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It includes complete steps from data wrangling and visualization to feature engineering and model evaluation.\n\n\n🧠 Key Features\n\nCleaning and imputation of missing values\n\nFeature engineering (e.g., binary encoding, aggregation)\n\nOutlier detection and treatment\n\nDecision Tree and Random Forest models\n\nModel tuning and pruning for interpretability\n\nClassification performance metrics (accuracy, recall, ROC curve)\n\n📊 Use Cases\n\nSales and marketing automation pipelines\n\nCRM analytics: prioritizing high-value leads\n\nApplied data science education and portfolio\n\n🛠️ Tools \u0026 Libraries\n\nPython · pandas · matplotlib · seaborn\n\nscikit-learn\n\nJupyter Notebook (exported as HTML)\n\n🚀 Example Question Answered\n\n“How do occupation, education level, and lead source affect conversion rates?”\n\n🚧 Future Enhancements\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdwade-eng%2Fcustomer-lead-conversion-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdwade-eng%2Fcustomer-lead-conversion-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdwade-eng%2Fcustomer-lead-conversion-analysis/lists"}