Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.preprocessing import StandardScaler
+import joblib
+
+# Load and preprocess data
+@st.cache_data
+def load_data():
+    # Replace this with your actual data loading method
+    data = pd.read_csv('employee_performance_data.csv')
+    return data
+
+# Train model
+@st.cache_resource
+def train_model(data):
+    X = data.drop('performance_score', axis=1)
+    y = data['performance_score']
+    
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    
+    scaler = StandardScaler()
+    X_train_scaled = scaler.fit_transform(X_train)
+    X_test_scaled = scaler.transform(X_test)
+    
+    model = RandomForestRegressor(n_estimators=100, random_state=42)
+    model.fit(X_train_scaled, y_train)
+    
+    return model, scaler
+
+# Streamlit app
+def main():
+    st.title('Employee Performance Prediction')
+    
+    data = load_data()
+    model, scaler = train_model(data)
+    
+    st.sidebar.header('Input Parameters')
+    
+    # Create input fields for user
+    age = st.sidebar.slider('Age', 18, 65, 30)
+    experience = st.sidebar.slider('Years of Experience', 0, 40, 5)
+    training_score = st.sidebar.slider('Training Score', 0.0, 100.0, 75.0)
+    project_completion = st.sidebar.slider('Project Completion Rate', 0.0, 100.0, 80.0)
+    
+    # Create a dataframe with user input
+    user_input = pd.DataFrame({
+        'age': [age],
+        'experience': [experience],
+        'training_score': [training_score],
+        'project_completion': [project_completion]
+    })
+    
+    # Scale the input
+    user_input_scaled = scaler.transform(user_input)
+    
+    # Make prediction
+    prediction = model.predict(user_input_scaled)
+    
+    st.subheader('Predicted Performance Score')
+    st.write(f"{prediction[0]:.2f}")
+    
+    st.subheader('Performance Interpretation')
+    if prediction[0] >= 90:
+        st.write("Excellent performance!")
+    elif prediction[0] >= 75:
+        st.write("Good performance")
+    elif prediction[0] >= 60:
+        st.write("Average performance")
+    else:
+        st.write("Needs improvement")
+
+if __name__ == '__main__':
+    main()