app

app.py

@@ -20,15 +20,11 @@ def update(name):
     data_selected = data_encoded[['EmployeeExperience', 'HealthBenefitsSatisfaction', 'SalarySatisfaction', 'Designation', 'HealthConscious',
                             'EmployeeFeedbackSentiments', 'Education', 'Gender', 'HoursOfTrainingAttendedLastYear', 'InternalJobMovement', 'Attrition']]
 
-    input_data = data_selected.drop(['Attrition'], axis=1)
-    target_data = data_selected[['Attrition']]
-    input_data = input_data[0:100]
-    validation_data = input_data[100:198]
+    validation_data = data_selected[100:198]
     validation_input_data = validation_data.drop(['Attrition'], axis=1)
     validation_target_data = validation_data[['Attrition']]
     reg = LinearRegression().fit(validation_input_data, validation_target_data)
     prediction_value = reg.predict(np.array([[2,2,1,3,1,2,0,1,40,1]]))
-    print(prediction_value)
     return f"Prediction : , {prediction_value}!"
 
 with gr.Blocks() as demo:

requirements.txt

@@ -0,0 +1,2 @@
+cufflinks
+sklearn