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This project is a simple implementation of a linear regression model using PyTorch library to predict SPI (Standardized Precipitation Index) values based on input precipitation data.
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The dataset consists of 20 rows of input data, each with 3 features, and a corresponding SPI value.
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The model is trained using mean squared error loss and gradient descent optimization.
The following libraries are required to run the code:
- To run the code, simply execute the script in a Python environment that has the required libraries installed. The script will output the final loss, weights, and bias of the trained model.
- Gradient Descent Algorithm with Linear Regression is used to train this model
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The input data is defined as a NumPy array with 20 rows and 3 columns. The target output is also defined as a NumPy array with 20 rows and 1 column.
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The weights and bias of the neural network model are initialized randomly and with requires_grad set to True, indicating that they need to be updated during training.
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The model is trained for a fixed number of iterations using gradient descent optimization. The loss function used is mean squared error loss.
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After training, the final loss, weights, and bias are printed to the console.
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Some potential areas of improvement for this project include:
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Use a larger and more diverse dataset to improve the accuracy of the model.
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Experiment with different neural network architectures and hyperparameters to find the best model configuration.
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Implement techniques such as early stopping and regularization to prevent overfitting.