Expand Preprocessing Capabilities

[ombhojane]

Enhance the preprocessing module to handle a wider range of data types and transformations.

• Implement advanced categorical encoding techniques (e.g., target encoding, weight of evidence)
• Add outlier detection and treatment methods
• Implement dimensionality reduction techniques (e.g., PCA, t-SNE)
• Update documentation with examples of advanced preprocessing techniques

[Sakeebhasan123456]

hey @ombhojane I would like to work on it

2. Advanced Categorical Encoding Techniques

• Objective: Improve model performance by implementing sophisticated encoding methods for categorical variables.
• Implementation:
  • Target Encoding: Replaced categorical variables with a mean of the target variable, reducing dimensionality and capturing target-related information.
  • Weight of Evidence (WoE) Encoding: Calculated the WoE for each category, which measures the predictive power of a feature in relation to the target variable.
  • Benefits: These techniques help in handling high-cardinality categorical variables and can lead to better model performance compared to traditional one-hot encoding.

3. Outlier Detection and Treatment Methods

• Objective: Identify and handle outliers to prevent them from adversely affecting model performance.
• Implementation:
  • Statistical Methods: Used Z-score and IQR (Interquartile Range) methods to detect outliers in numerical features.
  • Isolation Forest: Implemented the Isolation Forest algorithm for anomaly detection in high-dimensional data.
  • Treatment Strategies: Options included removing outliers, capping them at certain thresholds, or transforming them using logarithmic or Box-Cox transformations.

4. Dimensionality Reduction Techniques

• Objective: Reduce the number of features to simplify models, reduce overfitting, and improve visualization.
• Implementation:
  • Principal Component Analysis (PCA): Reduced dimensionality by projecting data onto principal components that capture the most variance.
  • t-Distributed Stochastic Neighbor Embedding (t-SNE): Used for visualizing high-dimensional data in 2D or 3D spaces.
  • Feature Selection Methods: Applied techniques like Recursive Feature Elimination (RFE) and SelectFromModel to select the most important features.
  • Visualization: Plotted the reduced dimensions to visualize the data clusters and understand the separability of classes.

5. Documentation and Examples

• Objective: Provide clear documentation and examples to help users understand and utilize the advanced preprocessing techniques.
• Implementation:
  • Updated Documentation: Expanded the project's documentation to include detailed explanations of each preprocessing step.
  • Code Examples: Included code snippets and Jupyter Notebook examples demonstrating how to apply each technique.
  • Tutorials: Developed step-by-step guides for replicating the preprocessing pipeline and integrating it with the model training process.

[ombhojane]

heyy @Sakeebhasan123456 you've already been assigned to another issue. we highly encourage to solve one issue at a time for quality contribution. you may work on this later.

[alo7lika]

Could you assign me this work? I want to contribute to it and add labels gssoc and hacktoberfest