Daniel Olutade

Computer Science graduate with practical experience in data analytics, machine learning, software development and technical support. Built a football analytics dashboard using Python, Pandas, Streamlit and predictive modelling to process player and match data, compare performance, and generate data-driven insights. Confident in troubleshooting, debugging, data cleaning and building user-focused technical solutions, with a strong interest in software engineering, data analytics and IT support roles.

Football Analytics Dashboard | Python, Streamlit, Pandas, Scikit-Learn, Plotly, GitHub
• Built an interactive football analytics dashboard for match prediction, player comparison, team analysis and recruitment scouting.
• Processed large football datasets across multiple leagues and seasons, applying data cleaning, feature engineering and performance scoring.
• Integrated machine learning models and visual dashboards to support data-driven football analysis.

Machine Learning Prediction System | Python, Scikit-Learn, TensorFlow, Pandas
• Built supervised learning models using datasets exceeding 40,000 records.
• Achieved 95% ROC AUC through feature engineering, hyperparameter optimisation and model evaluation.
• Applied PCA, SMOTE and class weighting techniques to improve model performance and handle class imbalance.

Lightweight Phishing Email Detection System | Python, NLP, Scikit-Learn
• Developed a phishing email detection system using TF-IDF text processing and email header analysis.
• Evaluated Logistic Regression, Random Forest and SVM classifiers against detection performance.
• Engineered lightweight feature extraction methods suitable for efficient deployment.