Developed Machine Learning Pipeline for Diamond Pricing

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Developed Machine Learning Pipeline for Diamond Pricing

  • Day: 2024-04-18
  • Time: 20:30 to 21:55
  • Project: Dev
  • Workspace: WP 2: Operational
  • Status: Completed
  • Priority: MEDIUM
  • Assignee: Matías Nehuen Iglesias
  • Tags: Machine Learning, Data Preprocessing, Feature Engineering, Model Evaluation, Python

Description

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_depth on errors.
  • [[Data Visualization]] with Matplotlib: Used plt.plot for 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.

Evidence

  • source_file=2024-04-18.sessions.jsonl, line_number=2, event_count=0, session_id=bcf78406363e3753262d960a447291306cc64b996f418c903c16d7a41d4421ec
  • event_ids: []

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