Dec-12-2019, 03:29 AM

Hi Forum,

How to predict with date as input for DecisionTreeRegressor model?

source: student_mark_result_dec_hist.csv

name day subject percentage

john 12/1/2019 maths 30

john 12/2/2019 maths 40

john 12/3/2019 maths 33

john 12/4/2019 maths 32

john 12/5/2019 maths 31

john 12/6/2019 maths 38

john 12/7/2019 maths 35

john 12/8/2019 maths 38

john 12/9/2019 maths 39

john 12/10/2019 maths 55

john 12/11/2019 maths 65

john 12/12/2019 maths 68

john 12/13/2019 maths 62

john 12/14/2019 maths 70

john 12/15/2019 maths 64

john 12/16/2019 maths 82

john 12/17/2019 maths 80

john 12/18/2019 maths 55

john 12/19/2019 maths 68

john 12/20/2019 maths 79

john 12/21/2019 maths 88

john 12/22/2019 maths 87

john 12/23/2019 maths 80

john 12/24/2019 maths 75

Now, i want to predict for 12/25/2019 and 11/30/2019 marks for subject-maths for name -john, Any ideas?

I was trying with below, but i doubt that's absolutely incorrect,

Sandeep

GANGA SANDEEP KUMAR

How to predict with date as input for DecisionTreeRegressor model?

source: student_mark_result_dec_hist.csv

name day subject percentage

john 12/1/2019 maths 30

john 12/2/2019 maths 40

john 12/3/2019 maths 33

john 12/4/2019 maths 32

john 12/5/2019 maths 31

john 12/6/2019 maths 38

john 12/7/2019 maths 35

john 12/8/2019 maths 38

john 12/9/2019 maths 39

john 12/10/2019 maths 55

john 12/11/2019 maths 65

john 12/12/2019 maths 68

john 12/13/2019 maths 62

john 12/14/2019 maths 70

john 12/15/2019 maths 64

john 12/16/2019 maths 82

john 12/17/2019 maths 80

john 12/18/2019 maths 55

john 12/19/2019 maths 68

john 12/20/2019 maths 79

john 12/21/2019 maths 88

john 12/22/2019 maths 87

john 12/23/2019 maths 80

john 12/24/2019 maths 75

Now, i want to predict for 12/25/2019 and 11/30/2019 marks for subject-maths for name -john, Any ideas?

I was trying with below, but i doubt that's absolutely incorrect,

import pandas as pd #import numpy as np from sklearn.tree import DecisionTreeRegressor from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score from sklearn import preprocessing raw_data=pd.read_csv('student_mark_result_dec_hist.csv',index_col=False) blankIndex=[''] * len(raw_data) raw_data.index=blankIndex le = preprocessing.LabelEncoder() for column_name in raw_data.columns: if raw_data[column_name].dtype == object: raw_data[column_name] = le.fit_transform(raw_data[column_name]) le_name_mapping = dict(zip(le.classes_, le.transform(le.classes_))) print(le_name_mapping) else: pass print('---->', raw_data[:]) X=raw_data[['name','day','subject']] y=raw_data['percentage'] model=DecisionTreeRegressor() model.fit(X,y) predictions=model.predict([ [0,24,0], [0,-1,0] ]) print(predictions) #here, i am not sure if [0.24.0],[0,-1,0] points to date 12/25/2019 and 11/30/2019, Any ideas?Best Regards,

Sandeep

GANGA SANDEEP KUMAR