import tensorflow as tf
import numpy as np
import pandas as pd
import numpy as np
from sklearn import linear_model
import tensorflow as tf
import numpy as np
df = pd.read_csv('/work/jass.csv')
df
Cijint64
Yiint64
0
887
10500
1
887
15000
2
887
6000
3
887
20000
4
887
12000
5
887
8000
6
887
18000
7
887
14500
reg = linear_model.LinearRegression()
reg.fit(df.drop('ViJ', axis='columns'),df.ViJ)
reg.coef_
reg.intercept_
reg.predict([[750, 35000, 15, 2000]])
/shared-libs/python3.7/py/lib/python3.7/site-packages/sklearn/base.py:451: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names
"X does not have valid feature names, but"
df2 = pd.read_csv('/work/jasso2.csv')
df2
Diaslibresint64
Yiint64
0
8
10500
1
10
15000
2
6
6000
3
12
20000
4
6
12000
5
6
8000
6
8
18000
7
10
14500
reg1 = linear_model.LinearRegression()
reg1.fit(df2.drop('ViJ2', axis='columns'),df2.ViJ2)
reg1.coef_
reg1.intercept_
reg1.predict([[20, 35000, 1, 3000]])
/shared-libs/python3.7/py/lib/python3.7/site-packages/sklearn/base.py:451: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names
"X does not have valid feature names, but"