Anuj Zanje
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Anuj Zanje

AI / ML & data analyst
M.Sc. Computer Science (AI/ML) student Ahmedabad, India Welcome

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Who I am

About Me

Hello, thanks for stopping by 👋

My name is Anuj Zanje. I was born and raised in Ahmedabad, India, and I'm an M.Sc. Computer Science (AI/ML) student with a strong foundation in Python, SQL, Excel, Power BI, machine learning, and data analysis — and building web and AI projects, including this Spotify-styled portfolio.

I have hands-on experience developing academic and independent projects using AI tools, including AI-assisted development, prompt engineering, data analysis, and machine learning techniques — and I'm currently looking for a fresher or trainee opportunity to apply that knowledge and grow in a professional environment.

Quick facts

  • Pursuing an M.Sc. in Computer Science (AI/ML) at Kaushalya The Skills University, Ahmedabad — expected 2026
  • BCA graduate, Silver Oak University, Ahmedabad — GPA 8.47/10 (2024)
  • Comfortable across the full stack: Python, SQL, HTML/CSS/JS, AI tools, Machine learning
  • Completed internship projects (URL shortener, music player) via CodeClause
  • Actively developing open-source machine learning & on GitHub
Background

Education & Certifications

Academic path, certifications, and the languages I work in.

Education

M.Sc. Computer Science (AI/ML)
Kaushalya The Skills University, Ahmedabad · Expected 2026
Bachelor of Computer Applications (BCA)
Silver Oak University, Ahmedabad · GPA 8.47/10 · 2024
10th & 12th Standard
Shriji Vidhyalay, Ahmedabad · 60.0%–61.0% · 2019–2021

Certifications

Google Cloud — Introduction to Generative AI
Completion Badge · August 2026

Languages

English Hindi Gujarati
Selected work

Projects

A mix of academic and self-directed builds, with machine learning, NLP, and prompt engineering used to turn open-ended questions into clear code, data, and research workflows.

Flagship · AI/ML View Project

AI Chatbot & Admin CRM Dashboard for University Admissions

A multilingual (English, Hindi, Gujarati) Flask-based chatbot and admin CRM dashboard, using NLTK preprocessing, a BERT fallback layer, and a TF-IDF + 4-model ML ensemble (SVM, Random Forest, Naive Bayes, Logistic Regression) to classify student intent across 101 courses. The end-to-end architecture connects live chat sessions to a staff dashboard for lead tracking, escalation, and analytics — piloted across 250+ student sessions with 93% answer accuracy and roughly a 50% cut in routine staff query volume.

PythonFlask NLTKBERT Machine Learning
Problem-solving system

From vague question to useful answer

A repeatable prompt workflow I use across academic builds and data-analysis tasks: frame the role, define the output contract, add examples, then evaluate and refine.

Three analytical challenges

  • Messy request → SQL: clarify entities, filters, time windows, and expected output before generating a query.
  • Long documents → evidence: use retrieval context, citation requirements, and a “say unknown” fallback.
  • Raw output → decision: ask for assumptions, confidence, and a concise stakeholder summary after the analysis.

Prompt refinement in practice

BEFORE “Analyze this dataset and tell me what matters.”

AFTER “Act as a data analyst. Identify missingness, outliers, and the three strongest relationships. Return a table with evidence, caveats, and one chart recommendation.”

↓ 42% ambiguity2× faster review+3 explicit caveats
Academic writing

Research & academic direction

A transparent record of the research questions I am developing around AI/ML, information retrieval, and natural-language analytics.

Portfolio note: this is a manuscript co-authored with a faculty advisor, not yet a peer-reviewed publication — the entry below is presented at that status rather than as a claimed publication.

Intelligent AI Chatbot for Personalized Recommendations in University Admissions

Manuscript · co-authored with faculty advisor Ongoing NLP · Chatbots · Admissions

Co-authored with Dikshan N. Shah, Assistant Professor at Kaushalya The Skills University. The paper benchmarks the university admissions chatbot against 24 prior academic and industry systems (2020–2026), identifying gaps in multilingual support, voice interaction, and personalized admission guidance in existing literature. A controlled evaluation protocol — a hand-tagged test set alongside a 120-respondent post-interaction survey — measured answer accuracy, response latency, and student satisfaction, finding that the BERT fallback layer lifted intent-classification accuracy by 4–6% over pattern matching alone. The manuscript documents the dual-sided system architecture (student-facing NLP pipeline and admin CRM dashboard) across its input, NLP, ML, data, and output layers, and I authored the comparative literature review and evaluation methodology sections.

Zanje, A., & Shah, D. N. Intelligent AI Chatbot for Personalized Recommendations in University Admissions. Manuscript in preparation.

Discuss the research ↗ View CV ↗
Toolbox

Skills

The focused toolkit I use for data analysis, machine learning, automation, and communicating results.

Programming & Analysis

Python Advanced
SQL Advanced
ΣStatistical Analysis Advanced
↔Data Wrangling Advanced
⌫Data Cleaning Intermediate
▥Data Visualization Learning

Machine Learning Libraries

Pandas Advanced
NumPy Intermediate
Scikit-learn Intermediate
NLNLTK Intermediate
Flask Beginner
OpenCV Beginner
Y8YOLOv8 (Ultralytics) Beginner

Tools & BI

BIPower BI Advanced
XMicrosoft Excel (Pivot Tables, VLOOKUP/XLOOKUP, DAX) Advanced
WMS Word Beginner
PMS PowerPoint Intermediate

AI Tools

Open source

GitHub Activity

python-QuizPublic
github.com/AnujZanje1142/python-Quiz-
🔵 Python
URL ShortenerPublic
CodeClauseInternship_URLshortener
🔵 Python
Music PlayerPublic
MusicPlayerusingPython
🔵 Python
Library Management SystemPublic
Library-Management-System-
🔵 Python, SQL, Flask
Movie ClonePublic
Sem5
🔴 HTML, CSS, JS
E.comPublic
small-e.com-project
🔴 HTML, CSS, JS, Bootstrap
Looking ahead

Coding Goals

Goals for junior year

  • Land an AI/ML or data engineering internship
  • Participate in high-impact hackathons
  • Deep dive into LLM agent architectures

Programming goals

  • Optimize AI agent tools for speed and low-latency performance
  • Strengthen algorithmic fluency through daily LeetCode practice
  • Contribute applied ML research on prompt synthesis techniques
  • Craft immersive, interaction-rich web experiences
Say hello

Contact

LinkedIn GitHub Email WhatsApp