Update README.md

README.md

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----
-license: unknown
----
+---
+license: unknown
+datasets:
+- GeoTrix/Datathon
+---
+# Model For Datathon
+
+This repo holds 2 final models that was used and made for Ristek Datathon. The first model is a Linear Regression Model,
+and the other is an LSTM Model. The datasets use to train the model rely with the yfinance python module, which is a module that was made by
+Yahoo! that stores the stock market data, from what price it opens, close, the highest price it reach on that day and the lowest and the volume.
+It was also slighly modified with some added features that was used for the Linear Regression Model, adding SMA, EMA and RSI. While the LSTM Model
+only relied only on the Closing price of the stock and without the added modified dataset due to during testing it generated the best result
+when just using the close feature.
+
+## Using Linear Regression Model
+
+Sample python code to see the linear regression model
+```
+from joblib import load
+import yfinance as yf
+
+# Grab the most recent stock data
+data = yf.download('AAPL', period='40d')
+
+# Adding SMA (Simple Moving Average) into the data
+data['SMA_5'] = data['Close'].rolling(window=5).mean()
+data['SMA_10'] = data['Close'].rolling(window=10).mean()
+
+# Adding EMA (Exponential Moving Average) into the data
+data['EMA_5'] = data['Close'].ewm(span=5, adjust=False).mean()
+data['EMA_10'] = data['Close'].ewm(span=10, adjust=False).mean()
+
+# Adding RSI (Relative Strength Index) into the data
+delta = data['Close'].diff()
+avg_gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
+avg_loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
+rs = avg_gain / avg_loss
+data['RSI'] = 100 - (100 / (1 + rs))
+
+# Grab the features of the final stock price to predict the next day stock
+feature = data[['Open', 'High', 'Low', 'Close', 'Volume', 'SMA_5', 'SMA_10', 'EMA_5', 'EMA_10', 'RSI']].tail().values
+
+# Load the models in your directory
+model = load('Path/Of/The/Model')
+predictions = model.predict(feature)
+
+# Predicts gives the next day stock
+print(f"Predicted: ${predictions[0]:.2f}")
+```
+
+## Using LSTM Model
+
+Sample python code to see the LSTM model
+```
+import yfinance as yf
+from tensorflow.keras.models import load_model
+from sklearn.preprocessing import MinMaxScaler
+import numpy as np
+
+# Grab the most recent stock data
+data = yf.download('AAPL', period='40d')
+
+# Normalize the data
+scaler = MinMaxScaler(feature_range=(0, 1))
+scaled_features = scaler.fit_transform(data[['Close']])
+
+# Grabbing the last 30 days of data for prediction
+windows_size = 30
+last_30_days = scaled_features[-windows_size:]
+
+# Shaping the data to match the model input
+x = np.reshape(last_30_days, (1, windows_size, 1))
+
+# Loading model
+model = load_model('Models/Stockly-lstm.keras')
+
+# Predicting the next day's stock price
+predictions = model.predict(x)
+predictions = scaler.inverse_transform(predictions)
+
+print(f"Predicted: ${predictions[0][0]:.2f}")
+```
+
+**NOTE:** These models are still very new and not that highly accurate. So don't really use them for actual usage to trade in the stock market,
+but rather have fun with it, since the model itself was made pretty rushed to complete the Datathon. If the model ever get updated to be more accurate
+this NOTE should be gone.