Technical Overview
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PreppyData is a platform designed to make data preprocessing easily customizable. It adopts a modular architecture to provide flexibility and transparency.

Architecture
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The application consists of the following key components:

1. **Preprocessing Engine**
   - A backend system that performs data transformations based on the options selected by the user.
   - Handles preprocessing tasks such as data encoding, outlier detection, and missing value handling.

2. **Data Management Module**
   - Manages data input and temporary storage while ensuring data security.
   - Supports data formats like CSV and XLSX.

Core Components
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1. **Preprocessing Engine**
     - **Data Encoding Module**
     - Offers various encoding techniques such as One-Hot Encoding and Label Encoding.
     - Converts categorical data into numerical formats suitable for analysis.
     - **Outlier Detection Module**
     - Provides methods like Z-Score, Interquartile Range (IQR), and Local Outlier Factor (LOF).
     - Identifies and processes outliers in the dataset.
     - **Missing Value Handling Module**
     - Offers strategies like Mean Imputation, Median Imputation, and Deletion.
     - Effectively handles missing data to improve dataset completeness.

2. **Data Management Module**
     - **File Handling System**
     - Supports uploading data in CSV and XLSX formats.
     - Ensures data is securely managed during the session and not stored permanently.
     - **Security Measures**
     - Processes data locally to protect user privacy.
     - Prevents unauthorized access or transmission of data.

Underlying Technologies
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- **Programming Language**
- Developed using Python for the backend.
  
- **Frameworks and Libraries**
- **Pandas, NumPy**: Used for efficient data processing and numerical computations.
- **Scikit-learn**: Utilized for implementing preprocessing algorithms.
- **Flask or Django**: Employed as the web framework for building the application.
  
- **Data Visualization**
- **Matplotlib, Seaborn**: Used as visualization tools for data analysis and evaluation.

Workflow Summary
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1. **Data Upload**
   - Users upload CSV or XLSX files.
   
2. **Selection of Preprocessing Options**
   - Users choose the desired preprocessing methods.
   
3. **Data Processing**
   - The Preprocessing Engine transforms the data based on selected options.
   
4. **Result Download**
   - Users download the processed data in their preferred format.