Memvisualisasikan hasil persetujuan pinjaman

Dalam layanan keuangan, memahami faktor-faktor yang memengaruhi tingkat persetujuan pinjaman sangat penting bagi pemberi pinjaman dan peminjam. Sebuah institusi keuangan telah melakukan studi dan mengumpulkan data aplikasi pinjaman, merinci jumlah yang diminta, skor kredit pemohon, status pekerjaan, dan hasil akhir dari proses persetujuan. Himpunan data yang kaya ini memberikan gambaran tentang dinamika halus dalam pengambilan keputusan pinjaman. Anda diminta menelaah himpunan data loan_approval_yield untuk memahami bagaimana jumlah pinjaman dan skor kredit memengaruhi hasil persetujuan.

DataFrame loan_approval_yield, seaborn sebagai sns, dan matplotlib.pyplot sebagai plt telah dimuat untuk Anda.

Latihan ini adalah bagian dari kursus

Perancangan Eksperimen dengan Python

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Use Seaborn to create the bar graph
sns.catplot(x="____", 
            y="____", 
            hue="____", 
            kind="____", 
            data=loan_approval_yield)
plt.title("Loan Approval Yield by Amount and Credit Score")
plt.show()

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Perancangan Eksperimen dengan Python

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

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: Mensintesis wawasan dari eksperimen yang kompleks Exercise 2: Memvisualisasikan hasil persetujuan pinjaman

Latihan Saat Ini

Exercise 3: Mengeksplorasi kepuasan pelanggan Exercise 4: Mengomunikasikan data eksperimen secara efektif Exercise 5: Menangani kompleksitas dalam data eksperimen Exercise 6: Periksa heteroskedastisitas pada masa simpan Exercise 7: Mengeksplorasi dan mentransformasikan data masa simpan Exercise 8: Menerapkan uji nonparametrik dalam analisis eksperimen Exercise 9: Memvisualisasikan dan menguji metode pengawetan Exercise 10: Menganalisis lebih lanjut teknik pengawetan makanan Exercise 11: Selamat!