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https://github.com/surajwate/advertising-sales-regression
Linear Regression of Advertising Sales Data
https://github.com/surajwate/advertising-sales-regression
Last synced: 10 days ago
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Linear Regression of Advertising Sales Data
- Host: GitHub
- URL: https://github.com/surajwate/advertising-sales-regression
- Owner: surajwate
- License: mit
- Created: 2024-07-11T13:20:00.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-08-13T21:49:56.000Z (3 months ago)
- Last Synced: 2024-08-14T00:43:30.397Z (3 months ago)
- Language: Python
- Size: 25.4 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Advertising Sales Regression
[](https://advertising-sales-regression.streamlit.app/)
This repository contains the source code for a Streamlit app that demonstrates Simple and Multiple Linear Regression models applied to advertising sales data. The models were created and explained in detail in the following blog posts:
- [Simple Linear Regression](https://surajwate.com/blog/SimpleLinearRegression/)
- [Multiple Linear Regression](https://surajwate.com/blog/MultipleLinearRegression/)
## Table of Contents
- [Overview](#overview)
- [Features](#features)
- [Installation](#installation)
- [Usage](#usage)
- [App Demo](#app-demo)
- [Data](#data)
- [Modeling](#modeling)
- [Contributing](#contributing)
- [License](#license)
## Overview
This project showcases the application of Simple and Multiple Linear Regression models to predict sales based on advertising data. The models were built using Python and deployed using Streamlit, a popular framework for creating interactive web applications. The app allows users to explore the data, visualize relationships, and make predictions based on the regression models.
## Features
- **Data Visualization**: Interactive plots to explore the relationship between advertising channels (TV, Radio, Newspaper) and sales.
- **Model Building**: Simple and Multiple Linear Regression models are built and explained.
- **Prediction Interface**: Users can input their own values for advertising spend to see the predicted sales.
- **Explanation**: The app includes brief explanations of how the models work and what the results mean.
## Installation
To run this application locally, you need to have Python installed. Then, follow these steps:
1. **Clone the repository:**
```bash
git clone https://github.com/surajwate/advertising-sales-regression.git
cd advertising-sales-regression
```
2. **Create a virtual environment (optional but recommended):**
```bash
python -m venv env
source env/bin/activate # On Windows use `env\Scripts\activate`
```
3. **Install the required packages:**
```bash
pip install -r requirements.txt
```
4. **Run the Streamlit app:**
```bash
streamlit run app.py
```
## Usage
Once the app is running, you'll be able to interact with the regression models through the web interface:
- **Input Data**: Adjust the sliders to input different values for TV, Radio, and Newspaper advertising budgets.
- **View Results**: The app will display the predicted sales based on the regression models.
- **Visualize**: Explore the interactive plots to understand the relationship between the different variables.
## App Demo
You can try out the live version of the app here: [Advertising Sales Regression App](https://advertising-sales-regression.streamlit.app/)
## Data
The dataset used in this project is the **Advertising Dataset**, which includes data on TV, Radio, and Newspaper advertising budgets and their corresponding sales. This dataset is a classic example used to explain linear regression.
- **Features**:
- `TV`: Advertising budget spent on TV (in thousands of dollars).
- `Radio`: Advertising budget spent on Radio (in thousands of dollars).
- `Newspaper`: Advertising budget spent on Newspapers (in thousands of dollars).
- **Target**:
- `Sales`: Sales generated (in thousands of units).
## Modeling
### Simple Linear Regression
- **Model**: Predicts sales based on a single independent variable (e.g., TV advertising).
- **Interpretation**: The slope of the regression line indicates how much sales are expected to increase for each additional dollar spent on TV advertising.
### Multiple Linear Regression
- **Model**: Predicts sales based on multiple independent variables (TV, Radio, and Newspaper advertising).
- **Interpretation**: Shows how sales are influenced by each advertising channel, accounting for the effects of the others.
## Contributing
Contributions are welcome! If you have suggestions, bug reports, or improvements, feel free to open an issue or submit a pull request.
1. Fork the repository.
2. Create a new branch (`git checkout -b feature-branch`).
3. Make your changes.
4. Commit your changes (`git commit -m 'Add new feature'`).
5. Push to the branch (`git push origin feature-branch`).
6. Open a pull request.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.