https://github.com/iotchulindrarai/milk_sales_production
Collect and preprocess historical sales data of Sujal Dairy Pvt. Ltd. to understand trends. Implement regression models like linear regression, decision trees, or advanced models like LSTM, based on data complexity. Validate model accuracy using metrics such as RMSE, MAE, and R-squared.
https://github.com/iotchulindrarai/milk_sales_production
lstm machine-learning mae milk-production r-squared rmse salesprediction
Last synced: 5 months ago
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Collect and preprocess historical sales data of Sujal Dairy Pvt. Ltd. to understand trends. Implement regression models like linear regression, decision trees, or advanced models like LSTM, based on data complexity. Validate model accuracy using metrics such as RMSE, MAE, and R-squared.
- Host: GitHub
- URL: https://github.com/iotchulindrarai/milk_sales_production
- Owner: IotchulindraRai
- License: gpl-3.0
- Created: 2024-12-26T16:13:34.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-01-25T04:07:15.000Z (10 months ago)
- Last Synced: 2025-04-05T09:25:59.575Z (8 months ago)
- Topics: lstm, machine-learning, mae, milk-production, r-squared, rmse, salesprediction
- Language: Jupyter Notebook
- Homepage: https://medium.com/@chulinrai/sales-prediction-and-production-of-milk-industry-using-machine-learnbing-lstm-0eed912fc265
- Size: 316 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
For sales prediction #NovaNectar Services Pvt. Ltd., I collect and preprocess the historical sales data of Sujal Dairy Pvt. Ltd., incorporating key variables such as seasonality, promotions, and economic indicators. These factors are crucial for understanding trends and patterns in the sales data. Next, I implement regression algorithms, including linear regression, decision trees, or more advanced models like LSTM networks, depending on the complexity and structure of the data.
Once the models are trained on the historical data, I validate their accuracy by evaluating performance using key metrics like RMSE (Root Mean Squared Error), MAE (Mean Absolute Error), and R-squared. These metrics provide insight into how well the model predicts future sales.
To further enhance accuracy, I optimize the hyperparameters of the models, fine-tuning them to achieve the best performance. After training and optimizing the models, I provide actionable insights that can guide business strategies, such as inventory management, promotional planning, and pricing strategies.
https://github.com/user-attachments/assets/1d599c97-112f-4f30-aecb-fdc9175dc0fe