GDP Forcasting

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.

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 …

資料科學的日常研究議題

Complete mathematical and statistical analysis of linear regression model

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.

Exploring the confidence-Interval concept and bootstrapping.

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 moving squared sample Pearson product-moment correlation coefficient incrementally.

Compute a squared sample Pearson product-moment correlation coefficient.

Using multiple linear regression model to predict customer demand in order to make business decision

project to predict smartphone sales based on the marketing budget spent on advertising using three platforms involves collecting data on marketing spending and smartphone sales, and using statistical and machine learning techniques to build a model that can predict future smartphone sales based on changes in marketing budget.

Predicting annual highest of sneakers on StockX

The comparison of multiple Machine Learning models refers to training, evaluating, and analyzing the performance of different algorithms on the same dataset to identify which model performs best for a specific predictive task.

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.

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

Functional specification to calculate per country's happiness score