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

Hybrid Deep MILP Planner (HD-MILP-Plan)

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…

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…

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.

Code for the Journal of Cleaner Production paper: Data-driven Assessment of Room Air Conditioner Efficiency for Saving Energy (https://doi.org/10.1016/j.jclepro.2022.130615).

Program Description and Certificates for the MicroMasters Program in Business Analytics

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.

A predictive model for postgraduate program enrollment based on historical data, student rankings, and various influencing factors.

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

Recipe Site Traffic Prediction: Utilising machine learning to forecast high traffic recipes on a recipe website. Improve user engagement and traffic with data-driven decisions.

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.

MPS Analytics Coursework [2020 - 2021]

Data-driven Decision Making project to predict location of oil and petroleum refineries in United States

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.

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.

Visual analysis application for the data collected by a Formula Student car during driving, aiming to evaluate and compare the drivers of the team.

Google Advanced Data Analytics Projects: WAZE and Salifort Motors

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.

Python code that predicts which course a student will register for based on their previous registration history.