mlr-org/mlr3fselect

Feature selection package of the mlr3 ecosystem.

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ing-bank/probatus

SHAP-based validation for linear and tree-based models. Applied to binary, multiclass and regression problems.

Language: Python - Size: 17.5 MB - Last synced at: 16 days ago - Pushed at: about 2 months ago - Stars: 144 - Forks: 41

Ehsan-Behzadi/A-Machine-Learning-Approach-Using-the-Pima-Indians-Diabetes-Dataset

This repository features a machine learning project utilizing the Pima Indians Diabetes Dataset to predict diabetes risk. It explores data preprocessing, model training, and evaluation using techniques such as Naive Bayes and K-Nearest Neighbors (KNN) . The aim is to highlight the impact of various health factors on diabetes prediction.

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imharshag/NIDS-using-ML

This project showcases a Network Intrusion Detection System (NIDS) designed to bolster cybersecurity defenses against evolving threats

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alejimgon/Housing-Prices-Kaggle-Competition-Model

This repository contains my current model for the Housing Prices Kaggle competition.

Language: Python - Size: 27.3 KB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 0 - Forks: 0

Edanur-Y/Abalone-Age-Prediction-with-Regression-Models

Comparing the performances of simple linear, multiple linear, multi-layer perceptron and k-nearest neighbors regressions on abalone data to predict the age.

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mohiteamit/telecom-churn-casestudy

Telecom Churn Case Study | Machine Learning | IIITB

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MvMukesh/FeatureSelection-Framework-ML

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

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Yasmin-Camargo/ferramentas-de-inteligencia-artificial

Atividades práticas elaborados em Python, no ambiente Google Colab, para a disciplina de ferramentas de inteligencia artificial

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bhavya844/Predicting-CO2-Emissions-and-Renewable-Energy-for-Sustainable-Development

The project was done in the last semester which included the use of Time Series Forecating and regression models to predict the CO2 and Renewable Energy Consumption by countries in any given year.

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larenwell/ai-ml-rfe-optuna-ferreyros-challenge

At ferreyros, prioritizing sales opportunities for spare parts and services allows them to provide the best service to customers who need it most. This challenge is to use historical opportunity data to estimate the probability of closing new opportunities.

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Harshraj1301/Meijer---Real-Estate-Predictive-Model

Developed a predictive real estate model leveraging XG Boost Regressor, integrating web-scraped market data with existing datasets to forecast daily store visits, achieving a MAPE of 13.3%, enabling strategic retail location decisions

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alejo1630/grades_college_analysis

A Jupyter Notebook with the analysis and prediction of Final Grades (Pass/Fail) for students of mechatronics engineering in several mechanic courses.

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tomartushar/Credit-Card-Fraud-Detection

An ensemble of machine learning models for detecting fraudulent credit card transactions, utilizing advanced techniques for feature selection, data imbalance handling, and hyperparameter tuning.

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RimTouny/Feature-Selection

Delved into advanced techniques to enhance ML performance during the uOttawa 2023 ML course. This repository offers Python implementations of Naïve Bayes (NB) and K-Nearest Neighbor (KNN) classifiers on the MCS dataset.

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SHOCKWAVE07/AirPressureSystemFailure

The goal of this project is to develop a predictive model that accurately detects failures in the Air Pressure System (APS) of heavy Scania trucks. The APS is responsible for generating pressurized air used in various functions of the truck, such as braking and gear changes. By detecting failures in the APS system.

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venkatesh-eranti/Housing_case-study

A real estate company that has a dataset containing the prices of properties in the Delhi region. It wishes to use the data to optimise the sale prices of the properties based on important factors such as area, bedrooms, parking, etc

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vaitybharati/Feature-Engg

Feature-Engg

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EhtashamBillah/Variable-Selection-By-Recursive-Feature-Elimination-RFE-

All independent variables do not have the similar impact on dependent variable. Here we will try to find the independent varibles that have most significant impact on dependent variable to make the ML algorithm fast and accurate by utilizing RFE.

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now-youre-gittin-it/attrition-analysis

Predict the attrition (Yes/No) of employees, identify factors significantly impacting it, and finally state recommendations on how to mitigate the attrition.

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heet-shahh/Uber-Data-Analysis

Unlocking Insights and Accurate Price Predictions for Uber Rides through Extensive Data Analysis.

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Tejindersingh1/Tumor-Prediction-with-ML

Tumor prediction from microarray data using 10 machine learning classifiers. Feature extraction from microarray data using various feature extraction algorithms.

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bhavishyasharma10/Employee-turnover-prediction

Understanding and predicting the factors leading to employees leaving and finding relations between them. Also finding the importance of a feature according to ML models.

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ashomah/Bike-Sharing-in-Washington-DC

Bike Sharing in Washington D.C.

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garimagupta123/LeadScore_LogisticRegression_Assignment

Logistic regression model build on lead score data to score leads on the basis of their probability of conversion.

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olga1590/Employee-Attrition_ML_with_ANN

ML with ANN

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elsheikh21/malware-analysis

using Drebin dataset to distinguish between malwares and not malwares

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lyx199504/click-fraud-cat-rfe

本项目是论文《CAT-RFE：点击欺诈的集成检测框架》的实验代码。

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Vishal1999-33/Logistic-Regression

The classification goal is to predict whether the client will subscribe (1/0) to a term deposit (variable y).

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mosama1994/Heart-Attack-Detection-using-Machine-Learning

Used CDC dataset for heart attack detection in patients. Balanced the dataset using SMOTE and Borderline SMOTE and used feature selection and machine learning to create different models and compared them based on metrics such as F-1 score, ROC AUC, MCC, and accuracy.

Language: Python - Size: 125 MB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 0 - Forks: 0

mohan-aditya05/churn_modelling

Case Study for Churn Modelling in a NGO

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mohan-aditya05/credit_card_spend_regression

Language: Jupyter Notebook - Size: 8.16 MB - Last synced at: about 2 years ago - Pushed at: about 6 years ago - Stars: 1 - Forks: 1

ashomah/HR-Analytics

HR Analytics Dataset

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red-dragon65/MLStockForecasting

Forecast stocks using machine learning algorithms. This is the final project for Data Science 3580.

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chinmayeeguru/Bike-Sharing-Linear-Regression-Model

To model the demand for shared bikes with the available independent variables

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roshansridhar/Multimodal-Sentiment-Analysis

Engaged in research to help improve to boost text sentiment analysis using facial features from video using machine learning.

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ashomah/King-County-House-Sales

King County House Sales

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ryanquinnnelson/CMU-02718-Patient-Mortality-Classification-using-ML

Fall 2020 - Computational Medicine - course project

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ryanquinnnelson/CMU-02718-Biomarker-Discovery-using-ML

Fall 2020 - Computational Medicine - course project

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KhyaatiNayak/Multiple-Linear-Regression-Model-on-Car-prices

This project was done in the 4th semester of Btech data science. Took an online dataset about cars. Understood, cleaned, visualized and prepared our data for building a regression model. Using Recursive feature elimination and Backward selection as a method of feature selection we built a final model. Then tested the assumptions that we made about the data before we started building the model. Lastly predicted the car prices and evaluated our model based on its performance in both in and out sample testing. This was done using Python.

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AkashSDas/classify-benign-or-malignant

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

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AkashSDas/predict-hr-stay-or-leave

Sampling unbalanced dataset using SMOTE and creating a classifier to classify if a HR will stay or leave.

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sakusuma/CarPricePrediction

A Chinese automobile company Geely Auto aspires to enter the US market by setting up their manufacturing unit there and producing cars locally to give competition to their US and European counterparts. They have contracted an automobile consulting company to understand the factors on which the pricing of cars depends. Specifically, they want to understand the factors affecting the pricing of cars in the American market, since those may be very different from the Chinese market. The company wants to know: Which variables are significant in predicting the price of a car How well those variables describe the price of a car Based on various market surveys, the consulting firm has gathered a large dataset of different types of cars across the Americal market.

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MonirZaman/Feature-Selection-in-Machine-Learning

Size: 1.01 MB - Last synced at: about 2 years ago - Pushed at: almost 6 years ago - Stars: 0 - Forks: 1

Premchand95/Evaluation-of-machine-learning-algorithms-on-walmart-stock-price-data

CSCI 54900 INTELLIGENT SYSTEMS PROJECT

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marycboardman/Random-Acts-of-Pizza

A machine learning project using the data from this Kaggle competition: https://www.kaggle.com/c/random-acts-of-pizza

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