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import pandas as pd
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
import streamlit as st
import joblib
# Load the trained model and dataset
model = joblib.load('best_regression_model.pkl')
df = pd.read_csv('Startups.csv')
# Preprocessor setup for scaling numerical and encoding categorical features
preprocessor = ColumnTransformer(
transformers=[
('num', StandardScaler(), ['R&D Spend', 'Administration', 'Marketing Spend']),
('cat', OneHotEncoder(), ['State'])
]
)
# Pipeline for preprocessing and model prediction
pipeline = Pipeline(steps=[('preprocessor', preprocessor), ('regressor', model)])
pipeline.fit(df[['R&D Spend', 'Administration', 'Marketing Spend', 'State']], df['Profit'])
# Prediction function
def price_pred(rd, administration, marketing, state):
input_data = pd.DataFrame({
'R&D Spend': [rd],
'Administration': [administration],
'Marketing Spend': [marketing],
'State': [state]
})
prediction = pipeline.predict(input_data)[0]
return prediction
# Main function for Streamlit app layout
def main():
st.set_page_config(page_title="Profit Prediction", page_icon="💰", layout="wide")
# Header Section
st.title("💼 Profit Prediction Tool")
st.markdown("""
Welcome to the **Profit Prediction Tool**! This tool uses machine learning to predict the profit of a company
based on different financial parameters. Please input the values for **R&D Spend**, **Administration Spend**,
and **Marketing Spend** along with the **State** to get the predicted profit.
""")
# Sidebar Section for better organization
st.sidebar.header("Enter Your Company Details")
state = st.sidebar.selectbox('Select your State', df['State'].unique())
rd = st.sidebar.number_input('R&D Spend Amount ($)', 0, int(df['R&D Spend'].max()), step=1000)
administration = st.sidebar.number_input('Administration Spend Amount ($)', 0, int(df['Administration'].max()), step=1000)
marketing = st.sidebar.number_input('Marketing Spend Amount ($)', 0, int(df['Marketing Spend'].max()), step=1000)
# Display entered data for user confirmation
st.markdown("### You entered:")
st.write(f"**State**: {state}")
st.write(f"**R&D Spend**: ${rd:,.2f}")
st.write(f"**Administration Spend**: ${administration:,.2f}")
st.write(f"**Marketing Spend**: ${marketing:,.2f}")
# Prediction Button
if st.sidebar.button('Predict Profit'):
profit = price_pred(rd, administration, marketing, state)
profit = float(profit)
st.markdown(f"### The Predicted Profit is: **${profit:,.2f}**")
# Add some styling with markdown and display information
st.markdown("""
---
### About the Model
The model is built using a **regression algorithm** that takes into account the R&D spend, administration expenses,
and marketing spend for a company to predict its profit. The model was trained using real-world company data.
""")
if __name__ == '__main__':
main()
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