https://github.com/shaadclt/future-sales-prediction-linearregression
This repository provides a sales prediction model using linear regression for an advertising dataset. The model aims to predict sales based on various advertising channels, such as TV, radio, and newspaper.
https://github.com/shaadclt/future-sales-prediction-linearregression
linear-regression
Last synced: 6 months ago
JSON representation
This repository provides a sales prediction model using linear regression for an advertising dataset. The model aims to predict sales based on various advertising channels, such as TV, radio, and newspaper.
- Host: GitHub
- URL: https://github.com/shaadclt/future-sales-prediction-linearregression
- Owner: shaadclt
- Created: 2022-09-29T10:47:34.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2023-07-06T16:17:03.000Z (over 2 years ago)
- Last Synced: 2025-04-09T17:05:02.360Z (6 months ago)
- Topics: linear-regression
- Language: Jupyter Notebook
- Homepage:
- Size: 146 KB
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Future Sales Prediction for Advertising Dataset using Linear Regression
This repository provides a sales prediction model using linear regression for an advertising dataset. The model aims to predict sales based on various advertising channels, such as TV, radio, and newspaper.
## Dataset
The dataset used in this project consists of historical data on advertising expenditures and corresponding sales for different channels. The dataset includes the following columns:
- TV: Advertising budget for TV in thousands of dollars.
- Radio: Advertising budget for radio in thousands of dollars.
- Newspaper: Advertising budget for newspaper in thousands of dollars.
- Sales: Sales in thousands of units.
The dataset is available in the file `Advertising.csv`.
## Model
Linear regression is a statistical approach used to model the relationship between a dependent variable (sales) and one or more independent variables (TV, radio, and newspaper). This model assumes a linear relationship between the variables.
In this repository, we have implemented a linear regression model using Python and the scikit-learn library. The code for training and evaluating the model can be found in the `Future Sales Prediction.ipynb` Jupyter Notebook.
## Getting Started
To get started with the sales prediction model, follow these steps:
1. Clone this repository to your local machine.
2. Install the required dependencies.
3. Ensure you have Jupyter Notebook installed. If not, install it using `pip install jupyter`.
4. Open the `Future Sales Prediction.ipynb` Notebook using Jupyter Notebook.
5. Follow the instructions in the Notebook to train and evaluate the linear regression model.
6. Once trained, you can use the model to make predictions on new advertising data.
## Results
After training the linear regression model on the advertising dataset, you will be able to evaluate its performance and make predictions. The Notebook provides visualizations and metrics to assess the model's accuracy and effectiveness in predicting sales based on advertising budgets.
## Contributions
Contributions to this repository are welcome. If you have any suggestions, bug fixes, or enhancements, please submit a pull request. We appreciate your contributions!
## License
This project is licensed under the MIT License. See the `LICENSE` file for more details.
## Acknowledgments
We would like to acknowledge the authors of the advertising dataset used in this project for providing valuable data for analysis and modeling.
## Contact
For any questions or inquiries, please contact [Mohamed Shaad] at [shaadclt@gmail.com].