{"id":22959953,"url":"https://github.com/oscartma/regression-project","last_synced_at":"2025-10-26T05:11:10.080Z","repository":{"id":263014454,"uuid":"889076512","full_name":"OscarTMa/Regression-Project","owner":"OscarTMa","description":"Regression is a fundamental supervised machine learning technique used to predict continuous numerical outcomes based on input features. ","archived":false,"fork":false,"pushed_at":"2024-11-22T08:25:54.000Z","size":314,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-08-09T16:33:38.715Z","etag":null,"topics":["mae","mse","r-squared"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/OscarTMa.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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":"2024-11-15T15:07:56.000Z","updated_at":"2024-11-22T08:25:57.000Z","dependencies_parsed_at":null,"dependency_job_id":"35315aa2-51e7-445b-93e0-4ff6a4f5bd2b","html_url":"https://github.com/OscarTMa/Regression-Project","commit_stats":null,"previous_names":["oscartma/regression-project"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/OscarTMa/Regression-Project","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/OscarTMa%2FRegression-Project","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/OscarTMa%2FRegression-Project/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/OscarTMa%2FRegression-Project/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/OscarTMa%2FRegression-Project/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/OscarTMa","download_url":"https://codeload.github.com/OscarTMa/Regression-Project/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/OscarTMa%2FRegression-Project/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":281059632,"owners_count":26437061,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-10-26T02:00:06.575Z","response_time":61,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"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":["mae","mse","r-squared"],"created_at":"2024-12-14T18:29:43.486Z","updated_at":"2025-10-26T05:11:10.065Z","avatar_url":"https://github.com/OscarTMa.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Regression-Project\n\n## Table of Contents\n1. [Description](#description)\n2. [Installation](#installation)\n3. [Usage](#usage)\n4. [Project Structure](#project-structure)\n5. [Contributing](#contributing)\n6. [License](#license)\n7. [Workflows](#workflows)\n\n## Description\nRegression is a fundamental supervised machine learning technique used to predict continuous numerical outcomes based on input features. This project focuses on applying regression to [The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic prices and the demand for clean air', J. Environ. Economics \u0026 Management, vol.5, 81-102, 1978.] using a structured dataset. The analysis is designed to provide insights into the relationships between input features and the target variable while also delivering an accurate predictive model.\n\n## Key Concepts Covered\n1.Exploratory Data Analysis (EDA)\n  Understand the data through visualization, summary statistics, and correlation analysis.\n\n2.Data Preprocessing\n\n- Handle missing values and outliers.\n- Transform and encode categorical variables.\n- Standardize or normalize numerical features.\n\n3.Modeling and Evaluation\n\n- Experiment with different regression techniques (Linear Regression, Decision Trees, Gradient Boosting, etc.).\n- Use metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R²) to evaluate model performance.\n\n4.Feature Importance and Interpretability\n\n- Understand which features influence predictions the most.\n- Visualize model predictions versus actual values.\n  \n## Project Goals\n1.Build a robust regression model to predict [target variable, e.g., house prices].                                      \n2.Explore and visualize patterns in the data.                                                 \n3.Highlight practical insights for stakeholders, such as the most influential factors affecting the target variable.                                          \n4.This project provides three distinct **workflows** to enable deployment and environment setup across various platforms. Each is designed for specific use cases\n\n## Technologies Used\n- Python for data processing and modeling.\n- Pandas and NumPy for data manipulation.\n- Matplotlib and Seaborn for visualizations.\n- Scikit-learn for machine learning algorithms and metrics.\n- EC2 AWS\n- Streamlit Cloud\n- Ngrok\n\n\n## Installation\n1. Clone this repository:\n   ```bash\n   git clone https://github.com/oscar/Regression-Project.git\n\n## Usage\njupyter notebook notebooks/exploratory_analysis.ipynb\n\n## Project Structure\nRegression-Project/                                                    \n│                                                    \n├── data/                                                                    \n│   ├── raw/                                                                      \n│   ├── processed/                                                                \n│                                                    \n├── notebooks/                                                                    \n│   ├── exploratory_analysis.ipynb                                                     \n│   ├── regression_model.ipynb                                                          \n│                                                    \n├── scripts/                                                                      \n│   ├── data_preprocessing.py                                                    \n│   ├── model_training.py                                                    \n│   ├── evaluation.py                                                    \n│                                                    \n├── visuals/                                                                      \n│                                                    \n├── README.md                                                                     \n├── requirements.txt                                                              \n├── LICENSE                                                                       \n├── .gitignore                      \n│   ├── workflows                                                                                     \n│   ├── workflows_aws                                                                                 \n│   ├── workflows_ngrok                                       \n\n\n## Contributing\nContributions are welcome! Please open an issue or submit a pull request for any improvements.\n\n## License\nThis project is licensed under the MIT License. See the LICENSE file for more details.\n\n## Workflows \n\n**Workflows: Automating Deployment and Setup**                                            \nThis project provides three distinct workflows to enable deployment and environment setup across various platforms. Each is designed for specific use cases, as detailed below.\n\n1. Workflows/streamlit.yml: **Deploying to Streamlit Cloud**                          \nThis workflow facilitates automatic deployment of the application to Streamlit Cloud, a free hosting service specifically designed for Streamlit-based applications.\n\n   **Key Features:**\n    - **Core File**: .github/workflows/streamlit.yml.            \n    - Automatically installs dependencies from requirements.txt.                  \n    - Configures environment variables and prepares necessary files, such as kaggle.json, for data access.                   \n     - Ideal for simple and rapid deployments of Streamlit applications.\n  \n     **Benefits:**\n     - Free hosting with public access.\n     - Streamlined management of dependencies and environment setup.\n\n2. workflows_ngrok/streamlit_ngrok_solution: **Running with Ngrok**\nThis workflow is intended to expose the local Streamlit application to the internet using Ngrok, a tool for creating secure HTTPS tunnels to localhost.\n\n   **Key Features:**\n    - Core File: workflows_ngrok/streamlit_ngrok_solution.\n    - Configures Ngrok to create a tunnel and expose the application.\n    - **Currently Non-Functional** due to recent changes in Ngrok's tunneling and endpoint APIs.\n  \n   **Current Limitations:**\n    - Ngrok updates have broken compatibility with the current implementation.\n    - Ongoing efforts aim to adapt this workflow to the latest Ngrok standards.\n   **Future Use:**\n    - This workflow has potential for temporary public exposure of local applications without a dedicated hosting platform.\n\n3. workflows_aws/ec2_AWS.txt: **Deploying to AWS EC2**\nThis workflow outlines the steps for deploying the application on an AWS EC2 instance. It is suited for scalable and customizable environments.\n\n   **Key Steps:**\n    - **Launch an EC2 Instance:** Use the AWS Management Console or AWS CLI to create an instance.\n    - **User Data File:** Copy the commands in workflows_aws/ec2_AWS.txt and paste them into the \"User Data\" field during the instance setup. These commands:\n        - Install Python, pip, and Streamlit.                  \n        - Install dependencies from requirements.txt.                  \n        - Launch the application on the configured port.                    \n\n    - **Benefits:**\n       - Full control over the runtime environment.\n       - Scalability to handle varying levels of traffic and usage.                                    \n\n## Summary\nEach workflow serves a distinct purpose:\n\n- Streamlit Cloud: For quick and easy deployment.\n- Ngrok: For temporary public testing (under development).\n- AWS EC2: For robust and scalable deployments.\n\nFollow the instructions in each workflow file to implement the desired deployment strategy.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Foscartma%2Fregression-project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Foscartma%2Fregression-project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Foscartma%2Fregression-project/lists"}