Predict Macro Economic Trends using Kaggle Financial Dataset

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.


Each project comes with 2-5 hours of micro-videos explaining the solution.

Code & Dataset

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Project Experience

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What will you learn

Understanding the problem statement
Importing the Train and Test dataset directly from the source
Performing basic EDA
Checking for null values and making imputations using appropriate methods
Dropping rows with null values
Understanding how a Linear model works and underlying assumptions
Multicollinearity, Autocorrelation, linearity and Normal distribution
Applying Ridge Regression for training
Applying GLMnet model for training
Applying elasticnet model for training
Converting Dataframe into DMatrix
Applying XGBoost model for making predictions
Defining parameters for XGBoost
Defining evaluation metrics
Plotting graphs for the results obtained to select the best model
Making final predictions with the best-selected model

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.

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Curriculum For This Mini Project

Introduction & Installation
Data Set Overview
Problem Statement
Data Analysis - Missing Values
Next Steps
Split Data Set into Train and Test
Linear Regression - Assumptions
Linear Regression - Model Creation
Robust Linear Regression
Ridge Regression
Extreme Gradient Boosting