File size: 6,156 Bytes
d7717b4 41a5a36 d7717b4 226dd1b d7717b4 5ce59f6 ad457bf 226dd1b 7849dfa 5ce59f6 f9d5aed 5ce59f6 226dd1b 7849dfa 5ce59f6 226dd1b 5ce59f6 ad457bf 292f81f 94b741a a88018b 94b741a 292f81f d7717b4 bbe236d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 |
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"):
# 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 characteristic-based recommendations
st.subheader("Suggestions for retaining the employee:")
if job_satisfaction == 1 or environment_satisfaction == 1:
st.write("Enhance job and environment satisfaction through initiatives such as recognition programs and improving workplace conditions.")
if years_since_last_promotion > 5:
st.write("Implement a transparent promotion policy and provide opportunities for career advancement.")
if years_with_curr_manager > 5:
st.write("Offer opportunities for a change in reporting structure to prevent stagnation and promote growth.")
if percent_salary_hike < 5:
st.write("Consider adjusting salary and benefits packages to remain competitive and reward employee loyalty.")
if training_times_last_year < 2:
st.write("Invest in employee development through training programs and continuous learning opportunities.")
if over_time:
st.write("Evaluate workload distribution and consider implementing measures to prevent overwork, such as workload balancing and flexible scheduling.")
if relationship_satisfaction == 1:
st.write("Foster positive relationships and a supportive work environment through team-building activities and open communication channels.")
if monthly_income < 5000:
st.write("Review compensation structures and adjust salaries to align with industry standards and employee expectations.")
if num_companies_worked > 5:
st.write("Identify reasons for high turnover and address issues related to job stability, career progression, and organizational culture.")
if work_life_balance == 1:
st.write("Promote work-life balance initiatives, such as flexible work arrangements and wellness programs, to support employee well-being.")
# General recommendation for all negative predictions
st.write("Conduct exit interviews to gather feedback and identify areas for improvement in retention strategies.")
if __name__ == "__main__":
main() |