{"id":19359111,"url":"https://github.com/uragirii/machine-learning-practice","last_synced_at":"2026-06-14T12:32:08.447Z","repository":{"id":122226717,"uuid":"196706290","full_name":"uragirii/Machine-Learning-Practice","owner":"uragirii","description":"This repository consists of all different algorithms I applied on the various Datasets. 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This repository consists of simple python code for working on common datasets.\n\n## Datasets:\n\nI've downloaded datasets from [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/index.php) and [Kaggle](https://www.kaggle.com). Datasets belong to respective owners. Further Information about each can be found on their website.\nI've worked on :\n- [Adult Salary Dataset](https://archive.ics.uci.edu/ml/datasets/Adult)\n- [Air Quality Index](https://archive.ics.uci.edu/ml/datasets/Air+quality)\n- [Bank Marketing Dataset](https://archive.ics.uci.edu/ml/datasets/Bank+Marketing)\n- [Breast Cancer Dataset](https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29)\n- [Boston Housing Dataset](https://www.kaggle.com/c/boston-housing)\n- [Titanic Surivor Dataset](https://www.kaggle.com/c/titanic)\n\n## Additional Information:\nThe dataset must be downloaded and saved in the directory `Datasets`. Each Dataset has a different file and sometimes different algorithms are applied on same datasets for checking the various accuracy percentage. Please Note that these are simple and very popular datasets and preprocessing is either done by the code or they are loaded preprocessed from `scikit-learn` library.\n\n## Requirements:\nDepending on the files, the requirements may change but common requirements are:\n- `Pandas` (for DataFrame and data storage)\n- `NumPy` (for calculations)\n- `Matplotlib` (for visualizing the output of algorithms)\n\n_I recommend opening files in Spyder IDE as many files may not work as Scripts_\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Furagirii%2Fmachine-learning-practice","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Furagirii%2Fmachine-learning-practice","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Furagirii%2Fmachine-learning-practice/lists"}