Syllabus |
Slides and Assignments |
Project |
Lecturer
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- Watch this repo to get notified for new issues.
- Watch this repo to get notified for new issues.
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- Clone this repo to your local machine (Do NOT do this inside your DSCI-633 directory):
git clone https://github.com/hil-se/fds.git
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- Copy ONLY the folder assignments/ from this repo to your repo DSCI-633 folder.
fds/
|-- assignments ---
|-- *** |
DSCI-633/ |
|-- assignments <---
Inside the DSCI-633 repo, it should look like mine. Clarification: this is just for using the assignment code in your own repo. Just copy and paste from local directories, do not try to link these two repos in any way.
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- Run A1.py:
DSCI-633/assignments/Preparation> python A1.py
SepalLengthCm 5.0
SepalWidthCm 3.0
PetalLengthCm 1.6
PetalWidthCm 0.2
Species Iris-setosa
Name: 20, dtype: object
0 Iris-setosa
1 Iris-setosa
2 Iris-setosa
3 Iris-setosa
4 Iris-setosa
...
130 Iris-virginica
131 Iris-virginica
132 Iris-virginica
133 Iris-virginica
134 Iris-virginica
Name: Species, Length: 135, dtype: object
SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm Species
0 5.1 3.5 1.4 0.2 Iris-setosa
1 4.9 3.0 1.4 0.2 Iris-setosa
2 4.7 3.2 1.3 0.2 Iris-setosa
3 4.6 3.1 1.5 0.2 Iris-setosa
4 5.0 3.6 1.4 0.2 Iris-setosa
5 5.4 3.9 1.7 0.4 Iris-setosa
6 4.6 3.4 1.4 0.3 Iris-setosa
7 5.0 3.4 1.5 0.2 Iris-setosa
8 4.4 2.9 1.4 0.2 Iris-setosa
9 4.9 3.1 1.5 0.1 Iris-setosa
10 5.4 3.7 1.5 0.2 Iris-setosa
11 4.8 3.4 1.6 0.2 Iris-setosa
12 4.8 3.0 1.4 0.1 Iris-setosa
13 5.7 3.8 1.7 0.3 Iris-setosa
14 5.1 3.8 1.5 0.3 Iris-setosa
15 5.4 3.4 1.7 0.2 Iris-setosa
16 5.1 3.7 1.5 0.4 Iris-setosa
17 4.6 3.6 1.0 0.2 Iris-setosa
18 5.1 3.3 1.7 0.5 Iris-setosa
19 4.8 3.4 1.9 0.2 Iris-setosa
20 5.0 3.0 1.6 0.2 Iris-setosa
21 5.0 3.4 1.6 0.4 Iris-setosa
22 5.2 3.5 1.5 0.2 Iris-setosa
23 5.2 3.4 1.4 0.2 Iris-setosa
24 4.7 3.2 1.6 0.2 Iris-setosa
25 4.8 3.1 1.6 0.2 Iris-setosa
26 5.4 3.4 1.5 0.4 Iris-setosa
27 5.2 4.1 1.5 0.1 Iris-setosa
28 5.5 4.2 1.4 0.2 Iris-setosa
29 4.9 3.1 1.5 0.1 Iris-setosa
30 5.0 3.2 1.2 0.2 Iris-setosa
31 5.5 3.5 1.3 0.2 Iris-setosa
32 4.9 3.1 1.5 0.1 Iris-setosa
33 4.4 3.0 1.3 0.2 Iris-setosa
34 5.1 3.4 1.5 0.2 Iris-setosa
35 5.0 3.5 1.3 0.3 Iris-setosa
36 4.5 2.3 1.3 0.3 Iris-setosa
37 4.4 3.2 1.3 0.2 Iris-setosa
38 5.0 3.5 1.6 0.6 Iris-setosa
39 5.1 3.8 1.9 0.4 Iris-setosa
40 4.8 3.0 1.4 0.3 Iris-setosa
41 5.1 3.8 1.6 0.2 Iris-setosa
42 4.6 3.2 1.4 0.2 Iris-setosa
43 5.3 3.7 1.5 0.2 Iris-setosa
44 5.0 3.3 1.4 0.2 Iris-setosa
Iris-setosa 1.000000
Iris-setosa 1.000000
Iris-setosa 1.000000
Iris-setosa 1.000000
Iris-setosa 1.000000
Iris-versicolor 0.992155
Iris-versicolor 0.999921
Iris-versicolor 0.999999
Iris-versicolor 0.997254
Iris-versicolor 0.999990
Iris-virginica 1.000000
Iris-virginica 0.840581
Iris-virginica 0.999999
Iris-virginica 0.999988
Iris-virginica 0.753482
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- Modify A1.py to print out the 12th training data point.
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- Modify A1.py to print out the "SepalWidthCm" column.
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- Modify A1.py to print out training data points with "SepalWidthCm" < 2.5.
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- Modify A1.py so that a Decision Tree Classifier is used to make the prediction instead of Gaussian Naive Bayes. Use the Decision Tree Classifier from scikit-learn directly (without any specific parameters).
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- Run A1.py again and snapshot the output (including the command python A1.py).
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- Save the snapshot as A1.png and put it under assignments/Prepation/ in your repo. Should look like this. Note: the example snapshot will look different from the correct one.
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- Commit to the remote server of Github.
- 6 (out of 7) points will be received if all the required files can be found in the submitted repo.
- The rest 1 point will be given based on whether the screenshot A1.png is correct.
- Note: be sure to add your github repo url to the Google sheet since this is how this and all the future assignments will be graded.