hlalit/MachineLearning-Santander-customer-behavior-prediction

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Created almost 8 years ago
Updated almost 8 years ago

hlalit/MachineLearning-Santander-customer-behavior-prediction

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Implementation of a recommendation system for Santander Bank dataset. Dataset is based on monthly data of customer transactions for 1.5 years.

Non-Reflecting-Boundary-Conditions-NRBC

Implemented the non-reflecting boundary conditions of Poinsot and Lele, JCP 1992 with Fortran. Input files have been provided Validation has been done using a subsonic compressible channel flow test case second order spatial differencing has been used and 4th order Runge Kutta scheme for time advancement Compressible finite difference solver implemented

DataAnalytics-World-Bank

Visualizing data on the World Bank Dataset of youth unemployment

Jupyter Notebook

2D-Lid-Driven-Cavity

Implementation of an 2D finite difference incompressible code in Python to compute the Navier Stokes equations for a two dimensional lid driven cavity problem

DataAnalytics-Customer-Retention-E-Commerce-Website

customer sign up and retention trends are analyzed for an e-commerce website. The documentation is available in the PDF document. The data analysis was done using Jupyter Notebook, the code for which is also uploaded

Jupyter Notebook