Course: PGPDSBA Capstone Project (Oct 2024) This project builds a machine learning model to predict home loan defaults, aimed at assisting a bank’s consumer credit department in improving the decision-making process for home equity lines of credit. Following the Equal Credit Opportunity Act’s guidelines, the goal is to create an empirically derived, statistically sound model […]
Course: Financial Risk Analysis – Part B (Sep 2024) This Financial Risk Analysis project involves market risk analysis, where the mean and std deviation of stock returns are calculated to gain insights into stock performance and volatility. This analysis is a valuable tool for investors, financial institutions, and stakeholders in making informed decisions related to […]
Course: Financial Risk Analysis – Part A (Sep 2024) This Financial Risk Analysis project focuses on default prediction and financial analysis, aiming to assess a company’s ability to meet its debt obligations. This analysis is a valuable tool for investors, financial institutions, and stakeholders in making informed decisions related to creditworthiness and investment strategies. Deliverables: […]
Course: Market Research Analysis – Part B (Sep 2024) The project involves conducting Market Basket Analysis using a thorough analysis of Point of Sale (POS) Data for providing recommendations through which a grocery store can increase its revenue by coming up with attractive combo & discounts. Deliverables: Business Report Recency Frequency and Monetory (RFM) Analysis […]
Course: Market Research Analysis – Part A (Sep 2024) This project aims to find the underlying buying patterns of the customers of an automobile part manufacturer based on the past 3 years of the Company’s transaction data and hence recommend customized marketing strategies for different segments of customers. Deliverables: Business Report Recency Frequency and Monetory […]
Course: Time Series Forecasting (Aug 2024) Analyzed historical monthly sales data of a company. Created multiple forecast models for two different products of a particular Wine Estate and recommended the optimum forecasting model to predict monthly sales for the next 12 months along with appropriate lower and upper confidence limits. Deliverables: Python Notebook Gituhub: ABC […]
Course: Introduction to SQL (Jul 2024) New-Wheels sales have been dipping steadily in the past year and due to the critical customer feedback and ratings online, there is a drop in new customers every quarter, which is concerning. The data is not organized and is dumped as flat files only to be used occasionally. Your […]
Course: Unsupervised Learning (May 2024) Analyze the stocks data, grouping the stocks based on the attributes provided, and sharing insights about the characteristics of each group. Deliverables: Python Notebook Business Report Gituhub: Trade&Ahead – Unsupervised Learning
Course: Model Tuning (Apr 2024) “ReneWind” is a company working on improving the machinery/processes involved in the production of wind energy using machine learning and has collected data of generator failure of wind turbines using sensors. The objective is to build various classification models, tune them and find the best one that will help identify […]
Course: Ensemble Techniques (Mar 2024) Analyze the data of Visa applicants, build a predictive model to facilitate the process of visa approvals, and based on important factors that significantly influence the Visa status recommend a suitable profile for the applicants for whom the visa should be certified or denied. Deliverables: Python Notebook Gituhub: EasyVisa – […]
Course: Supervised Learning – Classification (Feb 2024) Analyze the data of INN Hotels to find which factors have a high influence on booking cancellations, build a predictive model that can predict which booking is going to be canceled in advance, and help in formulating profitable policies for cancellations and refunds. Deliverables: Python Notebook Gituhub: INN […]
Course: Supervised Learning – Foundations (Feb 2024) Analyze the used devices dataset, build a model which will help develop a dynamic pricing strategy for used and refurbished devices, and identify factors that significantly influence the price. Deliverables: Python Notebook Gituhub: ReCell – Supervised Learning – Regression