Pinned Repositories
Lendingclub-loan
1 Introduction LendingClub is a peer to peer lending company in which their product allows consumers to both invest and borrow loans. They offer multiple kinds of loans like student loans, personal loans, auto refinancing loans and even business loans. The borrowers who are interested in obtaining loans will get a loan grade assigned to them which affect their interest rates and the amount of money they can borrow. A lot of the LendingClub data leads to insightful conclusions about the borrowing and investing patterns of all kinds of individuals. Through our investigation, we will explain patterns and similarities of the behaviors of borrowers and investors. 1.1 Questions of Interest We intend to start off with exploratory data analysis of all the factors involved to find patterns and relationships. We will look at the data from multiple angles to get a sense of the intricacies that lie within the data. We will additionally match the trend we see in the data to external events to try to explain why such is happening. We will also conduct time series decomposition in regards to the average loan amounts being requested. We will take a look at the trend and the seasonality so that we can better forecast spikes in demand. After, we will try to fit prediction models in order to answer a couple questions: namely whether a loan request from a client should be funded or not from the perspective of the bank, and what interest rate a borrower would get for a loan from the perspective of a client. After finding good models, we will deconstruct them in order to get a deeper sense of the important aspects in such decisions. 1.2 Dataset The dataset we are using is a compilation of data on loans issued by LendingClub from the period 2007 to 2015. The data includes information on the current loan status (how much has been funded so far, how much has been paid off, etc) as well as information about the borrower (occupation, income, credit score, etc). This data lends itself to a variety of interesting financial analysis, notably time series analysis since the data is date stamped. We will touch on a number of variables present in the dataset throughout the course of this analysis. We will consolidate the meanings of all these variables here for future reference. • loan_amnt: listed amount of the loan applied for by the borrower • funded_amnt: total amount committed to that loan at that point in time • funded_amnt_inv: total amount committed by investors for that loan at that point in time • term: number of payments on the loan. Values are in months and can be either 36 or 60 • int_rate: interest rate on the loan University of California, Davis • installment: monthly payment owed by the borrower • grade: loan grade that corresponds to the risk of the loan • loan_status: current status of the loan • total_bal_il: total current balance of all installment accounts • emp_title: job title of the borrower • next_pymnt_d: next scheduled payment date • sec_app_mort_acc: number of mortgage accounts at time of application for the secondary applicant More information about the dataset can be found here: https://www.kaggle.com/wendykan/lending-club-loan-data
Airbnb
The-Doomsday
The Doomsday Algorithm
train-testimage
US-federal-gov
We’re going to look at US federal government spending broken down by agency. In this assignment we’ll learn: • The ‘group by’ model of computation • How to process data spread over many files The goal is to learn about the limits of data processing on a local laptop or desktop
usgov
CS
Case_study
Hawks
you will be doing exploratory spatial data analysis birds known as Swainson Hawks.
Machine-Learning-Projects
Machine Learning Experiments and Work
onedimentionalarray
Write a program that reads students’ data from a file into two parallel arrays, it calculates the class average, sorts the parallel arrays in descending order, writes the sorted arrays to another file, searches the parallel arrays and displays the results (see next page).
SullyVo's Repositories
SullyVo/Lendingclub-loan
1 Introduction LendingClub is a peer to peer lending company in which their product allows consumers to both invest and borrow loans. They offer multiple kinds of loans like student loans, personal loans, auto refinancing loans and even business loans. The borrowers who are interested in obtaining loans will get a loan grade assigned to them which affect their interest rates and the amount of money they can borrow. A lot of the LendingClub data leads to insightful conclusions about the borrowing and investing patterns of all kinds of individuals. Through our investigation, we will explain patterns and similarities of the behaviors of borrowers and investors. 1.1 Questions of Interest We intend to start off with exploratory data analysis of all the factors involved to find patterns and relationships. We will look at the data from multiple angles to get a sense of the intricacies that lie within the data. We will additionally match the trend we see in the data to external events to try to explain why such is happening. We will also conduct time series decomposition in regards to the average loan amounts being requested. We will take a look at the trend and the seasonality so that we can better forecast spikes in demand. After, we will try to fit prediction models in order to answer a couple questions: namely whether a loan request from a client should be funded or not from the perspective of the bank, and what interest rate a borrower would get for a loan from the perspective of a client. After finding good models, we will deconstruct them in order to get a deeper sense of the important aspects in such decisions. 1.2 Dataset The dataset we are using is a compilation of data on loans issued by LendingClub from the period 2007 to 2015. The data includes information on the current loan status (how much has been funded so far, how much has been paid off, etc) as well as information about the borrower (occupation, income, credit score, etc). This data lends itself to a variety of interesting financial analysis, notably time series analysis since the data is date stamped. We will touch on a number of variables present in the dataset throughout the course of this analysis. We will consolidate the meanings of all these variables here for future reference. • loan_amnt: listed amount of the loan applied for by the borrower • funded_amnt: total amount committed to that loan at that point in time • funded_amnt_inv: total amount committed by investors for that loan at that point in time • term: number of payments on the loan. Values are in months and can be either 36 or 60 • int_rate: interest rate on the loan University of California, Davis • installment: monthly payment owed by the borrower • grade: loan grade that corresponds to the risk of the loan • loan_status: current status of the loan • total_bal_il: total current balance of all installment accounts • emp_title: job title of the borrower • next_pymnt_d: next scheduled payment date • sec_app_mort_acc: number of mortgage accounts at time of application for the secondary applicant More information about the dataset can be found here: https://www.kaggle.com/wendykan/lending-club-loan-data
SullyVo/Airbnb
SullyVo/CS
Case_study
SullyVo/Hawks
you will be doing exploratory spatial data analysis birds known as Swainson Hawks.
SullyVo/Machine-Learning-Projects
Machine Learning Experiments and Work
SullyVo/onedimentionalarray
Write a program that reads students’ data from a file into two parallel arrays, it calculates the class average, sorts the parallel arrays in descending order, writes the sorted arrays to another file, searches the parallel arrays and displays the results (see next page).
SullyVo/self_checkoutregister
Write a program that acts as a self-checkout register at a store and lets the user buy 2 items. The program will prompt the user and read in: - the name of item 1, such as mechanical pencil. - the price of item 1 and number of items 1, such as: 2 2.50 - the name of item 2, such as binder - the price of item 2 and the number of items 2, such as:
SullyVo/The-Doomsday
The Doomsday Algorithm
SullyVo/train-testimage
SullyVo/US-federal-gov
We’re going to look at US federal government spending broken down by agency. In this assignment we’ll learn: • The ‘group by’ model of computation • How to process data spread over many files The goal is to learn about the limits of data processing on a local laptop or desktop
SullyVo/usgov