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Overview:

Explore the dataset and potentially gain valuable insight into your data science project through interesting features. The dataset was developed for a portfolio optimization graduate project I was working on. The goal was to the monetize risk of company deleveraging by associated with changes in economic data. Applications of the dataset may include. To see the data in action visit my analytics page. Analytics Page & Dashboard and to access all 295,000+ records click here.

• Mortgage-Backed Securities
• Geographic Business Investment
• Real Estate Analysis

For any questions, you may reach us at research_development@goldenoakresearch.com. For immediate assistance, you may reach me on at 585-626-2965. Please Note: the number is my personal number and email is preferred

Statistical Themes:

Note: in total there are 75 fields the following are just themes the fields fall under Home Owner Costs: Sum of utilities, property taxes.

• Second Mortgage: Households with a second mortgage statistics.
• Home Equity Loan: Households with a Home equity Loan statistics.
• Debt: Households with any type of debt statistics.
• Mortgage Costs: Statistics regarding mortgage payments, home equity loans, utilities and property taxes
• Home Owner Costs: Sum of utilities, property taxes statistics
• Gross Rent: Contract rent plus the estimated average monthly cost of utility features
• Gross Rent as Percent of Income Gross rent as the percent of income very interesting
• High school Graduation: High school graduation statistics.
• Population Demographics: Population demographic statistics.
• Age Demographics: Age demographic statistics.
• Household Income: Total income of people residing in the household.
• Family Income: Total income of people related to the householder.

Sources, if you wish to get the data your self :)

2012-2016 ACS 5-Year Documentation was provided by the U.S. Census Reports. Retrieved May 2, 2018, from

• Census estimate, error, and location data
• Census location information

Access All 325,258 Location of Our Most Complete Database Ever:

Providing you the potential to monetize risk and optimize your investment portfolio through quality economic features at unbeatable price. Access all 295,000+ records on an incredibly small scale, see links below for more details:

• Full Dataset: View Full Dataset
• Real Estate Research: View Research

Introduction

Financial loan services are leveraged by companies across many industries, from big banks to financial institutions to government loans. One of the primary objectives of companies with financial loan services is to decrease payment defaults and ensure that individuals are paying back their loans as expected. In order to do this efficiently and systematically, many companies employ machine learning to predict which individuals are at the highest risk of defaulting on their loans, so that proper interventions can be effectively deployed to the right audience.

Description

This dataset has been taken from Coursera's Loan Default Prediction Challenge and will provide you the opportunity to tackle one of the most industry-relevant machine learning problems with a unique dataset that will put your modeling skills to the test. The dataset contains 255,347 rows and 18 columns in total.

Features

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F5934442%2F4601b71040400d531d38ba6db2d59a29%2Floan_desc.png?generation=1694520603948387&alt=media" alt="">

Image credits: Image by vectorjuice on Freepik

Please, provide an upvote👍if the dataset was useful for your task. It would be much appreciated😄

Many of your staff, grant recipients and partners may be eligible for loan forgiveness. Typically, to quality you must be employed by a U.S. federal, state, local, or tribal government, a 501(c)3 non-profit or a non-profit organization that provides a qualifying service (including military service). You can tailor these resources to spread the word about the PSLF program. Please consider sharing in your newsletters, social media feeds or at grant recipient convenings and conferences! Subject: Changes to Public Service Loan Forgiveness (PSLF) Program Offer More Options for Loan Forgiveness [INSERT STATE] Employees May Now Be Eligible The COVID-19 pandemic resulted in financial hardship for many, including members of the human services workforce. As a [INSERT STATE] employee, you may now be eligible for federal student loan forgiveness for your important public service, even if you were not eligible before. ACF has created a PSLF landing page that includes resources for you to share. It includes the March 31 webinar hosted by the Office of Early Childhood Development, in partnership with the Department of Education, attended by over 17,000 early educators. A webinar for the broader human services community was held on May 26th. Both recordings, as well as PDFs and Frequently Asked Questions, are housed on the site. Please help us share this news with the broader human services workforce, including all of you who work here at [INSERT STATE]. The Department of Education issued a waiver that allows you to get credit for past payments even if you didn’t make the payment on time, didn’t pay the full amount due, or weren’t on a the right repayment plan. Until Oct. 31, 2022, federal student loan borrowers can get credit for payments that previously didn’t qualify for Public Service Loan Forgiveness (PSLF). Many people in the human services sector (including those that work in government and nonprofits) qualify for this program but don’t know about it. See if you qualify . Because of the COVID-19 emergency, the U.S. Department of Education announced a change to Public Service Loan Forgiveness (PSLF) program rules. For a limited time, borrowers may receive credit for past periods of repayment that would otherwise not qualify for loan forgiveness. The waiver expires October 31, 2022. See if you qualify and apply today ! Did you know that for a limited time, borrowers may receive credit for past periods of repayment that would otherwise not qualify for the Public Service Loan Forgiveness program? Read the FAQs to learn more and see if you qualify. Click to Retweet to Twitter Click to Retweet to Twitter Click to Retweet to Twitter Metadata-only record linking to the original dataset. Open original dataset below.

Company Information:

The data set is based upon https://www.kaggle.com/prateikmahendra/loan-data"> Lending Club Information . - TheIrish Dummy Banks is a peer to peer lending bank based in the ireland, in which bank provide funds for potential borrowers and bank earn a profit depending on the risk they take (the borrowers credit score). Irish Fake bank provides loan to their loyal customers. The complete data set is borrowed from Lending Club For more basic information about the company please check out the wikipedia article about the company. This dataset is copied and clean from kaggle but it has been changed. The any kind of similarity is just for learning purposes. I dont have any intention for Plagiarism I just like to be clear myself.

<a src="https://en.wikipedia.org/wiki/Lending_Club"> Lending Club Information </a>

The central idea and coding is abstract from Kevin mark ham youtube video series, Introduction to machine learning with scikit-learn video series. You can find link under resources section.

Data Description

• LoanStatNew Description

• addr_state The state provided by the borrower in the loan application

• annual_inc The self-reported annual income provided by the borrower during registration.

• annual_inc_joint The combined self-reported annual income provided by the co-borrowers during registration

• application_type Indicates whether the loan is an individual application or a joint application with two co-borrowers

• collection_recovery_fee post charge off collection fee

• collections_12_mths_ex_med Number of collections in 12 months excluding medical collections

• delinq_2yrs The number of 30+ days past-due incidences of delinquency in the borrower's credit file for the past 2 years

• desc Loan description provided by the borrower

• dti A ratio calculated using the borrower’s total monthly debt payments on the total debt obligations, - - - excluding mortgage and the requested LC loan, divided by the borrower’s self-reported monthly income.

• dti_joint A ratio calculated using the co-borrowers' total monthly payments on the total debt obligations, - excluding mortgages and the requested LC loan, divided by the co-borrowers' combined self-reported monthly income

• earliest_cr_line The month the borrower's earliest reported credit line was opened

• emp_length Employment length in years. Possible values are between 0 and 10 where 0 means less than one year

• and 10 means ten or more years.

• emp_title The job title supplied by the Borrower when applying for the loan.*

• fico_range_high The upper boundary range the borrower’s FICO at loan origination belongs to.

• fico_range_low The lower boundary range the borrower’s FICO at loan origination belongs to.

• funded_amnt The total amount committed to that loan at that point in time.

• funded_amnt_inv The total amount committed by investors for that loan at that point in time.

• grade LC assigned loan grade

• home_ownership The home ownership status provided by the borrower during registration. Our values are: RENT, OWN, MORTGAGE, OTHER.

Context

This data set includes customers who have paid off their loans, who have been past due and put into collection without paying back their loan and interests, and who have paid off only after they were put in collection. The financial product is a bullet loan that customers should pay off all of their loan debt in just one time by the end of the term, instead of an installment schedule. Of course, they could pay off earlier than their pay schedule.

Content

Loan_id A unique loan number assigned to each loan customers

Loan_status Whether a loan is paid off, in collection, new customer yet to payoff, or paid off after the collection efforts

Principal Basic principal loan amount at the origination

terms Can be weekly (7 days), biweekly, and monthly payoff schedule

Effective_date When the loan got originated and took effects

Due_date Since it’s one-time payoff schedule, each loan has one single due date

Paidoff_time The actual time a customer pays off the loan

Pastdue_days How many days a loan has been past due

Age, education, gender A customer’s basic demographic information

This data includes filings related to mortgage foreclosure in Allegheny County. The foreclosure process enables a lender to take possession of a property due to an owner's failure to make mortgage payments. Mortgage foreclosure differs from tax foreclosure, which is a process enabling local governments to take possession of a property if the owner fails to pay property taxes. As Pennsylvania is a judicial foreclosure state, a lender files for foreclosure through the court system. Foreclosure data in the court system is maintained by the Allegheny County Department of Court Records. Data included here is from the general docket, and a mortgage foreclosure docket created to help homeowners maintain ownership of their property following an initial filing. Several different types of legal filings may occur on a property involved in the foreclosure process. At this time, only the most recent filing in a case is included in the data found here, but we hope to add all filings for a case in the coming months. After a property enters the foreclosure process, several potential outcomes are possible. Some of the more common outcomes include: borrowers may come to an agreement with the lender for unpaid debt; borrowers may sell the property to satisfy part or all of the debt; borrowers may voluntarily relinquish ownership to the lender; lenders may decide not to pursue the foreclosure any further; and the property may proceed all the way through a sheriff sale, where it is sold to a new owner. Before September 2022, the data presented here included only the final filing for the month in which each case (represented by Case ID) is opened; since then the feed has changed so we now have a new last_activity field, which gets updated whenever there is a new filing in the case with the date of the last filing for the month. The last_activity value gives some indication of which cases are still ongoing. (However, the new feed does not include the docket_type field, so these are blank for cases started after August 2022.) To view the detailed mortgage foreclosure filings for each property represented in this dataset, please visit the Department of Court Records Website, and enter the Case ID for a property to pull-up detailed information about each foreclosure case, including parties, docket entries, and services. Changelog 2022-12-14: Loaded data back to September (which had been missing due to the schema migration). Added a new last_activity field. Data since September 2022 is missing the docket_type value, for now those new values will be set to '' (empty string). Visualizations

This data collection provides information on the characteristics of a national sample of housing units, including apartments, single-family homes, mobile homes, and vacant housing units. Unlike previous years, the data are presented in nine separate parts: Part 1, Work Done Record (Replacement or Additions to the House), Part 2, Housing Unit Record (Main Record), Part 3, Worker Record, Part 4, Mortgages (Owners Only), Part 5, Manager and Owner Record (Renters Only), Part 6, Person Record, Part 7, Mover Group Record, Part 8, Recodes (One Record per Housing Unit), and Part 9, Weights. Data include year the structure was built, type and number of living quarters, occupancy status, access, number of rooms, presence of commercial establishments on the property, and property value. Additional data focus on kitchen and plumbing facilities, types of heating fuel used, source of water, sewage disposal, heating and air-conditioning equipment, and major additions, alterations, or repairs to the property. Information provided on housing expenses includes monthly mortgage or rent payments, cost of services such as utilities, garbage collection, and property insurance, and amount of real estate taxes paid in the previous year. Also included is information on whether the household received government assistance to help pay heating or cooling costs or for other energy-related services. Similar data are provided for housing units previously occupied by respondents who had recently moved. Additionally, indicators of housing and neighborhood quality are supplied. Housing quality variables include privacy of bedrooms, condition of kitchen facilities, basement or roof leakage, breakdowns of plumbing facilities and equipment, and overall opinion of the structure. For quality of neighborhood, variables include use of exterminator services, existence of boarded-up buildings, and overall quality of the neighborhood. In addition to housing characteristics, some demographic data are provided on household members, such as age, sex, race, marital status, income, and relationship to householder. Additional data provided on the householder include years of school completed, Spanish origin, length of residence, and length of occupancy. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR -- https://doi.org/10.3886/ICPSR02912.v2. We highly recommend using the ICPSR version as they made this dataset available in multiple data formats.

This data set contains candidate and political committee loan information for the last 17 years. Data includes loans received, loan repayments, interest payments, and loans forgiven. Descriptions attached to this dataset do not constitute legal definitions; please consult RCW 42.17A and WAC Title 390 for legal definitions and additional information regarding political finance disclosure requirements. CONDITION OF RELEASE: This publication and or referenced documents constitutes a list of individuals prepared by the Washington State Public Disclosure Commission and may not be used for commercial purposes. This list is provided on the condition and with the understanding that the persons receiving it agree to this statutorily imposed limitation on its use. See RCW 42.56.070(9) and AGO 1975 No. 15.

Introduction:

This dataset analysis aims to explore and analyze a Credit Score dataset to gain insights into customer creditworthiness and segmentation. The dataset contains information on various factors that influence credit scores, such as payment history, credit utilization ratio, number of credit accounts, education level, and employment status. The analysis will utilize the k-means algorithm to perform clustering and identify distinct groups of customers based on their credit scores.

The Credit Score dataset comprises a collection of records, each representing an individual's credit profile. The features included in the dataset are as follows:

The data set Contains following all features:

**Description of All features **:

(1). Age: This feature represents the age of the individual.

(2). Gender: This feature captures the gender of the individual.

(3). Marital Status: This feature denotes the marital status of the individual.

(4). Education Level: This feature represents the highest level of education attained by the individual.

(5). Employment Status: This feature indicates the current employment status of the individual.

(6). Credit Utilization Ratio: This feature reflects the ratio of credit used by the individual compared to their total available credit limit.

(7). Payment History: It represents the monthly net payment behaviour of each customer, taking into account factors such as on-time payments, late payments, missed payments, and defaults.

(8). Number of Credit Accounts: It represents the count of active credit accounts the person holds.

(9). Loan Amount: It indicates the monetary value of the loan.

(10). Interest Rate: This feature represents the interest rate associated with the loan.

(11). Loan Term: This feature denotes the duration or term of the loan.

(12). Type of Loan: It includes categories like “Personal Loan,” “Auto Loan,” or potentially other types of loans.

This data collection provides information on the characteristics of a national sample of housing units. Data include the year the structure was built, type and number of living quarters, presence of a garage, occupancy status, access, number of rooms and bedrooms, presence of commercial establishments on the property, and property value. Additional data focus on kitchen and plumbing facilities, types of heating fuel used, source of water, sewage disposal, heating and air conditioning equipment, and major additions, alterations, or repairs to the property. Information provided on housing expenses includes monthly mortgage or rent payments, cost of services such as utilities, garbage collection, and property insurance, and amount of real estate taxes paid in the previous year. Similar data are provided for housing units previously occupied by respondents who had recently moved. Supplemental sections provide data on energy-related characteristics, such as the presence of storm doors, storm windows, and other types of insulation, and use of supplemental heating equipment. Additionally, indicators of housing and neighborhood quality are supplied. Housing quality variables include privacy of bedrooms, condition of kitchen facilities, basement or roof leakage, cracks or holes in walls, ceilings, and floors, breakdowns of plumbing facilities and equipment, use of exterminator service, and respondent's overall opinion of structure. For quality of neighborhood, variables include existence of boarded-up buildings, noise, lack of street lighting, heavy traffic, objectionable odors, crime, and respondent's overall opinion of neighborhood. Extensive information is provided on mobile homes including type of foundation, width of home, quality of the structure, problems, if any with installation of mobile home on the present site, and amount of real estate and property taxes, and site rent. Information on condominiums and cooperatives covers number of units in the development, amount of mortgage payment, real estate tax, condominium fee, and utility costs. In addition to housing characteristics, demographic data are provided on the household members, such as sex, age, race, marital status, relationship to the household head, and income. Additional data are provided on the head of the household including years of school completed, Hispanic origin, and length of residence. For each employed respondent travel-to-work information such as principal mode of transportation, carpool occupancy, type of public transportation used, and time and distance from home to work was also collected. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR09016.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

This data collection is one in a series of financial surveys of consumers conducted annually since 1946. In a nationally representative sample, the head of each spending unit (usually the husband, the main earner, or the owner of the home) was interviewed. The basic unit of reference in the study was the spending unit, but some family data are also available. The questions in the 1954 survey covered the respondent's attitudes toward national economic conditions and price activity, as well as the respondent's own financial situation. Other questions examined the spending unit head's occupation, and the nature and amount of the spending unit's income, debts, liquid assets, changes in liquid assets, savings, investment preferences, and actual and expected purchases of cars and other major durables. In addition, the survey explored in detail the subject of housing, e.g., previous and present home ownership, value of respondent's dwelling, and mortgage information. This was the first year that questions were asked regarding plans to make additions and repairs to homes. The 1954 survey emphasized the respondent's contractual payments, e.g., mortgages, rent, property taxes, and installment debt. Respondents were asked about the desirability of using an installment plan and the wisest place to put savings. A separate subsection of the survey contained questions for farmers. (The separate farmer's questionnaire, used in the 1947-1953 surveys, was dropped.) Personal data include number of people in the spending unit, age, sex, and education of the head, and the race and sex of the respondent. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR03608.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

This dataset includes one dataset which was custom ordered from Statistics Canada.The table includes information on housing suitability and shelter-cost-to-income ratio by number of bedrooms, housing tenure, status of primary household maintainer, household type, and income quartile ranges for census subdivisions in British Columbia. The dataset is in Beyond 20/20 (.ivt) format. The Beyond 20/20 browser is required in order to open it. This software can be freely downloaded from the Statistics Canada website: https://www.statcan.gc.ca/eng/public/beyond20-20 (Windows only). For information on how to use Beyond 20/20, please see: http://odesi2.scholarsportal.info/documentation/Beyond2020/beyond20-quickstart.pdf https://wiki.ubc.ca/Library:Beyond_20/20_Guide Custom order from Statistics Canada includes the following dimensions and variables: Geography: Non-reserve CSDs in British Columbia - 299 geographies The global non-response rate (GNR) is an important measure of census data quality. It combines total non-response (households) and partial non-response (questions). A lower GNR indicates a lower risk of non-response bias and, as a result, a lower risk of inaccuracy. The counts and estimates for geographic areas with a GNR equal to or greater than 50% are not published in the standard products. The counts and estimates for these areas have a high risk of non-response bias, and in most cases, should not be released. All the geographies requested for this tabulation have been cleared for the release of income data and have a GNR under 50%. Housing Tenure Including Presence of Mortgage (5) 1. Total – Private non-band non-farm off-reserve households with an income greater than zero by housing tenure 2. Households who own 3. With a mortgage1 4. Without a mortgage 5. Households who rent Note: 1) Presence of mortgage - Refers to whether the owner households reported mortgage or loan payments for their dwelling. 2015 Before-tax Household Income Quartile Ranges (5) 1. Total – Private households by quartile ranges1, 2, 3 2. Count of households under or at quartile 1 3. Count of households between quartile 1 and quartile 2 (median) (including at quartile 2) 4. Count of households between quartile 2 (median) and quartile 3 (including at quartile 3) 5. Count of households over quartile 3 Notes: 1) A private household will be assigned to a quartile range depending on its CSD-level location and depending on its tenure (owned and rented). Quartile ranges for owned households in a specific CSD are delimited by the 2015 before-tax income quartiles of owned households with an income greater than zero and residing in non-farm off-reserve dwellings in that CSD. Quartile ranges for rented households in a specific CSD are delimited by the 2015 before-tax income quartiles of rented households with an income greater than zero and residing in non-farm off-reserve dwellings in that CSD. 2) For the income quartiles dollar values (the delimiters) please refer to Table 1. 3) Quartiles 1 to 3 are suppressed if the number of actual records used in the calculation (not rounded or weighted) is less than 16. For cases in which the renters’ quartiles or the owners’ quartiles (figures from Table 1) of a CSD are suppressed the CSD is assigned to a quartile range depending on the provincial renters’ or owners’ quartile figures. Number of Bedrooms (Unit Size) (6) 1. Total – Private households by number of bedrooms1 2. 0 bedrooms (Bachelor/Studio) 3. 1 bedroom 4. 2 bedrooms 5. 3 bedrooms 6. 4 bedrooms Note: 1) Dwellings with 5 bedrooms or more included in the total count only. Housing Suitability (6) 1. Total - Housing suitability 2. Suitable 3. Not suitable 4. One bedroom shortfall 5. Two bedroom shortfall 6. Three or more bedroom shortfall Note: 1) 'Housing suitability' refers to whether a private household is living in suitable accommodations according to the National Occupancy Standard (NOS); that is, whether the dwelling has enough bedrooms for the size and composition of the household. A household is deemed to be living in suitable accommodations if its dwelling has enough bedrooms, as calculated using the NOS. 'Housing suitability' assesses the required number of bedrooms for a household based on the age, sex, and relationships among household members. An alternative variable, 'persons per room,' considers all rooms in a private dwelling and the number of household members. Housing suitability and the National Occupancy Standard (NOS) on which it is based were developed by Canada Mortgage and Housing Corporation (CMHC) through consultations with provincial housing agencies. Shelter-cost-to-income-ratio (4) 1. Total – Private non-band non-farm off-reserve households with an income greater than zero 2. Spending less than 30% of households total income on shelter costs 3. Spending 30% or more of households total income on shelter costs 4. Spending 50% or more of households total income on shelter costs Note: 'Shelter-cost-to-income...

Business Understanding

The loan providing companies find it hard to give loans to the people due to their insufficient or non-existent credit history. Because of that, some consumers use it as their advantage by becoming a defaulter. Suppose you work for a consumer finance company which specialises in lending various types of loans to urban customers. You have to use EDA to analyse the patterns present in the data. This will ensure that the applicants are capable of repaying the loans are not rejected. When the company receives a loan application, the company has to decide for loan approval based on the applicant’s profile. Two types of risks are associated with the bank’s decision: If the applicant is likely to repay the loan, then not approving the loan results in a loss of business to the company If the applicant is not likely to repay the loan, i.e. he/she is likely to default, then approving the loan may lead to a financial loss for the company. The data given below contains the information about the loan application at the time of applying for the loan. It contains two types of scenarios: The client with payment difficulties: he/she had late payment more than X days on at least one of the first Y instalments of the loan in our sample, All other cases: All other cases when the payment is paid on time. When a client applies for a loan, there are four types of decisions that could be taken by the client/company:

Approved: The Company has approved loan Application Cancelled: The client cancelled the application sometime during approval. Either the client changed her/his mind about the loan or in some cases due to a higher risk of the client he received worse pricing which he did not want. Refused: The company had rejected the loan (because the client does not meet their requirements etc.). Unused offer: Loan has been cancelled by the client but at different stages of the process.

Business Objectives

The case study aims to identify patterns which indicate if a client has difficulty paying their instalments which may be used for taking actions such as denying the loan, reducing the amount of loan, lending (too risky applicants) at a higher interest rate, etc. This will ensure that the consumers capable of repaying the loan are not rejected. Identification of such applicants, using EDA is the aim of this case study. In other words, the company wants to understand the driving factors (or driver variables) behind loan default, i.e., the variables which are strong indicators of default. The company can utilise this knowledge for its portfolio and risk assessment.

Data Understanding

1. application_data.csv It contains all the information of the client at the time of application. The data is about whether a client has payment difficulties. 2. previous_application.csv It contains information about the client’s previous loan data. It contains the data whether the previous application had been Approved, Cancelled, Refused or Unused offer. 3. columns_description.csv It is data dictionary which describes the meaning of the variables.

The solution is made in 2 different ipymb files First file contains detailed analysis (EDA) on application_data to identify the important features which help us to identify the defaulters Second file contains data where we inner join the records (application_data, previous_application) with same the SK_ID_CURR