Python Data Analysis and OOP

📘 Overview
A compact collection of core Python projects demonstrating concepts of File Handling, Object-Oriented Programming, and Data Analysis using NumPy, pandas, and Matplotlib.
Each program is modular, commented, and includes time complexity insights for better understanding and interview readiness.

📂 Projects

1️⃣ File Text Analytics Tool
• Concepts: File handling, regex, collections.Counter
• Function: Reads a text file, cleans content using regex, and counts word frequencies.
• Complexity: Time — O(n) | Space — O(k)
• Run:
python file_text_analytics.py

2️⃣ OOP Banking System
• Concepts: Inheritance, Encapsulation, Modular Design
• Function: Simulates banking operations (deposit, withdraw, interest) using class hierarchy.
• Complexity: Time — O(1) | Space — O(1)
• Run:
python oop_banking_system.py

3️⃣ Matrix Computation and Data Visualization
• Concepts: NumPy, pandas, Matplotlib
• Function: Performs matrix addition/multiplication and visualizes Sales-Profit trends.
• Complexity: Time — O(n²–n³) | Space — O(n²)
• Run:
python data_analysis_module.py

⚙️ Requirements
Before running the projects, install the required libraries:
pip install numpy pandas matplotlib

📁 Folder Structure

Python-Data-Analysis-and-OOP ┣ file_text_analytics.py ┣ oop_banking_system.py ┣ data_analysis_module.py ┗ README.md

🎯 Key Learning Outcomes
• Practical implementation of file handling and regex in text analytics
• Application of core OOP principles — inheritance, encapsulation, modularity
• Hands-on use of NumPy, pandas, and Matplotlib for computation and visualization