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Combining climate data (air pollution, temperature, precipitation, etc.) and social media/consumer behavior data to examine the relationship between people’s perception of climate change and air quality in specific regions (e.g. states in the US) and predict possible future trends.
We leverage machine learning and data analysis to address real-world challenges in the copper industry. Our documentation encompasses data preprocessing, feature engineering, classification, regression, and model selection. Explore how we've enhanced predictive capabilities to optimize manufacturing solutions.
Culled from the UCI Machine Learning Repository, the Dry Bean Dataset (licensed under CC BY 4.0) provides valuable insights into bean classification and is a valuable resource for machine learning enthusiasts.
We use machine learning and data analysis to predict resale prices of Singapore flats. Our documentation covers data preprocessing, feature engineering, regression, and model selection. Discover how we improved predictions to optimize solutions.
This project helps user to perform trend analysis, pattern recognition, and deriving data insights through exploratory data analysis (EDA) for the Airbnb data. Created an interactive Powerbi dashboard to analyze Airbnb data.