Intelligence artificielle et données
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Cours, examens, TD, TP et exercices de intelligence artificielle et données. Thèmes couverts : machine learning, apprentissage automatique, big data, data science, fouille de données.
This document focuses on the application of Correspondence Analysis (AFC), a statistical method for exploring relationships between categorical variables in contingency tables. The study examines the association between employee age categories and job roles through statistical tests, including a chi-squared test for independence. Key findings reveal significant dependence between these variables and associations like the proximity of 'Cadre' with '< 30 years.' Methodology involves construction and interpretation of line and column profiles, as well as visualizing relationships using multidi...
This document outlines the methodology and statistical analysis for the Factorial Correspondence Analysis (AFC) applied to two qualitative variables. It includes contingency tables representing the distribution of employees by age and category. The document further explores the relationships between these variables and tests for independence using the chi-squared test.
The document details the methodology for conducting Principal Component Analysis (PCA) on automobile data using Python. It involves data preparation by centering and scaling using the StandardScaler from scikit-learn. PCA is performed, and eigenvalues and explained variance are calculated to determine key components. Graphical tools such as the Scree plot and variance-explained graphs help identify two principal components to retain. Finally, the proximity between vehicle models is analyzed through factor coordinates and their contributions to the principal axes.
Ce document traite de l'utilisation de l'analyse en composantes principales (ACP) à l'aide de Python, et spécifiquement de la bibliothèque 'scikit-learn'. Il présente un ensemble de données sur les véhicules, en expliquant comment préparer et analyser ces données à l'aide de PCA. L'accent est mis sur l'importance de centrer et réduire les variables avant de procéder à l'ACP.
This document outlines a course workshop on data mining as part of the Business Analytics & Data Science master's program. It includes a section on principal component analysis (PCA) applied to vehicle data with specific tasks for analysis. The focus is on interpreting correlations and distributions of vehicles using PCA techniques.
The document outlines a practical workshop for Principal Component Analysis (PCA) applied to six continuous variables recorded from data on 20 cars. Using R programming and the FactoMineR and factoextra packages, correlations between variables are analyzed to identify patterns and influences in the factor space. The methodology includes generating a correlation matrix, interpreting factor axes, plotting correlation circles, and examining individual contributions. Results provide insights into the distribution of vehicles across the factor map, supported by graphical visualizations.
This document outlines an academic workshop focused on data mining techniques used within the context of business analytics and data science. It covers principal component analysis (PCA) of vehicle data, including tasks such as correlation analysis, factor interpretation, and data visualization techniques using R packages. Practical coding examples are provided to assist in performing PCA and interpreting results.
This document outlines the application of Principal Component Analysis (PCA) on a dataset containing six continuous variables related to 20 cars. The primary focus is on understanding correlations between variables using a correlation matrix and interpreting the first two PCA axes with the help of variable-factor correlations and a correlation circle. Additionally, distributions of cars are analyzed on the first factor plane using contributions and squared cosine values of observations. The analysis is conducted using SPAD software, with graphical outputs such as histograms and factor maps...
The document outlines essential methods and techniques in data exploration, specifically principal component analysis, correspondence analysis, and unsupervised classification methods. It emphasizes analytical frameworks and the use of advanced statistical tools for data interpretation. The methodology involves leveraging software such as R and Anaconda to process and visualize data effectively. The findings aim to improve understanding of complex datasets and inform business analytics strategies.
This document outlines an atelier focused on data mining within the Business Analytics & Data Science program for the academic year 2021-2022. It covers principal component analysis, correspondence analysis, and unsupervised classification methods. Additionally, it provides references to relevant resources such as cran.r-project.org and anaconda.com.
The document focuses on Principal Component Analysis (PCA) as a technique for dimensionality reduction and visualization of high-dimensional data. PCA centers on creating new variables, the principal components, that summarize quantitative data while minimizing information loss. It involves steps like data standardization, eigenvalue decomposition, and projections to lower dimensions, enabling visualization of relationships between individuals and variables. The methodology is applied to datasets of varying scales, emphasizing the identification of homogeneous groups, variable relevance, an...
The document outlines key concepts and methods in Business Analytics and Data Science, focusing on Principal Component Analysis (PCA) as a technique for data summarization. It discusses various methodologies, aims for visualization, and the importance of understanding relationships between variables. The text also emphasizes dimensionality reduction and the significance of centering and scaling data for meaningful analysis.
This document discusses the principles and methodologies of data mining, emphasizing its relevance and application in various fields. It highlights the intersection of statistics and information technology to uncover valuable insights from large datasets. The document also explores different methodologies for effective data mining practices.
This document presents a data mining project focused on classifying road accidents in France for the year 2020. It includes data importation, variable selection, descriptive statistics, correlation analysis, PCA, and K-means clustering. The findings highlight the relationships among various factors contributing to road accidents.
This document presents assessment items on Decision Support Systems focusing on data warehousing concepts like subject-oriented collections, integration, and data historicity. The methodology for practical application is illustrated via the design of a data warehouse schema for healthcare analytics, including primary keys, boolean attributes, and dimensional modeling. A separate practical scenario evaluates vehicle rental contracts using financial metrics to ensure solution scalability and operational service levels. Core calculations about storage dimensions and performance are included to...
This document contains a detailed Big Data exam focusing on Apache Spark, Hadoop, Hive, and Pig. It evaluates knowledge of Spark APIs, functionalities, and components, alongside Hadoop architecture and Hive capabilities. The exam covers practical aspects of data processing, querying systems, and programming paradigms in Big Data frameworks. Core methodologies include in-memory computations, lazy evaluations, and using specific tools for tasks like transformations, data analysis, and SQL integration.
The document outlines exam questions and exercises for a course on decision support systems. It includes theoretical questions comparing OLTP and OLAP databases, the modeling of data warehouses, and the differentiation between slow and fast-changing dimensions. Practical exercises involve designing data warehouse schemas for a toy manufacturer and a telecom operator, incorporating star and snowflake schema designs, and addressing specific business queries on sales, customer behavior, and TV audience analytics. The methodology emphasizes the application of dimensional modeling techniques for...
The document elaborates on exponential smoothing techniques, particularly their application in time series forecasting for data with no clear trend or seasonality. It describes simple exponential smoothing (SES) and its extension by Holt and Winters for handling linear trends and seasonality. The simplicity and effectiveness in short-term predictions are highlighted, with practical examples and use of software like R for implementation. Key mathematical formulations, selection of initial values, and evaluation methods like the Mean Absolute Error (MAE) are discussed.
This document provides a comprehensive guide on how to create and manage a data set in SPSS after conducting surveys. It discusses the critical steps involved in data entry, including the definition and encoding of variables and the practical preparation of the SPSS data file. The document also covers types of questions and their coding, ensuring the data is ready for statistical analysis.
This document outlines systematic steps for preparing and encoding a database within SPSS software, including variable definition and encoding. It describes various data input methods based on question types, such as single-answer, multiple-choice, Likert scales, ranking, and open-ended responses. Additionally, it touches on variable property modifications, such as creating new variables and aggregating data. It concludes with guidance for entering and managing data within SPSS, emphasizing the importance of consistent numbering and saving practices.
This document outlines the preparation of data encoding using SPSS software. It includes the definition of variables, data entry procedures, and modifications of variable properties. Specific examples of different types of questions and how they should be encoded in SPSS are provided.
The document centers on evaluating the decision of a clinic to adopt Big Data technologies for its information system. It emphasizes the potential advantages, such as improved data processing and patient care, while addressing associated challenges like system complexity and data security. Practical recommendations are suggested to optimize implementation strategies. The analysis concludes with an overall assessment of the feasibility of the clinic's decision within the given constraints.
This document discusses the decision of a clinic to implement a Big Data-based information system. It outlines the potential benefits and considerations of such a system. Students are required to submit a PDF document detailing their analysis by the deadline.
This document provides a detailed introduction to Hadoop, a core component of Big Data solutions, discussing its history, architecture, and ecosystem. It outlines the framework's features such as distributed file storage (HDFS), fault tolerance, scalability, and cost efficiency, while highlighting how Hadoop addresses the limitations of traditional RDBMS for big data. The ecosystem includes tools like HDFS, MapReduce, YARN, Hive, Pig, HBase, and Spark, among others, which enable efficient data storage, processing, and analysis. Furthermore, it emphasizes Hadoop's main use cases and its adva...