Case Study Of iPhone Purchases

This repository explores the use of K-Nearest Neighbors (KNN) for predicting customer purchase behavior in the context of iPhone sales. It combines machine learning, data analysis, and visualization.

Data Exploration & Cleaning:

• Analyze and pre-process the iPhone purchase dataset to understand data distribution, handle missing values, and prepare it for modeling.

KNN Model Building:

• Train and evaluate KNN models with different K values to optimize prediction accuracy for iPhone purchases.

Exploratory Data Analysis (EDA):

• Utilize Seaborn library to visualize data trends, analyze feature relationships, and gain insights into customer behavior.

Tableau Integration:

• Create interactive data visualizations and model performance dashboards in Tableau for enhanced understanding and communication.

Comprehensive Documentation:

• Jupyter notebook (.ipynb file) details the complete code and analysis, while a Word document (.docx) provides a breakdown of the end-to-end process, challenges faced, and key takeaways.

This project showcases the potential of KNN in understanding customer purchase behavior and making informed business decisions. The interactive Tableau dashboards and detailed documentation make the insights easily accessible and actionable.

Tableau Dashboard -

https://public.tableau.com/app/profile/shruti.thorle/viz/iPhonePurchaseAnalysis/Dashboard1