#### Exercise 0: Environment and libraries
##### The exercise is validated if 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:
```console
array([ 4., 13., 6.])
```
###### For question 2, is the filled train set the following?
```console
array([[ 7., 6., 5.],
[ 4., 13., 5.],
[ 1., 20., 8.]])
```
###### For question 3, is the filled test set the following?
```console
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.])`.
```console
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?
```console
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?
```console
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?
```console
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?
```console
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']` :
```console
#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"]`?
```console
#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?
```console
# 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?
```console
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!**
