| tags: | |
| - time-series | |
| - regression | |
| - svr | |
| - stock-prediction | |
| - scikit-learn | |
| # SVR Model for AAPL Price Prediction | |
| This repository hosts a trained **Support Vector Regression (SVR)** model and its necessary preprocessing components (MinMaxScaler) for predicting the closing price of **AAPL**. | |
| ## Model Details | |
| - **Algorithm:** Support Vector Regression (SVR) with RBF Kernel | |
| - **Features (Input Sequence Length):** 100 days | |
| - **Target:** Single-step prediction (the price of day $T+1$) | |
| - **Training Period:** 2020-11-24 to 2025-11-23 | |
| ## Inference | |
| To use this model, you must provide a sequence of the last **100** scaled closing prices. | |
| 1. Load the `svr_model.joblib` and `minmax_scaler.joblib`. | |
| 2. Scale your 100-day input sequence using the loaded `MinMaxScaler`. | |
| 3. Run the prediction. | |
| 4. Inverse transform the prediction using the loaded `MinMaxScaler` to get the final dollar value. | |