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"
```