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final in the name of Jesus

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Jesselyn Mu
2025-04-10 15:14:58 +07:00
parent 1856e8ebb3
commit 53ca7c3072
52 changed files with 65816 additions and 20235 deletions
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28
pages/Prediksi.py
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@ -14,7 +14,7 @@ import time
import json
reg_model = pickle.load(open('regression_model_final.sav', 'rb'))
class_model = pickle.load(open('clasification_final_model.sav', 'rb'))
class_model = pickle.load(open('clasification_final_model_smote.sav', 'rb'))
train_file_path = 'X_train.csv'
with open("feature_explanation.json", "r") as f:
@ -78,7 +78,6 @@ def process_employee_data(df):
df["income_6_months"] = df["income"] * 6
df["total_income_work"] = df["income"] * df["active_work_months"]
df["absence_ratio"] = df["absent_90D"] / (df["active_work"] / 90)
df["income_dependant_ratio"] = df["income"] / (df["dependant"] + 1)
df["work_efficiency"] = df["avg_time_work"] / 8
@ -95,15 +94,6 @@ def process_employee_data(df):
# Work Stability Score
df['work_stability_score'] = df['active_work_months'] / (df['absent_90D'] + 1)
# Married-Dependent Ratio
def married_dependent_ratio(row):
if row['marriage_stat'] == 'Married':
return row['dependant'] + 1
else:
return 1
df['married_dependent_ratio'] = df.apply(married_dependent_ratio, axis=1)
# Job Income to Position Score
position_score_mapping = {'Junior': 2, 'Staff': 1, 'Senior': 3, 'Manager': 4}
df['position_score'] = df['position'].map(position_score_mapping)
@ -119,20 +109,6 @@ def process_employee_data(df):
0.6 * df['job_satisfaction'] + 0.4 * df['performance_rating']
)
# Resign Risk Indicator
def resign_risk_indicator(row):
if row['age_years'] < 30 and row['active_work_months'] < 12:
return "High"
elif 1 <= row['active_work_months'] <= 36:
return "Medium"
else:
return "Low"
df['resign_risk_indicator'] = df.apply(resign_risk_indicator, axis=1)
# Adjusted Work Time
df['adjusted_work_time'] = df['avg_time_work'] * (1 - (df['absent_90D'] / ((df['active_work_months'] * 90) + 1)))
job_satisfaction_mapping = {1.0: 'Low', 2.0: 'Medium', 3.0: 'High', 4.0: 'Very High'}
df['job_satisfaction'] = df['job_satisfaction'].map(job_satisfaction_mapping)
@ -407,7 +383,7 @@ def show_prediction():
# Kolom kategori
cat_feature = ['departemen', 'position', 'domisili', 'marriage_stat', 'job_satisfaction',
'performance_rating', 'education', 'active_work_category', 'resign_risk_indicator', 'jenis_kelamin']
'performance_rating', 'education', 'active_work_category', 'jenis_kelamin']
X_test_class = df[expected_columns_class]
X_test_reg = df[expected_columns_reg]