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SOMOSPIE (Soil Moisture Spatial Inference Engine) consists of a Jupyter Notebook and a suite of machine learning methods to process inputs of available coarse-grained soil moisture data at its native spatial resolution. Features include the selection of a geographic region of interest, prediction of missing values across the entire region of int…
Alpha principles for the ethical use of AI and Data Driven Technologies in Ontario | Proposition de principes pour une utilisation éthique des technologies axées sur les données en Ontario
BeNeutral helps people save money and the environment by making their homes energy-efficient. We calculate co2 emitted by a house, advise how to reduce it and encourage to offset the rest by planting trees. The goal is for a household to achieve and maintain carbon-neutrality.
Minimize the risks and maximize the benefits of using data-driven technologies within government processes, programs and services through transparency. | Réduire les risques et à maximiser les avantages liés à l’utilisation de technologies axées sur les données, dans le cadre de processus, programmes et services gouvernementaux, grâce à la trans…
Unleashed the power of data science to analyze the performance of golfers from the PGA tour. Built ML models and compared Strokes Gained to traditional metrics, resulting in insightful findings and actionable recommendations for golfers at all levels. Showcased advanced data analysis, decision trees, and visualizations in this comprehensive project
Analyzing Retail data to explore the dataset and answer a main question which is how we can make more money by improving weak areas and represent the data in an interactive way.
The project dives into transaction records of an online retail business to uncover hidden relationships between products. The overall goal is a data-driven approach to enhance the customer shopping experience, improve loyalty, boost profitability, tailor marketing strategies, and optimize inventory management via strategic business decisions.
A critical problem in EdTech is converting potential customers into paid customers. Performed EDA to identify the key factors driving the lead conversion process and built an ML model (using Decision Trees and Random Forest) that identifies which leads are more likely to convert.
This academic article explores the factors that influence the success of data initiatives, the importance of data in modern organizations, and the role of data strategy in fostering a data-driven culture.
Dive into my Data Science Projects Repository, featuring a Spam SMS Classifier, NIA Dashboard, H1N1 Vaccine Prediction, and NYC Taxi Fare Prediction. Each project showcases my skills in data cleaning, exploratory analysis, modeling, and visualization, offering valuable insights and methodologies for data enthusiasts and practitioners.
Built a classification model to predict clients who are likely to default on their loans. With the challenge of a limited dataset was able to build and tune a Random Forest Model maximized for a recall score of 80%. Significant EDA and feature analysis were done to identify key features and make business recommendations moving forward.
Built a linear regression model to predict house prices in Boston. The final model is generalized and perfectly predicts prices with a 100% r-squared. Significant EDA and feature analysis were done to identify key features and make business recommendations moving forward.