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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.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=0, 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")
# Predict button
if st.button("Predict"):
# Convert numerical features to strings
age = str(age)
monthly_income = str(monthly_income)
num_companies_worked = str(num_companies_worked)
percent_salary_hike = str(percent_salary_hike)
training_times_last_year = str(training_times_last_year)
years_since_last_promotion = str(years_since_last_promotion)
years_with_curr_manager = str(years_with_curr_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],
'WorkLifeBalance': [work_life_balance],
'YearsSinceLastPromotion': [years_since_last_promotion],
'YearsWithCurrManager': [years_with_curr_manager]
})
# Reorder columns to match the expected order
input_data = input_data[['Age', 'Department', 'EnvironmentSatisfaction', 'JobRole', 'JobSatisfaction',
'MonthlyIncome', 'NumCompaniesWorked', 'OverTime', 'PercentSalaryHike',
'RelationshipSatisfaction', 'TrainingTimesLastYear', 'WorkLifeBalance',
'YearsSinceLastPromotion', 'YearsWithCurrManager']]
# 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]*100:.2f}%")
if __name__ == "__main__":
main() |