/Hands-On: Symbolic AI Projects

Hands-On: Symbolic AI Projects

These projects focus on traditional machine learning algorithms and techniques, perfect for building a strong foundation in data science.

    • Email Spam Detection System

      Develop a robust machine learning classifier to accurately identify spam emails using models such as Naive Bayes, Support Vector Machines, or ensemble methods. Emphasize text preprocessing, feature extraction, and model evaluation.

      Dataset: UCI Spambase Dataset. Categories: Classification, Natural Language Processing, Text Analysis, Binary Classification.

    • Credit Card Fraud Detection

      Create a predictive model to detect fraudulent credit card transactions while addressing class imbalance. Explore anomaly detection and ensemble methods.

      Dataset: Kaggle Credit Card Fraud Dataset. Categories: Classification, Anomaly Detection, Imbalanced Learning, Financial Machine Learning.

    • House Price Prediction

      Develop a regression model to predict housing prices based on features such as size, location, and amenities. Experiment with linear regression, random forests, and gradient boosting.

      Dataset: Kaggle House Prices Dataset. Categories: Regression, Feature Engineering, Ensemble Methods, Real Estate Analytics.