rfe
There are 98 repositories under rfe topic.
AutoViML/featurewiz
Use advanced feature engineering strategies and select best features from your data set with a single line of code. Created by Ram Seshadri. Collaborators welcome.
vikrantarora25/Car-Price-Prediction-Highly-Comprehensive-Linear-Regression-Project-
A Linear Regression model to predict the car prices for the U.S market to help a new entrant understand important pricing variables in the U.S automobile industry. A highly comprehensive analysis with detailed explanation of all steps; data cleaning, exploration, visualization, feature selection, model building, evaluation & MLR assumptions validity.
Hameem1/Step-Detection-using-Machine-Learning
Implements an entire machine learning pipeline to train and evaluate a Random Forest Classifier on labeled gait data for walking. Data generated during the experiment has led to helpful insights in to the problem domain.
mansipatel2508/Network-Intrusion-Detection-with-Feature-Extraction-ML
The given information of network connection, model predicts if connection has some intrusion or not. Binary classification for good and bad type of the connection further converting to multi-class classification and most prominent is feature importance analysis.
ashomah/HR-Analytics
HR Analytics Dataset
Danfoa/parkinson-progression-prediction-with-speech-tests
Computer Intelligence subject final project at UPC.
bioinfoUQAM/CASTOR_KRFE
Alignment-free method to identify and analyse discriminant genomic subsequences within pathogen sequences
pramodini18/Car-Price-Prediction
A multiple linear regression model for the prediction of car prices.
labrijisaad/Car-Price-Prediction
Car Price Prediction
stxupengyu/SVM-RFE
SVM classification, RFE feature selection
nafisa-samia/Automobile-Price-Prediction-using-Linear-Regression
Predict the vehicle price from the open source Auto data set using linear regression. In this data set, we have prices for 205 automobiles, along with other features such as fuel type, engine type,engine size,etc.
iici-psiddineni/ML_Telecom_Churn
Machine Learning Telecom Churn Model
ashomah/King-County-House-Sales
King County House Sales
shromana98/Diabetes-Prediction
The primary aim of this project is to accurately identify individuals at risk of diabetes based on different features.
anikch/Bike-rental-prediction-based-on-env-season
Building a model to predict demand of shared bikes. It will be used by the management to understand how exactly the demands vary with different features. They can accordingly manipulate the business strategy to meet the demand levels.
ChaitanyaC22/House-Price-Prediction-Project-for-a-US-based-housing-company
The goal of this project is to garner data insights using data analytics to purchase houses at a price below their actual value and flip them on at a higher price. This project aims at building an effective regression model using regularization (i.e. advanced linear regression: Ridge and Lasso regression) in order to predict the actual values of prospective housing properties and decide whether to invest in them or not.
ChaitanyaC22/Telecom-Churn-Prediction
In this project, data analytics is used to analyze customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn, and identify the main indicators of churn. The project focuses on a four-month window, wherein the first two months are the ‘good’ phase, the third month is the ‘action’ phase, while the fourth month is the ‘churn’ phase. The business objective is to predict the churn in the last i.e. fourth month using the data from the first three months.
kshitij-raj/Bike-Share-Prediction
Predictive model that tells important factors(or features) affecting the demand for shared bikes
mohd-faizy/feature-engineering-hacks
This repository contains a collection of hacks and tips for feature engineering. It is a great resource for anyone who wants to learn how to improve the performance of their machine learning models.
pramodini18/Real-estate-case-study
To identify the variables affecting house prices :Multiple Linear Regression in Python using statsmodels and RFE
RimTouny/Dynamic-DNS-Traffic-Analysis-for-Data-Exfiltration-Detection-with-Kafka
Crafting static and dynamic models for data exfiltration detection via DNS traffic analysis. Static model trained on batch data, while dynamic model simulates a continuous stream. Rigorous analysis, feature engineering, and model training conducted. Implementation part of AI for Cyber Security Master's assignment at the University of Ottawa, 2023.
rushhemant/Lead-Scoring-Case-Study
Build a logistic regression model to assign a lead score between 0 and 100 to each of the leads which can be used by the company to target potential leads. A higher score would mean that the lead is hot, i.e. is most likely to convert whereas a lower score would mean that the lead is cold and will mostly not get converted.
Shrawan-Kumar/BoomBikes-Linear-Regression-Assignment
A US bike-sharing provider BoomBikes has recently suffered considerable dips in their revenues due to the ongoing Corona pandemic. The company is finding it very difficult to sustain in the current market scenario. So, it has decided to come up with a mindful business plan to be able to accelerate its revenue as soon as the ongoing lockdown comes to an end, and the economy restores to a healthy state.
SMQuadri/Geely-Car-Price-Prediction
Machine Learning Project
Xamweis/digi-impact-eco-socio
project for the practice of webscraping, APIs, machine learning, feature selection
anikch/Telecom-churn-analysis-and-prediction
Analyze customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn (usage-based churn) and identify the main indicators of churn.
arnabberawork/Telecom-Churn-Group-Case-Study
M.S. and EPGP Assignment | Kaggle Competition - Predict customer churn in the telecom sector using machine learning models and exploratory data analysis to identify key factors driving churn
DavidPanduro/financial_fraud_detection
Previsão de Fraude Financeiro
GvHemanth/Bike_Sharing_Linear_Reg
Predicting the variables that effects the revenue of the bike sharing company after a serious drop-fall during the covid-19 pandemic.
kshitij-raj/House-Price-Prediction
Regression Model using regularisation to predict the actual value of the prospective properties and decide whether to invest in them or not.
kshitij-raj/Telecom-Churn-Prediction
Build a classification model for reducing the churn rate for a telecom company
marileano/Determining-Trade-Union-Status
In this project we built a model to predict whether a person will remain in a hypothetical trade union called the United Data Scientists Union (UDSU).
Palak-15/Housing-Case-Study-with-RFE
Consider 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. Essentially, the company wants — To identify the variables affecting house prices, e.g. area, number of rooms, bathrooms, etc. To create a linear model that quantitatively relates house prices with variables such as number of rooms, area, number of bathrooms, etc. To know the accuracy of the model, i.e. how well these variables can predict house prices.
rakibhhridoy/MachineLearning-FeatureSelection
Before training a model or feed a model, first priority is on data,not in model. The more data is preprocessed and engineered the more model will learn. Feature selectio one of the methods processing data before feeding the model. Various feature selection techniques is shown here.
renatokano/cn-feature-selection-and-dimensionality-reduction
[Codenation] Feature Selection w/ Recursive Feature Elimination (aka RFE) and Dimensionality Reduction using Principal Component Analysis (aka PCA)
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.