Update app.py

app.py

@@ -26,41 +26,149 @@ def main():
     st.write("This app helps HR practitioners predict employee attrition using a trained CatBoost model.")
     st.write("Please provide the following information to make a prediction:")
 
-    # Define layout with two columns
-    col1, col2 = st.columns(2)
+    # Define layout with three columns
+    col1, col2, col3 = st.columns(3)
 
     # Column 1
     with col1:
-        age = st.slider("Age", min_value=18, max_value=70)
-        monthly_income = st.slider("Monthly Income", min_value=1000, max_value=20000)
-        num_companies_worked = st.slider("Number of Companies Worked", min_value=0, max_value=10)
-        percent_salary_hike = st.slider("Percent Salary Hike", min_value=10, max_value=25)
-        training_times_last_year = st.slider("Training Times Last Year", min_value=0, max_value=6)
-        years_since_last_promotion = st.slider("Years Since Last Promotion", min_value=0, max_value=15)
-        years_with_curr_manager = st.slider("Years With Current Manager", min_value=0, max_value=15)
+        age = st.number_input("Age", min_value=18, max_value=70)
+        monthly_income = st.number_input("Monthly Income")
+        num_companies_worked = st.number_input("Number of Companies Worked")
+        percent_salary_hike = st.number_input("Percent Salary Hike", min_value=10, max_value=25)
+        training_times_last_year = st.number_input("Training Times Last Year", min_value=0, max_value=6)
 
     # Column 2
     with col2:
+        department = st.selectbox("Department", ['Sales', 'Research & Development', 'Human Resources'])
+        environment_satisfaction = st.selectbox("Environment Satisfaction", [1, 2, 3, 4])
+        job_role = st.selectbox("Job Role", ['Sales Executive', 'Research Scientist', 'Laboratory Technician',
+                                              'Manufacturing Director', 'Healthcare Representative', 'Manager',
+                                              'Sales Representative', 'Research Director', 'Human Resources'])
+        job_satisfaction = st.selectbox("Job Satisfaction", [1, 2, 3, 4])
+        work_life_balance = st.selectbox("Work Life Balance", [1, 2, 3, 4])
+
+    # Column 3
+    with col3:
         over_time = st.checkbox("Over Time")
-        work_life_balance = st.select_slider("Work Life Balance", options=[1, 2, 3, 4])
-        environment_satisfaction = st.select_slider("Environment Satisfaction", options=[1, 2, 3, 4])
-        job_satisfaction = st.select_slider("Job Satisfaction", options=[1, 2, 3, 4])
-        relationship_satisfaction = st.select_slider("Relationship Satisfaction", options=[1, 2, 3, 4])
+        relationship_satisfaction = st.selectbox("Relationship Satisfaction", [1, 2, 3, 4])
+        years_since_last_promotion = st.number_input("Years Since Last Promotion")
+        years_with_curr_manager = st.number_input("Years With Current Manager")
 
     # Create a DataFrame to hold the user input data
     input_data = pd.DataFrame({
         'Age': [age],
+        'Department': [department],
+        'EnvironmentSatisfaction': [environment_satisfaction],
+        'JobRole': [job_role],
+        'JobSatisfaction': [job_satisfaction],
         'MonthlyIncome': [monthly_income],
         'NumCompaniesWorked': [num_companies_worked],
+        'OverTime': [over_time],
         'PercentSalaryHike': [percent_salary_hike],
+        'RelationshipSatisfaction': [relationship_satisfaction],
         'TrainingTimesLastYear': [training_times_last_year],
-        'YearsSinceLastPromotion': [years_since_last_promotion],
-        'YearsWithCurrManager': [years_with_curr_manager],
-        'OverTime': [over_time],
         'WorkLifeBalance': [work_life_balance],
+        'YearsSinceLastPromotion': [years_since_last_promotion],
+        'YearsWithCurrManager': [years_with_curr_manager]
+    })
+
+    # Make predictions
+    prediction = model.predict(input_data)
+    probability = model.predict_proba(input_data)[:, 1]
+
+    # Display prediction
+    if prediction[0] == 0:
+        st.success("Employee is predicted to stay (Attrition = No)")
+    else:
+        st.error("Employee is predicted to leave (Attrition = Yes)")
+
+        # Offer recommendations for retaining the employee
+        st.subheader("Suggestions for retaining the employee:")
+        st.markdown("- Invest in orientation programs and career development for entry-level staff, which could contribute to higher retention.")
+        st.markdown("- Implement mentorship programs and career development initiatives aimed at engaging and retaining younger employees.")
+        st.markdown("- Offer robust training and development programs and regular promotions to foster career growth. This investment in skills and career advancement can contribute to higher job satisfaction and retention.")
+        st.markdown("- Recognize the diverse needs of employees based on marital status and consider tailoring benefits or support programs accordingly.")
+        st.markdown("- Consider offering benefits that cater to the unique needs of married, single, and divorced employees.")
+        st.markdown("- Introduce or enhance policies that support work-life balance for employees with families.")
+        st.markdown("- Recognize the unique challenges and opportunities within each department and tailor retention strategies accordingly.")
+
+    # Display probability
+    st.write(f"Probability of Attrition: {probability[0]:.2f}")
+
+if __name__ == "__main__":
+    main()
+import streamlit as st
+import pickle
+import pandas as pd
+from catboost import CatBoostClassifier
+
+# Load the trained model and unique values from the pickle file
+with open('model_and_key_components.pkl', 'rb') as file:
+    saved_components = pickle.load(file)
+
+model = saved_components['model']
+unique_values = saved_components['unique_values']
+
+# Define the Streamlit app
+def main():
+    st.title("Employee Attrition Prediction App")
+    st.sidebar.title("Model Settings")
+
+    # Sidebar inputs
+    with st.sidebar.expander("View Unique Values"):
+        st.write("Unique values for each feature:")
+        for column, values in unique_values.items():
+            st.write(f"- {column}: {values}")
+
+    # Main content
+    st.write("Welcome to the Employee Attrition Prediction App!")
+    st.write("This app helps HR practitioners predict employee attrition using a trained CatBoost model.")
+    st.write("Please provide the following information to make a prediction:")
+
+    # Define layout with three columns
+    col1, col2, col3 = st.columns(3)
+
+    # Column 1
+    with col1:
+        age = st.number_input("Age", min_value=18, max_value=70)
+        monthly_income = st.number_input("Monthly Income")
+        num_companies_worked = st.number_input("Number of Companies Worked")
+        percent_salary_hike = st.number_input("Percent Salary Hike", min_value=10, max_value=25)
+        training_times_last_year = st.number_input("Training Times Last Year", min_value=0, max_value=6)
+
+    # Column 2
+    with col2:
+        department = st.selectbox("Department", ['Sales', 'Research & Development', 'Human Resources'])
+        environment_satisfaction = st.selectbox("Environment Satisfaction", [1, 2, 3, 4])
+        job_role = st.selectbox("Job Role", ['Sales Executive', 'Research Scientist', 'Laboratory Technician',
+                                              'Manufacturing Director', 'Healthcare Representative', 'Manager',
+                                              'Sales Representative', 'Research Director', 'Human Resources'])
+        job_satisfaction = st.selectbox("Job Satisfaction", [1, 2, 3, 4])
+        work_life_balance = st.selectbox("Work Life Balance", [1, 2, 3, 4])
+
+    # Column 3
+    with col3:
+        over_time = st.checkbox("Over Time")
+        relationship_satisfaction = st.selectbox("Relationship Satisfaction", [1, 2, 3, 4])
+        years_since_last_promotion = st.number_input("Years Since Last Promotion")
+        years_with_curr_manager = st.number_input("Years With Current Manager")
+
+    # Create a DataFrame to hold the user input data
+    input_data = pd.DataFrame({
+        'Age': [age],
+        'Department': [department],
         'EnvironmentSatisfaction': [environment_satisfaction],
+        'JobRole': [job_role],
         'JobSatisfaction': [job_satisfaction],
-        'RelationshipSatisfaction': [relationship_satisfaction]
+        'MonthlyIncome': [monthly_income],
+        'NumCompaniesWorked': [num_companies_worked],
+        'OverTime': [over_time],
+        'PercentSalaryHike': [percent_salary_hike],
+        'RelationshipSatisfaction': [relationship_satisfaction],
+        'TrainingTimesLastYear': [training_times_last_year],
+        'WorkLifeBalance': [work_life_balance],
+        'YearsSinceLastPromotion': [years_since_last_promotion],
+        'YearsWithCurrManager': [years_with_curr_manager]
     })
 
     # Make predictions
@@ -87,4 +195,4 @@ def main():
     st.write(f"Probability of Attrition: {probability[0]*100:.2f}%")
 
 if __name__ == "__main__":
-    main()
+    main()