Predict customer purchase intent in an e-commerce environment using session and behavioral data. 🎯
This project focuses on predicting whether a website visitor will make a purchase during an online shopping session.
By identifying visitors with high purchase intent, businesses can optimize targeting, personalization, and retargeting strategies, ultimately boosting revenue and conversion rates. 💰
Target: REVENUE (Categorical)
- 🟢 Buy
- 🔴 Not Buy
Business Impact:
- 🎯 Improved marketing targeting
- ✨ Personalized user experience
- 📈 Higher conversion rates and sales
We use the Online Shoppers Purchasing Intention Dataset, which contains detailed information about visitor sessions.
Features include:
- 📝 Pages visited
- ⏱️ Session duration
- 💳 Cart value
- 📱 Device type
- 🌐 Traffic source
- 🛍️ Past purchases
Sources:
Administrative,Informational,ProductRelatedpages viewedAdministrative_Duration,Informational_Duration,ProductRelated_DurationBounceRates,ExitRates,PageValuesSpecialDay(seasonal promotions) 🎉Monthof the visit 📆OperatingSystems,Browser,Region,TrafficType🖥️VisitorType(New/Returning) 👤Weekendindicator 🛌
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Data Preprocessing 🧹
- Handle missing values
- Encode categorical features
- Normalize numerical features
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Exploratory Data Analysis (EDA) 🔍
- Analyze session patterns
- Identify trends and correlations with purchase behavior
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Modeling 🤖
- Classification models
- Model evaluation with accuracy, precision, recall, F1-score
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Deployment 🚀
- Predict purchase intent in real-time for live sessions
- Integrate predictions with personalization and marketing tools
- Identify sessions with high likelihood of purchase 🛍️
- Optimize marketing spend and retargeting campaigns 💸
- Increase conversion rate and customer satisfaction 😃
This project uses publicly available datasets.