📊 Data Analysis
Master essential data exploration, cleaning, and visualization techniques that form the foundation of successful machine learning projects. Learn how to understand your data before applying any models.
📈 Exploratory Data Analysis (EDA)
Learn systematic approaches to explore datasets, uncover patterns, detect anomalies, and understand data distributions before modeling.
- BBC News Text Classification (Text)
- Adult Income Prediction (Tabular)
- Oxford Pets Classification (Image)
- Interactive tutorials with code examples
🧹 Data Cleaning
Handle missing values, outliers, duplicates, and inconsistencies to prepare high-quality datasets for analysis and modeling.
- Missing value imputation strategies
- Outlier detection and treatment
- Data type conversions
- Handling duplicates
⚙️ Feature Engineering
Transform raw data into meaningful features that improve model performance through encoding, scaling, and creating new features.
- Categorical encoding techniques
- Numerical feature scaling
- Feature creation and extraction
- Feature selection methods
📊 Statistical Analysis
Apply statistical methods to understand relationships, test hypotheses, and validate assumptions in your data.
- Descriptive statistics
- Hypothesis testing
- Correlation analysis
- Statistical distributions