# AI - Data Preprocessing
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## Introduction
**Data preprocessing** is the initial stage of the Data Mining and Machine Learning process that involves ++cleaning++, ++transforming++, and ++organizing++ raw data into a usable, structured format to improve its quality, accuracy, and suitability for analysis or modeling.

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### Why is it Necessary?
1. **Addresses Data Inconsistencies:** Raw data often contains inconsistencies, noise, and errors that can hinder analysis and modeling efforts.
2. **Handles Incomplete Data:** Datasets can be incomplete, requiring preprocessing to fill in missing values or impute them appropriately.
3. **Standardizes Format:** It ensures all data sets have a uniform design and format, making it easier for machines to understand and process.
4. **Improves Model Performance:** By enhancing data quality, preprocessing leads to more accurate and effective machine learning models and data mining results.
5. **Facilitates Analysis:** Clean, structured data makes it easier to identify patterns, extract meaningful insights, and make informed decisions.

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### Common Techniques!
- **++Data Cleaning++**
- handling missing values
- removing duplicates
- correcting inconsistencies.
- **++Data Transformation++**
- normalization
- scaling
- required by algorithms
- **++Feature Scaling++**
- adjust numerical features to a common scale
- preventing certain features from dominating
- **++Feature Engineering++**
- converting non-numerical data into a numerical format
- (like text labels)
- **++Categorical Data Encoding++**
- creating new features from existing data
- improve model performance
- capture more complex relationships

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## Practical Examples
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## Conclusion
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:::spoiler Relevant Resource
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