuses on building and optimizing a deep learning neural network to predict the success of applicants funded by Alphabet Soup Charity.

Manipulated Facial Image Detection is a machine learning project aimed at identifying altered facial images, such as deepfakes or morphs. It leverages advanced image analysis to detect manipulations, addressing challenges like misinformation and digital deception.

This magical tool estimates car prices with a Linear Regression model, trained on Quikr Car listings. Powered by Python and scikit-learn, it considers company, model, year, and mileage. Simply input your car's details and get an instant price prediction!

UOC University - Applied Data Science Engineering - Fundamentals of Data Science - PEC_7 || Data preprocessing in Python. Libraries: Scikit-learn and Pandas. ||

Customer Churn Rate Predicticted by Machine learning models

This project uses machine learning to predict and analyze employee attrition in Company.By developing three predictive models,it identifies key factors influencing turnover,providing actionable insights to mitigate attrition challenges.The analysis focuses on enhancing job satisfaction,work-life balance and career growth opportunities.

Diabetes prediction

Building a predictive model to predict views of Ted Talks in YouTube from dataset of past events using Machine Learning models

With imbalanced observed data, a search for the best model is conducted. The bank is seeing its customers leave. Wondering if there are patterns to their decision to exit, the bank wishes to anticipate for this trend. When the positive class is the minority in an imbalanced dataset, a model need to be trained for robustness.

Practical tasks that were performed during the course Specialization DS

Credit Card Fraud Detection Using Machine Learning

🐾 A lightweight & extensible library to create complex multi-model and multi-modal pipelines, including ``Ensembles`` and ``Meta-Models``

Problem to solve: Predict if a candidate would be hired based on specific characteristics; what are the most important features a candidate must have to have higher possibilities of getting the job?

Machine Learning projects