Podcast
Questions and Answers
What is the mean income of males in the dataset?
What is the mean income of males in the dataset?
5446.46
What is the mean income of females in the dataset?
What is the mean income of females in the dataset?
4643.47
What is the median of the LoanAmount field?
What is the median of the LoanAmount field?
128.0
What is the mode of the Dependents field?
What is the mode of the Dependents field?
Signup and view all the answers
What is the standard deviation of the Loan_Amount_Term field?
What is the standard deviation of the Loan_Amount_Term field?
Signup and view all the answers
The number of missing values in the dataset is: df.isnull().sum()
The number of missing values in the dataset is: df.isnull().sum()
Signup and view all the answers
What is the probability of taking a loan?
What is the probability of taking a loan?
Signup and view all the answers
What is the probability of taking a loan for clients with a positive credit history?
What is the probability of taking a loan for clients with a positive credit history?
Signup and view all the answers
Study Notes
Here are the study notes:
Loan Status Dataset
- The Loan Status dataset contains information about clients, including:
- Loan ID
- Gender
- Marital status
- Number of dependents
- Education level
- Self-employment status
- Applicant's income
- Loan amount
- Loan term
- Credit history
- Property area
- Loan status
Descriptive Statistics
- The mean income of males is 5446.46 and females is 4643.47
- The median of LoanAmount is 128.0
- The mode of Dependents is 0
- The standard deviation of Loan_Amount_Term is 65.12
Missing Values
- The number of missing values for each column is:
- Loan_ID: 0
- Gender: 13
- Marital status: 3
- Dependents: 15
- Education: 0
- Self-employment: 32
- Applicant's income: 0
- Loan amount: 22
- Loan term: 14
- Credit history: 50
- Property area: 0
- Loan status: 0
Probability of Taking a Loan
- The probability of taking a loan is 0.6873
- The probability of taking a loan with a positive credit history is 0.7958
Graphical Representation
- Histograms and boxplots can be used to visualize the distribution of LoanAmount and Dependents
- The seaborn library can be used to create informative and attractive statistical graphics
Cumulative Distribution Function
- The cumulative distribution function (CDF) can be used to calculate the probability of a client having an income less than 2000
- The CDF is calculated using the norm.cdf function from the scipy.stats module
- The result shows that approximately 6.51% of clients have an income less than 2000
t-test
- The t-test can be used to compare the means of two groups
- One-sample t-test can be used to compare the mean of a sample to a known population mean
- Two-sample t-test can be used to compare the means of two independent samples
- The t-test assumes normality of the data and equal variances between the groups
- The results of the t-test can be used to accept or reject the null hypothesis
Studying That Suits You
Use AI to generate personalized quizzes and flashcards to suit your learning preferences.
Related Documents
Description
This quiz covers probability and statistics concepts based on the LoanStatus.csv dataset, including data analysis and interpretation. It tests your understanding of statistical methods and their application to real-world problems.
