Implemented ensemble classifiers for data preprocessing

• Day: 2023-04-19
• Time: 19:50 to 20:00
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Ensemble Learning, Random Forest, Data Preprocessing, Python, Scikit-Learn

Description

Session Goal

The session aimed to explore and implement ensemble classifiers, specifically focusing on handling categorical features and preprocessing tasks in data science projects.

Key Activities

• Discussed and implemented ensemble classifiers like Random Forest and Gradient Boosting for classification tasks involving categorical features. Provided Python code examples using scikit-learn.
• Demonstrated filtering of dataframes to include only labeled data and trained a Random Forest classifier to obtain probability scores.
• Addressed NaN values in input data through removal, imputation, and using models like XGBoost that handle missing values, with accompanying code examples.
• Resolved an index error in NumPy arrays by converting them to pandas DataFrames, allowing for proper indexing and data manipulation.
• Converted numpy ndarrays to pandas DataFrames by defining column names to resolve AttributeError issues.

Achievements

• Successfully implemented ensemble classifiers for handling categorical features.
• Developed robust data preprocessing techniques for dealing with NaN values and data manipulation errors.

Pending Tasks

• Further exploration of ensemble methods for different types of data and more complex datasets.

Evidence

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• event_ids: []