https://github.com/m-rishab/gold-price-prediction-model
https://github.com/m-rishab/gold-price-prediction-model
css decision-trees flask html machine-learning-algorithms python3 random-forest-classifier
Last synced: 2 months ago
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- Host: GitHub
- URL: https://github.com/m-rishab/gold-price-prediction-model
- Owner: m-rishab
- Created: 2024-01-15T10:57:02.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-01-16T08:32:18.000Z (over 2 years ago)
- Last Synced: 2025-03-15T14:14:38.962Z (about 1 year ago)
- Topics: css, decision-trees, flask, html, machine-learning-algorithms, python3, random-forest-classifier
- Language: Jupyter Notebook
- Homepage:
- Size: 161 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Gold Price Prediction Model
## Overview
This project aims to predict gold prices using a Random Forest Regression algorithm, a supervised learning technique that employs ensemble learning for regression.
## Algorithm Definition
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.
## Flowchart
## Steps
### 1. Data Collection
The first step involves collecting relevant data from various sources such as financial news websites, government reports, and social media.
### 2. Data Preparation and Cleaning
After data collection, the dataset undergoes cleaning, which includes eliminating duplicates, filling in blanks, and formatting the data appropriately for analysis.
### 3. Feature Selection and Engineering
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).
### 4. Model Training
The machine learning model is trained using historical data. This involves selecting the right algorithm and optimizing its settings for optimal performance.
### 5. Evaluation of the Model
After training, the model needs to be evaluated by comparing its predictions with real gold prices to assess its accuracy and performance.
## Demo
https://github.com/m-rishab/Gold-Price-Prediction-Model/assets/113618652/6d57c042-2a5e-43af-b442-b84cd297cf15
```python
# 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)