# Documentation for `preprocessor` Directory of DataAnalysisToolkit

The `preprocessor` directory in the DataAnalysisToolkit contains tools for preprocessing data, an essential stage in preparing data for analysis and machine learning.

## Data Preprocessor (`data_prep.py`)

### Overview

The `DataPreprocessor` class is designed for preprocessing datasets, with a focus on data standardization. Standardization is a key preprocessing step that scales data features to have a mean of 0 and a standard deviation of 1, ensuring that all features contribute equally to the analysis and improving algorithm convergence.

### Usage

```python
preprocessor = DataPreprocessor(df)
preprocessor.standardize(['age', 'income'])
```

### Methods

- `__init__(self, data)`: Initialize the DataPreprocessor with a pandas DataFrame.
- `standardize(self, columns)`: Standardize specified columns in the dataset.

### Example

Standardizing Numeric Columns in a DataFrame:

```python
data_preprocessor = DataPreprocessor(df)
data_preprocessor.standardize(['height', 'weight', 'salary'])
```

### Extended Summary

Data standardization is particularly useful in machine learning, where features with different scales can disproportionately influence the model. By standardizing features, you ensure a balanced contribution from all features and potentially improve the performance of many machine learning algorithms. The `DataPreprocessor` class leverages sklearn's `StandardScaler` to perform this operation efficiently.

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The `preprocessor` directory is pivotal in the DataAnalysisToolkit, providing essential functionalities for data preparation. By using the `DataPreprocessor` class, users can easily prepare their datasets for more effective and accurate data analysis and machine learning model training.