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This project aimed to develop a model for predicting sales based on advertising spending across different channels (TV, radio, and newspaper). The analysis revealed a positive correlation between TV advertising spend and sales, allowing the model to accurately predict sales trends based on TV advertising investments.

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Sales-Prediction-Using-Python

Google Colab Python

Pandas Numpy Matplotlib Seaborn Scikit-learn

This project aimed to develop a model for predicting sales based on advertising spending across different channels (TV, radio, and newspaper). The analysis revealed a positive correlation between TV advertising spend and sales, allowing the model to accurately predict sales trends based on TV advertising investments.

Furthermore, the model demonstrates good overall accuracy and reliability, with an R-squared value of 0.86, indicating that it explains a significant portion of the variance in sales. Evaluation metrics, such as RMSE (2.13) and cross-validated RMSE (1.69), further support the model's accuracy and its ability to generalize to new, unseen data.

These findings suggest that the model is a valuable tool for businesses looking to optimize their advertising strategies and forecast sales outcomes. While the current model performs well, there is always room for improvement. Future work could explore incorporating additional variables or refining the model further to enhance its predictive capabilities.

The model accurately predicts sales based on advertising, especially TV, with high reliability

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This project aimed to develop a model for predicting sales based on advertising spending across different channels (TV, radio, and newspaper). The analysis revealed a positive correlation between TV advertising spend and sales, allowing the model to accurately predict sales trends based on TV advertising investments.

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