Closed Varunshiyam closed 2 weeks ago
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| Name | Status | Preview | Comments | Updated (UTC) |
|---|---|---|---|---|
| ml-nexus | ✅ Ready (Inspect) | Visit Preview | 💬 Add feedback | Nov 5, 2024 1:46pm |
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2 weeks ago 👋 Thank you for opening this pull request! We appreciate your contribution to improving this project. Your PR is under review, and we'll get back to you shortly. Don't forget to mention the issue you solved!.
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github-actions[bot]
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2 weeks ago 🎉🎉 Thank you for your contribution! Your PR #773 has been merged! 🎉🎉
Related Issues or bug
The goal of this project is to predict the sales revenue generated from different advertising budgets across media channels like TV, radio, and newspapers. By predicting the influence of advertisement spending on sales, this project aims to provide actionable insights to optimize ad budgets and maximize returns.
Fixes: #770
Proposed Changes
The project analyzes the impact of advertisement budgets across various media channels (TV, radio, newspaper) on sales revenue. By employing multiple regression models, it predicts sales as a function of ad spending in each channel. Key performance metrics such as R-squared, Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) are used to evaluate and compare model accuracy. This project uses the advertising dataset from Kaggle to train and test these models.