Kushagr Ittan

Data Scientist

https://talent.gravityer.com/kushagr-ittan

I am a student of B.Tech. in AIML at University School of Automation and Robotics

Delhi, India

Key Strengths

Demonstrated interest in Data Science and Machine Learning through projects like 'HCV-Liver-Fibrosis-Predictor' and 'Salaahkar'.
Experience with Python for data science tasks, including predictive modeling, explainable AI (SHAP), and handling class imbalance (SMOTE).
Familiarity with various ML models (XGBoost, RF, K-Means) and frameworks (Streamlit, LangChain, Llama 3.2).
Exposure to data visualization and dashboarding with Streamlit.
Experience with Jupyter Notebooks for data exploration and model development.

Cultural & Operational Fit

Cultural Fit Analysis

The candidate's projects are primarily personal and demonstrate a strong inclination towards self-learning and exploration in AI/ML. The diversity of projects, from AR experiences to RAG systems, suggests a curious and adaptable mindset. However, the lack of team-based or professional experience makes it difficult to fully assess cultural fit in a collaborative corporate environment. The focus on personal projects aligns with a proactive, self-starter culture.

Soft Skills & Operational Fit

Insufficient data to assess soft skills or operational fit. Project descriptions indicate an ability to work on self-directed initiatives.

AI is analyzing your overall score…

Identifying your key strengths…

Evaluating your skill match against the job requirements…

Assessing your cultural and operational fit

Projects

HCV-Liver-Fibrosis-Predictor

May 5, 2026 – Present

Predictive modeling of liver fibrosis in HCV patients using 8 ML models (XGBoost, RF, K-Means). Features an explainable AI (XAI) pipeline with SHAP, SMOTE for class imbalance, and a Streamlit dashboard for real-time clinical inference.

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Salaahkar

May 3, 2026 – Present

Salaahkar 2.0 is an Agentic RAG study assistant built for college students. Powered by Llama 3.2 and LangChain, it locally indexes course PDFs to provide context-aware tutoring, syllabus mapping, and active recall. Features a Streamlit UI and ChromaDB backend, optimized for private, offline learning on Linux. 📚🤖

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project-musafir

March 5, 2026 – Present

project-musafir — GitHub repository

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ai-_interview_analysis

January 23, 2026 – Present

ai-_interview_analysis — GitHub repository

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Student-Grade-Predictor

November 18, 2025 – November 18, 2025

Student-Grade-Predictor — GitHub repository

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hardware-hackfest2.0

October 4, 2025 – October 5, 2025

hardware-hackfest2.0 — GitHub repository

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HW_HFest2.0

October 4, 2025 – October 4, 2025

HW_HFest2.0 — GitHub repository

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MathMirage

April 29, 2025 – April 29, 2025

MathMirage is an immersive AR experience built in Lens Studio for Mathethon 2025. It features a custom-designed virtual environment with advanced tonemapping techniques to enhance visual realism. The experience overlays interactive mathematical illusions and concepts in augmented space, making learning both engaging and visually captivating.

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logikal

April 23, 2025 – April 23, 2025

logikal — GitHub repository

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Angel_Eyes

March 2, 2025 – April 29, 2025

Angel_Eyes — GitHub repository

View Project

Key Strengths

Demonstrated interest in Data Science and Machine Learning through projects like 'HCV-Liver-Fibrosis-Predictor' and 'Salaahkar'.
Experience with Python for data science tasks, including predictive modeling, explainable AI (SHAP), and handling class imbalance (SMOTE).
Familiarity with various ML models (XGBoost, RF, K-Means) and frameworks (Streamlit, LangChain, Llama 3.2).
Exposure to data visualization and dashboarding with Streamlit.
Experience with Jupyter Notebooks for data exploration and model development.

Cultural & Operational Fit

Cultural Fit Analysis

Soft Skills & Operational Fit

Insufficient data to assess soft skills or operational fit. Project descriptions indicate an ability to work on self-directed initiatives.

Kushagr Ittan

Key Strengths

Cultural & Operational Fit

Top Skills

Skills

Projects

Key Strengths

Cultural & Operational Fit