{"id":20165336,"url":"https://github.com/ayodimeji1/ai_linear_regression","last_synced_at":"2026-05-11T15:41:30.228Z","repository":{"id":260973973,"uuid":"882863818","full_name":"Ayodimeji1/AI_Linear_Regression","owner":"Ayodimeji1","description":null,"archived":false,"fork":false,"pushed_at":"2024-11-04T04:39:11.000Z","size":1236,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-13T14:52:18.858Z","etag":null,"topics":["artificial-intelligence","linear-regression","machine-learning","matplotlib","numpy","sckit-learn","seaborn"],"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/Ayodimeji1.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":"2024-11-03T23:56:32.000Z","updated_at":"2024-11-04T04:41:39.000Z","dependencies_parsed_at":"2024-11-04T01:19:35.257Z","dependency_job_id":"ca830dd5-0933-46c8-982b-a9cc62773e8f","html_url":"https://github.com/Ayodimeji1/AI_Linear_Regression","commit_stats":null,"previous_names":["ayodimeji1/linear_regression","ayodimeji1/ai_linear_regression"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ayodimeji1%2FAI_Linear_Regression","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ayodimeji1%2FAI_Linear_Regression/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ayodimeji1%2FAI_Linear_Regression/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ayodimeji1%2FAI_Linear_Regression/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Ayodimeji1","download_url":"https://codeload.github.com/Ayodimeji1/AI_Linear_Regression/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241601324,"owners_count":19988900,"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","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":["artificial-intelligence","linear-regression","machine-learning","matplotlib","numpy","sckit-learn","seaborn"],"created_at":"2024-11-14T00:37:28.991Z","updated_at":"2026-05-11T15:41:30.180Z","avatar_url":"https://github.com/Ayodimeji1.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Linear Regression Project\n\n## Overview\n\nThis project is focused on demonstrating the concepts and practical implementation of Linear Regression, a fundamental technique in machine learning used for predictive modeling. We'll be predicting house prices based on features from a real estate dataset. The project utilizes a Jupyter Notebook to walk through the steps of applying linear regression on a dataset, covering data preparation, model training, and evaluation.\n\n## Table of Contents\n\n- [Features](#features)\n- [Project Structure](#project-structure)\n- [Installation](#installation)\n- [Usage](#usage)\n- [Dependencies](#dependencies)\n- [Configuration](#configuration)\n- [Project Details](#project-details)\n- [License](#license)\n\n## Features\n\n- **Data Loading and Preparation**: Prepares and cleans the dataset for analysis.\n- **Exploratory Data Analysis (EDA)**: Visualizations and statistical analysis to understand data distribution.\n- **Model Implementation**: Simple and multiple linear regression models.\n- **Model Evaluation**: Metrics such as Mean Squared Error (MSE), R-squared, and visualization of residuals.\n- **Interactive Jupyter Notebook**: Contains code snippets, outputs, and explanations.\n\n## Project Structure\n\n```\nLinear_Regression-main/\n│\n├── Linear_Regression.ipynb                 # Jupyter Notebook for the project\n└── README.md                                # Project documentation\n```\n\n## Installation\n\n### Prerequisites\n- **Python 3.8+**\n- **Jupyter Notebook** or **Jupyter Lab**\n\n### Setup\n\n1. **Clone the repository**:\n   ```\n   git clone https://github.com/Ayodimeji1/Linear_Regression.git\n   cd Linear_Regression-main\n   ```\n   \n2. **Install required packages**:\n   ```\n   pip install numpy pandas matplotlib scikit-learn\n   ```\n\n## Usage\n\n1. **Launch Jupyter Notebook**:\n   ```\n   jupyter notebook\n   ```\n\n2. **Open `Linear_Regression.ipynb`** in the Jupyter interface and execute the cells step-by-step to follow the analysis and model implementation.\n\n## Dependencies\n\n- **NumPy**: For numerical operations\n- **Pandas**: For data manipulation\n- **Matplotlib/Seaborn**: For data visualization\n- **Scikit-learn**: For model training and evaluation\n- **Jupyter Notebook**: For interactive coding environment\n\n## Configuration\n\n- **Data File**: Ensure any dataset needed is placed in the correct path or modified in the notebook to point to the location of your data file.\n- **Python Environment**: Use a virtual environment to avoid dependency conflicts.\n\n## Project Details\n\nThe notebook provides a hands-on approach to understanding linear regression. It includes:\n\n- **Simple Linear Regression**: Modeling a single feature against a target variable.\n- **Multiple Linear Regression**: Extending to multiple features for more robust predictive capabilities.\n- **Evaluation Metrics**: Discusses and displays metrics like R-squared, MSE, and visualizations to assess model performance.\n\n\n\n## License\n\nThis project is licensed under the MIT License. \n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fayodimeji1%2Fai_linear_regression","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fayodimeji1%2Fai_linear_regression","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fayodimeji1%2Fai_linear_regression/lists"}