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README.md

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+
+---
+tags:
+- time-series
+- regression
+- svr
+- stock-prediction
+- technical-analysis
+- scikit-learn
+---
+# SVR Model for AAPL Price Prediction (Technical Indicators)
+
+This repository hosts a trained **Support Vector Regression (SVR)** model and its necessary preprocessing components (StandardScaler) for predicting the closing price of **AAPL**.
+
+## Model Details
+- **Algorithm:** Support Vector Regression (SVR) with RBF/Linear Kernel (Tuned by Grid Search)
+- **Features:** 37 features derived from technical analysis (SMA, Volatility, Returns) with lookbacks up to 252 days.
+- **Target:** Next day's closing price.
+- **Training Period:** 2023-01-01 to 2024-12-31
+
+## Inference
+To use this model, you must correctly calculate and input all 37 technical features (including moving averages and volatility ratios) for the day prior to the prediction.
+
+1.  Load the `svr_model.joblib` and `standard_scaler.joblib`.
+2.  Calculate the 37 features for day $T$.
+3.  Scale the 37 features using the loaded `StandardScaler`.
+4.  Run the prediction.
+