Update app.py

app.py

@@ -26,58 +26,41 @@ 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 three columns
-    col1, col2, col3 = st.columns(3)
+    # Define layout with two columns
+    col1, col2 = st.columns(2)
 
     # Column 1
     with col1:
-        age = st.slider("Age", min_value=18, max_value=70, value=30)
-        business_travel = st.selectbox("Business Travel", options=['Travel_Rarely', 'Travel_Frequently', 'Non-Travel'])
-        department = st.selectbox("Department", options=['Sales', 'Research & Development', 'Human Resources'])
-        education_field = st.selectbox("Education Field", options=['Life Sciences', 'Other', 'Medical', 'Marketing', 'Technical Degree', 'Human Resources'])
-        environment_satisfaction = st.select_slider("Environment Satisfaction", options=[1, 2, 3, 4], value=2)
-        gender = st.radio("Gender", options=['Female', 'Male'])
-        job_role = st.selectbox("Job Role", options=['Sales Executive', 'Research Scientist', 'Laboratory Technician', 'Manufacturing Director', 'Healthcare Representative', 'Manager', 'Sales Representative', 'Research Director', 'Human Resources'])
-        job_satisfaction = st.select_slider("Job Satisfaction", options=[1, 2, 3, 4], value=2)
+        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)
 
     # Column 2
     with col2:
-        monthly_income = st.slider("Monthly Income", min_value=1000, max_value=20000, value=5000)
-        num_companies_worked = st.slider("Number of Companies Worked", min_value=0, max_value=10, value=2)
         over_time = st.checkbox("Over Time")
-        percent_salary_hike = st.slider("Percent Salary Hike", min_value=10, max_value=25, value=15)
-        relationship_satisfaction = st.select_slider("Relationship Satisfaction", options=[1, 2, 3, 4], value=2)
-        stock_option_level = st.select_slider("Stock Option Level", options=[0, 1, 2, 3], value=1)
-
-    # Column 3
-    with col3:
-        training_times_last_year = st.slider("Training Times Last Year", min_value=0, max_value=6, value=2)
-        work_life_balance = st.select_slider("Work Life Balance", options=[1, 2, 3, 4], value=2)
-        years_at_company = st.slider("Years at Company", min_value=0, max_value=40, value=5)
-        years_since_last_promotion = st.slider("Years Since Last Promotion", min_value=0, max_value=15, value=3)
-        years_with_curr_manager = st.slider("Years With Current Manager", min_value=0, max_value=17, value=5)
+        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])
 
     # Create a DataFrame to hold the user input data
     input_data = pd.DataFrame({
         'Age': [age],
-        'BusinessTravel': [business_travel],
-        'Department': [department],
-        'EducationField': [education_field],
-        'EnvironmentSatisfaction': [environment_satisfaction],
-        'Gender': [gender],
-        'JobRole': [job_role],
-        'JobSatisfaction': [job_satisfaction],
         'MonthlyIncome': [monthly_income],
         'NumCompaniesWorked': [num_companies_worked],
-        'OverTime': [over_time],
         'PercentSalaryHike': [percent_salary_hike],
-        'RelationshipSatisfaction': [relationship_satisfaction],
-        'StockOptionLevel': [stock_option_level],
         'TrainingTimesLastYear': [training_times_last_year],
-        'WorkLifeBalance': [work_life_balance],
-        'YearsAtCompany': [years_at_company],
         'YearsSinceLastPromotion': [years_since_last_promotion],
-        'YearsWithCurrManager': [years_with_curr_manager]
+        'YearsWithCurrManager': [years_with_curr_manager],
+        'OverTime': [over_time],
+        'WorkLifeBalance': [work_life_balance],
+        'EnvironmentSatisfaction': [environment_satisfaction],
+        'JobSatisfaction': [job_satisfaction],
+        'RelationshipSatisfaction': [relationship_satisfaction]
     })
 
     # Make predictions
@@ -101,7 +84,7 @@ def main():
         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}")
+    st.write(f"Probability of Attrition: {probability[0]*100:.2f}%")
 
 if __name__ == "__main__":
-    main()
+    main()