Data Analysis Projects in LahoreData Analysis Projects in Lahoreš Sharing Task 2 of my Python Programming Internship at CodeAlpha (https://www.linkedin.com/company/codealpha/)!
This project is a Stock Portfolio Tracker built in Python. The user enters stock names and quantities, and the program calculates the total investment value using a hardcoded dictionary of stock prices (like AAPL, TSLA, GOOGL, and more).
It validates input, lets you add multiple stocks, displays a clean summary table and optionally saves the results to a .txt or .csv file.
I also built a bonus browser-based version using HTML, CSS, and JavaScript with a live-updating portfolio table, a dropdown to select stocks and a one-click CSV download feature.
Through this task, I got hands-on practice with:
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Dictionaries for structured data storage
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Input/output handling and validation
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Basic arithmetic for real-world calculations
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File handling (.txt and .csv export)
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Translating backend logic into an interactive front-end
Thankful to CodeAlpha for this practical learning experience on to the next task!
š GitHub repo link: https://lnkd.in/dswwqvYU
(https://lnkd.in/dswwqvYU)hashtag#CodeAlpha (https://www.linkedin.com/search/results/all/?keywords=%23codealpha&origin=HASH_TAG_FROM_FEED) hashtag#PythonProgramming (https://www.linkedin.com/search/results/all/?keywords=%23pythonprogramming&origin=HASH_TAG_FROM_FEED) hashtag#Internship (https://www.linkedin.com/search/results/all/?keywords=%23internship&origin=HASH_TAG_FROM_FEED) hashtag#LearningByDoing (https://www.linkedin.com/search/results/all/?keywords=%23learningbydoing&origin=HASH_TAG_FROM_FEED) hashtag#SoftwareDevelopment (https://www.linkedin.com/search/results/all/?keywords=%23softwaredevelopment&origin=HASH_TAG_FROM_FEED) hashtag#StudentDeveloper (https://www.linkedin.com/search/results/all/?keywords=%23studentdeveloper&origin=HASH_TAG_FROM_FEED) hashtag#BSCS (https://www.linkedin.com/search/results/all/?keywords=%23bscs&origin=HASH_TAG_FROM_FEED) Built AuraChat v3.0 ā a fully offline Document Intelligence desktop app in pure Python. Users upload any PDF, Word, or TXT file and ask questions in plain English. The system returns cited answers with confidence scores instantly.
Technical highlights:
ā Custom NLP engine using TF-IDF scoring + hybrid token overlap analysis
ā 1,700Ć faster indexing than baseline on 500-page documents
ā Multi-threaded processing ā UI never freezes during heavy indexing
ā Supports PDF, DOCX, and TXT file formats
ā Zero external APIs ā runs completely offline on the user's machine
ā 23 production-grade bugs identified and resolved before delivery
This is not a demo. This is production-ready software built with clean architecture, full error handling, keyboard shortcuts, chat export, source citations, and confidence indicators