https://github.com/sanjaiveeran44/shopping-intent-analasys
This project predicts whether a visitor to an e-commerce website will make a purchase based on their browsing behavior. It includes data analysis, preprocessing, and machine learning modeling to understand online shopping patterns.
https://github.com/sanjaiveeran44/shopping-intent-analasys
matplotlib matplotlib-pyplot numpy pandas python3 sklearn tensorflow
Last synced: about 1 month ago
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This project predicts whether a visitor to an e-commerce website will make a purchase based on their browsing behavior. It includes data analysis, preprocessing, and machine learning modeling to understand online shopping patterns.
- Host: GitHub
- URL: https://github.com/sanjaiveeran44/shopping-intent-analasys
- Owner: sanjaiveeran44
- Created: 2025-10-05T12:21:34.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2025-10-05T13:45:48.000Z (about 1 month ago)
- Last Synced: 2025-10-05T14:37:32.331Z (about 1 month ago)
- Topics: matplotlib, matplotlib-pyplot, numpy, pandas, python3, sklearn, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 647 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Online Shopping Intention Analysis
# Project Overview
This project predicts whether a user visiting an e-commerce website will make a purchase. It uses historical user behavior data to perform:
- Exploratory Data Analysis (EDA)
- Data preprocessing (encoding, scaling)
- Machine learning model training
- Performance evaluation
The goal is to understand user behavior and predict purchase intent.
# Dataset
**Source:** Kaggle — Online Shoppers Intention Dataset
**Target Variable:** `Revenue` (0 = No purchase, 1 = Purchase)
**Key Features:**
| Feature | Description |
|---------|-------------|
| ProductRelated_Duration | Total time spent on product pages |
| BounceRates | Fraction of visitors leaving after one page |
| ExitRates | Fraction of exits per page |
| PageValues | Value of pages contributing to revenue |
| VisitorType | New or Returning visitor |
| Weekend | Whether session occurred on weekend |
| Month | Month of the visit |
# Project Steps
## 1. Data Loading & Inspection
- Load dataset into a Jupyter Notebook
- Inspect columns, data types, and missing values
## 2. Exploratory Data Analysis (EDA)
- Target distribution plots (buyers vs non-buyers)
- Numeric and categorical feature analysis
- Correlation heatmap
## 3. Data Preprocessing
- Handle missing values
- Encode categorical features
- Scale numeric features
- Train/Test split
## 4. Model Training
- Train Random Forest Classifier
- Evaluate using accuracy, confusion matrix, and classification report
## 5. Prediction
- Predict purchase intent for new user sessions
# Usage
Clone the repository and run the notebook: