Feature Engineering Enhancement for ML Models
Budget: £20 – £250 GBP
I'm seeking an expert to improve the feature engineering process for my completed Python-based Machine Learning pipeline, specifically focusing on feature engineering, extraction and the EDA stage. The primary objective is to boost model accuracy as much as possible.
Key Tasks:
- Refine and fine-tune the feature extraction process to increase overall model performance.
- Analyze the current feature engineering and EDA process and identify areas for improvement.
Ideal Candidate:
- Proficient in Python and experienced in Machine Learning.
- Deep understanding and practical experience in feature engineering, with a focus on feature extraction.
- Skilled in working with numerical data.
- Proven track record of enhancing model accuracy through improved feature engineering.
The .py files are completed - I just need someone to improve the eda.py and feature_engineering.py process, so that the models work better. I think something is wrong with these stages.
This is for two models - XGBoost and Log Regression. The .py files for these are also completed.
Key Tasks:
- Refine and fine-tune the feature extraction process to increase overall model performance.
- Analyze the current feature engineering and EDA process and identify areas for improvement.
Ideal Candidate:
- Proficient in Python and experienced in Machine Learning.
- Deep understanding and practical experience in feature engineering, with a focus on feature extraction.
- Skilled in working with numerical data.
- Proven track record of enhancing model accuracy through improved feature engineering.
The .py files are completed - I just need someone to improve the eda.py and feature_engineering.py process, so that the models work better. I think something is wrong with these stages.
This is for two models - XGBoost and Log Regression. The .py files for these are also completed.