On this repo there is a University project about the course of Signal, Image and Video where a constraint was to bulid a project where AI is just a narrow task and not the whole implementation of the project. I will just use AI as a comparison with traditional techniques of video processing.

An Interactive Approach to Understanding Deep Learning with Keras

Predicting house prices using ML regression models (Linear, RandomForest, XGBoost) with full pipeline from data cleaning to model optimization.

Abundance-based species distribution models

Useful tools for constructing species distribution models

✨ Stock Price Prediction Using Tesla Dataset ✨ In this project, I analyzed Tesla’s historical stock data to forecast future closing prices using machine learning models like Random Forest Regressor. Through data cleaning, feature engineering, and rich visual analytics, I explored patterns in price trends, volatility, and trading volume.

Built a machine learning model to predict visa approval outcomes using real-world immigration data. Applied EDA, feature engineering, and ensemble models (Random Forest, XGBoost) to uncover factors influencing visa decisions and streamline the OFLC review process.

Machine Learning project for Kaggle’s Titanic: Machine Learning from Disaster competition. Achieved 0.76555 public leaderboard score using advanced feature engineering and Random Forest Classifier.

This project builds a predictive model to estimate visa approval likelihood using candidate and job-related features. It showcases an end-to-end machine learning workflow with EDA, feature engineering, and model tuning to automate parts of the visa evaluation process.

Data science project to predict the "Team of the Season" in the Premier League using player performance analytics from the last 13 years.

Sentiment Analysis is a Natural Language Processing (NLP) technique used to identify the emotional tone behind a piece of text — typically classified as positive, negative, or neutral.

🫀 Heart Disease Risk Prediction This project focuses on predicting the risk of heart disease using machine learning techniques. It includes thorough Exploratory Data Analysis (EDA), dimensionality reduction using Principal Component Analysis (PCA), model training, evaluation, and visualization of the results

An AI-powered marketing strategy advisor that predicts bank client subscription likelihood using machine learning and Google Gemini for personalized marketing recommendations. Includes model selection, tuning, and a Streamlit web app for real-time predictions.

Machine Learning System Development - Diabetes Prediction

Built LinkedGen to gain hands-on ML skills; data curation/EDA, fine-tuning DistilGPT2 on Colab, and integrating a working UI with reproducible, deployable workflows. LinkedGen generates professional, tone-controlled LinkedIn posts from user input using a fine-tuned DistilGPT2 model, backed by a clean data pipeline and a Streamlit interface.

This project develops a machine learning model to detect fraudulent bank account applications using synthetic financial data, identifying key fraud indicators like device_os_windows and applicant residency history. Dataset Source: https://www.kaggle.com/datasets/sgpjesus/bank-account-fraud-dataset-neurips-2022

This repository performs automated parameter optimization to maximize calcium Ca2+ influx in the trunk of a CA1 pyramidal neuron model while minimizing calcium influx in oblique dendrites, using the Optuna framework for hyperparameter tuning.

Model Tuning: Collaborated with "ReneWind," a company focused on enhancing wind energy production through machine learning. Analyzed sensor data on turbine generator failures, developed and tuned various classification models, and evaluated their performance to select the most accurate one for predicting failures and minimizing maintenance costs.

Deep learning framework for multi-horizon financial time series forecasting using RNN, GRU, and LSTM. Incorporates hyperparameter optimization, visualization, and multivariate sequence prediction across Open, High, Low, Close, and Volume indicators.

Machine Learning System Development - Diabetes Prediction