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data-driven-decisions

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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…

  • Updated May 22, 2024
  • Jupyter Notebook

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…

  • Updated Jun 23, 2021
Data-Science-in-Golf-Strokes-Gained-vs-Traditional-Metrics

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

  • Updated Feb 9, 2023
  • Jupyter Notebook
Loan-Default-Prediction

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.

  • Updated Dec 16, 2022
  • Jupyter Notebook

This project involved analyzing AdventureWorks bike sales data to uncover key insights into sales performance by country, customer segments, and products. The findings informed strategies for targeted marketing, market expansion, promotional timing, and product quality improvements.

  • Updated Aug 24, 2024
  • Python

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.

  • Updated Oct 29, 2024
  • Jupyter Notebook

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