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A Hybrid Prediction Model combines multiple machine learning or deep learning techniques to improve prediction accuracy and performance. Unlike traditional models that rely on a single approach, hybrid models integrate different algorithms to leverage their strengths while minimizing weaknesses.
For example, a hybrid model for stock price prediction might use: Statistical methods for trend analysis Machine learning models (Random Forest, XGBoost) for pattern recognition Deep learning models (LSTMs, CNNs) for sequence-based prediction This combination results in more accurate, reliable, and robust predictions.
STEP 1: Data Collection
Collects data from multiple sources (databases, APIs, sensors, user inputs). Cleans the data (removes duplicates, handles missing values, normalizes features). Extracts relevant features for better prediction.
STEP 2: Model Selection & Training
Chooses different models (e.g., Decision Trees, SVM, CNN, LSTM). Trains each model separately on specific data types (structured, unstructured, time series). Fine-tunes hyperparameters to optimize performance.
STEP 3: Hybrid Model Integration
Combines outputs from different models using ensemble techniques (stacking, boosting, weighted averaging). A meta-model (like a neural network) refines the final prediction. The hybrid system balances accuracy and adaptability.
STEP 4: Model Testing & Validation
The hybrid model is tested using unseen data. Performance is evaluated with metrics like Accuracy, Precision, Recall, and RMSE. Adjustments are made to improve efficiency and correctness.
STEP: 5: Prediction & Real-Time Deployment
The final model is deployed as a web app, API, or embedded system. Users input new data, and predictions are generated in real-time. Results are displayed in charts, confidence scores, and reports.
