Update app.py

app.py

@@ -25,35 +25,35 @@ def main():
     st.write("This app predicts employee attrition using a trained CatBoost model.")
 
     # Add inputs for user to input data
-    age = st.slider("Age", min_value=18, max_value=70, value=30)
-    distance_from_home = st.slider("Distance From Home", min_value=1, max_value=30, value=10)
-    environment_satisfaction = st.slider("Environment Satisfaction", min_value=1, max_value=4, value=2)
-    hourly_rate = st.slider("Hourly Rate", min_value=30, max_value=100, value=65)
-    job_involvement = st.slider("Job Involvement", min_value=1, max_value=4, value=2)
-    job_level = st.slider("Job Level", min_value=1, max_value=5, value=3)
-    job_satisfaction = st.slider("Job Satisfaction", min_value=1, max_value=4, value=2)
-    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)
-    stock_option_level = st.slider("Stock Option Level", min_value=0, max_value=3, value=1)
-    training_times_last_year = st.slider("Training Times Last Year", min_value=0, max_value=6, value=2)
-    work_life_balance = st.slider("Work Life Balance", min_value=1, max_value=4, value=2)
-    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=15, value=3)
+    col1, col2, col3 = st.columns(3)
+    with col1:
+        age = st.slider("Age", min_value=18, max_value=70, value=30)
+        environment_satisfaction = st.slider("Environment Satisfaction", min_value=1, max_value=4, value=2)
+        job_satisfaction = st.slider("Job Satisfaction", min_value=1, max_value=4, value=2)
+        monthly_income = st.slider("Monthly Income", min_value=1000, max_value=20000, value=5000)
+        over_time = st.checkbox("Over Time")
+    with col2:
+        distance_from_home = st.slider("Distance From Home", min_value=1, max_value=30, value=10)
+        job_involvement = st.slider("Job Involvement", min_value=1, max_value=4, value=2)
+        num_companies_worked = st.slider("Number of Companies Worked", min_value=0, max_value=10, value=2)
+        percent_salary_hike = st.slider("Percent Salary Hike", min_value=10, max_value=25, value=15)
+        stock_option_level = st.slider("Stock Option Level", min_value=0, max_value=3, value=1)
+    with col3:
+        training_times_last_year = st.slider("Training Times Last Year", min_value=0, max_value=6, value=2)
+        work_life_balance = st.slider("Work Life Balance", min_value=1, max_value=4, value=2)
+        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=15, value=3)
     
     # Create a DataFrame to hold the user input data
     input_data = pd.DataFrame({
         'Age': [age],
         'DistanceFromHome': [distance_from_home],
         'EnvironmentSatisfaction': [environment_satisfaction],
-        'HourlyRate': [hourly_rate],
-        'JobInvolvement': [job_involvement],
-        'JobLevel': [job_level],
         'JobSatisfaction': [job_satisfaction],
         'MonthlyIncome': [monthly_income],
         'NumCompaniesWorked': [num_companies_worked],
         'OverTime': [over_time],
+        'JobInvolvement': [job_involvement],
         'PercentSalaryHike': [percent_salary_hike],
         'StockOptionLevel': [stock_option_level],
         'TrainingTimesLastYear': [training_times_last_year],
@@ -62,17 +62,6 @@ def main():
         'YearsWithCurrManager': [years_with_curr_manager]
     })
     
-    # Suggestions for retaining the employee
-    if predicted_to_leave:
-        st.subheader("Suggestions for Retaining the Employee:")
-        st.markdown("- Invest in orientation programs and career development for entry-level staff to 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 promotion 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.")
-
     # Make predictions
     prediction = model.predict(input_data)
     probability = model.predict_proba(input_data)[:, 1]