This repository contains my final project for the Diploma in Data Science at
Concordia University (Feb–Sep 2022). The notebook PROJECT_FINALE_Osagie.ipynb
shows an end-to-end applied data science workflow in Python, from data loading
and cleaning through exploratory analysis, feature engineering, and predictive
modelling.
The project was completed as part of the final capstone for the program and demonstrates how I approach a real-world analytics problem using modern Python tools.
- Work through a complete data science pipeline in a single notebook.
- Clean and preprocess a real-world tabular dataset.
- Explore the data with descriptive statistics and visualizations.
- Build and evaluate one or more machine-learning models.
- Communicate results clearly with tables, charts, and narrative text.
Through this project I practiced:
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Structuring a reproducible Jupyter notebook for an applied data-science task.
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Cleaning and transforming raw data into model-ready features.
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Visualizing patterns and relationships in the data.
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Training and evaluating machine-learning models using appropriate metrics.
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Explaining findings in a way that is accessible to a non-technical audience.
This project was completed as part of the Diploma in Data Science at Concordia University, Montreal (2022)