def addEmployee():
if not request.json or not 'name' in request.json:
return jsonify({"result": False, "msg": "Failed to Add Employee!"})
# Get Prediction for the Employee Turnover - Create Dataframe
df = pd.DataFrame()
print(request.json)
df['Age'] = [request.json['age']]
df['DistanceFromHome'] = [request.json['distFromHome']]
df['Children'] = [request.json['children']]
df['Num CompaniesWorked'] = [request.json['numCompaniesWorked']]
df['Training '] = [request.json['trainingHours']]
df['Years At Company'] = [request.json['yearsAtCompany']]
df['Years In Current Role'] = [request.json['yearsInCurrentRole']]
df['YearsSinceLastPromotion'] = [request.json['yearsSinceLastPromo']]
df['YearsWithCurSupervisor'] = [request.json['yearsWithCurrSupervisor']]
df['StandardHours'] = [request.json['standardHours']]
if (request.json['department'] == '1'):
df['Department_Cutting'] = [1]
df['Department_Pressing'] = [0]
df['Department_Sewing'] = [0]
if (request.json['department'] == '2'):
df['Department_Cutting'] = [0]
df['Department_Pressing'] = [1]
df['Department_Sewing'] = [0]
if (request.json['department'] == '3'):
df['Department_Cutting'] = [0]
df['Department_Pressing'] = [0]
df['Department_Sewing'] = [1]
if (request.json['gender'] == '1'):
df['Gender_Female'] = [1]
df['Gender_Male'] = [0]
if (request.json['gender'] == '2'):
df['Gender_Female'] = [0]
df['Gender_Male'] = [1]
if (request.json['jobSatisfaction'] == '1'):
df['Job_Satisfaction_1'] = [1]
df['Job_Satisfaction_2'] = [0]
df['Job_Satisfaction_3'] = [0]
df['Job_Satisfaction_4'] = [0]
if (request.json['jobSatisfaction'] == '2'):
df['Job_Satisfaction_1'] = [0]
df['Job_Satisfaction_2'] = [1]
df['Job_Satisfaction_3'] = [0]
df['Job_Satisfaction_4'] = [0]
if (request.json['jobSatisfaction'] == '3'):
df['Job_Satisfaction_1'] = [0]
df['Job_Satisfaction_2'] = [0]
df['Job_Satisfaction_3'] = [1]
df['Job_Satisfaction_4'] = [0]
if (request.json['jobSatisfaction'] == '4'):
df['Job_Satisfaction_1'] = [0]
df['Job_Satisfaction_2'] = [0]
df['Job_Satisfaction_3'] = [0]
df['Job_Satisfaction_4'] = [1]
if (request.json['maritalStatus'] == '0'):
df['Martial_Status_Single'] = [1]
df['Martial_Status_Married'] = [0]
df['Martial_Status_Divorced'] = [0]
if (request.json['maritalStatus'] == '1'):
df['Martial_Status_Single'] = [0]
df['Martial_Status_Married'] = [1]
df['Martial_Status_Divorced'] = [0]
if (request.json['maritalStatus'] == '2'):
df['Martial_Status_Single'] = [0]
df['Martial_Status_Married'] = [0]
df['Martial_Status_Divorced'] = [1]
if (request.json['salary'] == '0'):
df['Salary_Low'] = [1]
df['Salary_Average'] = [0]
df['Salary_High'] = [0]
if (request.json['salary'] == '1'):
df['Salary_Low'] = [0]
df['Salary_Average'] = [1]
df['Salary_High'] = [0]
if (request.json['salary'] == '2'):
df['Salary_Low'] = [0]
df['Salary_Average'] = [0]
df['Salary_High'] = [1]
if (request.json['overtime'] == '0'):
df['OverTime_No'] = [1]
df['OverTime_Yes'] = [0]
if (request.json['overtime'] == '1'):
df['OverTime_No'] = [0]
df['OverTime_Yes'] = [1]
if (request.json['absenteeism'] == '0'):
df['Absenteeism Rate_Low'] = [1]
df['Absenteeism Rate_Medium'] = [0]
df['Absenteeism Rate_High'] = [0]
if (request.json['absenteeism'] == '1'):
df['Absenteeism Rate_Low'] = [0]
df['Absenteeism Rate_Medium'] = [1]
df['Absenteeism Rate_High'] = [0]
if (request.json['absenteeism'] == '2'):
df['Absenteeism Rate_Low'] = [0]
df['Absenteeism Rate_Medium'] = [0]
df['Absenteeism Rate_High'] = [1]
if (request.json['performanceRating'] == 'A'):
df['PerformanceRating_A'] = [1]
df['PerformanceRating_B'] = [0]
df['PerformanceRating_C'] = [0]
df['PerformanceRating_D'] = [0]
if (request.json['performanceRating'] == 'B'):
df['PerformanceRating_A'] = [0]
df['PerformanceRating_B'] = [1]
df['PerformanceRating_C'] = [0]
df['PerformanceRating_D'] = [0]
if (request.json['performanceRating'] == 'C'):
df['PerformanceRating_A'] = [0]
df['PerformanceRating_B'] = [0]
df['PerformanceRating_C'] = [1]
df['PerformanceRating_D'] = [0]
if (request.json['performanceRating'] == 'D'):
df['PerformanceRating_A'] = [0]
df['PerformanceRating_B'] = [0]
df['PerformanceRating_C'] = [0]
df['PerformanceRating_D'] = [1]
if (request.json['education'] == '1'):
df['Education_1'] = [1]
df['Education_2'] = [0]
df['Education_3'] = [0]
df['Education_4'] = [0]
if (request.json['education'] == '2'):
df['Education_1'] = [0]
df['Education_2'] = [1]
df['Education_3'] = [0]
df['Education_4'] = [0]
if (request.json['education'] == '3'):
df['Education_1'] = [0]
df['Education_2'] = [0]
df['Education_3'] = [1]
df['Education_4'] = [0]
if (request.json['education'] == '4'):
df['Education_1'] = [0]
df['Education_2'] = [0]
df['Education_3'] = [0]
df['Education_4'] = [1]
if (request.json['workLifeBalance'] == '1'):
df['Work Life Balance_1'] = [1]
df['Work Life Balance_2'] = [0]
df['Work Life Balance_3'] = [0]
df['Work Life Balance_4'] = [0]
if (request.json['workLifeBalance'] == '2'):
df['Work Life Balance_1'] = [0]
df['Work Life Balance_2'] = [1]
df['Work Life Balance_3'] = [0]
df['Work Life Balance_4'] = [0]
if (request.json['workLifeBalance'] == '3'):
df['Work Life Balance_1'] = [0]
df['Work Life Balance_2'] = [0]
df['Work Life Balance_3'] = [1]
df['Work Life Balance_4'] = [0]
if (request.json['workLifeBalance'] == '4'):
df['Work Life Balance_1'] = [0]
df['Work Life Balance_2'] = [0]
df['Work Life Balance_3'] = [0]
df['Work Life Balance_4'] = [1]
# Load the XGBoost Model
param_dist = {
'objective': 'binary:logistic',
'n_estimators': 180,
'eta': 0.1,
'gamma': 0.05,
'max_depth': 3
}
clf = xgb.XGBModel(**param_dist)
clf.load_model(TURNOVER_MODEL)
# Predict Turnover from the XGBoost Model
prediction = clf.predict(df.to_numpy())
print(prediction)
# Get Turnover Drivers
# explainer = lime.lime_tabular(df.to_numpy(), feature_names=df.columns, class_names=[0,1], kernel_width=3)
# Load the SVR Model
svr = pickle.load(open(TIMETILLTURNOVER_MODEL, 'rb'))
# Predict Time Till Turnover
timetillturnover = svr.predict(df.to_numpy())[0]
employee = Employee(request.json['name'],
request.json['age'],
request.json['department'],
request.json['phone'],
request.json['email'],
request.json['address'],
request.json['distFromHome'],
request.json['education'],
request.json['gender'],
request.json['jobSatisfaction'],
request.json['maritalStatus'],
request.json['children'],
request.json['salary'],
request.json['numCompaniesWorked'],
request.json['overtime'],
request.json['performanceRating'],
request.json['standardHours'],
request.json['trainingHours'],
request.json['workLifeBalance'],
request.json['yearsAtCompany'],
request.json['yearsInCurrentRole'],
request.json['yearsSinceLastPromo'],
request.json['yearsWithCurrSupervisor'],
request.json['absenteeism'],
request.json['recruitmentDate'],
request.json['loggedInId'],
turnover=prediction[0],
timetillturnover=timetillturnover)
result = Employee.addEmployee(employee, mysql)
return jsonify({"result": True, "msg": "Successfully Added Employee!"})
