As an engineer, I want to work in a company which understands the challenges of today and tomorrow. I wish to innovate all the time as it means better understanding the world around me. I am enthusiastic about machine learning and I am studying for data science as it regroups mathematics, creating algorithms and investigating data. This portfolio shows the projects I have worked on. link to my linkedin account : https://fr.linkedin.com/in/pierre-masse

1. ENGINEER
PORTFOLIO
Pierre Massé

2. As an engineer, I want to work in a company which
understands the challenges of today and tomorrow.
I wish to innovate all the time as it means better
understanding the world around me.
I am enthusiastic about machine learning and I am
studying for data science as it regroups
mathematics, creating algorithms and investigating
data.
This portfolio shows the projects I have worked on.

3. Data Science project
2015-2016 NBA players analysis
the challenges

4. The purpose of the project was to find patterns between the way NBA players behave during basketball games. To achieve this,
we chose to concatenate different databases containing players characteristics (age, salary, position) and players statistics per
36 minutes played (field goal attempt/success, fouls, minutes played…).
Data corrections
The fist step of this analysis was to identify and
remove the first database outliers. “Garbage time”
players have outstanding statistics due to the lack of
time they spent on the field. Under these
circumstances, players that played less than 250
minutes in the whole season were deleted.
The second step of this correction was to correct
the skewness of each variable with a function
modification to match a normal repartition.
Correlation Matrix
Correlation was declared valid when the p-value
was under 5% and the correlation greater than 0,5.
This level, relatively traditional, ensured us that the
correlation was statistically significant.
Correlation board
Corellation
level
c > 0,8
0,8 > c > 0,5
Correlation type
● Salary, Salary per min and Salary per game
● Game started and Min Played
● Field Goal, Field Goal Attempt, Point and PER
● 3Point, 3Point Attempt, Point and PER
● 2Point, 2Point Attempt, Point and PER
● Free-throws, Free-throws Attempt, Point and PER
● Offensive Rebound, Defensive Rebound and Total Rebound
● Field Goal is correlated to the Free Throws and 2Point but not by the 3Point
● Field Goal Attempt is correlated to the 2Point, 2Point Attempt, Free
Throws but not by the 3Point
● Offensive Rebound and Defensive Rebound are correlated to the block
● Turnover are correlated to Assist
● 3 Points and 3 Points Attempt are anti correlated to the Field Goal %
● 3 Points is anti-correlated to Block, Defensive Rebound, Offensive Rebound
DataScienceproject
NBAplayersanalysis Objectives and database selection

5. After glancing rapidly at the different databases axis, some other outliers were identified through the contribution table and
the player’s repartitioning map. These were indeed far from the axis. Five players were removed including Stephen Curry and
Michael Beasley. Stephen Curry broke the record of “3 points” in a year : 402, when the last highest was 270. In that same
season, Michael Beasley rediscovered NBA after a few seasons in China, and, as a consequence, his wages were the lowest
possible, but he played a lot. The quality/price of this player did not fit the model, he was an outlier. The 3 remaining players
were the last “garbage time” players.
PCA - Interpretation
The analysis of the database’s scree plot indicated that no
more than 3 axis explained the database. In this condition,
and according to the previous results, these are the meanings
of all of them :
• Axis 1 : player efficiency
• Axis 2 : offensive / defensive player
• Axis 3 : ratio time played / wage of the player
DataScienceproject
NBAplayersanalysis PCA - Outliers
Axe 1 - 2 Axe 2 - 3

6. Helped by the dendogram decision tree, 9 different clusters were identified. This is the result of the clustering.
DataScienceproject
NBAplayersanalysis
Clustering
Axe 1 - 2Axe 1 - 3 Axe 2 - 3
Cluster %database interpretation
1 12%
Point guard player. He is organising the team mouvements, as a consequence, he doesn't get a lot of
rebounds
2 13% 3 points shooter
3 16% "Trigger-happy" players. Love to shoot, with a poor efficiency in reaching the target
4 13% "Garbage time" players. They play a little time per match
5 9% Defensive players, poor attackers
6 15% Middling players : average in every sectors.
7 15% Defensive minded players
8 8% Offensive minded players
9 13% Superstar players. Great athletes for great salaries

7. Data Science project
NBA players analysis
Conclusion
In the NBA, being efficient in scoring “3 points” is the most valuable skill.
Most of the players who have this skill earn more than an average player.
This can be explained by the fact that “3 points” are harder to get, but also
add entertainment to the game.
As a surprise, age doesn’t account for anything. Neither on the wages nor
performances. This might be one of the biggest surprise of the database.

8. Data Mining project
Predicting alcoholic behavior
in students

9. DataMiningproject
Studentalcoholicbehavior The purpose of the project was to make a model that would be predictive to evaluate the impact of alcohol on student
behavior. The database at our disposal was filled with information about student’s behavior and environment : age, sex,
how often they go out per week, the number of drinks consumed per day and per week, if they have a romantic partner
and their performances in school… To achieve the project’s goal, we defined an alcoholic as a person who drinks at least
twice a day three times a week.
Objectives & definition
After balancing the data, our first result revealed gender played a key role in determining the alcohol intake, when 60%
were males whereas 40% were females. This result is completed by the predictive factors for weekly and daily
consumption. Weekly consumption is driven more by failure, health and the student‘s family support. Week-end
consumption is more determined by the number of times they go out, their parent’s jobs and the type of school they are
attending.
First results
Weekly consumption
Predictive factors
Weekly consumption
Predictive factors

10. DataMiningproject
Studentalcoholicbehavior Whether weekly consumption or week-ends consumption, family support is also a really important factor. But what leads
to alcoholic behavior is in the first case failure and in the second case the type of classes students are attending
(engineering, business, design). For weekly consumption, health condition could be seen as a consequence of such
behavior. For week-end consumption, the number of time the student goes out is a predictor, as well as the type of job
their families are doing.
Analysis
These two graphs focus on the daily consumption. The first one is showing a predictive analysis for the whole students
population. It shows how important is the difference between the number of student consuming alcohol and those who do
not. We made two different types of models : neural and chaid. To complete the study, this second graph shows how
accurate the two models are.
Predictions and model validation

11. Data Science project
Predicting alcoholic behavior in
students
Conclusion
At school, like everywhere else, failure needs to be well supervised. From
this analysis we have demonstrated that failures can lead to serious
diseases and addictions. In order to succeed, family support is also really
important. Schools also play a role in helping their student towards
success. Like in companies, the environment and the management are key
performance indicators for students.

12. SQL FundamentalsData Journalism
R 101TOEIC test
Certifications
English test passed in may
2016. This certification is valid
until may 2018.
My score is 915 out of 990.
Basics on finding database and
telling stories about it. This
course has introduced me to
data based communication.
Certification for the basic use
of R.
Certification for the basic use
of SQL.

13. Pierre Massé
pierre.masse@epfedu.fr
Phone : +33 6 82 71 61 87