Deliverable 1: Once you have found all variables that may explain loan repayment,prepare a document(in Microsoft Word),and save itas "Project 1: Lending Club Loan Analysis FirstName LastName...

LOAN STATS
Table of Contents
Executive Summary    2
Dependent and Independent Variables    3
Descriptive Analysis    4
Pivot table    6
Correlation    7
Regression    8
Conclusion    9
Executive Summary
This report will go through the dependent and independent variables and how they effect the lending process. The significance of these factors in loan processing and permitting loan amount and interest rate to the interest. Finally, we discovered a positive link between these variables. We have used certain methodology to check the relationship among these variables namely, Regression Analysis, Descriptive Statistics and Correlation with Pivot table analysis. Using both recent and old data, descriptive analytics seeks out patterns and connections. Because it only describes patterns and associations without going any further, it is frequently referred to as the most basic kind of data analysis. Discovering out whether there is a link among variables and then figuring out its strength and course of motion are the main goals of correlation assessment, also known as bivariate. We have evaluated a large amount of data to determine the severity and relationships between the variables with the aid of these analyses.
Loan Club Analysis
In experimental research, an impartial variable is one that you alternate or adjust to study its consequences. It is named "impartial" due to the fact it's far unaffected by using another research factors. An established variable is one this is altered because of the change of an independent variable. Your independent variable "relies upon" on the final results you're inquisitive about measuring.
Dependent and Independent Variables
Five Independent Variables
Interest rate
Interest rate are the lending rate that is fully independent variable since rates are carried out by the government to lend money in the economy it is not related to any individuals.
Grade
Grade of the employee job is set by the job profile not related to any loan procedure.
Employee length
Employee total work experience is what counts here to analyze the persistence of the employee in an organization.
Home ownership
Ownership of the home is actually a important deal in financing.
Annual income
Annual income of the individual is also an important thing but it is not reliable on the loan procedure.
Dependent Variables
Loan Amount
Loan amount is decided by the loan manager. Hence, it is fully dependent on the procedure.
Funded amount investment
Funded amount also dependent on kind of risk profile individual has.
Term
Term of the loan is also decided by the Risk profile of the individual.
Installments
Loan payment is also decided by the loan procedure and protocols
Sub Grade
Sub grade is dependent on the Loan category.
Descriptive Analysis
    funded amount investment
     
    int rate
     
    
    
    
    
    Mean
    10409.1343
    Mean
    0.12027873
    Standard Error
    35.7741305
    Standard Error
    0.00018687
    Median
    8975
    Median
    0.1186
    Mode
    5000
    Mode
    0.1099
    Standard Deviation
    7135.66127
    Standard Deviation
    0.03727466
    Sample Variance
    50917661.8
    Sample Variance
    0.0013894
    Kurtosis
    1.05578176
    Kurtosis
    -0.4490431
    Skewness
    1.10459363
    Skewness
    0.29296992
    Range
    35000
    Range
    0.1917
    Minimum
    0
    Minimum
    0.0542
    Maximum
    35000
    Maximum
    0.2459
    Sum
    414137819
    Sum
    4785.4097
    Count
    39786
    Count
    39786
    installment
    
    
    Mean
    324.733637
    Standard Error
    1.04742167
    Median
    280.61
    Mode
    311.11
    Standard Deviation
    208.923212
    Sample Variance
    43648.9084
    Kurtosis
    1.24138885
    Skewness
    1.12684455
    Range
    1289.5
    Minimum
    15.69
    Maximum
    1305.19
    Sum
    12919852.5
    Count
    39786
    annual_inc
    
    
    Mean
    68979.0668
    Standard Error
    319.669435
    Median
    59000
    Mode
    60000
    Standard Deviation
    63762.6345
    Sample Variance
    4065673561
    Kurtosis
    2303.21957
    Skewness
    30.9413311
    Range
    5996000
    Minimum
    4000
    Maximum
    6000000
    Sum
    2744401150
    Count
    39786
Transformation of the Data
    term
    
    
    Mean
    42.4484995
    Standard Error
    0.05333678
    Median
    36
    Mode
    36
    Standard Deviation
    10.6387822
    Sample Variance
    113.183687
    Kurtosis
    -0.9107631
    Skewness
    1.04368705
    Range
    24
    Minimum
    36
    Maximum
    60
    Sum
    1688856
    Count
    39786
Pivot table
We may get an estimate of the total loan amount available for different experience individuals with varied interest rates using the table above. This table will demonstrate the significance of employment perseverance in the financing process.
We can assess the amount possessed by the various kinds of individuals in terms of living standards using the table above.
Each column heading is transformed into a selection of records that the user may readily manipulate in a pivot table, which performs the unique discipline of statistics for the user.
Records-containing columns can be easily deleted, added, or moved in any direction inside the table. Here, lengthy tables of uncooked data may be condensed into understandable summaries.
There are several ways to summarise data using frequencies and averages.1. Ease of use
A pivot table's key benefit is how simple it is to use. By moving the columns to the standout areas of the table, you may quickly summarise the data. The mouse button may also be used to alter columns.
Data analysis
Excel pivot tables allow you to manage enormous volumes of data at once. These tables provide you the ability to sort through hundreds of statistical data points and compile the information into simple, quantifiable findings. never again feel threatened by a large table.
Facts Summary Finding certain rows and columns can occasionally be challenging. You may use pivot tables to condense information into a single, simple graph. Users can insert and label information in rows and columns in accordance with their preferences and needs.
A pivot table may be used to manipulate data to display any possible styles. This can aid in producing precise statistical forecasts.
Brief messages
Using pivot tables, reporting is both simpler and more effective. It can save hours of tedious manual reporting of artwork. Additionally, it offers quick access to to outside resources.
Making decisions
Making quick decisions without compromising the precision of the quantitative fact requires the use of pivot tables.
Last but not least, pivot tables are a need for every business analyst, financial expert, marketer, revenue manager, or senior executive who wants to have an influence on the bottom line of their organisation.
Correlation
     
    Funded amount investment
    int rate
    Funded amount investment
    1
    
    int rate
    0.3077154
    1

     
    int rate
    term
    int rate
    1
    
    term
    0.45288431
    1
    
         
    Loan amount
    instalment
    Loan amnt
    1
    
    Instalment
    0.93020927
    1
A statistical term known as correlation describes how closely different variables move in unison with one another. A positive correlation is said to exist between two variables if they move in the same direction. They have a negative correlation if they travel in the opposite directions.
Because they may be used to predict future trends and manage risk in a portfolio, correlations play an important role in finance. These days, correlations between properties may be easily computed using a variety of software programmes and internet sites. The development and pricing of derivatives and other complex economic devices depend heavily on correlations, along with other statistical norms.
The idea of correlation is frequently employed in modern finance. For instance, a trader can utilise previous correlations to forecast whether a change in hobby prices or commodity costs will cause a company's shares to rise or decline. Additionally, a portfolio manager can try to lower their risk by making sure that no single asset in their portfolio has an excessively high correlation with another.
Regression
    Regression Statistics
    Multiple R
    0.26712961
    R Square
    0.071358229
    Adjusted R Square
    0.071334886
    Standard Error
    6932.73567
    Observations
    39786
According to the data, we can conclude that an individual's yearly income has a greater influence on the loan manager's amount decision. A higher T statistic indicates a significant link between the variables. R square is also 71%.
In statistics, a scalar dependent variable y and one or more explanatory factors (or independent variables) designated X are modelled using a linear technique called linear regression. Simple linear regression is used when there is only one explanatory variable.
Analyses using linear regression determine whether one or more predictor variables adequately account for the dependent (or criterion) variable. Five major hypotheses underlie the regression:
connection that is linear
Normality across variables
minimal to no multicollinearity
zero autocorrelation
Homoscedasticity
When there are numerous independent factors in a regression, the coefficient indicates the predicted increase in the dependent variable when one of the independent variables is increased while keeping the other independent variables constant.
The statistical indicator of how closely the data resemble the fitted regression line is called R-squared. For multiple regression, it is sometimes referred to as the coefficient of determination or the coefficient of multiple determination.
The fraction of the response variable variance that is explained by a linear model is the simple definition of R-squared. Alternative Formula: R-squared = Explanated Variation / Total Variation
R-squared ranges from 0% to 100% always:
0% means that no variability in the response data around the mean is explained by the model.
100% means that all of the variability in the response data around the mean is explained by the model.
Conclusion
We may draw the conclusion that the loan application process is entirely based on a few factors that are crucial to comprehend in order to determine the reliability of the customer. In order to speed up the application process, decrease inconsistencies, give a comprehensive image of the property, and detect any red flags and blockages, legal verification is a crucial step in the home loan process. The loan application procedure requires legal confirmation.
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