Developed and Troubleshot Machine Learning Pipeline

📅 2024-04-12 — Session: Developed and Troubleshot Machine Learning Pipeline

🕒 19:25–20:10
🏷️ Labels: Machine Learning, Git, Python, Sgdregressor, Gridsearchcv
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The goal of this session was to develop a machine learning pipeline using the diamonds dataset, restructure the Git repository, and troubleshoot Git errors.

Key Activities

• Model Development Plan: Outlined a structured plan for developing a machine learning model, including data preprocessing, model training, evaluation, and addressing production issues.
• Git Repository Restructuring: Followed a guide to organize and update the Git repository, including adding and removing files and managing commits.
• Git Error Resolution: Addressed the ‘Couldn’t Find Remote Ref’ error by checking remote branches and setting upstream branches.
• Local Branch Integration: Integrated the local Git branch with remote repositories using merge and rebase strategies.
• Git Log Utilization: Used git log to view and customize commit history.
• Model Training Code Revision: Revised code for model training using SGDRegressor, including data preprocessing and evaluation.
• Error Handling in Python: Resolved FileNotFoundError by understanding file paths and setting a default project directory in Python scripts.
• Grid Search Setup and Analysis: Set up and analyzed GridSearchCV for hyperparameter tuning of SGDRegressor.

Achievements

• Developed a comprehensive plan for the machine learning model.
• Successfully restructured the Git repository and resolved Git errors.
• Revised and improved model training and evaluation code.
• Set up and analyzed grid search for hyperparameter tuning.

Pending Tasks

• Further refine the machine learning model based on grid search insights.
• Continue monitoring and optimizing the Git repository structure.