📅 2023-04-19 — Session: Implemented ensemble classifiers for data preprocessing
🕒 19:50–20:00
🏷️ Labels: Ensemble Learning, Random Forest, Data Preprocessing, Python, Scikit-Learn
📂 Project: Dev
⭐ Priority: MEDIUM
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.