{"id":23820081,"url":"https://github.com/blacksujit/amazon-ml-challange","last_synced_at":"2026-05-17T10:37:12.080Z","repository":{"id":269302926,"uuid":"859412336","full_name":"Blacksujit/Amazon-ML-challange","owner":"Blacksujit","description":"This is our submission to the amazon ML challange to the final round","archived":false,"fork":false,"pushed_at":"2024-12-22T14:47:49.000Z","size":7,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-21T22:42:43.366Z","etag":null,"topics":["2024","amazon","amazon-ml-challange-2024","amazon-ml-challenge","amazon-web-services","bigdata","core","core-machine-learning","core-ml","data-science","data-structures-and-algorithms","data-visualization","datasets","deep-learning","deployement-strategy","jupyter-notebook","machine-learning"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Blacksujit.png","metadata":{"files":{"readme":"READme.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-09-18T16:05:14.000Z","updated_at":"2024-12-22T14:51:33.000Z","dependencies_parsed_at":"2024-12-22T15:36:07.665Z","dependency_job_id":"c71b391f-d83c-4525-80f9-04ce908cde17","html_url":"https://github.com/Blacksujit/Amazon-ML-challange","commit_stats":null,"previous_names":["blacksujit/amazon_ml_challange"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Blacksujit/Amazon-ML-challange","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Blacksujit%2FAmazon-ML-challange","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Blacksujit%2FAmazon-ML-challange/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Blacksujit%2FAmazon-ML-challange/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Blacksujit%2FAmazon-ML-challange/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Blacksujit","download_url":"https://codeload.github.com/Blacksujit/Amazon-ML-challange/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Blacksujit%2FAmazon-ML-challange/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33135105,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-17T09:28:26.183Z","status":"ssl_error","status_checked_at":"2026-05-17T09:27:52.702Z","response_time":107,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["2024","amazon","amazon-ml-challange-2024","amazon-ml-challenge","amazon-web-services","bigdata","core","core-machine-learning","core-ml","data-science","data-structures-and-algorithms","data-visualization","datasets","deep-learning","deployement-strategy","jupyter-notebook","machine-learning"],"created_at":"2025-01-02T07:18:49.203Z","updated_at":"2026-05-17T10:37:12.064Z","avatar_url":"https://github.com/Blacksujit.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Amazon ML Challenge - Machine Learning Model for Data Classification and Analysis\r\n\r\n## Overview\r\n\r\nThis repository provides a solution to the Amazon ML Challenge, which involves building a machine learning model for analyzing and classifying data. The notebook included applies modern machine learning techniques to preprocess data, train models, evaluate performance, and visualize results.\r\n\r\n## Dataset Description\r\n\r\nThe dataset contains features that represent [add a description of the data based on the notebook]. This includes:\r\n\r\nNumerical Features: [List numerical features].\r\n\r\nCategorical Features: [List categorical features].\r\n\r\nTarget Variable: [Description of the target variable].\r\n\r\n## Observations from the Dataset:\r\n\r\nMissing values exist in [specific columns].\r\n\r\nData contains outliers in [specific columns].\r\n\r\nCategorical data needs encoding for machine learning models.\r\n\r\n## Project Workflow\r\n\r\n**Step 1:** Problem Understanding\r\n\r\nObjective: Develop a model that predicts [specific task] with high accuracy.\r\n\r\nApproach: Use supervised learning techniques for [classification/regression] tasks.\r\n\r\n**Step 2:** Data Preprocessing\r\n\r\n## Missing Data Handling:\r\n\r\nNumerical features: Imputed using [median/mean].\r\n\r\nCategorical features: Imputed with [most frequent category].\r\n\r\n**Encoding:**\r\n\r\nUsed [One-Hot Encoding/Label Encoding] for categorical variables.\r\n\r\n**Scaling:**\r\n\r\nStandardized numerical features using [standard scaling].\r\n\r\n## Data Splitting:\r\n\r\nSplit dataset into training, validation, and test sets in an 80-10-10 ratio.\r\n\r\n**Step 3:** Exploratory Data Analysis (EDA)\r\n\r\nVisualized distributions and relationships using matplotlib and seaborn.\r\n\r\nIdentified key trends and feature importance.\r\n\r\n**Step 4:** Model Building\r\n\r\nUsed a [specific model, e.g., Random Forest] for prediction.\r\n\r\nHyperparameter tuning using [Grid Search/Randomized Search].\r\n\r\n****Step 5**:** Model Evaluation\r\n\r\n## Metrics Used:\r\n\r\nAccuracy for classification.\r\n\r\n**F1-Score** for imbalanced datasets.\r\n\r\nMean Squared Error (MSE) for regression.\r\n\r\n**Step 6:** Results Visualization\r\n\r\nPlotted feature importance and residuals.\r\n\r\n**Step 7:** Deployment (Optional)\r\n\r\nProvided an approach for deploying the model using [Flask/FastAPI].\r\n\r\n## Setup Instructions\r\n\r\n**Prerequisites**\r\n\r\nPython 3.8 or above\r\n\r\n**Libraries:**\r\n\r\npandas\r\n\r\nnumpy\r\n\r\nmatplotlib\r\n\r\nseaborn\r\n\r\nscikit-learn\r\n\r\n## Installation:\r\n\r\nClone the repository:\r\n\r\ngit clone [repository_url]\r\ncd [repository_folder]\r\n\r\nInstall dependencies:\r\n\r\npip install -r requirements.txt\r\n\r\nOpen and run the notebook:\r\n\r\njupyter notebook Amazon_ML_Model.ipynb\r\n\r\n## Results:\r\n\r\n**Model Performance:**\r\n\r\n[Insert specific evaluation metrics and scores]\r\n\r\n**Key Insights:**\r\n\r\n[Summarize findings, e.g., important features or significant trends].\r\n\r\n## Future Improvements\r\n\r\n**Feature Engineering:**\r\n\r\nAdd domain-specific derived features.\r\n\r\n**Model Optimization:**\r\n\r\nExperiment with advanced techniques like XGBoost, LightGBM, or Neural Networks.\r\n\r\n## Deployment:\r\n\r\nPackage the solution into an API for real-time predictions.\r\n\r\n## Contribution\r\n\r\nContributions are welcome! Fork the repository, make changes, and submit a pull request. For questions or suggestions, please open an issue.\r\n\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fblacksujit%2Famazon-ml-challange","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fblacksujit%2Famazon-ml-challange","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fblacksujit%2Famazon-ml-challange/lists"}