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README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+Model Card: TERPredictor V1
+📌 Model Name
+TERPredictor V1 – A linear regression model for predicting the Total Expense Ratio (TER) of mutual fund Regular Plans.
+
+📖 Overview
+TERPredictor V1 is a regression model trained to estimate the 'Regular Plan - Total TER (%)' of mutual funds based on various financial features. It uses a simple linear regression approach and achieves near-perfect performance on the test set. Due to the unusually high accuracy, this model is best suited for exploratory analysis and feature relationship interpretation, rather than generalization to unseen data.
+
+📊 Intended Uses
+Expense Ratio Estimation: Estimate TER for new or hypothetical mutual fund structures.
+
+Outlier Detection: Identify funds with unusually high or low TERs.
+
+Feature Impact Analysis: Understand which components most influence TER.
+
+🧠 Model Architecture
+Attribute	Value
+Model Type	Linear Regression
+Framework	scikit-learn
+Input Features	10 float64 columns
+Target Variable	Regular Plan - Total TER (%)
+Identifier Dropped	Scheme Name (object)
+📚 Training Details
+Dataset Size: 1,622 samples
+
+Train/Test Split: 1297 / 325
+
+Missing Values: None
+
+Preprocessing:
+
+Dropped identifier column (Scheme Name)
+
+No normalization required due to linear model simplicity
+
+📈 Evaluation Metrics
+Metric	Value
+Mean Squared Error (MSE)	0.000001
+R-squared (R²)	0.999999
+⚠️ Note: These metrics suggest potential data leakage or a deterministic relationship between features and target. Use with caution.
+
+🚀 How to Use
+python
+from terpredictor import TERModel
+
+model = TERModel.load_pretrained("your-huggingface-username/terpredictor-v1")
+input_data = {
+    "feature_1": 0.12,
+    "feature_2": 0.03,
+    ...
+}
+predicted_ter = model.predict(input_data)
+⚠️ Limitations
+Potential Data Leakage: Extremely high R² may indicate the target is directly derived from input features.
+
+Limited Generalization: Not recommended for predicting TER on unseen or structurally different funds.
+
+No Feature Engineering: Model assumes raw features are sufficient.
+
+📄 License
+MIT License
+
+👤 Author
+Created by [Your Name or Organization]
+
+📚 Recommendations for Open-Sourcing
+Include full training code and preprocessing steps
+
+Provide detailed explanation of evaluation metrics
+
+Add cautionary notes about performance anomalies
+
+Consider publishing a cleaned or anonymized version of the dataset