This project aims to predict gold prices using a Random Forest Regression algorithm, a supervised learning technique that employs ensemble learning for regression.
Random Forest Regression is a supervised learning algorithm that uses an ensemble learning method for regression. It leverages the power of multiple decision trees to make accurate predictions.
The first step involves collecting relevant data from various sources such as financial news websites, government reports, and social media.
After data collection, the dataset undergoes cleaning, which includes eliminating duplicates, filling in blanks, and formatting the data appropriately for analysis.
Machine learning models require features for predictions. Relevant features for gold price prediction may include economic indicators (e.g., inflation rates, interest rates, GDP) and market-specific factors (e.g., gold production, gold demand, geopolitical events).
The machine learning model is trained using historical data. This involves selecting the right algorithm and optimizing its settings for optimal performance.
After training, the model needs to be evaluated by comparing its predictions with real gold prices to assess its accuracy and performance.
Gold.Price.Prediction.Models.-.Google.Chrome.2024-01-15.20-54-01.mp4
# Sample code snippet
from gold_price_prediction_model import GoldPricePredictor
# Load the trained model
model = GoldPricePredictor.load_model('path/to/trained/model')
# Make predictions
predictions = model.predict(gold_features)
# Display results
print("Predicted Gold Price:", predictions)