├── Dockerfile
├── README.md
├── app.py
└── requirements.txt


/Dockerfile:
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 1 | # Use an official Python runtime as a parent image
 2 | FROM python:3.6-slim
 3 | 
 4 | # Set the working directory to /app
 5 | WORKDIR /app
 6 | 
 7 | # Copy the current directory contents into the container at /app
 8 | ADD . /app
 9 | 
10 | # Install any needed packages specified in requirements.txt
11 | RUN pip install -r requirements.txt
12 | 
13 | # Make port 80 available to the world outside this container
14 | #EXPOSE 80
15 | 
16 | # Define environment variable
17 | ENV NAME World
18 | 
19 | # Run app.py when the container launches
20 | CMD ["python", "app.py"]
21 | 


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/README.md:
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 1 | # Docker_Python_DataScience
 2 | Some DataScience Test with docker + python + SciKit-learn
 3 | 
 4 | <h1>Version 1</h1>
 5 | <ul>
 6 |   <li>Install Docker</li>
 7 |   <li>clone the Repo</li>
 8 |   <li>>docker build -t "tag_name" .</li>(example - docker build -t dspython .) 
 9 |   <li> docker run "tag_name"</li>(example - docker run dspython)
10 | </ul>
11 | 
12 | app.py:
13 |      
14 | Uses scikit-learn machine learning library (from requirements.txt) and takes sample fruit dataset (height,weight,texture) and predicts the fruit based on the input dataset. 
15 | 
16 | #Docker
17 | #Python
18 | #SciKit-learn
19 | #MachineLearning
20 | 


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/app.py:
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 1 | from sklearn import tree
 2 | from sklearn import neighbors
 3 | 
 4 | #[size,weight, texture]
 5 | X = [[181, 80, 44], [177, 70, 43], [160, 60, 38], [154, 54, 37], [166, 65, 40],
 6 |      [190, 90, 47], [175, 64, 39],
 7 |      [177, 70, 40], [159, 55, 37], [171, 75, 42], [181, 85, 43]]
 8 | 
 9 | Y = ['apple', 'apple', 'orange', 'orange', 'apple', 'apple', 'orange', 'orange',
10 |      'orange', 'apple', 'apple']
11 | 
12 | #classifier - DecisionTreeClassifier
13 | clf_tree = tree.DecisionTreeClassifier();
14 | clf_tree = clf_tree.fit(X,Y);
15 | 
16 | #classifier - neighbour
17 | clf_neighbors = neighbors.KNeighborsClassifier();
18 | clf_neighbors = clf_neighbors.fit(X,Y);
19 | 
20 | #test_data
21 | test_data = [[190,70,42],[172,64,39],[182,80,42]];
22 | 
23 | #prediction
24 | prediction_tree = clf_tree.predict(test_data);
25 | prediction_neighbors = clf_neighbors.predict(test_data);
26 | 
27 | print("prediction of DecisionTreeClassifier:",prediction_tree);
28 | 
29 | print("prediction of Neighour:",prediction_neighbors);
30 | 


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/requirements.txt:
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1 | numpy
2 | scipy
3 | scikit-learn
4 | 


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