KSAT Quest: Regression_DataNauts

"UKSAT Uncovered: From Raw Data to Refined Insight"

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KSAT Quest: Regression_DataNauts

Inspiration

Soil permeability (KSAT) plays a critical role in various fields such as agriculture and hydrology. The challenge was to predict KSAT using the UKSAT dataset, which contains soil attributes. We were inspired by the idea that machine learning could help predict KSAT more efficiently, saving time and resources compared to traditional measurement methods.

What it does

This project aims to predict the saturated hydraulic conductivity (KSAT) of soil samples using machine learning models. By processing and analyzing the UKSAT dataset, the model predicts KSAT based on soil properties, such as texture and water retention, and evaluates model performance with different dataset sizes.

How we built it

  1. Data Cleaning: Removed samples with missing or invalid data. Standardized the dataset to ensure consistency.
  2. Feature Selection: Applied correlation analysis and feature importance techniques to identify the most relevant features for KSAT prediction.
  3. Modeling: Explored multiple models
  4. Subset Experiments: Ran experiments using progressively smaller datasets (removing 2,000 samples at a time) and evaluated model performance using RMSLE and R².
  5. Hyperparameter Tuning: Used grid and random search to optimize the models for the best performance.
  6. Visualization: Created plots to visualize the relationship between model performance and training data size.

Challenges we ran into

  • High computational load due to repeated experiments on different subsets of data.
  • Overfitting concerns with smaller datasets, requiring careful model selection and tuning.
  • Visualizing results across multiple iterations to interpret trends clearly.
  • Time management with the complexity of cleaning, modeling, and tuning all aspects of the project.

Accomplishments that we're proud of

  • Successfully predicted KSAT using a variety of machine learning models.
  • Created a detailed analysis of how model performance scales with the size of the training dataset.
  • Documented and shared the project with a comprehensive report and well-organized code repository.
  • Managed to tune models effectively, even with limited data, leading to accurate predictions.

What we learned

  • Clean and well-prepared data is crucial for model success.
  • Feature selection significantly improves model accuracy and reduces overfitting.
  • Experimenting with smaller datasets can reveal interesting insights into how models perform when data is limited.
  • Hyperparameter tuning and cross-validation are essential to achieving optimal performance.

What's next for KSAT Quest: Regression_DataNauts

  • Expand the dataset: We aim to incorporate additional data sources for even more robust predictions.
  • Model improvements: Test additional algorithms and fine-tune them further for better performance.
  • User-friendly interface: Develop a tool or API to allow users to easily input soil data and get Ksat predictions.
  • Public release: Share the project on public platforms like GitHub to contribute to the data science community and receive feedback.
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