https://github.com/wa-lead/ml485_blind_preprocessing_prediction_comp
This project aims to achieve the best prediction results by applying various preprocessing techniques and blind data engineering.
https://github.com/wa-lead/ml485_blind_preprocessing_prediction_comp
data-engineering data-visualization machine-learning python
Last synced: 10 days ago
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This project aims to achieve the best prediction results by applying various preprocessing techniques and blind data engineering.
- Host: GitHub
- URL: https://github.com/wa-lead/ml485_blind_preprocessing_prediction_comp
- Owner: Wa-lead
- Created: 2023-05-17T13:54:50.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2023-05-17T14:09:13.000Z (about 3 years ago)
- Last Synced: 2025-01-26T15:46:02.782Z (over 1 year ago)
- Topics: data-engineering, data-visualization, machine-learning, python
- Language: Jupyter Notebook
- Homepage:
- Size: 1.38 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# ML485_blind_preprocessing_prediction_comp
This project aims to achieve the best prediction results by applying various preprocessing techniques and blind data engineering.
# Data Preprocessing
To gain an understanding of the data structure and the separation of classes, the initial step involved using UMAP (Uniform Manifold Approximation and Projection). Additionally, the data was imputed, and generic data engineering techniques were applied. Finally, feature selection was performed using mutual information.
# Prediction
Several models were evaluated, and XGBoost emerged as the top-performing model for prediction.
# Gain Further Insights into the Data
To gain insights into the weaknesses of the model, various techniques were employed to understand the reasons behind incorrect predictions (details can be found in the "main.ipynb" file).
Please refer to the "main.ipynb" file for a more comprehensive analysis and implementation details.
" Won first place btw 🥱 "