https://github.com/iamabhaytiwari343/mushroom_classification
The goal of this Project is to predict whether a mushroom is edible or poisonous based on its physical characteristics.
https://github.com/iamabhaytiwari343/mushroom_classification
classification lgbmclassifier machine-learning matplotlib pandas python seaborn streamlit
Last synced: 7 months ago
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The goal of this Project is to predict whether a mushroom is edible or poisonous based on its physical characteristics.
- Host: GitHub
- URL: https://github.com/iamabhaytiwari343/mushroom_classification
- Owner: iamabhaytiwari343
- Created: 2024-09-01T14:55:13.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-09-15T18:07:26.000Z (about 1 year ago)
- Last Synced: 2025-01-22T06:19:49.919Z (9 months ago)
- Topics: classification, lgbmclassifier, machine-learning, matplotlib, pandas, python, seaborn, streamlit
- Language: Jupyter Notebook
- Homepage: https:/mushroomclassificationapp2024.streamlit.app/
- Size: 94.1 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: readme.md
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README
# LGBM classifier
LGBM (Light Gradient Boosting Machine) is a gradient boosting framework that uses tree-based learning algorithms. It's designed to be efficient, fast, and high-performance. LGBM is particularly well-suited for large datasets and complex machine learning tasks.\
* Histogram-based algorithm: LGBM uses histograms to approximate the distribution of data, significantly reducing memory usage and computation time.
* Exclusive feature bundling: This technique merges features with similar values, further improving computational efficiency.
* Categorical feature support: LGBM can directly handle categorical features without requiring one-hot encoding.
* Gradient-based one-side sampling (GOSS): GOSS focuses on data points with high gradients, improving training speed and generalization performance.
* Exclusive feature importance: This feature provides insights into the importance of each feature in the model.
# streamlit app
* Python: Ensure you have Python 3.7 or later installed.
* Streamlit: Install Streamlit using pip
* Open a terminal or command prompt.
* Navigate to the directory where your app file is located.
* Run the following command - streamlit run app.py
# Data Visualization / Data Cleaning