About
Highly analytical and results-driven Computer Science Engineering student with hands-on experience in data analysis, machine learning model development, and natural language processing. Proven ability to leverage Python, statistical modeling, and data visualization to derive actionable insights and build innovative solutions, complemented by strong leadership skills from co-leading TEDx VIT Bhopal.
Work
Pune, Maharashtra, India
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Summary
Conducted comprehensive data analysis and machine learning model development to predict academic outcomes and enhance data processing workflows.
Highlights
Developed and compared Linear, Multiple Linear, Polynomial, and Decision Tree Regression models, accurately predicting student exam scores based on study hours, parental education, and academic history.
Executed comprehensive data preprocessing using one-hot encoding, feature scaling, and transformation techniques with pandas and NumPy, significantly enhancing model accuracy and robustness.
Validated model assumptions and ensured reliability by rigorously evaluating performance with MSE, R-squared, and Adjusted R-squared, coupled with in-depth residual analysis using Matplotlib and scikit-learn.
Volunteer
TEDx VIT Bhopal
|Co-Lead
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Summary
Co-led the strategic planning, execution, and promotion of TEDx VIT Bhopal, ensuring a high-impact event and fostering community engagement.
Highlights
Co-led the end-to-end planning and execution of TEDx VIT Bhopal, overseeing team coordination, event logistics, and on-ground operations for a seamless experience.
Directed speaker selection, content curation, and multi-channel marketing strategies, effectively driving audience engagement and promoting the event.
Cultivated strategic partnerships with sponsors, speakers, and university stakeholders, enhancing outreach and significantly elevating the overall event impact.
Skills
Programming Languages
Python, Java, SQL.
Developer Tools
VS Code, Google Colab, GitHub.
Technologies/Frameworks
Scikit-Learn, TensorFlow, Power BI, NLP, Excel, Pandas, NumPy, NLTK, Streamlit, HTML, CSS, JavaScript, AWS S3.
Data Analysis & Machine Learning
Linear Regression, Multiple Linear Regression, Polynomial Regression, Decision Tree Regression, Data Preprocessing, One-hot Encoding, Feature Scaling, Transformation Techniques, Model Performance Evaluation (MSE, R-squared, Adjusted R-squared), Residual Analysis, Natural Language Processing (NLP), Entity Extraction, Keyword Extraction, Vector Embeddings, Similarity Scoring, Data Visualization (Matplotlib).