{"id":27373409,"url":"https://github.com/audy21/machine-learning-exploratory","last_synced_at":"2026-04-13T04:46:05.502Z","repository":{"id":285522422,"uuid":"958423273","full_name":"audy21/machine-learning-exploratory","owner":"audy21","description":"A recent Machine Learning playground, to get a better knowledge and practices.","archived":false,"fork":false,"pushed_at":"2025-04-30T02:24:40.000Z","size":58682,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-08-02T13:25:12.091Z","etag":null,"topics":["matplotlib","pandas","python","pytorch","scikit-learn","tensorflow"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Jupyter Notebook\n\n## Featured Projects\n\n### [Building an E-Commerce Clothing Classifier Model](https://github.com/audy21/machine-learning-exploratory/blob/main/Building%20an%20E-Commerce%20Clothing%20Classifier%20Model/notebook.ipynb)\n- **Objective**: Develop a machine learning model to classify clothing items based on their features.\n- **Tools Used**: Scikit-learn, Pandas, Matplotlib.\n- **Output/Findings**: Achieved high accuracy in classifying clothing items, providing insights into feature importance for classification.\n\n### [Predicting Movie Rental Durations](https://github.com/audy21/machine-learning-exploratory/blob/main/Predicting%20Movie%20Rental%20Durations/notebook.ipynb)\n- **Objective**: Predict the rental duration of movies based on customer and movie attributes.\n- **Tools Used**: Scikit-learn, Pandas, Seaborn.\n- **Output/Findings**: Built a regression model with good predictive performance, identifying key factors influencing rental durations.\n\n### [Predicting Temperature in London](https://github.com/audy21/machine-learning-exploratory/blob/main/Predicting%20Temperature%20in%20London/notebook.ipynb)\n- **Objective**: Forecast daily temperatures in London using historical weather data.\n- **Tools Used**: Scikit-learn, Pandas, Matplotlib.\n- **Output/Findings**: Developed a time-series model that accurately predicts temperature trends, aiding in weather forecasting.\n\n### [Predicting Traffic Volume with PyTorch](https://github.com/audy21/machine-learning-exploratory/blob/main/Predicting%20Traffic%20Volume%20with%20PyTorch/notebook.ipynb)\n- **Objective**: Predict traffic volume on roads using deep learning techniques.\n- **Tools Used**: PyTorch, Pandas, NumPy.\n- **Output/Findings**: Implemented a neural network model that effectively predicts traffic volume, highlighting the impact of time and weather on traffic.\n\n### [Predictive Modeling for Agriculture](https://github.com/audy21/machine-learning-exploratory/blob/main/Predictive%20Modeling%20for%20Agriculture/notebook.ipynb)\n- **Objective**: Build a predictive model to assist in agricultural decision-making.\n- **Tools Used**: Scikit-learn, Pandas, Seaborn.\n- **Output/Findings**: Created a model that predicts crop yields based on environmental and soil factors, providing valuable insights for farmers.\n\n## Conclusion\nThis repository showcases diverse applications of data analysis and machine learning, demonstrating the power of these techniques in solving real-world problems across various domains.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faudy21%2Fmachine-learning-exploratory","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faudy21%2Fmachine-learning-exploratory","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faudy21%2Fmachine-learning-exploratory/lists"}