https://github.com/shubhamsoni98/classification-with-decision-tree

This project predicts iPhone purchases using demographic data (gender, age, salary). A Decision Tree Classifier was used, achieving 88.16% accuracy. Insights from the model can refine marketing strategies, optimize product offerings, and boost sales by targeting key customer segments.
https://github.com/shubhamsoni98/classification-with-decision-tree

algorithms anaconda classification data data-science descision-tree jupyter-notebook machine-learning prediction python

Last synced: 19 days ago
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This project predicts iPhone purchases using demographic data (gender, age, salary). A Decision Tree Classifier was used, achieving 88.16% accuracy. Insights from the model can refine marketing strategies, optimize product offerings, and boost sales by targeting key customer segments.

• Host: GitHub
• URL: https://github.com/shubhamsoni98/classification-with-decision-tree
• Owner: shubhamsoni98
• Created: 2024-09-17T07:55:34.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-09-17T08:01:03.000Z (2 months ago)
• Last Synced: 2024-10-16T19:51:45.414Z (about 1 month ago)
• Topics: algorithms, anaconda, classification, data, data-science, descision-tree, jupyter-notebook, machine-learning, prediction, python
• Language: Jupyter Notebook
• Homepage:
• Size: 3.59 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Classification-with-Decision-Tree

This project predicts iPhone purchases using demographic data (gender, age, salary). A Decision Tree Classifier was used, achieving 88.16% accuracy. Insights from the model can refine marketing strategies, optimize product offerings, and boost sales by targeting key customer segments.

# iPhone Purchase Status Prediction

## Overview

This project aims to predict whether a customer will purchase an iPhone based on demographic factors such as gender, age, and salary. By applying a Decision Tree Classifier, the project provides actionable insights into customer behavior, enabling businesses to optimize marketing and sales strategies.

## Objectives

- **Predict iPhone Purchases**: Determine the likelihood of a customer purchasing an iPhone using demographic data.

- **Understand Influencing Factors**: Analyze how gender, age, and salary affect purchase decisions.

- **Enhance Marketing Strategies**: Utilize insights to target marketing efforts and refine product offerings.

## Solution

### Data Collection

- **Dataset**: `iphone_purchase_records.csv`

  - **Columns**: `Gender`, `Age`, `Salary`, `Purchase Iphone`