This package contains functions to find similarities between arrays

Use PyConforMap to generate a simple scatter plot to map conformational landscapes of intrinsically disordered proteins, and quantify conformational diversity.

Analysis of real estate sales data. Tasks include understanding dataset structure, variable conversion, descriptive analysis, pairwise comparisons, linear relationship analysis, multiple regression modeling, feature selection using stepwise methods, final model summary, assumptions checking, and LASSO variable selection. Results are documented.

Bayesian Statistics: Linear Regression and multi-linear models and related concepts (multicollinearity, correlation coefficient etc) on iris dataset in pymc3

In this project is a programming assignment wherein you have to build a multiple linear regression model for the prediction of demand for shared bikes. You will need to submit a Jupyter notebook for the same.

Calculating the Pearson Correlation Coefficient for big data

Data vizualitation and several graphs, based on the dataset: AllCountries.csv

Feature Selection using Metaheuristics Made Easy: Open Source MAFESE Library in Python

The Pearson correlation coefficient program is a C++ program that takes input from the user in the form of two vectors of float values, representing two variables X and Y.

"A set of Jupyter Notebooks on feature selection methods in Python for machine learning. It covers techniques like constant feature removal, correlation analysis, information gain, chi-square testing, univariate selection, and feature importance, with datasets included for practical application.

Pharmaceutical analysis utilizing Python, Pandas, & Matplotlib. Generate summary statistics, create bar charts, scatter plots, and calculate correlation and regression.

Assignment 2 for CS F320: Foundations for Data Science

Classification using Pyspark

In this repository, four famous correlation algorithms have been implemented. Pearson, spearman, Chatterjee, and MIC correlation algorithm implemented

Social Choice Mechanisms for Recommender Systems. A report of the work is included that describes the project

SUTD ISTD 2021 50.034 Introduction to Probability & Statistics 1D Project

A program that will return the Pearson correlation coefficient of the stocks entered. One coefficient is returned for each possible pair. The program will plot a heat map and will return a CSV file containing the correlation of each possible stock pair.

Exploring correlations in an isobaric labeling mass spectrometry-based proteomic data set

Performed exploratory data analysis (EDA) in python on the world happiness report datasets (for years 2015, 2016, 2017, 2018, and 2019) from Kaggle; to analyze how measurements of well-being can effectively help assess the progress of nations across the world.

Predicting the Time Elapsed from Pickup to Delivery