Loan Status Analysis

Project Description The main goal of this project is to identify and predict the loan status of lenders. To figure out whether the dataset has time series characteristic, two cross-validation methods (K-fold, TimeSplit) are also used. In this project, RandomForest is the main model to predict and analyze the loan status. (The Dataset for model training includes 916567 rows and 10 columns from 2007 to 2017.) Data can be found in my GitHub Repository Response Variable Loan_Stat -> Including 3 status, Fully Paid, Charged Off, and Default Explanatory Variables Annual_Inc -> Annual income Emp_Length -> Employment length Dti -> The debt-to-income ratio of the borrower Delinq_2yrs -> The number of times the borrower had been 30+ days past due on a payment in the past 2 years Term -> Borrowing term Grade -> History credit grading Inq_Last_6mths -> The borrower’s number of inquiries by creditors in the last 6 months Purpose -> Purpose for borrowing Feature Engineering loan_stat Fully_Paid -> 0 Defult, Charged-Off` -> 1 Grade A, B, C, D, E, F, D -> 1, 2, 3, 4, 5, 6, 7 Purpose Debt_Consolidation -> 1 Other -> 0 1 2 3 4 5 6 7 8 #getting dummies for loan_status data_df['loan_status'] = data_df['loan_status']....

Stock Prediction System with Telegram Bot 🤖

Abstract Our topic focuses on constructing a stock forecasting system. This system can provide the user with the basic information of stock, stock price prediction, K-line chart, and the individual stock news. In the prediction aspect, our system uses four machine learning models, including Random Forest, XGBoost, LightGBM, and LSTM. To train our machine models, we use 250 different technical indicators as the variables and inputs. In order to make our model become better and more precise, we also used Shap and Skater the observe the reasoning process of the machine learning and improved our models by analyzing observation and changing parameters....

The Effect of CSR Decoupling on Corporations' Financial Performance

Abstract This research project mainly focuses on identifying the CSR decoupling behavior for the multi-international firms and utilizing the decoupling indicators to find if there is a relationship between firms’ CSR decoupling and financial indicators. Data Sources The sample used in this study is composed of listed and OTC companies in Taiwan. The data sources include: Company performance indicators and operating profiles of related companies are obtained from the Taiwan Economic Journal (TEJ) database....