This project provides a CSGO Skin Price Prediction System that integrates real-time data collection, price prediction (ARIMA, Prophet, LSTM, GAN), and model verification. It also includes RESTful APIs for easy access.

https://www.youtube.com/watch?v=MA5-19DV08U

202412-22-CSGO-Skin-Price-Predictor/
│
├── consequence/                    # Prediction results
├── csgo_market_data/               # Market data (raw and aggregated)
├── data_raw/                       # Reddit and Google trend data
├── predict_and_verification/       # Models and verification scripts
├── realtime_app/                   # Flask application and prediction utilities
│   ├── app.py                      # Main Flask app
│   ├── realtime_predict.py         # Combines ARIMA, Prophet, LSTM, and GAN for predictions
│   ├── store_data.py               # Store and preprocess data
│   ├── plot_data.py                # Generate plots
│   └── csgo_items.db               # Preprocessed SQLite database for skins
├── collect.py                      # General data collection
├── dailyactive_collect.py          # Collect daily active player data
├── dailyactive_peak_collect.py     # Collect peak player data
├── google_trend_collect.py         # Collect Google Trends data
├── reddit_praw.py                  # Reddit post classification
└── data_complete.json              # Combined market and activity data (user-provided)

• Purpose: Collect price history data for CSGO items from Steam Market.
• Parameters:
  • years (int): Specifies how many years of data to collect. If the range exceeds the data's earliest date, it fetches all available data.
  • Modify items_to_collect list in the script to specify items to fetch.
• Example Usage:

  python collect.py --years 5

• Output:
  • Saved price history CSV files in ../csgo_market/prices.

• Purpose: Collect daily active player data.
• Parameters:
  • None. Adjust the collection logic in the script if needed.
• Example Usage:

  python dailyactive_collect.py

• Output:
  • Generates a CSV file with daily active player data in data_raw/.

• Purpose: Collect peak player data.
• Parameters:
  • None. Adjust the logic if a different data source is needed.
• Example Usage:

  python dailyactive_peak_collect.py

• Output:
  • Generates a CSV file with peak player data in data_raw/.

• Purpose: Fetch Google Trends data for CSGO-related keywords.
• Parameters:
  • keywords (list): A list of terms to search trends for (e.g., "CS:GO", "AK-47").
  • timeframe (str): Time range for the trends (e.g., "today 12-m", "all").
  • region (str): Country or region for localized trends (default is global).
• Example Usage:

  python google_trend_collect.py --timeframe "today 12-m"

• Output:
  • Saves Google Trends data to data_raw/csgo_trend_data.csv.

• Purpose: Classify Reddit posts from r/GlobalOffensive into predefined categories.
• Parameters:
  • Modify KEYWORDS in the script to customize post classification.
  • max_retries (int): Maximum retries for fetching data in case of errors.
• Example Usage:

  python reddit_praw.py

• Output:
  • Classified posts saved to data_raw/reddit_post/.

• Purpose: Combines ARIMA, Prophet, LSTM, and GAN models for comprehensive price prediction.
• Parameters:
  • items_to_collect (list): List of item names to predict.
  • ARIMA: Modify the (p, d, q) parameters in the script.
  • Prophet: Customize seasonality (e.g., yearly, weekly) and changepoint sensitivity.
  • LSTM: Adjust hyperparameters like window_size, epochs, and batch_size.
  • GAN: Modify noise dimensions and training epochs.
• Example Usage:

  python realtime_app/realtime_predict.py

• Output:
  • Predictions (CSV and plots) saved in ../realtime/predict/csv and ../realtime/predict/picture.

• Purpose: Verify ARIMA model predictions against historical data.
• Parameters:
  • --item (str): A specific weapon name, or a path to a .txt or .json file containing a list of weapon names.
• Example Usage:

  python predict_and_verification/arima_verification.py --item items.txt

• Output:
  • Metrics and plots saved in consequence/verification/arima.

• Purpose: Verify LSTM model predictions.
• Parameters:
  • --item (str): A specific weapon name, or a path to a .txt or .json file containing a list of weapon names.
  • Change window_size, epochs, or other LSTM parameters within the script.
• Example Usage:

  python predict_and_verification/lstm_verification.py --item items.json

• Output:
  • Metrics and plots saved in consequence/verification/lstm.

• Purpose: Verify GAN-based predictions.
• Parameters:
  • --item (str): A specific weapon name, or a path to a .txt or .json file containing a list of weapon names.
  • Modify GAN architecture or training parameters in the script.
• Example Usage:

  python predict_and_verification/gan_verification.py --item items.json

• Output:
  • Metrics and plots saved in consequence/verification/gan.

• Purpose: Verify Prophet model predictions.
• Parameters:
  • --item (str): A specific weapon name, or a path to a .txt or .json file containing a list of weapon names.
  • Customize seasonality settings and forecast periods.
• Example Usage:

  python predict_and_verification/prophet_verification.py --item items.txt --yearly True --future_days 365

• Output:
  • Metrics and plots saved in consequence/verification/prophet.

• Navigate to the realtime_app/ directory and start the Flask server:

  python app.py

• Homepage:

  • URL: http://127.0.0.1:5001/
  • Description: Displays the introduction page.

• Price Prediction:

  • URL: http://127.0.0.1:5001/predict/<item_name>
  • Parameters:
    • item_name: The name of the item to predict (e.g., AK-47__Redline_(Battle-Scarred)).
  • Example:

    http://127.0.0.1:5001/predict/AK-47__Redline_(Battle-Scarred)
    

  • Description:
    • Collects price history if unavailable.
    • Combines ARIMA, Prophet, LSTM, and GAN predictions.
    • Returns predictions and percentage changes.

• Historical Data:

  • URL: http://127.0.0.1:5001/historical/<item_name>
  • Parameters:
    • item_name: The name of the item (e.g., AK-47__Redline_(Battle-Scarred)).
  • Example:

    http://127.0.0.1:5001/historical/AK-47__Redline_(Battle-Scarred)
    

• Price History Plot:

  • URL: http://127.0.0.1:5001/plot/price_history/<skin_name>
  • Parameters:
    • skin_name: The name of the skin to plot (e.g., AK-47__Redline_(Battle-Scarred)).
  • Example:

    http://127.0.0.1:5001/plot/price_history/AK-47__Redline_(Battle-Scarred)
    

• Daily Activity Plot:

  • URL: http://127.0.0.1:5001/plot/daily_activity
  • Description:
    • Generates and returns a plot for daily active player trends.

This is a user-provided file combining market and activity data. Example structure:

[
    {
        "date": "2024-01-01",
        "peak_players": 123456,
        "items": {
            "ak47": {
                "positive_count": 500,
                "negative_count": 50
            }
        },
        "Tournaments": {"positive_ratio": 0.8},
        "Version_Updates": {"positive_ratio": 0.7},
        "Market_and_Price": {"positive_ratio": 0.9},
        "trends": {
            "CS:GO": 120,
            "Global Offensive": 100,
            "CS2": 110,
            "tournament": 90,
            "market_activity": 95,
            "update_trend": 80
        }
    }
]

Stored in ../csgo_market/prices, formatted as:

date,price,volume
2024-01-01,1.23,500
2024-01-02,1.25,550
...

Stored in ../csgo_market/daily_activity.csv, formatted as:

date,active_players
2024-01-01,123456
2024-01-02,124789
...

Request:

GET http://127.0.0.1:5001/predict/AK-47__Redline_(Battle-Scarred)

Response:

{
  "item_name": "AK-47__Redline_(Battle-Scarred)",
  "current_price": 123.45,
  "forecast": {
    "2024-12-01": 124.56,
    "2024-12-02": 125.78,
    ...
  },
  "changes": {
    "Tomorrow": "+2.34%",
    "3 Days": "+5.67%",
    "7 Days": "+8.90%",
    "1 Month": "+12.34%",
    ...
  }
}