📅 2024-04-18 — Session: Developed Machine Learning Pipeline with GridSearchCV
🕒 20:30–21:55
🏷️ Labels: Python, Machine Learning, Data Preprocessing, Gridsearchcv
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to enhance a machine learning pipeline by implementing data preprocessing, model development, hyperparameter tuning, and evaluation techniques.
Key Activities
- [[Data Visualization]]: Created scatter plots for geometric log variables to understand their relationship with pricing.
- Feature Engineering: Evaluated feature relevance in model development using exploratory data analysis.
- Data Preprocessing: Developed a preprocessing pipeline using
scikit-learn, including outlier removal and feature transformations. - Model Implementation: Implemented a Random Forest model with
GridSearchCVfor hyperparameter tuning. - Model Evaluation: Assessed model performance using diagnostic plots and metrics post hyperparameter tuning.
- Optimization: Explored strategies to manage overfitting in decision tree models, focusing on
max_depthandmin_samples_leaf.
Achievements
- Successfully created and visualized data relationships and preprocessing steps.
- Implemented a robust Random Forest model with optimized hyperparameters.
- Evaluated model performance with comprehensive metrics and visual diagnostics.
Pending Tasks
- Further exploration of feature engineering to enhance model accuracy.
- Additional experiments with different models and hyperparameter settings to improve performance.