---
title: "Jagarajupalli Manasa - AI‑Driven Book Recommendation System (Ongoing Project) in Bengaluru | Tal"
description: "Meet Jagarajupalli Manasa, Aspiring Data Analyst | Building skills in Power BI | SQL | Python | Advanced Excel & Azure | based in Bengaluru."
image: "https://tal.club/meet/jagarajupalli/share-image"
---

# Jagarajupalli Manasa on Tal

## Headline

Aspiring Data Analyst \| Building skills in Power BI \| SQL \| Python \| Advanced Excel & Azure \|

## About

Hello, I'm a BCA graduate with a passion for combining technology, creativity, and problem\-solving, I thrive at the intersection of AI innovation and practical application\. I’ve explored the power of AI/ML tools, prompt engineering, and no\-code development, and applied these to real\-world projects like a Pomodoro Timer with AI\-generated motivation and an Efficient Dairy Management System simulating a full\-stack architecture — all built and deployed using platforms like Bolt\.new and Netlify\. Through my internship at UTL Technologies, I worked with real datasets in healthcare, experimented with LLMs, and gained insight into how AI can transform everyday tasks\. Whether it’s streamlining operations, building user\-focused tools, or simplifying development with no\-code solutions, I enjoy creating systems that are intelligent, efficient, and easy to use\. \*Prompt Engineering Guides:\* Hands\-on resources to help you master the art of crafting prompts for LLMs and generative AI tools \(think: chatbots, creative writing, data analytics and beyond\)\. \- \*AI & No\-Code Toolkits:\* Curated collections and step\-by\-step walkthroughs for building intelligent workflows, automating everyday tasks, and integrating AI into customer support, web development, and business operations—no advanced coding required\. \- \*Personalized Learning Programs:\* From live workshops to on\-demand recorded sessions—learn how to deploy AI agents, automate data routines, and turn your wildest technical ideas into real projects\. \- \*Consulting for Early\-Stage Projects:\* Got an idea but don’t know where to start? I offer 1:1 consulting for students and startups—guiding you on AI tool selection, building MVPs, and scaling project outcomes\. 💡 Always learning\. 🤝 Always building\. 🚀 Always open to new opportunities\.

## Location

Bengaluru

## Work experience

AI‑Driven Book Recommendation System \(Ongoing Project\) at Self\-Employed, Sep 2025 \- Present · 11 mos, Bangalore Rural, Karnataka, India, What I’m building An ML‑powered web app that recommends books using a hybrid approach—collaborative filtering plus content‑based similarity—to cut decision fatigue and improve discovery across genres\. Core features \(MVP\) Onboarding: pick genres/authors \+ quick ratings to seed a profile\. Home feed: For You, Because you liked…, Trending, Fresh picks\. Search: title/author/description with genre/year/rating filters\. Book page: cover, authors, description, tags, similar titles, rate/add to shelf\. Feedback loop: ratings/shelves refine recommendations\. Admin: CSV import \(Goodbooks‑10k\), one‑click retrain, metrics dashboard\. Tech stack Data/ML: Python, pandas, scikit‑learn, TF‑IDF \+ cosine, item‑item CF \(v1\), path to SVD/KNN \(v2\)\. Backend: FastAPI \(REST\), background retraining, model registry\. Frontend: React, responsive UI with carousels and instant search\. Storage: PostgreSQL; versioned model/vectorizer artifacts\. Ops: Health checks, env‑based config, basic observability\. What’s done Requirements, schema, preprocessing plan\. TF‑IDF pipeline and item‑item similarity design\. Hybrid scoring \(CF \+ content \+ popularity\) with fallbacks\. API contracts and UI wireframes \(onboarding, home, book page\)\. Admin flows \(import, retrain, metrics\) scoped\. In progress Data cleaning and vectorizer/index build\. Popularity aggregates, interaction thresholds\. API stubs for recs and similar items\. Evaluation scripts: RMSE/MAE, Precision@K/Recall@K/NDCG@K\. Diversity reranking \(MMR\) and “Why this” explanations\. Next up Full offline eval and metrics dashboard\. A/B flags for hybrid weights and exploration\. Optional phase‑2: Google Books covers, SendGrid weekly digests\. Call for feedback Diversity metrics that correlate with reader satisfaction\. Cold‑start UX patterns that work for book discovery\. Open datasets with richer book metadata\. \#MachineLearning \#RecommenderSystems \#DataScience \#AIProducts \#Python \#FastAPI \#React \#MVP \#Books \#EdTech

Pomodoro Timer with AI Integration at Frontend Developer \(Personal Project\), May 2025 \- Jul 2025 · 3 mos, Bengaluru, Karnataka, India, Tech Stack: JavaScript, HTML, CSS, OpenAI APIs Tools Used: Bolt\.new, Netlify Developed a productivity timer app based on the Pomodoro Technique to help users manage focused work sessions and breaks\. Integrated AI\-generated motivational prompts using OpenAI APIs to enhance user engagement\. Built using Bolt\.new’s no\-code platform and deployed via Netlify with a responsive, user\-friendly interface\. Demonstrated skills in UI/UX, event\-based JavaScript, and no\-code deployment\.

Efficient Dairy System \(Academic Project\) at RJS FIRST GRADE COLLEGE, Jan 2025 \- Jun 2025 · 6 mos, Bengaluru, Karnataka, India, Tech Stack: React \(simulated\), Node\.js \(simulated\), TypeScript, PostgreSQL, Tailwind CSS, JWT Tools Used: Bolt\.new, PostgreSQL, Netlify Designed and deployed a no\-code full\-stack system for managing dairy inventory, supplier records, and order tracking\. Simulated frontend \(React\) and backend \(Node\.js\) architecture with secure JWT\-based authentication\. Incorporated AI\-generated summaries for automated reporting using prompt engineering techniques\. Published via Netlify, showcasing real\-world application flow and database interaction without traditional coding\.

Intern at UTL Technologies ltd\., Feb 2025 \- Apr 2025 · 3 mos, Bengaluru, Karnataka, India, \* Delivered four ML modules in healthcare \(diabetes, breast cancer, heart disease, drug\-review sentiment\) using Python, Scikit learn, NLTK\. \* Built reproducible pipelines: imputation for biologically implausible values, feature scaling, one hot encoding, TF IDF; stratified splitting\. \* Trained Logistic Regression, Random Forest, SVC, KNN, Multinomial NB; compared via accuracy, precision, recall, F1, and confusion matrices\. \* Produced interpretable visualizations \(EDA, correlations, confusion matrices\) and structured documentation for methods and outcomes\. \* Prioritized clinical relevance by focusing on recall for positive cases and transparent trade\-offs in model selection\.

## Education

Bachelor of Computer Applications, Computer Science, Rjs first grade college

Intermediate \(BIPC\), Sri Chaitanya Junior College, Tanapalli, Tirupati

[View the original profile](https://tal.club/meet/jagarajupalli)

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