Understanding Model Evaluation in Lead Scoring: A Practical Walkthrough

In this project, we explored model evaluation metrics using a Lead Scoring dataset. The goal was to identify which factors most influence lead conversion and evaluate how well our model can predict them. Below are the key concepts and lessons learned throughout the assignment.

1. Understanding the Lead Scoring Problem

A lead scoring model helps businesses identify which leads (potential customers) are most likely to convert. By assigning a score to each lead, sales teams can focus their efforts where it matters most — improving conversion rates and efficiency.

In our dataset, the target variable (converted) indicates whether a lead converted (1) or not (0). The features included data such as:

• lead_score
• number_of_courses_viewed
• interaction_count
• annual_income

2. Data Preparation

Before modeling, we performed crucial preprocessing steps:

• Handling Missing Values: Dropped rows with missing entries to ensure clean data.
• Feature Scaling: Standardized numeric values using StandardScaler for better model performance.
• Train-Test Split: Divided data into training (80%) and testing (20%) sets to evaluate generalization.

3. Building and Training the Model

We used Logistic Regression, a common algorithm for binary classification problems.
It predicts the probability that a lead will convert, allowing us to make decisions using a threshold (typically 0.5).

The model was trained and evaluated using scikit-learn.

4. Key Evaluation Metrics

Model performance isn’t just about accuracy — we looked deeper into precision, recall, F1 score, and ROC AUC.

ROC AUC (Receiver Operating Characteristic – Area Under Curve)

• Measures the model’s ability to distinguish between classes.
• A perfect model has an AUC of 1.0, while 0.5 means random guessing.
• Our model achieved an AUC of 0.72, indicating good performance.

Precision and Recall

• Precision measures how many predicted positives were actually positive.
• Recall measures how many actual positives were captured by the model.
• These two metrics balance between accuracy and completeness.
• Our precision and recall both stood around 0.54, showing moderate balance.

F1 Score

• The F1 score combines precision and recall into one metric using the harmonic mean.
• It’s especially useful when dealing with imbalanced datasets.
• Our model’s F1 score was 0.54, showing consistent alignment with other metrics.

5. Cross-Validation for Model Robustness

To ensure stability, we applied 5-Fold Cross-Validation — splitting the dataset into 5 parts, training on 4, and testing on 1 iteratively.
The standard deviation across folds was 0.006, showing that the model’s performance is consistent and reliable.

6. Hyperparameter Tuning (Best C)

The C parameter in logistic regression controls regularization strength — preventing overfitting.
We used GridSearchCV to find the best value of C from [0.000001, 0.001, 1].
The optimal value found was C = 1, meaning minimal regularization worked best for our data.

7. Feature Importance

We analyzed the absolute coefficients from our logistic model to identify the most influential features.
The top-performing feature was lead_score, confirming its strong predictive power in determining lead conversion likelihood.

Final Takeaways

Conclusion

This assignment provided hands-on experience in evaluating classification models, understanding performance trade-offs, and applying data-driven tuning for optimal results.

By combining analytical reasoning with technical tools like Logistic Regression, ROC curves, and GridSearchCV, we learned how to assess and improve a predictive model — skills fundamental to every data scientist’s toolkit.