--- license: apache-2.0 language: - en inference: true --- # HR Attrition Model This model predicts the survival function of employees based on various features using a Cox Proportional Hazards model. The model aims to estimate how long an employee is likely to stay at the company based on a variety of factors. ## Model Description The HR Attrition Model leverages the Cox Proportional Hazards method to predict employee retention. Key features used in this model include demographic information, job details, and work-life balance metrics. The model is trained on the "HR_Attrition" dataset and is designed to help HR departments understand employee attrition risks. ## Features The following features are used for predictions: - Age - DistanceFromHome - Education - NumCompaniesWorked - PercentSalaryHike - TotalWorkingYears - TrainingTimesLastYear - WorkLifeBalance - YearsInCurrentRole - YearsSinceLastPromotion - YearsWithCurrManager - BusinessTravel (Travel_Rarely, Travel_Frequently) - Department (Research & Development, Sales) - EducationField (Life Sciences, Medical, Marketing, Other, Technical Degree) - Gender (Male) - JobRole (Research Scientist, Sales Executive, Laboratory Technician, Manufacturing Director, Healthcare Representative, Manager, Sales Representative, Research Director) - MaritalStatus (Married, Single) - OverTime (Yes) ## Usage To use this model, you need to load the model and pass the input features in the required format. ### Example Here is an example of how to use the model to predict the survival function: ```python import joblib import pandas as pd class HRAttritionModel: def __init__(self, model_path): self.model = joblib.load(model_path) self.features = ['Age', 'DistanceFromHome', 'Education', 'NumCompaniesWorked', 'PercentSalaryHike', 'TotalWorkingYears', 'TrainingTimesLastYear', 'WorkLifeBalance', 'YearsInCurrentRole', 'YearsSinceLastPromotion', 'YearsWithCurrManager', 'BusinessTravel_Travel_Rarely', 'BusinessTravel_Travel_Frequently', 'Department_Research & Development', 'Department_Sales', 'EducationField_Life Sciences', 'EducationField_Medical', 'EducationField_Marketing', 'EducationField_Other', 'EducationField_Technical Degree', 'Gender_Male', 'JobRole_Research Scientist', 'JobRole_Sales Executive', 'JobRole_Laboratory Technician', 'JobRole_Manufacturing Director', 'JobRole_Healthcare Representative', 'JobRole_Manager', 'JobRole_Sales Representative', 'JobRole_Research Director', 'MaritalStatus_Married', 'MaritalStatus_Single', 'OverTime_Yes'] def predict_survival(self, input_data): df = pd.DataFrame([input_data], columns=self.features) survival_function = self.model.predict_survival_function(df) return survival_function.T # Load the model and make a prediction model = HRAttritionModel('cox_model.pkl') sample_input = {'Age': 41, 'DistanceFromHome': 1, 'Education': 2, 'NumCompaniesWorked': 1, 'PercentSalaryHike': 11, 'TotalWorkingYears': 8, 'TrainingTimesLastYear': 0, 'WorkLifeBalance': 1, 'YearsInCurrentRole': 4, 'YearsSinceLastPromotion': 0, 'YearsWithCurrManager': 5, 'BusinessTravel_Travel_Rarely': 1, 'BusinessTravel_Travel_Frequently': 0, 'Department_Research & Development': 0, 'Department_Sales': 1, 'EducationField_Life Sciences': 1, 'EducationField_Medical': 0, 'EducationField_Marketing': 0, 'EducationField_Other': 0, 'EducationField_Technical Degree': 0, 'Gender_Male': 1, 'JobRole_Research Scientist': 0, 'JobRole_Sales Executive': 0, 'JobRole_Laboratory Technician': 0, 'JobRole_Manufacturing Director': 0, 'JobRole_Healthcare Representative': 0, 'JobRole_Manager': 0, 'JobRole_Sales Representative': 0, 'JobRole_Research Director': 0, 'MaritalStatus_Married': 0, 'MaritalStatus_Single': 1, 'OverTime_Yes': 0} prediction = model.predict_survival(sample_input) print(prediction)