https://github.com/dwade-eng/customer-lead-conversion-analysis
This project explores a real-world lead conversion dataset, using a structured machine learning pipeline to classify leads into likely or unlikely converters. It includes complete steps from data wrangling and visualization to feature engineering and model evaluation.
https://github.com/dwade-eng/customer-lead-conversion-analysis
html matplotlib pandas python3 scikit-learn seaborn
Last synced: about 2 months ago
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This project explores a real-world lead conversion dataset, using a structured machine learning pipeline to classify leads into likely or unlikely converters. It includes complete steps from data wrangling and visualization to feature engineering and model evaluation.
- Host: GitHub
- URL: https://github.com/dwade-eng/customer-lead-conversion-analysis
- Owner: dWADE-ENG
- Created: 2025-09-21T23:30:28.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-09-21T23:32:58.000Z (9 months ago)
- Last Synced: 2025-09-22T01:17:54.944Z (9 months ago)
- Topics: html, matplotlib, pandas, python3, scikit-learn, seaborn
- Language: HTML
- Homepage:
- Size: 1.02 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Customer-Lead-Conversion-Analysis
This project explores a real-world lead conversion dataset, using a structured machine learning pipeline to classify leads into likely or unlikely converters. It includes complete steps from data wrangling and visualization to feature engineering and model evaluation.
🧠 Key Features
Cleaning and imputation of missing values
Feature engineering (e.g., binary encoding, aggregation)
Outlier detection and treatment
Decision Tree and Random Forest models
Model tuning and pruning for interpretability
Classification performance metrics (accuracy, recall, ROC curve)
📊 Use Cases
Sales and marketing automation pipelines
CRM analytics: prioritizing high-value leads
Applied data science education and portfolio
🛠️ Tools & Libraries
Python · pandas · matplotlib · seaborn
scikit-learn
Jupyter Notebook (exported as HTML)
🚀 Example Question Answered
“How do occupation, education level, and lead source affect conversion rates?”
🚧 Future Enhancements