# Examples and Use Cases for DataAnalysisToolkit

## Introduction

This document provides examples and use cases illustrating how the DataAnalysisToolkit can be employed in various data analysis scenarios. Each example includes code snippets and explanations to demonstrate the toolkit's functionality.

## Use Case 1: Basic Data Analysis

### Scenario

Performing basic statistical analysis on a dataset of sales data.

### Example Code

```python
from data_analysis_toolkit import DataAnalysisToolkit

# Load data
analyzer = DataAnalysisToolkit('sales_data.csv')

# Basic statistics
statistics = analyzer.calculate_budget_statistics('revenue')
print(statistics)

# Detecting outliers
outliers = analyzer.detect_outliers('revenue')
print(outliers)
```

### Description

This example demonstrates loading a CSV file and performing basic statistical analysis, including outlier detection.

## Use Case 2: Data Cleaning and Preprocessing

### Scenario

Preparing a dataset for machine learning, including handling missing values and encoding categorical variables.

### Example Code

```python
# Handle missing values
analyzer.handle_missing_values('age', strategy='mean')

# Encode categorical features
analyzer.encode_categorical_features()

# Export cleaned data
analyzer.export_data('cleaned_data.csv')
```

### Description

This example shows how to clean and preprocess data by handling missing values and encoding categorical features.

## Use Case 3: Data Visualization

### Scenario

Visualizing the distribution and relationship between variables in a dataset.

### Example Code

```python
# Histogram
analyzer.visualizer.histogram('price')

# Scatter plot
analyzer.visualizer.scatterplot('price', 'quantity')
```

### Description

Visualizations such as histograms and scatter plots help understand data distributions and relationships.

## Use Case 4: Advanced Analysis - Feature Engineering

### Scenario

Creating new features from existing data to improve model performance.

### Example Code

```python
# Binning a continuous variable
analyzer.feature_engineer.binning('age', bins=[0, 18, 35, 65, 100], labels=['Youth', 'Young Adult', 'Adult', 'Senior'])

# Interaction feature
analyzer.feature_engineer.create_interaction('price', 'quantity')
```

### Description

Feature engineering is critical for uncovering insights and enhancing model accuracy.

## Use Case 5: Generating Reports

### Scenario

Creating comprehensive reports for data analysis projects.

### Example Code

```python
# Generate HTML report
analyzer.report_generator.generate_html_report('data_analysis_report.html')
```

### Description

This showcases the report generation feature, useful for documentation and presentation purposes.

## Conclusion

These examples represent a fraction of what can be achieved with the DataAnalysisToolkit. Users are encouraged to explore the toolkit's capabilities and apply them to diverse data analysis scenarios.