Update model card

README.md

@@ -7,10 +7,6 @@ tags:
 - churn-prediction
 - telecom
 - mlops
-- customer-retention
-- gradient-boosting
-- random-forest
-- logistic-regression
 datasets:
 - telecom-customer-data
 metrics:
@@ -18,99 +14,201 @@ metrics:
 - f1
 - precision
 - recall
-- average_precision
-model_name: Telecom High-Value Customer Churn Prediction Model
+model_name: Telecom Churn Prediction Model
 ---
 
-# Telecom High-Value Customer Churn Prediction Model
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
 
-This model predicts churn probability for high-value customers in the telecom industry. It identifies customers at risk of leaving (churning) based on their usage patterns, recharge behavior, and engagement metrics over a 4-month period.
 
 ## Model Details
 
 ### Model Description
 
-**Best Model:** Gradient Boosting Classifier (ensemble with Random Forest and Logistic Regression)
+<!-- Provide a longer summary of what this model is. -->
+
 
-- **Developed by:** MLOps Team
-- **Model type:** Binary Classification (Churn vs. No Churn)
-- **Language(s):** en
+
+- **Developed by:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** en
 - **License:** mit
-- **Task:** Tabular Classification
+- **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
-- **Demo:** https://huggingface.co/Sam212000/telecom-churn-model
+- **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 telecom operators to:
-- **Identify at-risk customers:** Predict which high-value customers are likely to churn in the next month
-- **Enable proactive retention:** Trigger retention campaigns before customers actually leave
-- **Optimize resources:** Focus retention efforts on customers most likely to respond
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-**Input Format:** Customer features including:
-- Recharge amounts (months 6, 7, 8)
-- Call counts and duration (incoming/outgoing)
-- Data usage metrics
-- Engagement trends
+[More Information Needed]
 
-**Output:** Churn probability (0-1) with risk classification:
-- **Low Risk:** < 0.3
-- **Medium Risk:** 0.3 - 0.6
-- **High Risk:** 0.6 - 0.8
-- **Very High Risk:** > 0.8
+### Downstream Use [optional]
 
-### Downstream Use
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 
-- Integration with CRM systems for automated retention workflows
-- Dashboard visualizations for business analysts
-- A/B testing framework for retention strategies
+[More Information Needed]
 
 ### Out-of-Scope Use
 
-- **Not for non-telecom domains:** Model trained specifically on telecom usage patterns
-- **Not for real-time streaming:** Designed for batch/periodic prediction
-- **Not for individual customer targeting without privacy compliance:** Ensure GDPR/local privacy law compliance
-
-## Bias, Risks, and Limitations
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
-### Technical Limitations
+[More Information Needed]
 
-- **Temporal decay:** Model performance degrades as customer behavior patterns change over time; retraining recommended every 3-6 months
-- **Regional specificity:** Trained on specific telecom operator data; may not generalize to other regions or markets
-- **Feature dependency:** Requires complete 3-month history; customers with < 3 months of data will have reduced accuracy
+## Bias, Risks, and Limitations
 
-### Sociotechnical Considerations
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-- **Privacy:** Customer usage data is sensitive; ensure proper data handling and anonymization
-- **Fairness:** Monitor for bias across customer segments (age, location, plan type)
-- **Human oversight:** Model predictions should inform but not replace human judgment in retention decisions
+[More Information Needed]
 
 ### Recommendations
 
-- Regularly monitor model performance on new data
-- Implement feedback loops to capture which predicted churners were actually retained
-- Retrain model quarterly with updated data
-- Use model outputs as part of a broader retention strategy, not the sole decision factor
+<!-- 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
 
-```python
-from huggingface_hub import hf_hub_download
-import joblib
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- 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
+
+- **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
+
+<!-- This should link to a Data Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+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).
+
+- **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]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[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:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
 
-# Download model
-model_path = hf_hub_download(
-    repo_id="Sam212000/telecom-churn-model",
-    filename="gradient_boosting.pkl"
-)
+[More Information Needed]
 
-# Load model
-model = joblib.load(model_path)
 
-# Make predictions
-# (Input features must match training data format)