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what is best way of data cleaning for the good prediction through ANN

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what is best way of data cleaning for the good prediction through ANN
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Taylor
Taylor am 30 Nov. 2023
There is no single answer. The best method to clean your data is dependent on what your data represents. This is not a MATLAB question so much as it a question for your specific field of work.

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Shivansh
Shivansh am 4 Dez. 2023
Hi Sunita!
I understand that you want to know good practices for data cleaning to get better results from an Artificial neural network model. As mentioned in comments, it depends on the data and the problem statement.
There are a few common practices which can be leveraged for data cleaning purpose for an ANN model. They are:
  • Handling Missing Values: Identify and handle missing data using techniques such as imputation or deletion of records.
  • Outlier Detection and Treatment: Detecting and handling outliers using methods like Z-score, or transformation techniques.
  • Normalization and Standardization: Normalize or standardize the input features to bring them to a similar scale.
  • Dealing with Noise: Remove noise from the data by smoothing techniques, filtering, or using feature selection methods to focus on the most relevant features.
  • Handling Categorical Data: Convert categorical variables into a suitable format for ANN, such as one-hot encoding or label encoding.
  • Feature Engineering: Create new features or transform existing ones to better represent the underlying patterns in the data, which can enhance the predictive power of the model.
  • Data Splitting: Split the data into training, validation, and testing sets to evaluate the model's performance and prevent overfitting.
  • Handling Imbalanced Data: If the dataset is imbalanced, consider techniques such as oversampling, or undersampling.
You can refer to the following documentation to get a better understanding of data cleaning in MATLAB: https://www.mathworks.com/discovery/data-cleaning.html.
Hope it helps!

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