GDP Forcasting

資料科學的日常研究議題

An npm package to make it easier to deal with a handful of values, and try to model them in one of the most used mathematical models, with an R/Numpy-like accuracy algorithm

This project calculates the equation of the line of best fit of a given correlation

This repository contains all the Machine Learning projects I did using different Machine Learning methods. Python being the main software used.

Stata replication code and paper for an R-squared goodness-of-fit decomposition of income redistribution across 108 countries (1971–2010), using Shapley values via the rego method in Stata.

I leveraged an algorithmic approach to predict the price and carat of the diamond using Machine Learning. Various regression models have been trained and their performance has been evaluated using the R Squared Score followed by tuning of the hyperparameters of top models. I have also carried out a trade-off based on the R Squared Score and the …

Exploring the confidence-Interval concept and bootstrapping.

This project aims to enhance the accuracy and efficiency of stock market predictions by employing a sophisticated machine learning methodology. This project leverages the power of PySpark, a robust framework for distributed data processing, to handle large datasets and perform complex computations.

An introduction into the world of machine learning with a comprehensive Udemy online course, designed for beginners, to learn Python programming fundamentals and gain valuable insights into the practical applications of machine learning.

Builds a ranking model to predict the relevance score for query-product pairs in HomeDepot’s product search.

Implementation of Simple Linear Regression to analyze the relationship between experience and salary, including model evaluation using residuals and R² score.

developing several models (Linear Regression, Multiple Linear Regression, and Polynomial Regression) that will predict the price of the car using the variables or features. Then evaluating these models (in-sample, and cross-validation) using R-squared and Mean-Squared-Error metrics to find out which model is a better fit for this dataset.

Compute a squared sample Pearson product-moment correlation coefficient.

Compute a moving squared sample Pearson product-moment correlation coefficient incrementally.

Functional specification to calculate per country's happiness score

Predicting annual highest of sneakers on StockX

The project involves the multivariate regression analysis of a dataset.

Statistical analysis to predict the importance of various manufacturing parameters on fuel economy of a prototype car.