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ML.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6c4de9ae0d635f9a1fcb4a74fcd88387e43b577608319b1b77ec593c4475bdbb
+size 92817

app.py

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+import pandas as pd
+import streamlit as st
+
+from sklearn.preprocessing import StandardScaler
+from sklearn.compose import ColumnTransformer
+
+import joblib
+
+
+# Load data and update column names
+df = pd.read_csv('BTC-Hourly.csv')
+df.columns = df.columns.str.replace(r'[\s\.]', '_', regex=True)
+
+# Select dependent and independent variables
+x = df[["open", "high", "low", "close", "Volume_BTC", "Volume_USD"]]
+
+# Preprocessing (StandardScaler)
+preprocessor = ColumnTransformer(
+    transformers=[
+        ('num', StandardScaler(), ["open", "high", "low", "close", "Volume_BTC", "Volume_USD"])
+    ]
+)
+
+# Streamlit application
+def fiyat_pred(open, high, low, close, Volume_BTC, Volume_USD):
+    input_data = pd.DataFrame({
+        'open': [open],
+        'high': [high],
+        'low': [low],
+        'close': [close],
+        'Volume_BTC': [Volume_BTC],
+        'Volume_USD': [Volume_USD]
+    })
+    input_data_transformed = preprocessor.fit_transform(input_data)
+
+    model = joblib.load('ML.pkl')
+
+    prediction = model.predict(input_data_transformed)
+    return float(prediction[0])
+
+# Streamlit interface
+def main():
+    st.title("Prediction Model")
+    st.write("Enter Input Data")
+    
+    open = st.slider('Open', float(df['open'].min()), float(df['open'].max()))
+    high = st.slider('High', float(df['high'].min()), float(df['high'].max()))
+    low = st.slider('Low', float(df['low'].min()), float(df['low'].max()))
+    close = st.slider('Close', float(df['close'].min()), float(df['close'].max()))
+    Volume_BTC = st.slider('Volume BTC', float(df['Volume_BTC'].min()), float(df['Volume_BTC'].max()))
+    Volume_USD = st.slider('Volume USD', float(df['Volume_USD'].min()), float(df['Volume_USD'].max()))
+    
+    if st.button('Predict'):
+        fiyat = fiyat_pred(open, high, low, close, Volume_BTC, Volume_USD)
+        st.write(f'The predicted price is: {fiyat:.2f}')
+
+if __name__ == '__main__':
+    main()
+

requirements.txt

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+streamlit
+scikit-learn
+pandas
+tensorflow