📅 2024-04-18 — Session: Developed Machine Learning Pipeline for Diamond Pricing
🕒 20:30–21:55
🏷️ Labels: Machine Learning, Data Preprocessing, Feature Engineering, Model Evaluation, Python
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The primary goal of this session was to develop a comprehensive machine learning pipeline for predicting diamond prices, focusing on data preprocessing, feature engineering, and model optimization.
Key Activities
- [[Data Visualization]]: Created scatter plots to visualize geometric log variables and their relationship with price.
- Feature Engineering: Evaluated feature relevance for model development, particularly for diamond pricing, using exploratory data analysis.
- Data Preprocessing: Implemented a data preprocessing pipeline using Python and scikit-learn, including outlier removal and feature transformations.
- Preprocessor Understanding: Explained the importance of saving preprocessors like StandardScaler and OneHotEncoder for consistent data transformation.
- Model Implementation: Developed a Random Forest model with hyperparameter tuning using GridSearchCV.
- Log Transformation: Applied logarithmic transformation for regression modeling to handle target variables spanning multiple orders of magnitude.
- Model Evaluation: Evaluated model performance using GridSearchCV, calculating key metrics and creating diagnostic plots.
- Overfitting Management: Discussed strategies to manage overfitting in decision trees and visualized the effects of
max_depthon errors. - [[Data Visualization]] with Matplotlib: Used
plt.plotfor line plots to visualize training and test errors.
Achievements
- Successfully developed a robust machine learning pipeline for diamond pricing, incorporating data preprocessing, feature engineering, and model evaluation techniques.
Pending Tasks
- Further refinement of feature selection criteria based on exploratory data analysis.
- Additional hyperparameter tuning for improved model performance.