fix: add ex 8 data, fix ex8 correction, add itertools link

one_md_per_day_format/piscine/Week1/data/D01/ex8/winequality.names

@@ -0,0 +1,72 @@
+Citation Request:
+  This dataset is public available for research. The details are described in [Cortez et al., 2009]. 
+  Please include this citation if you plan to use this database:
+
+  P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. 
+  Modeling wine preferences by data mining from physicochemical properties.
+  In Decision Support Systems, Elsevier, 47(4):547-553. ISSN: 0167-9236.
+
+  Available at: [@Elsevier] http://dx.doi.org/10.1016/j.dss.2009.05.016
+                [Pre-press (pdf)] http://www3.dsi.uminho.pt/pcortez/winequality09.pdf
+                [bib] http://www3.dsi.uminho.pt/pcortez/dss09.bib
+
+1. Title: Wine Quality 
+
+2. Sources
+   Created by: Paulo Cortez (Univ. Minho), Antonio Cerdeira, Fernando Almeida, Telmo Matos and Jose Reis (CVRVV) @ 2009
+   
+3. Past Usage:
+
+  P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. 
+  Modeling wine preferences by data mining from physicochemical properties.
+  In Decision Support Systems, Elsevier, 47(4):547-553. ISSN: 0167-9236.
+
+  In the above reference, two datasets were created, using red and white wine samples.
+  The inputs include objective tests (e.g. PH values) and the output is based on sensory data
+  (median of at least 3 evaluations made by wine experts). Each expert graded the wine quality 
+  between 0 (very bad) and 10 (very excellent). Several data mining methods were applied to model
+  these datasets under a regression approach. The support vector machine model achieved the
+  best results. Several metrics were computed: MAD, confusion matrix for a fixed error tolerance (T),
+  etc. Also, we plot the relative importances of the input variables (as measured by a sensitivity
+  analysis procedure).
+ 
+4. Relevant Information:
+
+   The two datasets are related to red and white variants of the Portuguese "Vinho Verde" wine.
+   For more details, consult: http://www.vinhoverde.pt/en/ or the reference [Cortez et al., 2009].
+   Due to privacy and logistic issues, only physicochemical (inputs) and sensory (the output) variables 
+   are available (e.g. there is no data about grape types, wine brand, wine selling price, etc.).
+
+   These datasets can be viewed as classification or regression tasks.
+   The classes are ordered and not balanced (e.g. there are munch more normal wines than
+   excellent or poor ones). Outlier detection algorithms could be used to detect the few excellent
+   or poor wines. Also, we are not sure if all input variables are relevant. So
+   it could be interesting to test feature selection methods. 
+
+5. Number of Instances: red wine - 1599; white wine - 4898. 
+
+6. Number of Attributes: 11 + output attribute
+  
+   Note: several of the attributes may be correlated, thus it makes sense to apply some sort of
+   feature selection.
+
+7. Attribute information:
+
+   For more information, read [Cortez et al., 2009].
+
+   Input variables (based on physicochemical tests):
+   1 - fixed acidity
+   2 - volatile acidity
+   3 - citric acid
+   4 - residual sugar
+   5 - chlorides
+   6 - free sulfur dioxide
+   7 - total sulfur dioxide
+   8 - density
+   9 - pH
+   10 - sulphates
+   11 - alcohol
+   Output variable (based on sensory data): 
+   12 - quality (score between 0 and 10)
+
+8. Missing Attribute Values: None

one_md_per_day_format/piscine/Week1/day1.md

@@ -327,7 +327,7 @@ This question is validated if, without having used a for loop or having filled t
 
 The goal of this exercise is to learn to perform a basic data analysis on real data using NumPy.
 
-The data set that will be used for this exercise is the wine data set.
+The data set that will be used for this exercise is the red wine data set.
 
 https://archive.ics.uci.edu/ml/datasets/wine+quality
 
@@ -341,7 +341,7 @@ How to tell if a given 2D array has null columns?
 
 4. What is the average % of alcohol on all wines in the data set ? If needed, drop `np.nan` values
 
-5. Compute the minimum, the maximum, the 25th percentile, the 50th percentile, the 75 percentile, the median of the pH
+5. Compute the minimum, the maximum, the 25th percentile, the 50th percentile, the 75th percentile, the median (50th percentile) of the pH
 
 6. Compute the average quality of the wines having the 20% least sulphates
 
@@ -383,7 +383,7 @@ This slicing gives the answer `my_data[[1,6,11],:]`.
 
     > *Note: Using `percentile` or `median` may give different results depending on the duplicate values in the column. If you do not have my results please use `percentile`.*
 
-6. This question is validated if the answer is `5.222222222222222`. The first step is to get the percentile 20% of the column `sulphates`, then create a boolean array that contains `True` of the value is smaller than the percentile 20%, then select this rows with the column quality and compute the `mean`.
+6. This question is validated if the answer is ~`5.2`. The first step is to get the percentile 20% of the column `sulphates`, then create a boolean array that contains `True` of the value is smaller than the percentile 20%, then select this rows with the column quality and compute the `mean`.
 
 7. This question is validated if the output for the best wines is:
 
@@ -429,6 +429,8 @@ The expected output is:
 
 **Usage of for loop is not allowed, you may need to use the library** `itertools` **to create permutations**
 
+https://docs.python.org/3.9/library/itertools.html
+
 ## Correction
 
 This exercise is validated if the output is: