Pinned Repositories
Audit-Risk-Classification-and-Prediction-leveraging-Machine-Learning-
Our main objective is divided in to two parts: 1. Predict Audit Risk, i.e to build a model that accurately predicts the audit risk parameter before the audit team engages into the audit to provide an insight and prepare a robust audit plan ### 2. Classify high risk and fradulent firms, we need to build a model that based on past and current measures of risk and other parameters helps the audit team to indentify high risk and fraudulent firms ### In terms of machine learning to obtain the above objectives we have classified the problem into following parts: ### 1.Regression Problem: To build a prediction model to predict Audit Risk ### 2.Classification Problem: To identify or classify different firms into risky or non-risky categories
Exploratory_Data_Analysis_Visualization_Python
Data analysis and visualization with PyData ecosystem: Pandas, Matplotlib Numpy, and Seaborn
feature-engineering-and-feature-selection
A Guide for Feature Engineering and Feature Selection, with implementations and examples in Python.
Gun-Voilence-Statistical-Inference-Model-Using-R
Gun violence is one of the major issues ongoing in The United States. After each mass shooting, countless debates over the issue of gun control start to come up the surface and end without any significance changes in laws designed to combat the problem of gun control. One of the ways in which The United States government is trying to combat the issue of gun violence is by introducing the ‘Shall – Issue’ law. A Shall-issue law is one that requires that governments issue concealed carry handgun permits to any applicant who meets the necessary criteria. The criteria are as follows: • The applicant must be an adult. • The applicant must not have a significant criminal record. • The applicant must not have any history of mental illness. • If required by law, the applicant must complete a course in firearms safety training. If the above requirements are met, the granting authority has no discretion in the awarding of the licenses, and there is no requirement of the applicant to demonstrate "good cause". The problem we are trying to solve is to determine if the shall issue laws have any sort of affect in reducing the incidents of violent crimes. Our approach to solve this problem by analyzing historical data on crime in The United States
Stock-Price-Prediction-with-SAS
Time series models can be applied to a myriad of forecasting challenges. In this project, we explored the use of such models for forecasting stocks and using the forecasts to predict the performance of stock sectors. Specifically, we used the ARIMA(X), UCM and ESM models to predict the performance of fifteen stocks in the Oil and Gas, Public Utilities and Transportation sectors, and using the predicted stocks performance we predicted an overall forecast for the three sectors.
Mehra-Ashish's Repositories
Mehra-Ashish/Audit-Risk-Classification-and-Prediction-leveraging-Machine-Learning-
Our main objective is divided in to two parts: 1. Predict Audit Risk, i.e to build a model that accurately predicts the audit risk parameter before the audit team engages into the audit to provide an insight and prepare a robust audit plan ### 2. Classify high risk and fradulent firms, we need to build a model that based on past and current measures of risk and other parameters helps the audit team to indentify high risk and fraudulent firms ### In terms of machine learning to obtain the above objectives we have classified the problem into following parts: ### 1.Regression Problem: To build a prediction model to predict Audit Risk ### 2.Classification Problem: To identify or classify different firms into risky or non-risky categories
Mehra-Ashish/Exploratory_Data_Analysis_Visualization_Python
Data analysis and visualization with PyData ecosystem: Pandas, Matplotlib Numpy, and Seaborn
Mehra-Ashish/feature-engineering-and-feature-selection
A Guide for Feature Engineering and Feature Selection, with implementations and examples in Python.
Mehra-Ashish/Gun-Voilence-Statistical-Inference-Model-Using-R
Gun violence is one of the major issues ongoing in The United States. After each mass shooting, countless debates over the issue of gun control start to come up the surface and end without any significance changes in laws designed to combat the problem of gun control. One of the ways in which The United States government is trying to combat the issue of gun violence is by introducing the ‘Shall – Issue’ law. A Shall-issue law is one that requires that governments issue concealed carry handgun permits to any applicant who meets the necessary criteria. The criteria are as follows: • The applicant must be an adult. • The applicant must not have a significant criminal record. • The applicant must not have any history of mental illness. • If required by law, the applicant must complete a course in firearms safety training. If the above requirements are met, the granting authority has no discretion in the awarding of the licenses, and there is no requirement of the applicant to demonstrate "good cause". The problem we are trying to solve is to determine if the shall issue laws have any sort of affect in reducing the incidents of violent crimes. Our approach to solve this problem by analyzing historical data on crime in The United States
Mehra-Ashish/Stock-Price-Prediction-with-SAS
Time series models can be applied to a myriad of forecasting challenges. In this project, we explored the use of such models for forecasting stocks and using the forecasts to predict the performance of stock sectors. Specifically, we used the ARIMA(X), UCM and ESM models to predict the performance of fifteen stocks in the Oil and Gas, Public Utilities and Transportation sectors, and using the predicted stocks performance we predicted an overall forecast for the three sectors.