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Multi-Variant EDA
Food Hub
This project delves into imaginary Food Hub's dataset, utilizing Python for in-depth analysis and Microsoft PowerPoint for presentation. It seamlessly progresses into intricate multivariate analysis, starting with single-variable exploration and culminating in a precise Pearson's correlation model. By marrying Python's analytical prowess with PowerPoint's visual storytelling, the project unveils a comprehensive narrative of patterns and predictive insights within Food Hub's operations. This strategic integration of tools showcases the project's capacity to derive meaningful insights from a simulated dataset while effectively communicating findings through dynamic and visually engaging presentations.
Decision Tree Model
AllLife Bank Personal Loan Campaign
This project presents outcomes from a decision tree model designed to predict potential customers within the fictional AllLife Bank's existing customer base. The model is refined into Fully Grown, Pre-Pruned, and Post-Pruned Models, utilizing Python for rigorous analysis. The insights are compellingly communicated through Microsoft PowerPoint, illustrating the model's accuracy and practicality. Beginning with exploring the decision tree's growth, pruning, and optimization, this analysis leverages the robust capabilities of Python for intricate modeling. The subsequent presentation in MS PowerPoint ensures a visually appealing and accessible representation of the predictive model's outcomes, highlighting its effectiveness in strategic decision-making for the hypothetical AllLife Bank.