Update model card

README.md

@@ -17,316 +17,198 @@ metrics:
 model_name: Telecom Churn Prediction Model
 ---
 
-# Model Card for Telecom Churn Prediction Model
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
 
-This model predicts whether high-value telecom customers are likely to churn (cancel their service) based on their usage patterns over a 3-month period.
 
 ## Model Details
 
 ### Model Description
 
-This is an ensemble of scikit-learn classifiers trained to predict customer churn in the telecom industry. The model focuses specifically on high-value customers (top 70th percentile by recharge amount) and uses behavioral data from months 6, 7, and 8 to predict churn in month 9.
+<!-- Provide a longer summary of what this model is. -->
+
 
-- **Developed by:** MLOps Pipeline Team
-- **Shared by:** Sam2120
-- **Model type:** Binary classification ensemble (Logistic Regression, Random Forest, Gradient Boosting)
+
+- **Developed by:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
 - **Language(s) (NLP):** en
 - **License:** mit
-- **Finetuned from model:** Not applicable - trained from scratch
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
 
-### Model Sources
+<!-- Provide the basic links for the model. -->
 
-- **Repository:** https://github.com/Sam2120/telecom-churn-mlops
-- **Paper:** Not applicable
-- **Demo:** Hugging Face Spaces integration available
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
 
 ## Uses
 
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
 ### Direct Use
 
-This model is designed for:
-- **Customer retention teams** to identify at-risk high-value customers
-- **Marketing teams** to target retention campaigns
-- **Business analysts** to understand churn patterns and drivers
-- **Real-time churn risk scoring** via the provided FastAPI endpoint
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-The model accepts tabular customer data with features like call usage, recharge history, and internet usage, and outputs a churn probability (0-1) and binary prediction.
+[More Information Needed]
 
-### Downstream Use
+### Downstream Use [optional]
 
-- **Integration with CRM systems** for automated retention workflows
-- **Customer lifetime value (CLV) modeling** by combining churn predictions with revenue data
-- **Campaign optimization** to prioritize high-risk, high-value customers
-- **Fraud detection** for identifying unusual usage patterns
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
 
 ### Out-of-Scope Use
 
-This model should NOT be used for:
-- **Predicting churn for low-value customers** (model trained only on high-value segment)
-- **Long-term churn prediction** beyond the 1-month horizon it was trained for
-- **Different telecom markets** without retraining (model behavior may not generalize)
-- **Individual customer harassment** or punitive actions based solely on model predictions
-- **Credit scoring or loan decisions** (not designed for financial risk assessment)
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
 
 ## Bias, Risks, and Limitations
 
-### Technical Limitations
-- **Class imbalance:** The dataset has a natural churn rate of ~8-15%, which creates challenges in recall optimization
-- **Temporal leakage risk:** Features must be strictly from the "good phase" (months 6-8) to avoid data leakage
-- **Feature drift:** Telecom usage patterns change over time; model performance degrades without periodic retraining
-- **Missing value sensitivity:** Model requires complete feature sets; imputation strategies may introduce bias
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-### Sociotechnical Considerations
-- **Demographic bias:** If training data contains demographic information correlated with protected attributes, the model may inadvertently learn biased patterns
-- **Surveillance concerns:** Predictive models for customer behavior raise privacy considerations
-- **Feedback loops:** Aggressive retention campaigns targeting predicted churners may create self-fulfilling prophecies
+[More Information Needed]
 
 ### Recommendations
-- Regular model retraining (monthly/quarterly) to account for changing customer behaviors
-- A/B testing of retention interventions to measure true causal impact
-- Monitoring for demographic disparities in predictions across customer segments
-- Combining model predictions with human judgment for final retention decisions
-- Transparent communication with customers about data usage for service improvement
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 
 ## How to Get Started with the Model
 
-### Installation
-```bash
-pip install -r requirements.txt
-```
-
-### Quick Start
-```python
-import pickle
-import pandas as pd
-
-# Load the model
-with open("models/churn_model.pkl", "rb") as f:
-    model = pickle.load(f)
-
-# Prepare features (see src/feature_engineering.py for expected format)
-features = pd.DataFrame({
-    "total_calls_m6": [100],
-    "total_calls_m7": [95],
-    "total_calls_m8": [20],
-    # ... other features
-})
-
-# Predict
-prediction = model.predict(features)
-probability = model.predict_proba(features)[:, 1]
-print(f"Churn Risk: {probability[0]:.2%}")
-```
-
-### API Usage
-```bash
-curl -X POST "http://localhost:8000/predict" \
-  -H "Content-Type: application/json" \
-  -d '{"features": {"total_calls_m6": 100, ...}}'
-```
+Use the code below to get started with the model.
+
+[More Information Needed]
 
 ## Training Details
 
 ### Training Data
 
-**Data Source:** Telecom customer usage data spanning 4 months (months 6-9)
-
-**Dataset Characteristics:**
-- **Format:** Tabular CSV with wide format (one row per customer, columns per month)
-- **Size:** ~30,000 high-value customers (top 70th percentile by recharge amount)
-- **Features:** 30+ features including:
-  - **Usage features:** Total calls, duration, incoming/outgoing minutes (months 6-8)
-  - **Recharge features:** Recharge amounts and counts (months 6-8)
-  - **Internet features:** Data usage and sessions (months 6-8)
-  - **Derived features:** Average recharge, usage trends, month-over-month changes
-
-**Target Definition:**
-- Churn = 1 if customer has zero incoming calls AND zero outgoing calls AND zero mobile internet usage in month 9
-- Churn = 0 otherwise
-- Typical churn rate: ~8-15% among high-value customers
-
-**Preprocessing Steps:**
-1. Data validation (column checks, null handling)
-2. High-value customer identification (70th percentile threshold)
-3. Churn label definition based on month 9 usage
-4. Feature engineering (usage ratios, trends, averages)
-5. Train/test split: 80/20 with stratification by churn label
-
-### Training Procedure
-
-#### Preprocessing
-- **Feature Engineering:** `src/feature_engineering.py` creates rolling averages, month-over-month ratios, and interaction features
-- **SMOTE:** Applied for class balancing (sampling_strategy=0.5)
-- **PCA:** Optional dimensionality reduction (95% variance threshold)
-- **Scaling:** StandardScaler for Logistic Regression (embedded in pipeline)
+<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure 
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
 
 #### Training Hyperparameters
 
-**Logistic Regression:**
-- C: 1.0
-- Penalty: l2
-- Solver: lbfgs
-- Max iterations: 1000
-- Class weight: balanced
-
-**Random Forest:**
-- N estimators: 100
-- Max depth: 10
-- Min samples split: 5
-- Min samples leaf: 2
-- Class weight: balanced
-
-**Gradient Boosting:**
-- N estimators: 100
-- Max depth: 5
-- Learning rate: 0.1
-
-**Training regime:** fp32 (standard scikit-learn precision)
-
-#### Speeds, Sizes, Times
-- **Training time:** ~2-5 minutes on standard CPU
-- **Inference time:** <10ms per prediction
-- **Model size:** ~5-15 MB per model (pickled)
-- **Memory usage:** ~500MB during training (30K samples)
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
 
 ## Evaluation
 
+<!-- This section describes the evaluation protocols and provides the results. -->
+
 ### Testing Data, Factors & Metrics
 
 #### Testing Data
-- **Test set:** 20% stratified holdout from original data
-- **Size:** ~6,000 samples (typical)
-- **Churn rate:** Matched to training distribution (~8-15%)
+
+<!-- This should link to a Data Card if possible. -->
+
+[More Information Needed]
 
 #### Factors
-Evaluation is performed across:
-- Overall performance (all high-value customers)
-- Cross-validation (5-fold stratified) for robustness
-- Individual model comparison (Logistic Regression vs Random Forest vs Gradient Boosting)
 
-#### Metrics
-- **AUC-ROC:** Primary metric for ranking predictions
-- **F1-Score:** Balance between precision and recall
-- **Precision:** Minimize false alarms (don't waste retention budget)
-- **Recall:** Capture actual churners (don't miss at-risk customers)
-- **Average Precision:** Alternative to AUC for imbalanced data
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
 
-### Results
+[More Information Needed]
 
-Performance varies by model architecture. The ensemble selects the best model based on validation AUC.
+#### Metrics
 
-**Typical Performance (Random Forest - usually best):**
-- AUC: ~0.85-0.92
-- F1: ~0.70-0.80
-- Precision: ~0.65-0.75
-- Recall: ~0.75-0.85
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
 
-**Logistic Regression (most interpretable):**
-- AUC: ~0.80-0.88
-- F1: ~0.65-0.75
+[More Information Needed]
 
-**Gradient Boosting:**
-- AUC: ~0.83-0.90
-- F1: ~0.68-0.78
+### Results
+
+[More Information Needed]
 
 #### Summary
-The model achieves strong discriminative performance (AUC > 0.85) suitable for production deployment. Random Forest typically performs best, while Logistic Regression provides the most interpretable results for business stakeholders.
 
-## Model Examination
 
-### Feature Importance
-Top predictive features typically include:
-1. **Month-over-month usage decline** (steep drops in calls/data)
-2. **Average recharge amount** (lower recharge = higher risk)
-3. **Recent usage patterns** (month 8 vs month 6-7 averages)
-4. **Data usage consistency** (frequent data users who stop are high risk)
 
-### Interpretability
-- **Logistic Regression:** Coefficients indicate feature direction and magnitude
-- **Random Forest:** Feature importance scores available via `sklearn`
-- **SHAP/Partial Dependence:** Can be computed post-hoc for individual explanations
+## Model Examination [optional]
 
-## Environmental Impact
+<!-- Relevant interpretability work for the model goes here -->
 
-Carbon emissions are minimal given the small dataset and efficient scikit-learn algorithms.
+[More Information Needed]
 
-- **Hardware Type:** Standard CPU (no GPU required)
-- **Hours used:** <0.1 hours per training run
-- **Cloud Provider:** Local/AWS (configurable)
-- **Compute Region:** us-east-1 (configurable)
-- **Carbon Emitted:** <10g CO2eq per training run (estimated)
+## Environmental Impact
 
-## Technical Specifications
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
 
-### Model Architecture and Objective
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
 
-**Objective:** Minimize binary cross-entropy loss for churn prediction
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
 
-**Architecture:** Ensemble of three scikit-learn classifiers:
-1. Logistic Regression (linear baseline)
-2. Random Forest (tree-based, handles non-linearities)
-3. Gradient Boosting (sequential trees, high accuracy)
+## Technical Specifications [optional]
 
-**Pipeline:**
-```
-Input Features → [Optional SMOTE] → Classifier → Churn Probability
-```
+### Model Architecture and Objective
+
+[More Information Needed]
 
 ### Compute Infrastructure
 
+[More Information Needed]
+
 #### Hardware
-- **Minimum:** 2 CPU cores, 4GB RAM
-- **Recommended:** 4 CPU cores, 8GB RAM
-- **GPU:** Not required (CPU-only training)
+
+[More Information Needed]
 
 #### Software
-- **Python:** 3.10+
-- **Key dependencies:**
-  - scikit-learn 1.3+
-  - pandas 2.0+
-  - numpy 1.24+
-  - imbalanced-learn (SMOTE)
-  - mlflow (experiment tracking)
-  - fastapi (serving)
 
-## Citation
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 
 **BibTeX:**
-```bibtex
-@software{telecom_churn_mlops,
-  title = {Telecom Churn Prediction MLOps Pipeline},
-  author = {Sam2120},
-  year = {2024},
-  url = {https://github.com/Sam2120/telecom-churn-mlops}
-}
-```
+
+[More Information Needed]
 
 **APA:**
-Sam2120. (2024). *Telecom Churn Prediction MLOps Pipeline* [Software]. GitHub. https://github.com/Sam2120/telecom-churn-mlops
 
-## Glossary
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
 
-- **Churn:** Customer cancellation of telecom service
-- **High-Value Customer:** Top 70th percentile by average recharge amount in good phase months
-- **Good Phase:** Months 6, 7, 8 (stable period before prediction)
-- **Action Month:** Month 8 (last month with full data before prediction)
-- **SMOTE:** Synthetic Minority Over-sampling Technique (for class balancing)
-- **AUC:** Area Under the ROC Curve (discrimination metric)
-- **ARPU:** Average Revenue Per User
-- **MOU:** Minutes of Usage
+[More Information Needed]
 
-## More Information
+## More Information [optional]
 
-- Full documentation: See `docs/architecture.md`
-- API documentation: Available at `/docs` when running the FastAPI server
-- MLflow UI: Run `mlflow ui` to see experiment tracking
-- DVC pipeline: Run `dvc repro` to reproduce the full pipeline
+[More Information Needed]
 
-## Model Card Authors
+## Model Card Authors [optional]
 
-- MLOps Pipeline Team
-- Sam2120
+[More Information Needed]
 
 ## Model Card Contact
 
-For questions or issues, please open an issue on the GitHub repository: https://github.com/Sam2120/telecom-churn-mlops/issues
+[More Information Needed]
+
+