Robert Rusev

ML-FinFraud-Detector is a machine learning project for detecting financial transaction fraud. Utilizing XGBoost, precision-recall, and ROC curves, it provides accurate fraud detection. Explore feature importance, evaluate model performance, and enhance financial security with this comprehensive fraud detection solution.

ML-Premier-League-Wins-Predictor is my first machine learning project that predicts the number of wins for each team in the Premier League using linear regression. Explore the key factors that contribute to becoming a champion in one of the world's most competitive football leagues. Jupyter Notebook and code included.

Predict loan defaults using ML. Leverage Logistic Regression, Random Forest, XGBoost. Preprocess data, train models, analyze features. Make informed lending decisions. Jupyter Notebook and code.

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NLP-FinHeadlines-MoodTracker is a NLP project utilising sentiment analysis on financial news headlines. It employs a combination of CNN and LSTM layers to predict sentiment (positive, negative, neutral). The model incorporates an embedding layer, 1D convolution, max pooling, bidirectional LSTM, dropout, and dense layer for sentiment classification.

Stock Price Predictor: Leveraging historical data, macroeconomic indicators, LSTM and Prophet models for enhanced stock price forecasting. Analyze trends, patterns, and economic factors to gain insights and make data-driven predictions. Leverage advanced modeling techniques for reliable forecasts.