1-844-696-6465 (US)        +91 77600 44484        help@dezyre.com
two-sigma-financial-modeling-challenge.jpg

Predict Macro Economic Trends using Kaggle Financial Dataset

In this machine learning project, you will uncover the predictive value in an uncertain world by using various artificial intelligence, machine learning, advanced regression and feature transformation techniques.
4.54.5

Users who bought this project also bought

What will you learn

  • Application of linear regression
  • Application of non-linear regression
  • Application of LASSO and elastic net regression
  • Application of XGBoost model
  • Interpretation of models

What will you get

  • Access to recording of the complete project
  • Access to all material related to project like data files, solution files etc.

Prerequisites

  • Jupyter Notebook from Anaconda Installation
  • R and R-Studio Installation
  • At least 5 MBS internet speed
  • At least 4 GB RAM Machine
  • Language used: R

Project Description

Two Sigma is a technology company dedicated to finding value in the world’s data. Since its founding in 2001, Two Sigma has built an innovative platform that combines extraordinary computing power, vast amounts of information, and advanced data science to produce breakthroughs in investment management, insurance, and related fields. Economic opportunity depends on the ability to deliver singularly accurate forecasts in a world of uncertainty.

By accurately predicting financial movements, you will learn about scientifically-driven approaches to unlocking significant predictive capability.

Two Sigma is excited to find predictive value and gain a better understanding of the skills offered by the global data science crowd.

Instructors

 
Pradeepta

Curriculum For This Mini Project

 
  Introduction & Installation
00:05:02
  Data Set Overview
00:03:37
  Problem Statement
00:01:58
  Data Analysis - Missing Values
00:38:01
  Recap
00:02:03
  Next Steps
00:06:05
  Why MICE
00:03:23
  Split Data Set into Train and Test
00:05:39
  Linear Regression - Assumptions
00:06:22
  Linear Regression - Model Creation
00:03:29
  Robust Linear Regression
00:03:40
  Ridge Regression
00:11:09
  Extreme Gradient Boosting
00:07:51