Data Science with less than a year in Data Analytics & Machine Learning
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
I am a motivated and detail-oriented Computer Science and Design student with a clear and logical mindset and a practical approach to problem-solving. I am passionate about learning new technologies, working under pressure, and overcoming challenges effectively. I have a strong foundation in programming languages (Java, Python), web development and machine learning. I have gained hands-on experience through projects which I have worked on. Through these experiences, I have strengthened my technical, analytical, and teamwork skills, and I am eager to apply my knowledge to contribute effectively in a professional environment.
K.S Institute of Technology
Bachelor of Engineering
August 1, 2022 – June 30, 2026
New Angels Senior Secondary School
Intermediate
June 1, 2020 – May 31, 2021
New Angels Senior Secondary School
High School
June 1, 2018 – May 31, 2019
Take It Smart
Data Science Intern
February 1, 2026 – May 31, 2026
Bengaluru, Karnataka, India
Bioactivity Prediction of Chemical Compounds
June 24, 2026 – Present
• Built a machine learning model to predict bioactivity of chemical compounds using features from the ChEMBL dataset. • Performed data preprocessing, feature extraction using molecular descriptors, and train-test splitting. • Trained a Random Forest model and evaluated performance using accuracy and confusion matrix. • Gained experience in data analysis and model building with Python libraries such as Pandas, Scikit-learn, and NumPy.
House Price Prediction
June 24, 2026 – Present
• Developed a machine learning-based web application to predict house prices based on features like location, area, number of rooms, and amenities. • Performed data preprocessing and cleaning using Pandas and NumPy, including handling missing values and feature scaling. • Applied feature engineering techniques to improve model performance. • Trained regression models such as Linear Regression and Random Forest to predict housing prices. • Evaluated model performance using metrics like R2 score and Mean Squared Error (MSE). • Built an interactive Streamlit interface for user input and real-time predictions.
AI Blog Generator
June 24, 2026 – Present
• Built an automated blog generation system using n8n workflows to create content from real-time data sources. • Integrated SerpAPI/News APIs to fetch latest trending topics and AI-related news. • Used LLM APIs (Groq/OpenAI) to generate structured blog articles from raw data. • Designed prompts to produce SEO-friendly and readable blog content. • Automated content formatting and email delivery using Gmail integration. • Reduced manual effort by enabling fully automated daily blog creation and distribution.
HR Attrition Prediction
June 24, 2026 – Present
• Built a machine learning model to predict employee attrition using HR datasets, enabling proactive retention strategies. • Performed comprehensive EDA to uncover key factors driving attrition such as job satisfaction, overtime, salary, and tenure. • Applied feature engineering and encoding techniques (Label Encoding, One-Hot Encoding) to prepare categorical HR data for modelling. • Trained and compared multiple classification models including Logistic Regression, Random Forest, and Gradient Boosting; achieved high accuracy using cross-validation. • Evaluated model performance using accuracy, precision, recall, F1-score, ROC-AUC curve, and confusion matrix. • Developed an interactive Streamlit dashboard allowing HR teams to input employee data and receive real-time attrition risk predictions. • Identified top attrition drivers using feature importance analysis, providing actionable business insights.
AI Data Analyst Chatbot
June 24, 2026 – Present
• Developed an AI-powered data analyst chatbot that converts natural language queries into executable Python (Pandas) code for data analysis. • Loaded and processed datasets using Pandas and NumPy, enabling efficient data manipulation and transformation. • Utilized Groq LLM to interpret user queries and generate dynamic Python code for filtering, aggregation, and statistical analysis. • Executed generated code safely and displayed results through an interactive Streamlit interface. • Enabled users to perform data analysis, insights generation, and exploratory analysis without writing code manually. • Implemented code validation, sandbox execution, and error handling to ensure secure and reliable outputs. • Managed API keys securely using environment variables (Dotenv).
Cultural Fit Analysis
The candidate's project portfolio shows a strong interest in applying data science to diverse domains (bioactivity prediction, HR attrition, house prices, AI chatbots, blog generation). This diversity, coupled with an eagerness to learn new technologies and contribute to data-driven solutions, suggests a good cultural fit for an innovative and collaborative data science team. The candidate's academic background and internship align well with a data science career path.
Soft Skills & Operational Fit
The candidate demonstrates problem-solving, adaptability, and teamwork skills through project descriptions and internship experience. The ability to document workflows and collaborate with team members indicates a good operational fit for structured data science environments. Attention to detail is also highlighted.