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https://github.com/ayodimeji1/ai_linear_regression
https://github.com/ayodimeji1/ai_linear_regression
artificial-intelligence linear-regression machine-learning matplotlib numpy sckit-learn seaborn
Last synced: 4 days ago
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- Host: GitHub
- URL: https://github.com/ayodimeji1/ai_linear_regression
- Owner: Ayodimeji1
- Created: 2024-11-03T23:56:32.000Z (14 days ago)
- Default Branch: main
- Last Pushed: 2024-11-04T04:39:11.000Z (13 days ago)
- Last Synced: 2024-11-14T00:37:26.107Z (4 days ago)
- Topics: artificial-intelligence, linear-regression, machine-learning, matplotlib, numpy, sckit-learn, seaborn
- Language: Jupyter Notebook
- Homepage:
- Size: 1.18 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Linear Regression Project
## Overview
This 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.
## Table of Contents
- [Features](#features)
- [Project Structure](#project-structure)
- [Installation](#installation)
- [Usage](#usage)
- [Dependencies](#dependencies)
- [Configuration](#configuration)
- [Project Details](#project-details)
- [License](#license)
## Features
- **Data Loading and Preparation**: Prepares and cleans the dataset for analysis.
- **Exploratory Data Analysis (EDA)**: Visualizations and statistical analysis to understand data distribution.
- **Model Implementation**: Simple and multiple linear regression models.
- **Model Evaluation**: Metrics such as Mean Squared Error (MSE), R-squared, and visualization of residuals.
- **Interactive Jupyter Notebook**: Contains code snippets, outputs, and explanations.
## Project Structure
```
Linear_Regression-main/
│
├── Linear_Regression.ipynb # Jupyter Notebook for the project
└── README.md # Project documentation
```
## Installation
### Prerequisites
- **Python 3.8+**
- **Jupyter Notebook** or **Jupyter Lab**
### Setup
1. **Clone the repository**:
```
git clone https://github.com/Ayodimeji1/Linear_Regression.git
cd Linear_Regression-main
```
2. **Install required packages**:
```
pip install numpy pandas matplotlib scikit-learn
```
## Usage
1. **Launch Jupyter Notebook**:
```
jupyter notebook
```
2. **Open `Linear_Regression.ipynb`** in the Jupyter interface and execute the cells step-by-step to follow the analysis and model implementation.
## Dependencies
- **NumPy**: For numerical operations
- **Pandas**: For data manipulation
- **Matplotlib/Seaborn**: For data visualization
- **Scikit-learn**: For model training and evaluation
- **Jupyter Notebook**: For interactive coding environment
## Configuration
- **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.
- **Python Environment**: Use a virtual environment to avoid dependency conflicts.
## Project Details
The notebook provides a hands-on approach to understanding linear regression. It includes:
- **Simple Linear Regression**: Modeling a single feature against a target variable.
- **Multiple Linear Regression**: Extending to multiple features for more robust predictive capabilities.
- **Evaluation Metrics**: Discusses and displays metrics like R-squared, MSE, and visualizations to assess model performance.
## License
This project is licensed under the MIT License.