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 presents an examination on designing a decision tree model to predict customer interest in purchasing a product. Various factors such as gender, age, marital status, and income level are taken into account. The task involves constructing the decision tree using entropy or Gini index, converting it into a set of learning rules, and analyzing the strengths and weaknesses of the decision tree methodology. The document emphasizes practical application and assessment of decision tree learning techniques.
The document outlines a supervised exam focusing on decision tree methodology used to predict customer interest in a product based on features such as gender, age, marital status, and income level. Students are instructed to construct a decision tree using a chosen criterion (entropy or Gini index) and then convert it into a rule-based learning system. Additionally, the task involves discussing the advantages and limitations of decision tree methods. The exam emphasizes practical application of concepts in Machine Learning and the manipulation of algorithms for predictive modeling.
This document is an exam focused on machine learning concepts, specifically on building a decision tree model. It covers evaluating customer interest in purchasing a product based on various demographic factors. Students are required to demonstrate their understanding of decision trees, criteria choice, and provide insights into the methodology used.
The document focuses on analyzing data related to socio-professional groups and their consumption patterns for various food items. Principal Component Analysis (PCA) is utilized to identify underlying patterns and correlations among variables. The correlation matrix reveals significant associations, with 'Autre pain' and 'Raisin de table' exhibiting the highest correlation (0.959), while 'Autre pain' and 'Jus ordinaire' show the least. Total eigenvalue analysis suggests two primary factorial axes contributing to 88.59% of variability. The study interprets these axes, revealing distinct grou...
The document provides an in-depth explanation of quantitative research methods, emphasizing techniques for data collection, sampling strategies, and questionnaire design. It categorizes sampling methods into probabilistic and non-probabilistic approaches, highlighting their strengths and weaknesses. Additionally, it explores the use of SPSS software for data analysis, detailing processes such as variable definition, data input, and application of multidimensional analysis techniques. Finally, it covers questionnaire structuring and testing using various scales and question formats for effic...
BIG DATA: D finition " La notion de Big Data est un concept s tant popularis en 2012 pour traduire le fait que les entreprises sont confront es des volumes de donn es traiter de plus en plus consid rables et pr sentant un fort enjeux commercial et marketing.
This document provides an overview of Power BI components integrated with Excel, highlighting their functionality and installation process. It explains four key tools: Power Query, Power Pivot, Power View, and Power Map, detailing their roles in ETL, data modeling, visualization, and geospatial mapping. Installation instructions include activation for Excel versions and the integration of Office 365 Online for hosting and sharing. The guide outlines the simplicity of deploying Power BI tools within a secure, cloud-supported environment.
Power BI Desktop is a free tool for connecting, transforming, modeling, and visualizing data, extensively used by business intelligence professionals and analysts. It supports creating data models, designing visualizations, and compiling them into multi-page reports which can be shared with others using the Power BI Service. Users can connect to various data sources, clean and manipulate data through a query editor, and create visual representations of data such as charts and graphs. Reports can then be published for organization-wide collaboration, with Power BI Desktop offering three main...
Power BI Desktop is a free application that enables users to connect to data, transform it, and visualize it. Users can create reports and share them within their organization, leveraging various data sources. The tool is essential for data analysts and business intelligence professionals, allowing for the creation of visually rich reports.
This document describes the application of Multiple Linear Regression (MLR) using Python. Key methodological steps involve setting up the environment, loading data via libraries like Pandas, and using scikit-learn to create and fit the regression model. The document explores both performance metrics like R² and RMSE and provides initial visualization techniques for residuals and predicted vs actual values. An emphasis is placed on interpreting results and improving models within Python's data science ecosystem.
The document explains the calculation of the Gini Index to evaluate customer classification based on attributes like account balance, age range, and educational level. It begins by determining the overall Gini Index before splitting data, then evaluates Gini values for subgroups based on the 'M' categorical attribute. The analysis involves frequency distributions and numerical computations for each subgroup ('Low', 'Medium', 'High'), demonstrating how these contribute to the weighted Gini Index after splitting. This data-driven method highlights how decision-making in banking can be refined...
The document introduces supervised classification techniques, emphasizing decision tree algorithms. It compares traditional expert systems to machine learning-based classification, highlighting the inductive approach to generating classification rules. Decision trees are presented as interpretable models, with graphical representation and rule derivation, using attributes like temperature or age for medical and customer profiling scenarios. The text further describes algorithmic frameworks like ID3 and CART, explaining entropy and Gini index for node splitting, and concludes with a general...
This document provides an interactive quiz format to introduce key concepts in data mining, covering its predictive and descriptive applications. It addresses business motives behind data mining, such as uncovering hidden trends and enhancing profitability. Core techniques like decision trees, neural networks, and association rule mining are highlighted, alongside challenges such as ensuring privacy and overcoming technical limitations. Practical applications and critical success factors for data mining in various industries are also explored.
The document introduces the field of machine learning, starting with a historical overview of algorithms and data. It delves into supervised, unsupervised, and reinforcement learning, explaining their methodologies, advantages, and challenges. Deep learning is explored as an advanced subset, highlighting neural networks, computational advancements, and practical tools. Lastly, applications across fields such as vision, text recognition, personalization, and generative AI are showcased, emphasizing the vast potential and rapid advances in the domain.
Ce document propose une introduction au Machine Learning, en couvrant son historique, ses définitions, ainsi que les différents types d'apprentissage. Parmi les sujets abordés figurent l'apprentissage supervisé, non supervisé et par renforcement, sans oublier les applications du deep learning. L'évolution technologique et l'explosion des données ouvrent la voie à des performances impressionnantes dans divers domaines.
This document provides an overview of decision trees with a specific focus on the Gini Index as a metric. A banking use case illustrates predicting customers' loan repayment capacity through a decision tree framework. The step-by-step construction of the decision tree is documented, emphasizing rules extraction. Strengths and weaknesses of decision trees are critically analyzed, shedding light on their advantages and inherent limitations.
This document discusses decision trees and the Gini index in the context of predicting the ability of clients to repay loans. It provides examples and steps for constructing decision trees. Strengths and weaknesses of decision trees are also outlined, highlighting their interpretability and challenges with small datasets.
This document provides an introduction to data mining, focusing on its definition, interdisciplinary nature, and applications across various domains. It highlights the methods and algorithms involved in extracting meaningful insights from vast datasets and emphasizes the importance of data preparation. The applications of data mining include fraud detection, risk management, customer analysis, and healthcare diagnostics.
This document outlines an exam focusing on decision trees and linear regression. It includes exercises on decision tree algorithms and the application of linear regression using real data. Students are tasked with evaluating methods and interpreting statistical results.
This document provides a comprehensive overview of classification and clustering methods in data science. It distinguishes between supervised and unsupervised learning, explaining criteria for optimal clustering with similarity measures and specific metrics like Manhattan and Euclidean distances. The non-hierarchical methods, including k-means clustering and dynamic clouds, emphasize minimizing intra-class inertia and provide examples of their application in market segmentation. Hierarchical clustering techniques are also explored, including dendrogram construction to visualize classificati...
This document discusses classification methods in clustering, comparing hierarchical and non-hierarchical approaches. It dives into supervised and unsupervised learning, emphasizing the importance of measuring similarity within clusters. Additionally, it covers various types of distance measures for clustering, including examples involving numerical and binary variables.
This document is a practical exercise focusing on Correspondence Analysis (AFC), likely targeting master’s level students at the Université Virtuelle de Tunis in a BADS program. It may cover the methodology of AFC, including how to perform the analysis using real-world datasets and software tools. Through guided examples and exercises, the material teaches students how to interpret the results and draw insightful conclusions. The findings aim to equip students with practical knowledge essential for quantitative analysis in various fields.
This document introduces and applies correspondence analysis (CA) to multidimensional contingency tables. The methodology involves partitioning data into classes, visualizing their positions on principal component graphs, and exploring hierarchical relationships through ultrametric distances. Findings include detailed correspondence patterns and profiles derived from respondent data, along with specific metrics such as eigenvalues and chi-squared statistics, which provide insights into variable relationships.
This document covers concepts related to multidimensional analysis, including class partitioning and ultrametric distance evaluation. It presents an exercise involving brand name selection based on customer preferences using multidimensional data analysis methods. The results are summarized in tables and graphical representations, highlighting frequency distributions.