Welcome to AD_LAB! This repository serves as a digital lab notebook where I record my progress in Application Development, specifically focusing on the intersection of data-driven applications and preprocessing techniques.
I don't just write code; I document the "Why" and the "How." For every major task or tutorial found in this repository, I write a detailed, beginner-friendly article on Medium to help others navigate the same challenges I faced.
🔗 Read my full explanations here: Akshat on Medium
The lab is organized into two main sections:
| Folder | Description |
|---|---|
📁 Tutorial/ |
Step-by-step guides explaining core concepts like Pandas, Scikit-learn, and Pipeline structures. |
📁 Task/ |
Practical lab assignments and real-world data preprocessing challenges. |
This lab focuses on the building blocks of data-centric applications:
- Languages: Python
- Data Manipulation: Pandas, NumPy
- Machine Learning: Scikit-learn (Preprocessing, Imputation, Scaling)[1]
- Environment: Jupyter Notebooks
- Handling Missing Data (Mean/Median/Mode Imputation)
- Categorical Data Encoding (OneHot, Label Encoding)
- Data Scaling & Normalization
- Building Automated Data Pipelines
- Feature Engineering (Ongoing)
- Clone the repository:
git clone https://github.com/Akshat8510/AD_LAB.git
- Explore the Notebooks: Navigate to
Tutorialfor learning orTaskfor practical solutions. - Follow the Articles: If a code block seems complex, check my Medium Profile for the corresponding breakdown.
If you find these resources helpful or want to discuss Application Development, feel free to reach out:
- Medium: @akshat230405
- GitHub: @Akshat8510
- Email: [email protected]
Created with ❤️ to bridge the gap between "Doing" and "Understanding."