Developed an end-to-end machine learning solution to predict lead conversion and identify the key factors influencing customer acquisition. The project combines exploratory data analysis, feature engineering, predictive modeling, and interactive business intelligence dashboards to support data-driven sales and marketing decisions.
Organizations generate thousands of leads through multiple campaigns, but only a small percentage convert into customers. The objective of this project is to:
- Predict lead conversion using machine learning
- Identify high-performing lead sources and campaigns
- Analyze sales representative performance
- Build dashboards for business stakeholders
Programming
- Python
- Pandas
- NumPy
Machine Learning
- Scikit-learn
- XGBoost
- SMOTE
Visualization
- Matplotlib
- Seaborn
- Power BI
- Tableau
- Excel
Raw Data
↓
Exploratory Data Analysis
↓
Data Cleaning & Feature Engineering
↓
Machine Learning
↓
Model Evaluation
↓
Business Dashboards
Performed exploratory analysis to identify patterns affecting lead conversion, including:
- Lead stage distribution
- Lead source performance
- Campaign effectiveness
- Sales executive performance
- Conversion trends
- Missing value analysis
Created business-focused features including:
- Lead Age
- Interaction Hours
- First Contact Delay
- Contacted Status
- Follow-up Status
Models evaluated:
- Logistic Regression
- Logistic Regression (SMOTE)
- XGBoost Classifier
Evaluation Metrics:
- Accuracy
- Precision
- Recall
- F1 Score
- ROC-AUC
- Confusion Matrix
| Model | ROC-AUC |
|---|---|
| Logistic Regression | 0.916 |
| Logistic Regression (SMOTE) | 0.920 |
| XGBoost | 0.947 |
- Addressed severe class imbalance using SMOTE.
- XGBoost achieved the highest ROC-AUC (0.947), delivering the best overall predictive performance.
- Feature engineering significantly improved model performance and interpretability.
- Interactive dashboards enabled analysis of campaign effectiveness, lead sources, and sales performance.
Built interactive dashboards in:
- Excel
- Tableau
- Power BI
Key business insights include:
- Lead conversion trends
- Campaign performance
- Lead source analysis
- Sales executive performance
- KPI monitoring
├── data
├── notebooks
│ ├── 01_eda.ipynb
│ ├── 02_cleaning_feature_engineering.ipynb
│ └── 03_modeling.ipynb
├── images
├── README.md
└── requirements.txt
The dataset is not included in this repository due to confidentiality. The project structure and notebooks are provided for educational and portfolio purposes.
Akhina Manual