- Data Description Describe your dataset: source, variables, number of samples, etc.
Include data types and basic statistics.
- Data Exploration and Visualization Univariate Visualization: Plot and interpret single variables (e.g., histograms, boxplots).
Multivariate Visualization: Examine relationships between variables (e.g., scatter plots, heatmaps).
- Machine Learning Data Cleaning: Handle missing values, duplicates, and encode categorical variables.
Modelling and Evaluation: Use at least one classical ML model (e.g., Linear Regression) and one neural network (e.g., ANN). Evaluate performance using appropriate metrics.