streamlit app

app.py

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+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.beta_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("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)
+    
+    # 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],
+        'PercentSalaryHike': [percent_salary_hike],
+        'StockOptionLevel': [stock_option_level],
+        'TrainingTimesLastYear': [training_times_last_year],
+        'WorkLifeBalance': [work_life_balance],
+        'YearsSinceLastPromotion': [years_since_last_promotion],
+        '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]
+
+    # 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()