Automated Backward and Forward Selection On Python

Domain-Agnostic Supervised Learning with Hyperdimensional Computing

Implementation of backward elimination algorithm used for dimensionality reduction for improving the performance of risk calculation in life insurance industry.

Toolkit for Doing Research with ECMAScript-based Statistics (DRESS Kit)

As part of this project, I have used Machine Learning (classification) algorithms for classification of tumors in Human Breasts as Non-Cancerous/ Benign or Cancerous/ Malignant tumors.

Multiple linear regression model implementation with automated backward elimination (with p-value and adjusted r-squared) in Python and R for showing the relationship among profit and types of expenditures and the states.

Multiple linear regression has been used in order to provide a predictions regarding the common factors that affect the life expectancy.

Classification with Feature Selection and Extraction Methods

Built 20 multivariable logistic regression models to analyze the relationships between variables of local public health infrastructure and best practices

Year after year the percentage of carbon dioxide emissions in Jordan increase dramatically, and this lead to increase the temperature , to investigate this, the project aims to study the impact of CO2 emissions in on temperature and then use deep learning algorithm to predict the CO2 emission level through the next 10 years

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Artificial Intelligence -> Machine Learning (ML) -> Supervised Learning -> Regression -> Forward Selection, Backward Elimination, Stepwise Regression

According to WHO(world health organization) survey in 2014, the dataset contains nourished and malnourished child information (under 5). The job is to find out whether a child is malnourished or not when a new data will come applying machine learning algorithm.

Selecting the best startup to invest by analyzing the profit and its expense in different fields using the Multiple Linear Regression

This project estimates a multiple linear regression of 50 startups and how their expenses on R & D, administration, marketing, and location can be significant or not to their profits.

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Answer to how to select variables in data set and build simpler, faster, more reliable and interpretable ML models

A Python implementation of feature selection algorithms using k-Nearest Neighbor classification. This project implements three different search strategies for finding optimal feature subsets: Forward Selection, Backward Elimination, and Simulated Annealing.

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Analyzed financial reports of startups and developed a multiple linear regression model which was optimized using backwards elimination to determine which independent variables were statistically significant to the company's earnings.

Bu proje, çoklu doğrusal regresyon kullanılarak balık cins, uzunluk(1,2,3), yükseklik ve genişlik bilgilerine göre ağırlık tahmini yapmaktadır. Veri ön işleme, Backward Elimination ile öznitelik seçimi, model değerlendirme ve Flask tabanlı web arayüzü uygulanmıştır.

Classification model that can predict whether a tumor is Benign or Malignant.

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Feature Selection Using Python (Forward Selection and Backward Elimination)

Analysis of the Underlying Dynamics in the Stock Market: Stock Price of Southwest Airlines and Its Relationship with Other Stocks in the Market

Model regresi log-linear berganda untuk memprediksi harga sewa rumah urban IFLS. Menampilkan penanganan asumsi klasik (Log-Log Transformation) dan Backward Elimination.

Classification model to classify whether a customer is going to churn or not. Using the dataset EDA is done.

The goal of this assignment is to gain practical experience of performing regression on a small but realistic dataset, using a machine learning package.

Uses nearest neighbor algorithm to find which feature is the best indicator for a certain class attribute