Global Markets - FO

Pre-Placement offer for displaying high performer attributes

Machine Learning:•Examined the impact of transaction failures on the working of a biometric enabled payment system introduced in India to facilitate banking by the poor using Machine Learning AlgorithmTrading Strategies:• Developed Algorithmic Models to execute and back-test trading strategies and achieve returns of 14.56%, 16.37%, 10.98% on implementing Pitroski F-Score) strategies in Indian Markets• Extracted financial data from NSE, Prowessdx, Bloomberg LP by automating the web browser using Selenium, Beautiful SoupValuation:• Certified for completing a study on Business Valuation (WACC and APV) and assessed Busines cases of HBS and INSEAD

We analysed consumer decision-making involving pre-purchase information and post-purchase outcomes. In order to extract data for the project of various E-commerce websites we used Beautiful soup. After collecting the data we applied SVR(linear Kernel) Xgboost and Random Forrest models in order to get Relative Importance of various pre and post purchase variables.

Developed a web based recommendation engine by making use of user based collaborative filtering (CF) engine and combining content based recommendation results along with it. The system makes use of numerical ratings of similar items between the active user and other users of the system to assess the similarity between users’ profiles to predict recommendations of unseen items to active user. The system makes use of cosine similarity correlation to evaluate the similarity between users.