DATA SCIENCE ANALYST

Krishang Darji

Krishang Darji - Data Scientist & Analytics Expert

B.Tech Computer Science Engineering student with applied experience in data analysis, machine learning, and statistical modelling. Skilled in Python, SQL, Scikit-learn, and TensorFlow for building predictive models and scalable data workflows. Strong in EDA, real-world data handling, and data visualization to support decision-making. Passionate about continuous learning and innovation in data science.

Email +917778013901

Vadodara, Gujarat, India

Projects Completed

N/A

Accuracy Rate

N/A

Data Processed

Models Deployed

Professional Experience

Web Tech Coordinator

HEX SOCIETY ITM (SLS) Baroda University

2023 – 2024

Web Tech Coordinator at Hex Society, managing website development and user experience

Organizing tech workshops to promote technical skills and active member engagement within the club

Technical Skills

Languages

PythonSQLJavaScript

ML/DS

Scikit-learnTensorFlowpandasspaCy

Visualization

MatplotlibSeabornPlotlyPower BITableau

WebDev

FlaskReact.jsBootstrapTailwind CSSNode.js

Other Tools

GitJupyterVS CodePostman

Soft

Problem-solvingteamworkcommunicationadaptability

Data Science Projects

Mudravani – Bridging the Gap Between Silence and Speech

Developed a real-time AI system that translates static sign language into text and speech using Python, TensorFlow, and MediaPipe. Integrated OpenCV for webcam input and deployed with Flask, achieving 92% accuracy to improve communication for the deaf community.

PythonTensorFlowMediaPipeOpenCVFlask

HireSmart – AI-Powered Resume Screening Application

Build an AI-powered system to streamline hiring by evaluating resumes against job descriptions using NLP and machine learning. Implemented skill extraction, cosine similarity scoring, and dynamic visualizations with Python, Flask, React.js, spaCy, and scikit-learn.

PythonNLPMachine LearningFlaskReact.jsspaCyscikit-learn

House_O_Meter – Real Estate Price Forecasting

Developed a machine learning application to predict real estate prices using property features like location and size, applying Linear, Lasso, Ridge, and XGBoost models with feature engineering and hyperparameter tuning, achieving R² = 0.86 using Python, pandas, scikit-learn, and Matplotlib.