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README.md

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 license: apache-2.0
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+# 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)