What is the purpose of data preprocessing?

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Supervised and Unsupervised Learning are two primary types of machine learning, differing mainly in how they process and learn from data.

Neural networks are a type of machine learning model inspired by the structure and function of the human brain. They are designed to recognize patterns and relationships in data through a process of learning.

Overfitting is a common problem in machine learning where a model learns the training data too well, including its noise and outliers, resulting in excellent performance on the training set but poor generalization to new, unseen data.

The purpose of data preprocessing is to prepare raw data for analysis or modeling by cleaning and transforming it into a suitable format. This step is crucial because real-world data is often messy, incomplete, or inconsistent, which can negatively impact the performance of machine learning models or data analysis.

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Key goals of data preprocessing:

1. Data Cleaning:

   • Handle missing values, remove duplicates, and correct errors or inconsistencies.

   • Reduce noise and irrelevant information.

2. Data Transformation:

   • Normalize or scale features to a consistent range.

   • Convert categorical data into numerical format (e.g., encoding).

3. Data Reduction:

   • Reduce the dimensionality or size of data to improve efficiency and reduce computational cost.

4. Data Integration:

   • Combine data from multiple sources into a coherent dataset.

5. Improving Data Quality:

   • Enhance the accuracy, completeness, and reliability of data.

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