LSTM Model for Milk Production Predictions
Problem Statement:
The goal of this project was to accurately predict milk production from cows over time to optimize dairy farm operations and planning.
Goals:
To build an experiment with a LSTM (Long-Short-Term-Memory) model that can achieve high accuracy in predicting future milk production based on historical data
Techniques Used:
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Data Preprocessing: Handling missing values, normalizing data, and creating sequences for the LSTM model.
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Modeling: Implementing an LSTM model using Keras.
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Evaluation: Evaluating the model using metrics such as Mean Squared Error (MSE) and Root Mean Squared Error (RMSE).
Methodology
Results
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The LSTM model was used to predict the amount of milk that cows would produce in the future based on historical data. The accuracy of the model's predictions is measured using two metrics: Mean Squared Error (MSE) and Root Mean Squared Error (RMSE).
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The Mean Squared Error (MSE) is a measure of how close the model's predictions are to the actual milk production values, with a lower value indicating better accuracy. In this case, the MSE is 0.47, meaning the model's predictions are very close to the actual values.
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The Root Mean Squared Error (RMSE) is another way to measure prediction accuracy, and it is derived from the MSE. The RMSE value of 0.69 further confirms that the model's predictions are highly accurate and reliable.
Model Overview
