esssyjr/binary-classification-bike-buyer

This Project demonstrates the classification of bike buyers using various machine learning models. It includes data preprocessing, model implementation (Decision Tree, Logistic Regression, and Neural Network), and evaluation of model accuracies. The notebook provides a comprehensive guide for bike buyer classification

Bike Buyer Classification

This repository contains code for classifying bike buyers based on various features. The dataset consists of 1000 individuals and includes attributes like marital status, gender, income, education, and more. Three models were implemented: Decision Tree, Logistic Regression, and Neural Network.

Results:

• Decision Tree Accuracy: >90%
• Logistic Regression Accuracy: ~60%
• Neural Network Accuracy: >90%

Feel free to modify the code and experiment with different models to improve accuracy.

Usage

1. Clone the repository.
2. Adjust the file path in the code to your dataset location.
3. Run the code using a Python interpreter.

Citation

The data is gotten from kaggle named as ----Bike Buyers 1000 ----Data of 1000 rows with details of bike buyers with a categorical output variable I could not find the authors name and date published.