A powerful library extending VBA with over 100 functions for math, stats, finance, and data manipulation. It supports matrix operations, and user-defined functions, enhancing automation and analysis within Microsoft Office and LibreOffice environments for data management, financial calculations, an more.

Several examples of multivariate techniques implemented in R, Python, and SAS. Multivariate concrete dataset retrieved from https://archive.ics.uci.edu/ml/datasets/Concrete+Slump+Test. Credit to Professor I-Cheng Yeh.

Machine learning: Practical applications

Deep Learning Classification, LSTM Time Series, Regression and Multi-Layered Perceptrons with Tensorflow

Machine Learning Notebooks for various Algorithms with data file

This repository contains several machine learning projects done in Jupyter Notebooks

Machine learning algorithms

Basic Machine Learning using Sklearn: 📈📉 Multivariate Linear Regression

A hub that contains notebooks that implement Regression models, illustrates LR via Gradient Descent, compares K-means vs Spectral vs Hierarchical, compares PCA vs t-SNE

Predict housing prices to help a startup complete a beta version of its product using Multivariate Linear Regression

Repository contains all assignment solutions and dataset that I used in completing this course

Influence Measures and Diagnostic Plots for Multivariate Linear Models

This code is designed from scratch. It also generates it own n-dimensional data set.

Performed graphical and numerical EDA, correlation and residual error analysis, log normalization

Prediction of Abalone's age through its number of rings.

Using Zillow database and google API, plots nearby places of user input center location. Utilizing data obtained, finds relationship between real estate values and local amenities with multi-variable linear regression.

Using 21 categorical and numeric features in a multivariate linear regression to find that 79% of a home price can be positively affected by a combination of certain features like location, square feet, condition and age of the home.

A simple implementation of multivariate linear regression using gradient descent, built with NumPy and Matplotlib.

Data Science Project from DataCamp