Explorer la satisfaction client

La fusion de jeux de données est une compétence essentielle en analyse de données, surtout lorsque vous traitez des données liées provenant de sources différentes. Vous travaillez sur un projet pour une institution financière afin de comprendre la relation entre les taux d’approbation de prêts et la satisfaction client. Deux études distinctes ont été menées : l’une portant sur le rendement d’approbation des prêts selon divers facteurs, et l’autre sur la satisfaction client dans différentes conditions. Votre objectif est d’analyser comment le rendement d’approbation est corrélé à la satisfaction client, en tenant compte d’une autre variable comme les taux d’intérêt.

Les DataFrames loan_approval_yield et customer_satisfaction, ainsi que pandas sous pd, numpy sous np, seaborn sous sns et matplotlib.pyplot sous plt ont été chargés pour vous.

Cet exercice fait partie du cours

<cours>Conception expérimentale en Python</cours>

Exercice interactif pratique

Essayez cet exercice en complétant ce code d’exemple.

# Merge the two datasets
merged_data = pd.____(loan_approval_yield, 
                      ____, 
                      on='____')

Modifier et exécuter le code

Cet exercice fait partie du cours

<cours>Conception expérimentale en Python</cours>

IntermédiaireNiveau de compétence

4.8+

Commencer le cours gratuitement

Building knowledge in experimental design allows you to test hypotheses with best-practice analytical tools and quantify the risk of your work. You’ll begin your journey by setting the foundations of what experimental design is and different experimental design setups such as blocking and stratification. You’ll then learn and apply visual and analytical tests for normality in experimental data.

Exercise 1: Setting up experiments Exercise 2: Non-random assignment of subjects Exercise 3: Random assignment of subjects Exercise 4: Experimental data setup Exercise 5: Blocking experimental data Exercise 6: Stratifying an experiment Exercise 7: Which was stratified?Exercise 8: Normal data Exercise 9: Visual normality in an agricultural experiment Exercise 10: Analytical normality in an agricultural experiment

You'll delve into sophisticated experimental design techniques, focusing on factorial designs, randomized block designs, and covariate adjustments. These methodologies are instrumental in enhancing the accuracy, efficiency, and interpretability of experimental results. Through a combination of theoretical insights and practical applications, you'll acquire the skills needed to design, implement, and analyze complex experiments in various fields of research.

Exercise 1: Factorial designs: principles and applications Exercise 2: Understanding marketing campaign effectiveness Exercise 3: Heatmap of campaign interactions Exercise 4: Factorial designs and randomized block designs Exercise 5: Randomized block design: controlling variance Exercise 6: Implementing a randomized block design Exercise 7: Visualizing productivity within blocks by incentive Exercise 8: ANOVA within blocks of employees Exercise 9: Covariate adjustment in experimental design Exercise 10: Importance of covariates Exercise 11: Covariate adjustment with chick growth

Master statistical tests like t-tests, ANOVA, and Chi-Square, and dive deep into post-hoc analyses and power analysis essentials. Learn to select the right test, interpret p-values and errors, and skillfully conduct power analysis to determine sample and effect sizes, all while leveraging Python's powerful libraries to bring your data insights to life.

Exercise 1: Choosing the right statistical test Exercise 2: Choosing the right test: petrochemicals Exercise 3: Choosing the right test: human resources Exercise 4: Choosing the right test: finance Exercise 5: Post-hoc analysis following ANOVA Exercise 6: Anxiety treatments ANOVA Exercise 7: Applying Tukey's HSD Exercise 8: Applying Bonferoni correction Exercise 9: P-values, alpha, and errors Exercise 10: Analyzing toy durability Exercise 11: Visualizing durability differences Exercise 12: Role of significance levels Exercise 13: Power analysis: sample and effect size Exercise 14: Effect size purpose Exercise 15: Estimating required sample size for energy study

Hop into the complexities of experimental data analysis. Learn to synthesize insights using pandas, address data issues like heteroscedasticity with scipy.stats, and apply nonparametric tests like Mann-Whitney U. Learn additional techniques for transforming, visualizing, and interpreting complex data, enhancing your ability to conduct robust analyses in various experimental settings.

Exercise 1: Synthétiser des enseignements à partir d’expériences complexes Exercise 2: Visualiser le rendement d’approbation des prêts Exercise 3: Explorer la satisfaction client

Exercice actuel

Exercise 4: Communiquer efficacement des données expérimentales Exercise 5: Gérer les complexités dans les données expérimentales Exercise 6: Vérifier l’hétéroscédasticité de la durée de conservation Exercise 7: Explorer et transformer les données sur la durée de conservation Exercise 8: Appliquer des tests non paramétriques dans l’analyse expérimentale Exercise 9: Visualiser et tester les méthodes de conservation Exercise 10: Analyse approfondie des techniques de conservation des aliments Exercise 11: Félicitations !