HaiHuynh206/Lead_scoring_model
In this project, I leverage machine learning models including Logistic Regression, Decision Tree, Random Forest, XGBoost, CatBoost, and LightGBM to predict customer lead scoring. I apply WOE and SHAP for feature selection and use Optuna for hyperparameter turning, aiming to identify potential lead customers effectively.
This project helps sales and marketing teams automate the process of evaluating potential customers, known as lead scoring, within Customer Relationship Management (CRM) systems. By inputting customer data like online behavior and campaign interactions, it outputs a score indicating each customer's likelihood of conversion. Sales managers and marketers can then optimize their outreach and sales strategies based on these scores.
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Use this if you need to automatically prioritize sales leads to focus resources on the most promising potential customers.
Not ideal if you prefer manual, qualitative assessment of leads or have a very small, niche customer base where automation isn't critical.
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Apr 19, 2024
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