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Shipped impact, clean code, real results.
Selected data science projects by Amey Gawade.
Selected data science projects by Amey Gawade.
Data to decision: repeatable impact, metrics that move KPIs.
Data to decision: repeatable impact, metrics that move KPIs.
DidoSEO Web App 2023
DidoSEO Web App 2023
Key Projects
Key Projects
Focus of this project is develop a robust Multivariate Time Series Model utilizing a Gradient Boosting Regression (GBR) architecture to predict key Water Quality Parameters (Flow Rate, Pressure, and Temperature) at various sensor points for future planning, resource management, and setting a reliable baseline for normal system behavior.
For more details, click here
Python | Time Series | Machine Learning | Gradient Boosting | scikit-learn | BigQuery | GCP | Looker | Data Pipeline
Engineered a Multi-Output Classification model to accurately forecast building heating and cooling requirements. Through the use of sophisticated features like Roof Area and Overall Height, the project was able to identify energy inefficiencies. With approximately 99% accuracy, the XGBoost model showed outstanding predictive performance and practical insights for maximizing the use of energy resources and advancing sustainability.
This model serves as a robust tool for energy efficiency planning and data-driven decision-making in building management systems.
For more details, click here
Python | Classification | Machine Learning | Energy Efficiency | Data Analysis | XGBoost | Multi-Output Classification | SVM | Desicion Tree
Developed a BERT-based sentiment analysis model that classifies Amazon product reviews with high accuracy, achieving a 97% F1 score. Implemented advanced data processing, including emoji handling and neutral statement classification, to enhance model performance and improve sentiment prediction accuracy by 15%.
For more details, click here
Python | Sentiment Analysis | Apache Airflow | Bert | HuggingFace | Transformers | NLP | NoSQL | GCP
Sales Prediction based on Advertisement Spend [Simple Linear Regression Project ]
Sales Prediction based on Advertisement Spend [Simple Linear Regression Project ]
This project leverages simple linear regression to predict product sales based on TV advertisement spending. It provides insights into the relationship between ad budgets and sales, evaluates model performance, and visualizes predictions. Key metrics include an R² score of 0.797 and low error rates (MSE: 5.51, MAE: 1.87).
For more details, click here
Fin-S: Stock Prices Predictor and Sentiment Analysis
Fin-S: Stock Prices Predictor and Sentiment Analysis
Built a deep learning-powered web app that accurately predicts stock prices and provides sentiment analysis for financial news, improving predictive accuracy by 25%.
DidoSEO Web App
DidoSEO Web App
Reduced SEO analysis time by 38% and boosted website traffic by 87% through advanced SEO analytics and optimization techniques.
Robots.txt Generator [Streamlit App]
Robots.txt Generator [Streamlit App]
This Streamlit app allows users to generate and customize a robots.txt file by selecting user-agents, specifying disallowed paths, enabling crawler delay, and providing a sitemap URL.
For more details click here
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