Applied AI research focused on the quantitative evaluation and systematic optimization of NLU models.
This project was developed as part of my MSc in Artificial Intelligence at Berlin School of Business and Innovation (BSBI). The primary objective is to move beyond the "black box" nature of chatbots and apply rigorous data science methodologies to evaluate how effectively an AI understands and responds to users.
Through the analysis of interaction logs and NLU (Natural Language Understanding) datasets, this project identifies structural weaknesses in intent classification and suggests data-driven paths for performance optimization.
- Performance Benchmarking: Evaluating intent classification accuracy using metrics such as Precision, Recall, and F1-Score.
- Pattern Recognition: Identifying frequent "fallback" triggers and user digressions to improve the robustness of the conversation flow.
- Data-Driven Optimization: Using statistical analysis to determine where the training data requires more diversity or better entity labeling.
- Visualization of Insights: Transforming raw interaction logs into intuitive dashboards that help stakeholders understand AI reliability.
| Component | Technology | Role |
|---|---|---|
| Language | Python | Core analytical engine. |
| Data Handling | Pandas / NumPy | Cleaning and processing high-volume interaction logs. |
| Visualization | Matplotlib / Seaborn | Generating statistical heatmaps and trend charts. |
| Environment | Jupyter Notebook | Documenting the step-by-step research methodology. |
| Evaluation | Scikit-learn | Calculating confusion matrices and classification reports. |
The project follows a structured data science pipeline:
- Data Ingestion: Processing historical chatbot interaction logs.
- Exploratory Data Analysis (EDA): Visualizing the distribution of intents and identifying class imbalances.
- NLU Evaluation: Running test suites against the existing model to generate performance metrics.
- Optimization Strategy: Formulating specific recommendations for retraining the model based on the "Confused Intent" matrix.
- Python 3.9+
- Jupyter Notebook or Google Colab
- Clone the repository:
git clone [https://github.com/negilbabu/chatbot-analytics-optimization.git](https://github.com/negilbabu/chatbot-analytics-optimization.git)