https://github.com/mahtabranjbar/onlineshopping_analysis_dashboard
This project analyzes online shopper behavior using various machine learning models and EDA techniques.
https://github.com/mahtabranjbar/onlineshopping_analysis_dashboard
dashboard data dataanalysis eda machine-learning streamlit
Last synced: 5 months ago
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This project analyzes online shopper behavior using various machine learning models and EDA techniques.
- Host: GitHub
- URL: https://github.com/mahtabranjbar/onlineshopping_analysis_dashboard
- Owner: MahtabRanjbar
- Created: 2024-08-25T18:32:36.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-08-25T18:49:25.000Z (almost 2 years ago)
- Last Synced: 2025-07-10T22:15:48.175Z (12 months ago)
- Topics: dashboard, data, dataanalysis, eda, machine-learning, streamlit
- Language: Python
- Homepage:
- Size: 3.87 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Online Shoppers Intention Analysis
## Overview
This project analyzes online shopper behavior using various machine learning models. It includes an interactive Streamlit dashboard for exploratory data analysis (EDA) and model evaluation. The project evaluates multiple models and provides visual insights into the factors influencing shopper purchases. Here is the demo of Dashboard:
## Features
- **Streamlit Dashboard**: Interactive interface for exploring data and visualizing model performance.
- **Exploratory Data Analysis (EDA)**: Visualizations of key metrics, distributions, and relationships between variables.
- **Clustering Analysis**: Interactive clustering results to find patterns in shopper behavior.
- **Model Comparison**: ROC curves and performance metrics for comparing different machine learning models.
- **Model Comparison**: Evaluation of various classification models including:
- Naive Bayes
- K-Nearest Neighbors (KNN)
- Support Vector Machine (SVM)
- Random Forest
- Gradient Boosting
- AdaBoost
## Data
The dataset used is `online_shoppers_intention.csv`, which includes features related to shopper behavior and their purchase intentions.
## Installation
1. Clone the repository:
```bash
git clone https://github.com/MahtabRanjbar/Onlineshopping_analysis_dashboard.git
cd Onlineshopping_analysis_dashboard
```
2. Install the required packages:
```bash
pip install -r requirements.txt
```
## Usage
### Running the Streamlit Dashboard
To start the interactive dashboard, run the following command:
```bash
streamlit run src/streamlit_app.py
```
This will launch the Streamlit server and open the dashboard in your browser. You can explore various tabs including EDA, clustering analysis, and model comparison.
### Model Training and Evaluation
To train and evaluate the models, execute:
```bash
python src/model_training.py
```
This script trains multiple classification models, evaluates their performance, and saves the results, including confusion matrix plots and ROC curves.
## File Structure
- **data/:** Contains the dataset.
- **reports/:** Includes model evaluation reports and comparison plots.
- - **model_reports/:** Contains confusion matrix plots for each model.
- - **comparison/:** Contains ROC curve plots and model comparison tables.
- **src/:** Source code for model training, evaluation, and the Streamlit app.
- - **model_training.py:** Script for training and evaluating models.
- - **utils.py:** Utility functions for model evaluation and reporting.
- - **streamlit_app.py:** Streamlit application for data exploration and model comparison.