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import streamlit as st
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import pandas as pd
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from sklearn.ensemble import RandomForestRegressor
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from sklearn.model_selection import train_test_split
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@st.cache_data
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def load_data():
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df = pd.read_csv("demand.csv")
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df['Total Price'].fillna(df['Total Price'].mean(), inplace=True)
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return df
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def train_model(df, features, target):
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x = df[features]
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y = df[target]
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model = RandomForestRegressor()
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model.fit(x, y)
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return model
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def make_prediction(model, total_price, base_price):
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prediction = model.predict([[total_price, base_price]])
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return prediction[0]
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def main():
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st.title("Product Demand Prediction App")
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df = load_data()
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model = train_model(df, ['Total Price', 'Base Price'], 'Units Sold')
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total_price = st.number_input("Enter Total Price")
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base_price = st.number_input("Enter Base Price")
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if st.button("Predict"):
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prediction = make_prediction(model, total_price, base_price)
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st.write(f"Predicted Units Sold: {prediction}")
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if __name__ == "__main__":
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main()
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