Highly analytical and results-driven Entry-level Data Analyst with a Bachelor of Technology in Information Technology, specializing in leveraging advanced statistical methods and machine learning to drive actionable insights. Proficient in Python, SQL, and data visualization tools, I have successfully developed predictive models that improved forecasting accuracy by 15% and achieved 92% classification accuracy in complex data analysis projects. Eager to apply expertise in data cleaning, EDA, and automation to enhance organizational decision-making and optimize operational efficiency.
Machine Learning Engineer
Ghaziabad, Uttar Pradesh, India
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Summary
Engineered a Python-based web application leveraging advanced deep learning models and the USDA FoodData Central API to analyze food images and extract nutritional details.
Highlights
Developed a robust web application to accurately analyze food images and extract detailed nutritional content, including calorie information.
Achieved an average classification accuracy of 92%, significantly surpassing existing benchmarks by 25%.
Data Scientist / Forecasting Analyst
Ghaziabad, Uttar Pradesh, India
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Summary
Designed and implemented a comprehensive forecasting solution utilizing time series models and XGBoost algorithms to predict inventory demand based on historical sales and seasonal data.
Highlights
Developed a sophisticated forecasting solution leveraging time series models and XGBoost algorithms, achieving an average forecast accuracy of 85%.
Improved forecasting performance by 15% over baseline methods, significantly enhancing prediction reliability.
Reduced simulated stockout incidents by 10% and decreased excess inventory levels by 12% through optimized forecasting.
Quantitative Analyst / Data Scientist
Ghaziabad, Uttar Pradesh, India
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Summary
Built a predictive system and interactive dashboard using Streamlit and machine learning models to provide data-driven insights for stock market investors and traders.
Highlights
Designed and implemented a predictive system for stock prices, achieving 82% prediction accuracy and lowering the error rate by 18%.
Enabled over 500 simulated trades, resulting in a 12% improvement in portfolio return simulations.
Developed an interactive dashboard featuring customizable charts and heat maps to facilitate data-driven investment decisions.
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Bachelor of Technology
Information Technology
Published by
Peer-reviewed Research Journal/Conference
Summary
Authored and published a peer-reviewed research paper proposing a secure and transparent blockchain-based e-voting framework, demonstrating expertise in distributed systems and cryptography.
Python, SQL, R, C/C++.
Pandas, NumPy, Scikit-Learn, TensorFlow, Keras, LSTM, ARIMA, XGBoost, Linear Regression, Lasso Regression, Ridge Regression, Random Forest, Deep Learning, Machine Learning, Time Series Analysis, Predictive Modeling, Data Cleaning, Exploratory Data Analysis (EDA), Statistical Methods.
Tableau, Power BI, MS Excel, Matplotlib, Seaborn, Plotly, Streamlit, Dashboard Creation.
MySQL, MongoDB, PostgreSQL.
PySpark, Hadoop.
Jupyter Notebook, Git, GitHub, Flask, FastAPI, API Integration, Automation, Blockchain, Data Structures, Algorithms, Web Application Development, Clean Coding Practices, Open-Source Contribution.