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Python Projects
Introduction
The projects progress from beginner level to intermediate and advanced levels, covering essential concepts such as UI design, event handling, logical operations, file handling, data processing, NLP, and game development. By the end of the program, learners will be equipped with the ability to build practical applications, apply Python concepts effectively, and transition to advanced development or AI roles.

Python Projects

₹2,000.00Price

  • Founder Level Project – Simple Calculator App: A lightweight app that performs basic arithmetic operations with a simple user-friendly interface.

    Project Outcomes

    • A fully functional calculator that performs basic arithmetic operations accurately.
    • A clean, easy-to-use user interface for performing quick calculations.
    • Strong understanding of core programming concepts: logical operations, event handling, UI design, and application workflow.

    Medium Level Project – Personal Expense Tracker: It demonstrates how real-world financial data can be captured, processed, and presented in a user-friendly format. This project serves as an excellent foundation for developing further features like charts, advanced filtering, dashboards, or database integration.

    Project Outcomes

    • A working Python application that records, saves, and analyzes daily expenses.
    • Ability to track categorized expenses, generate summaries, and produce monthly spending reports.
    • Hands-on experience with essential Python skills:
      • File handling
      • Data structures
      • Functions & modular programming
      • Basic data analysis
    • Better understanding of real-world financial data management.

    Advanced Level Project – Hybrid AI Chatbot (Rule-Based + NLP): A Chabot capable of understanding user queries using both rule-based logic and NLP techniques.

    Project Outcomes

     1. Fully Functional Hybrid Chatbot

    • Delivers responses using both rule-based logic and NLP-driven intelligence.
    • Handles predictable queries and open-ended user inputs seamlessly.

     2. Strong Understanding of NLP Concepts

    Learners gain practical experience with:

    • Tokenization
    • Text preprocessing
    • Stemming / Lemmatization
    • Stop-word removal
    • Intent recognition
    • Similarity scoring

    These skills are foundational for building modern AI and conversational systems.

     3. Hands-on Experience with NLP Libraries

    Students work with widely-used Python libraries such as:

    • NLTK
    • spaCy
    • scikit-learn
    • Regex pattern engines.
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