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README.md
Exercise 0: Environment and libraries
The exercise is validated is all questions of the exercise are validated.
Activate the virtual environment. If you used conda
run conda activate your_env
.
Run python --version
.
Does it print Python 3.x
? x >= 8
Do import jupyter
, import numpy
, import pandas
, import matplotlib
and import sklearn
run without any error?
Exercise 1: Imputer 1
The exercise is validated if all questions of the exercise are validated.
For question 1 is validated if the imp_mean.statistics_
returns:
array([ 4., 13., 6.])
For question 2, is the filled train set the following?
array([[ 7., 6., 5.],
[ 4., 13., 5.],
[ 1., 20., 8.]])
For question 3, is the filled test set the following?
array([[ 4., 1., 2.],
[ 7., 13., 9.],
[ 4., 2., 4.]])
Exercise 2: Scaler
The exercise is validated if all questions of the exercise are validated.
For question 1, is the scaled train set as below? And by definition, the mean on the axis 0 should be array([0., 0., 0.])
and the standard deviation on the axis 0 should be array([1., 1., 1.])
.
array([[ 0. , -1.22474487, 1.33630621],
[ 1.22474487, 0. , -0.26726124],
[-1.22474487, 1.22474487, -1.06904497]])
For question 2, is the scaled test set the following?
array([[ 1.22474487, -1.22474487, 0.53452248],
[ 2.44948974, 3.67423461, -1.06904497],
[ 0. , 1.22474487, 0.53452248]])
Exercise 3: One hot Encoder
The exercise is validated if all questions of the exercise are validated.
For question 1, is the output the following?
| | ('C++',) | ('Java',) | ('Python',) |
|---:|-----------:|------------:|--------------:|
| 0 | 0 | 0 | 1 |
| 1 | 0 | 1 | 0 |
| 2 | 0 | 1 | 0 |
| 3 | 1 | 0 | 0 |
For question 2, is the output the following?
| | ('C++',) | ('Java',) | ('Python',) |
|---:|-----------:|------------:|--------------:|
| 0 | 0 | 0 | 1 |
| 1 | 0 | 1 | 0 |
| 2 | 0 | 0 | 0 |
| 3 | 1 | 0 | 0 |
Exercise 4: Ordinal Encoder
The exercise is validated if all questions of the exercise are validated
For question 1, is the output of the Ordinal Encoder on the train set the following?
array([[2.],
[0.],
[1.]])
Check that enc.categories_
returns[array(['bad', 'neutral', 'good'], dtype=object)]
.
For question 2, is the output of the Ordinal Encoder on the test set the following?
array([[2.],
[2.],
[0.]])
Exercise 5: Categorical variables
The exercise is validated if all questions of the exercise are validated
For question 1, are the number of unique values per feature outputted the following?
age 6
menopause 3
tumor-size 11
inv-nodes 6
node-caps 2
deg-malig 3
breast 2
breast-quad 5
irradiat 2
dtype: int64
For question 2, is the transformed test set by the OneHotEncoder
fitted on the train set as below? Make sure the transformer takes as input a dataframe with the columns in the order defined ['node-caps' , 'breast', 'breast-quad', 'irradiat']
:
#First 10 rows:
array([[1., 0., 1., 0., 0., 1., 0., 0., 0., 1., 0.],
[1., 0., 1., 0., 0., 1., 0., 0., 0., 1., 0.],
[1., 0., 1., 0., 0., 0., 0., 1., 0., 1., 0.],
[1., 0., 0., 1., 0., 1., 0., 0., 0., 1., 0.],
[1., 0., 0., 1., 0., 0., 0., 1., 0., 1., 0.],
[1., 0., 0., 1., 0., 0., 1., 0., 0., 1., 0.],
[1., 0., 0., 1., 0., 0., 1., 0., 0., 1., 0.],
[1., 0., 0., 1., 0., 1., 0., 0., 0., 1., 0.],
[1., 0., 0., 1., 0., 0., 1., 0., 0., 1., 0.],
[0., 1., 1., 0., 0., 0., 1., 0., 0., 0., 1.]])
For question 3, is the transformed test set by the OrdinalEncoder
fitted on the train set as below with the columns ordered as ["menopause", "age", "tumor-size","inv-nodes", "deg-malig"]
?
#First 10 rows:
array([[1., 2., 5., 0., 1.],
[1., 3., 4., 0., 1.],
[1., 2., 4., 0., 1.],
[1., 3., 2., 0., 1.],
[1., 4., 3., 0., 1.],
[1., 4., 5., 0., 0.],
[2., 5., 4., 0., 1.],
[2., 5., 8., 0., 1.],
[0., 2., 3., 0., 2.],
[1., 3., 6., 4., 2.]])
For question 4, is the column transformer transformed that is fitted on the X_train, transformed the X_test as below?
# First 2 rows:
array([[1., 0., 1., 0., 0., 1., 0., 0., 0., 1., 0., 1., 2., 5., 0., 1.],
[1., 0., 1., 0., 0., 1., 0., 0., 0., 1., 0., 1., 3., 4., 0., 1.]])
Exercise 6: Pipeline
For question 1, are the predictions on the test set the following?
array([0, 0, 2, 1, 2, 0, 2, 1, 1, 1, 0, 1, 2, 0, 1, 1, 0, 0, 2, 2, 0, 0,
0, 2, 2, 2, 0, 1, 0, 0, 1, 0, 1, 1, 2, 2, 1, 2, 1, 1, 1, 2, 1, 2,
0, 1, 1, 1, 1, 1])
and is the score on the test set 98%?
Note: Keep in mind that having a 98% accuracy is not common when working with real life data. Every time you have a score > 97% check that there's no leakage in the data. On financial data set, the ratio signal to noise is low. Trying to forecast stock prices is a difficult problem. Having an accuracy higher than 70% should be interpreted as a warning to check data leakage!