What are the methods to improve the efficiency and accuracy of my ML code?

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Data preprocessing: Clean and prepare your data by removing outliers and handling missing values.

Feature engineering: Select the most relevant features for your model and transform them appropriately.

Model selection: Choose the right model for your problem and adjust its parameters for optimal performance.

Cross-validation: Use cross-validation techniques to evaluate the performance of your model and prevent overfitting.

Regularization: Use regularization techniques such as L1 or L2 to reduce overfitting and improve model generalization.

Hyperparameter tuning: Optimize the hyperparameters of your model using grid search, random search, or other techniques.

Use of libraries and frameworks: Take advantage of pre-built libraries and frameworks such as scikit-learn, TensorFlow, or PyTorch to streamline your implementation.

Optimize hardware: Use hardware acceleration techniques such as GPUs or TPUs to speed up your computations.

Testing and debugging: Test your code thoroughly and use debugging tools to find and fix errors.

Continuous monitoring: Monitor your model performance over time and implement techniques such as retraining or updating your model to improve accuracy and efficiency.