Rapport BI

8/18/2019 Rapport BI

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ACKNOWLEDGEMENTS

“Silent gratitude isn’t much use to anyone.”

[Gladys Bronwyn Stern]

Our first words of recognition and gratitude are owed to

Mr. Kamel SMAILI, for allowing us the honor to be our

beloved teacher and supervisor, he responded to our

numerous requests, and helped us by his professionalism

and experience to accomplish a better job. He managed to

make us appreciate the "beauty" of Business Intelligence,

and through his marvelous way of teaching, we could

approach the fascinating world of Data Warehouse and

Data Mining.

We would like as well to express our appreciation to all the

students of this master, we’ve been so fortunate to be in

such an impeccable atmosphere. Thank you for your


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unending support, your devotion and for making this year

as rewarding and enjoyable to live.

Many thanks to Mr. Abdelatif Bouhlal, who’s no longer

teaching us, but from whom we’ve learned so much

regarding the art of eloquence. We’re deeply grateful to

you sir and we’ll truly never forget you.

Finally, the time spent next to all these persons has been

carved in our memories, they remain the example to

follow, and we’re hoping that someday, we will be able to

convey to our turn, as much as we were able to receive.


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TABLE OF CONTENTS

Acknowledgments ......................................................... 2

Table of contents ............................................................ 4

Life before Business Intelligence ................................... 6

BI at a glance ................................................................ 8

INSEE Presentation .................................................... 11

Phase 1: Identifying the project perimeter

Project goals ........................................................ 13

Project progress .................................................. 14

Phase 2: Data Sourcing and ETL

Data Understanding ........................................... 18

Data cleaning ..................................................... 19

Data acquisition & ETL Process ......................... 21

Pentaho’s ETL tool .............................................. 23

Phase 3: Conceiving the Data Warehouse

Data warehousing ............................................... 24

Conceptual Data Model ...................................... 26

Physical Data Model ............................................. 4

The Data marts ..................................................... 4


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Tools used ............................................................. 4

Phase 4 : Operating and dissecting the data Marts ..... 1

Data format & Calculated fields ........................... 4

Reports generation ................................................ 4

Phase 5 : Data Mining Phase ........................................ 4

Data Mining Presentation ..................................... 4

Analysing Data ...................................................... 4


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LIFE BEFORE BI

In the beginning was the data, and the data was hidden

away somewhere deep in the bowels of the corporate

databases, where only an elite of highly trained users wereable to reach it.

When access to this data was needed, the only way to get

at it, was to ask or even beg one of those highly trained

elite users for help (mainly if the person who’s asking forthese information isn’t a computer scientist). But when

the query finally made its way to the top of Mr. Elite

User’s in-tray, often several months later, the information

that trickled down, in the form of a spreadsheet or even a

printed report would be horrendously out-of-date.

As for whether Mr. Elite User was likely to understand the

business requirements asked in the first place and so avoid

supplying with wrong (or at best irrelevant) information.


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Business intelligence remains the solution to this hideous

problem; it sure not only, provides easy access to business

data with its architecture and its collection of integrated

operational as well as decision-support applications, but

improve the ability to study past behaviors and actions in

order to understand more where the organization or the

company stands.

Put simply, BI lets you make better business decisions

because it gives you access to the right information at the

right time.


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BI AT A GLANCE

A lot of vague terms were being tossed around to define

Business Intelligence: to one Business person, it means

market research, something we would call “competitive

intelligence.” To another person, “reporting” may be a

better term, even though business intelligence goes well

beyond accessing a static report. “Reporting” and

“analysis” are terms frequently used to describe business

intelligence too. Others will use terms such as “business

analytics” or “decision support,” both with varying

degrees of appropriateness.

How these terms differ matters poorly, unless you are

trying to compare market shares for different

technologies. What matters most is to use the terminology

that is most familiar to intended users and that has a

positive connotation. No matter which terminology you

use, keep the ultimate value of business intelligence in

mind which is providing a pertinent insight, so you can


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measure performance in order to take action at a time

when it is still possible, to eventually reach your goals.

Best of all, it lets you do it all yourself, rather than having

to depend on IT professionals to provide you with the data

you need at a time that suits their schedule; it allow you

also to track, understand, manage your business and

several others options such as :

Reporting: Reporting, as its name suggests, enables

you to format and deliver information to largeaudiences both inside and outside your organization

in the form of reports.

Query and analysis: Query and analysis tools provide

you with a means of interacting with businessinformation (by performing your own adhoc queries)

without having to understand the often complex

data that lies underneath this information.


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Performance management: Performance

management tools let you keep track of and analyze

key performance indicators and goals using

Dashboards, Scorecards, and Analytics.

What Business Intelligence Is not:

BI is neither a product nor a system.

A data warehouse may or may not be a component

of your business intelligence architecture, but a data

warehouse is not synonymous with businessintelligence.


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INSEE PRESENTATION

France's National Institute of Statistics and Economic

Studies (Institut National de la Statistique et des Études

Économiques: INSEE) is a Directorate General of theMinistry of the Economy, Finance, and Employment. It is

therefore a government agency whose personnel are

government employees, although not all belong to the

civil service. INSEE operates under government accounting

rules: it receives its funding from the State's generalbudget.

Getting to know INSEE

Main goal and missions, legislative framework, INSEE in

the European statistical system, brief history, INSEEresources, working at INSEE.

Official Statistics

The official statistical system collects the data needed to

compile quantitative results. In this capacity, it undertakes


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censuses and surveys, manages databases, and also draws

on administrative sources.

Quality at the INSEE

This quality rubric describes the rules, methods and

resources that enable official statistics to meet quality

requirements as well as possible. Such a description draws

direct inspiration from the fifteen principles and related

indicators from the European Statistics Code of Practice.

French, European and International statistical sites

Statistics production is conducted under a program, which

is a "decision" applicable to the Member States. INSEE

helps to design and implement multilateral cooperation

programs under the aegis of international organizations

such as Eurostat, U.N. institutions, the World Bank, and

the International Monetary Fund (IMF).

Seminars, conferences and fairs

Conferences and seminars organized by Insee or in which

Insee has participated.


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PHASE 1:

ACHIEVEMENT CONTEXT


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PROJECT GOALS & OBJECTIVES

The first thing that we had to do primarily is to define the

objectives and the goals for this BI project because it ispractically impossible to create or accomplish a valid

project without a solid understanding of the scope.

Mainly, the objectives of this BI project are:

To transform data into meaningful information

to support effective decisions by improving its

quality, consistency and completeness.

Build a data warehouse based on INSEE results

(the employee’s distribution, the repartition of

student loans, the population statistics, Rates

of death, birth, weeding …) to set the stage for

successful and effective data mining.

Deploy and exploit brightly the data warehouse

with the appropriate tools.

Generate specific and flexible reports.


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PROJECT PROGRESS

As a first and foremost important step in our BI project,

we strategically started with identifying the project

perimeter, we mainly analyzed and tried to understand allthe data in the given spreadsheets, then, we did set the

goals and the ultimate objectives relaying on every

specified note.

After identifying the project context and boundary, we

cleaned, swabbed and filtrated the data using the

appropriate ETL tools. ETL, which stands forEXTRACT

TRANSFORM AND LOAD, is the set of functions combined

into one solution that enables to “extract” data from

numerous databases, sources, applications and systems,

“transform” it as appropriate, and “load” it into another

database, a data mart or a data warehouse for analysis, or

send it along to another operational system to support a

business process. Creating a Data Warehouse was the

next phase: we tried to keep in mind that a DW is most

likely to success, if it’s highly organized and flexible.


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Then, we exploited and analyze all the Data Marts, using

the options offered by Cognos, we generate also severalreports, and adjust the values format too.

Subsequently to this stage, was the Data Mining Phase,

devoted to apply benefits from collections of data, to

improve business by predicting and understanding

behaviors. Finally, as BI is aimed to response to all types

1

2

3

4


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of issues, we inferred in this last phase descriptive or

explanatory models and we construed and interpret all

the results.


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PHASE 2:DATA SOURCING & ETL


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DATA UNDERSTANDING

After setting up our Bi project’s perimeter and goals, we

proceeded with a very central step which is the data

understanding. There are several things to be learned

about the data even after creating the Data Warehouse or

mining it, such as identifying entities and meanings of

individual attributes.

Fortunately, we didn’t have to collect data - a really crucial

phase, chiefly when it comes to several sources- the Excel

spreadsheets given were widely enough, but we did

however have our share of problems, problems relative

mainly to data comprehension, since some informationswere missed (mostly Dom’s data), other were misplaced,

per example: townships and township’s fractions… we had

to grasp the confusing or ambiguous combinations, and it

took us a long time to seize it.


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DATA CLEANING

Data understanding is not an obligatory one, but useful

from many aspects. Main role of data surveying in this

stage is finding out from the general structure of the data,

whether or not there is useful amount of informationenfolded in extracted or given data, which lead us to the

data cleaning phase. Basic as it is, its purpose is to get

healthy Data that can improve final modeling results. This

included checking the consistency of individual attribute

values and types, quantity, removing redundancy andfinding of outliers: we did detect a few anomalies

regarding the slight difference in the the number of

recruits compared to those accepted in internal contests,

especially when there is not free intake test (concours HF

file).

Checking in this phase deals with completeness and

correctness of data. Completness defines the proportion

and regularity of missing values in data. Correctness is

related to discovery of erroneous values present in data,

their extent and possible remedies.


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DATA ACQUISITION & ETL

PROCESS

It becomes very difficult to extract desired data. It is easy toimplement something that either misses the user’s

expectations or only partially satisfies them; Data

acquisition or the extract, transform, and load (ETL) process

is a complex set of activities whose sole principle is to

attain the most accurate and integrated data possible andmake it accessible to the enterprise through the data

warehouse.

It includes the following subprocesses :

Extracting which stands for copying the parts that we

needed to the data staging area for further work from the

INSEE’s excel spreadsheets, and purging the data that will

not be used.


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Transforming: Once the data was extracted into the data

staging area, we used as many possible transformations as

we could, including correcting misspellings, parsing the

data into standard formats (Like the PIB, we had to convert

it from the ME to the Euro), changing data into the

appropriate Type: the major problem with the data given is

that all the attributes and the values were Text, which is

really senseless, since there is dates involved, numeric

values …. We had also to combine the sources, by matching

and aggregating the information that has the same context,

or even the same structure.


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Loading: At the end of the transformation process, we were

able to load the data into CSV files, so that it can be easy to

import into the data base that will be created.

Nevertheless, we apply 80% of the ETL process manually,

the lens being that we had to have the cleanest DWpossible; 20% remaining was handled by an ETL tool called

Pentaho that will be explained in the next chapter.


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PENTAHO’S ETL TOOL

We have come a long way from the days where all set

activities had to be done manually: the BI industry has

developed a plethora of tools and technologies to support

the data acquisition process, we’ve chosen for our BIproject the Pentaho data integration, that offers first fully-

unified ETL, modeling and data visualization development

environment for Agile BI.

Here's a preview of Penaho interface while using it for

data transforming:


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PHASE 3:DESIGNING THE DATA

WAREHOUSE


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DATA WAREHOUSING

Data warehouses collect relevant data from multipledifferent data sources, rationalize, summarize it and

catalog it in large consistent, stable, accurate, long term

data stores which allows not only, for all types of

questions to be answered but provides insights into data

to answer the same question asked multiple differentways to support the decision making process.

Although specific vocabularies vary from organization to

organization, the data warehousing industry is in

agreement that the data warehouse lifecycle model is

fundamentally as described in the diagram of the next

page.

The model, which is a cycle rather than a serialized

timeline, consists of five major phases:


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Design: Practically speaking, the best data warehousing

practitioners are those who combine data with indicators

and other critical business metrics.

Prototype: Developing a unanimous working model of a

data warehouse or data mart design, suitable for actual

use. The purpose is to allow a back and forth between

design and prototype.


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Deploy: It is at this phase that the single most often

neglected component can undermine the whole process.

Operation: the day-to-day maintenance of the datawarehouse or mart, the data delivery services thatprovides to analysts to keep the warehouse or martcurrent.

Enhancement: In cases where external business

conditions change discontinuously or organizations

themselves undergo discontinuous changes.


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CONCEPTUAL DATA MODEL

The following diagram illustrates and defines the portionsthat our Data Warehouse will contain.

Part 1:

We made this portion of the conceptual model, according

to the following management rules:


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A contest refers to one and only category, type and intake

type, but categories may have assortment of contests, the

same thing can be applied to intake types and contest

types.

A socio-professional category has at least, one specific

equipment, and inversely equipment may have several

socio-professional categories.

Part 2:

To set up this part of our CDM, we were based on these

regulations:


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A Superior Class belongs to one precise category, but a

category encloses many superior classes. There is an

association between class categories, gender and the BAC

option that includes an effective and a percentage of a

precise date.

Part 3:

This is nearly the major bit of our CDM, which embraces

most of the entities that we have. We should mention

however, that we did merge some of the data, because

they share the same structure, such as domiciled births

and deaths.


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Sample of data Items


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PHYSICAL DATA MODEL

A Physical Data Model includes all the database

entities/tables/views, attributes/columns/fields and the

relationship between the entities that we have defined.

Database performance, indexing strategy, physical storage

and denormalization are important considerations of

creating the physical data model. How the database is

created is dependent to all the constraints implemented in

the PDM.


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DATA MARTS

In order to conceive our data marts, we had to form at first

our dimensions and our fact tables. We started by

denormalizing the physical implementation, so that we can

put one fact in numerous places.

Foremost, it improves usability by grouping all the

associated attributes in a table, thus reducing significantly

the total number of tables which a user will face.

Our dimensions are as follows:

Activite Dimension: Merge of two tables ( Activité and sous

activté) this dimension presents the activities and

subactivities related to each turf


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BAC Dimension: Merge of two tables (bac and bracnhe)

that nominates all the bac options.

Classes Dimension: Merge of two tables (classes

supérieures and categorie_classe) which proffer all the

superior classes and its categories.

Commune Dimension: combination of quite a lot of tables

(communes associées, cantons, fractions cantonales,

arrondissement, commune), this dimension remains the

geographic dimension that specifies the territory and the

ground.


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Equipement, sexe, categoeir_sp, type-recrutement, etat

dimensions: they refer respectively to the equipments,

gender, socio-professional category, recruitment type, and

the state of data (If the GDP is final, semi-final or

provisory).

Though, these dimensions won’t be handy, if it’s not for a

specific kind of tables, primary in each dimensial model

and containing the most useful facts, these tables are

called: the fact table.

Every fact table represents a many-to-many relationship

and every fact table encloses a set of two or more foreign

keys that join to their respective dimension tables.

This is a list of all the fact tables that we’ve gathered and

designed:

Etablissement: This fact table represents the number of

companies in each activity by year and district.

Etablissement details: this one represents the number of

companies in each sub activity.


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Serie_Bac: introduces the number of the students and the

percentage of girls per district.

Bibliotheque: clarifies the loans and the rate of registered

made by region and year.

Effectif class_sup : specifies the student’s number of a

specific category by gender, according of course to a

school year

Concours : presents the number of admitted present or

recruited persons that applied to a contest, and presentsthe percentage of women too.

Poucentage: shows the percentage of the used

equipments in every socio-professional category.

Mortalities: represents mortality rates per region peryear.

Marriage: introduces the number of weddings by

department and year.


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Nb_naissances_deces: this fact table stipulates the

number of domiciled births and deaths.

PIB_Region: defines the GDP, the GDP per person, the

GDP per job to all the districts.

PIB_Departement: sets apart the GDP, the GDP per

person, and the GDP per job to all the departments.

Population: This final fact table presents municipal

population, and the one who is counted separately of all

the municipalities per year.

Withal, conceiving the data warehouse environment

usually takes the form of replicating the dimension tables

and fact tables, and presenting sometimes these tables as

logical subsets or complete “pie-wedge” of the overall

model known as data marts.

However, our data warehouse includes three Data marts

sorted by realm or context, we distinguished three ones :


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The demographic Data Mart

This data mart treats everything that is related to

demography, like weddings, mortality, domiciled deaths

and births.

The Economic data Mart


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This one refers to all the economic values and

measures, such as the GDP, the percentage of equipments

used by activities, the number of companies…

The Education Data Mart

The last data mart shows how the dimensions related

to education are managed, as contests, type of contests,

rate of loans….


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THE TOOLS USED

In terms of tools used, the choice was difficult, given

the progression of advanced information technologies.

The choice was made carefully and was as follows:

At first, we used xamp to create our Mysql data basebecause of all the advantages that a Mysql db may offer,

such as:

The consolidated view of the base

Quickly testing of the reliability, security andperformance of the tables and the queries.

The robustness and ease of use of such an

Management System database.


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But since we used Cognos 7, we had to convert our data

base to an access one, because unfortunately Cognos does

not support a Mysql Base… so eventually, we had to

export it to an XML file, which gave rise to a format

problem : all the different types of data was converted o a

text type, so basically, we had to repair each field.

Even though, we refurbished the data base, we faced

several issues, especially when it comes to robustness of

Access and Cognos: concerning Acces, every time, we had


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PHASE 4:OPERATING & DISSECTING

THE DM


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DATA FORMAT & CALCULATED

FIELDS

Once we have completed all the steps of conceiving

the data warehouse, we finally got some data loaded, and

had to quarry it, but first we started by converting data

into its appropriate format: GDP to monetary type,

assigning the percentage sign, the Euro sign….

We also customized more than a few fields to make it easy

to understand or interpret, above all when it comes to

reporting, which will be stagger in the next chapter.

Calculated fields was a real help and release, we didn’thave to change our queries or create new ones, to obtain

Data… we used it most when in the data Mart related to

studies. We could view the result of a formula that uses

information from other fields in the cube.


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DATA FORMAT & CALCULATED

FIELDS

Cognos provides among other options the ability to create,

deploy and manage interactive, tabular or even graphical

reports, from multiple data sources. We tried to generate

the essential ones.


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The two reports present the births and deaths domiciled in

France, the first one is general, but the second concerns

only the departments of Dom for 2006 and 2007.


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The first two graphs of the previous page refers to the

number of weddings in 200- and 2007, and as we see ,

there is a slight difference between the two graphs, with Ile

de France remaining as the municipality with the highest

number. The other report presents the GDP of all the 26

regions, with Rhone Alpes as the fist region in term of GDP.

And the last report clarifies its evolution for Metropolitan

France (2000 to 2007), and shows that the GDP didn’t

retreat at all.


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PHASE 5:DATA MINING PHASE


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DATA MINING PRESENTATION

According to the Gartner Group, “Data mining is the

process of discovering meaningful new correlations,

patterns and trends by sifting through large amounts of

data stored in repositories, using pattern recognitiontechnologies as well as statistical and mathematical

techniques.” There are other definitions:

“Data mining is the analysis of observational data

sets to find unsuspected relationships and to

summarize the data in new ways”.

“Data mining is an interdisciplinary field bringing

togther techniques from machine learning, pattern

recognition, statistics, databases, and visualization

to address the issue of information extraction from

large data bases”.

However, we tried as hard as we could to describe,

estimate, predict, classify, cluster and associate the data

that we had.


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ANALYSING DATA

To analyze data, we’ve chosen to work with the tool

"WEKA”. The advantage being that this tool is programmed

in JAVA and therefore relatively fast. Moreover it is

extremely reliable. It has all the algorithms, classification

and searching functions. Besides it contains and offers a

large range of performance when it comes to graph

conceiving.

After configuring weka correctly and establishing the

connection, we retried the data that we wanted to

analyze, using the explorer interface of the tool.


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TEST METHODS

We were interested to decision trees and methods of K-

Means. We started by the decision trees, we applied the j-

48 algorithm ( an improved version of the algorithm C4.5

Quinlain).

Decision trees:

The figure below is a decision tree listing similardepartments in terms of births and deaths


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The second example concerns the decision tree

classification of GDP by department according to their

values

B- K- Means

In statistics and machine learning, k-means clustering is a

method of cluster analysis which aims to partition n-

observations into k clusters in which each observation

belongs to the cluster with the nearest mean. It is similar

to the expectation-maximization algorithm for mixtures of


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Gaussians in that they both attempt to find the centers of

natural clusters in the data as well as in the iterative

refinement approach employed by both algorithms.

We take advantage of this algorithm to test our data forGDP and number of marriages in the departments

The results are:

GDP (Gross domestic product)

K=2

=== Run i nf or mat i on ===

Scheme: weka. cl ust er er s. Si mpl eKMeans - N 2 - A"weka. cor e. Eucl i deanDi st ance - R f i r st - l ast " - I 500 - S 10Rel at i on: Quer yResul tI nst ances: 96At t r i but es: 2

pi b

nom_depar t ement Test mode: eval uat e on t r ai ni ng dat a

=== Model and eval uat i on on t r ai ni ng set ===

kMeans======

Number of i t er at i ons: 7Wi t hi n cl ust er sum of squared er r ors : 94. 93524745601803Mi ss i ng val ues gl obal l y r epl aced wi t h mean/ mode

Cl ust er cent r oi ds:Cl ust er #


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At t r i but e Ful l Dat a 0 1( 96) ( 78) ( 18)=============================================================================pi b 17670552083. 333310561371794. 8718 48477000000nom_depar t ement Ai nAi sne Ai n

Cl ust er ed I nst ances

0 78 ( 81%)1 18 ( 19%)

K=3

kMeans======

Number of i t er at i ons: 14Wi t hi n cl ust er sum of squared er r ors : 93. 52052880969502Mi ss i ng val ues gl obal l y repl aced wi t h mean/ mode

Cl ust er cent r oi ds:

Cl ust er#At t r i but e Ful l Dat a0 1 2

( 96)( 72) ( 21) ( 3)

==================================================================================================pi b 17670552083. 33339343222222. 2222 34178333333. 3333 101972000000nom_depar t ement Ai nAi sne Ai n Al pes- Mar i t i mes


0 72 ( 75%)1 21 ( 22%)

2 3 ( 3%)


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K=4

kMeans======


Cl ust er cent r oi ds:Cl ust er#At t r i but e Ful l Dat a0 1 2

( 96)( 72) ( 21) ( 3)==================================================================================================pi b 17670552083. 33339343222222. 2222 34178333333. 3333 101972000000nom_depar t ement Ai n

Ai sne Ai n Al pes- Mar i t i mes

K-4


0 72 ( 75%)1 21 ( 22%)2 3 ( 3%)

K=6

kMeans

======



Cl ust er#


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At t r i but e Ful l Dat a0 1 23 4 5

( 96)( 25) ( 27) ( 7)( 20) ( 3) ( 14)==================================================================================================================================================================================================pi b 17670552083. 33337723840000 14707333333. 3333 43717571428. 57143708000000 110004333333. 3333 28284500000nom_depar t ement Ai nAi sne Ai n Al pes- Mar i t i mesAl pes- de- Haut e- Pr ovence Bouches- du- Rh?neFi ni st ?r e


0 25 ( 26%)1 27 ( 28%)2 7 ( 7%)3 20 ( 21%)4 3 ( 3%)5 14 ( 15%)

K=7

kMeans======



Cl ust er#At t r i but e Ful l Dat a0 1 23 4 56

( 96)( 23) ( 18) ( 7)( 20) ( 3) ( 11)( 14)


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pi b 17670552083. 33337465304347. 8261 12792333333. 333343717571428. 5714 3708000000110004333333. 3333 29749727272. 727318354714285. 7143nom_depar t ement Ai nAl l i er Ai n Al pes- Mar i t i mesAl pes- de- Haut e- Pr ovence Bouches- du- Rh?neFi ni st ?r e Cal vados


0 23 ( 24%)1 18 ( 19%)2 7 ( 7%)3 20 ( 21%)4 3 ( 3%)5 11 ( 11%)6 14 ( 15%)

And K=10

kMeans======



Cl ust er#At t r i but e Ful l Dat a0 1 23 4 56 7 89

( 96)(16) (8) (7)(19) (3) (9)(12) (9) (5)(8 )======================================================================================================================pi b 17670552083. 33336707000000 13605250000 43717571428. 57143614684210. 5263 110004333333. 333331099777777. 7778 16968916666. 666711780333333. 3333 232170000008733875000nom_depar t ement Ai nAl l i er Ai n Al pes- Mar i t i mesAl pes- de- Haut e- Pr ovence Bouches- du- Rh?ne


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Haut e- Gar onne Cal vadosAi sne Fi ni st ?r e Char ent e


0 16 ( 17%)1 8 ( 8%)2 7 ( 7%)3 19 ( 20%)4 3 ( 3%)5 9 ( 9%)6 12 ( 13%)7 9 ( 9%)8 5 ( 5%)9 8 ( 8%)

Marriages

K=2

kMeans======

Number of i t er at i ons: 6Wi t hi n cl ust er sum of squared er r or s: 204. 48640349553273Mi ss i ng val ues gl obal l y repl aced wi t h mean/ mode


At t r i but e Ful l Dat a 01

( 200) ( 150)( 50)=======================================================================nbr decesdomi ci l l i e 5273. 02 3728. 21339907. 44nbr nai ssancesvi vant edomi ci l l i e 8232. 795 4988. 0617967nbrmar i ages 2738. 765 1862. 44675367. 72


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nom_depar t ement Val - d' Oi se Val - d' Oi seVal - de- Marne


0 150 ( 75%)1 50 ( 25%)

K=4

kMeans======

Number of i t er at i ons: 12Wi t hi n cl ust er sum of squared er r or s: 196. 85641391574097Mi ss i ng val ues gl obal l y repl aced wi t h mean/ mode


At t r i but e Ful l Dat a 01 2 3

( 200) ( 38)( 16) ( 71) ( 75)=================================================================================================nbr decesdomi ci l l i e 5273. 02 8254. 842112836. 3125 4866. 5493 2533. 52nbr nai ssancesvi vant edomi ci l l i e 8232. 795 13968. 210526404. 75 6736. 1127 2867. 0267nbrmar i ages 2738. 765 5206. 15795695. 125 2441. 3239 1139. 5067nom_depar t ement Val - d' Oi se Val - d' Oi seVal - de- Mar ne Mar ne Haut e- Mar ne


0 38 ( 19%)1 16 ( 8%)2 71 ( 36%)3 75 ( 38%)..

.K=10


65/66


66/66

CONCLUSION

This project certainly gave us a lot of trouble: some

problems were encountered during conceiving the Data

Warehouse and analyzing it, however, these problems

have been overcome and this is mainly thanks to the

support and assistance from members of the team.

However, this project allowed us to highlight the fact that

teamwork is the cornerstone of every labor.

Finally, we greatly appreciate the opportunity that was

given to us, since we could address issues of knowledge,

skills, adaptability, context and values.

Date post:	07-Jul-2018
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