Predicting How Long To Replace N Servers Node

My customer (a Data Center) give me a small dataset that contain time to replace N servers node.
I need to analysis and Build predicting program that can predict how long to replace N servers node.

Input Training:
Data of  “p027.txt” show below:

Minutes Units
23    1 
29    2 
49    3 
64    4 
74    4 
87    5 
96    6 
97    6  
109   7  
119   8  
149   9  
145   9  
154   10  
166   10 


Example Input test:
2
40
8
10
Example Output:

Predicting minutes for units : 
35
622
128
159
Visualization about Model:  







### Code:

import graphlab

# if not csv format, need speccify delimiter

sf = graphlab.SFrame.read_csv('http://www.ats.ucla/edu/stat/examples/chp/p027.txt',delimiter='t');

graphlab.canvas.set_target('ipynb')

sf.show(view="Scatter Plot", x="Minutes", y=Units")

Finally, what i need:  SFrame - Python

Resources you will need:



Graphlab library
SFrame library






Example Code:

import graphlab
sf = graphlab.SFrame.read_csv('http://www.ats.ucla.edu/stat/examples/chp/p027.txt',delimiter='t')
my_features1 = ["Units"]

my_features_model = graphlab.linear_regression.create(sf,target='Minutes',features=my_features1,validation_set=None)

repair_time = {
                           'Units':10
             }

print my_features_model.predict(repair_time)
Finish, Classify the problem to become linear regression single value. It’s my simple way to solve.