Topic: rfe Goto Github
Some thing interesting about rfe
Some thing interesting about rfe
rfe,This project tackles BoomBikes' post-Covid revenue decline by predicting shared bike demand after the lockdown. A consulting company identifies key variables impacting demand in the American market. The goal is to model demand, aiding BoomBikes in adapting its strategy to meet customer expectations and navigate market dynamics.
User: akashkriplani
rfe,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.
User: anikch
rfe,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.
User: anikch
rfe,Bike Sharing in Washington D.C.
User: ashomah
rfe,HR Analytics Dataset
User: ashomah
rfe,King County House Sales
User: ashomah
rfe,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.
User: autoviml
rfe,Feature Selection Examples
User: balamurali-m
rfe,Alignment-free method to identify and analyse discriminant genomic subsequences within pathogen sequences
Organization: bioinfouqam
rfe,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.
User: chaitanyac22
rfe,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.
User: chaitanyac22
rfe,Computer Intelligence subject final project at UPC.
User: danfoa
rfe,Bank Customer Behaviour Prediction
User: datasciencevishal
rfe,Previsão de Fraude Financeiro
User: davidpanduro
rfe,HDB flats resale price prediction. Neural network in Python. Machine learning models in R. Data pre-processing, feature engineering and feature selection mainly in R.
User: derekngoh
rfe,A telemarketing model to predict campaign subscriptions in a portuguese bank institution.
User: domingosdeeulariadumba
rfe,Multiclass classification model of penguins species.
User: domingosdeeulariadumba
rfe,Building logistic classifier model (RFE)
User: gulshank0719
rfe,Predicting the variables that effects the revenue of the bike sharing company after a serious drop-fall during the covid-19 pandemic.
User: gvhemanth
rfe,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.
User: hameem1
rfe,Machine Learning Telecom Churn Model
User: iici-psiddineni
rfe,Predictive model that tells important factors(or features) affecting the demand for shared bikes
User: kshitij-raj
rfe,Regression Model using regularisation to predict the actual value of the prospective properties and decide whether to invest in them or not.
User: kshitij-raj
rfe,Build a classification model for reducing the churn rate for a telecom company
User: kshitij-raj
rfe,Car Price Prediction
User: labrijisaad
rfe,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.
User: mansipatel2508
Home Page: https://colab.research.google.com/drive/1cymmyp2Bz-nYPKPnJNxtZdlYd7kdKhjd
rfe,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).
User: marileano
rfe,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.
User: nafisa-samia
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.
User: palak-15
rfe,A multiple linear regression model for the prediction of car prices.
User: pramodini18
rfe, To identify the variables affecting house prices :Multiple Linear Regression in Python using statsmodels and RFE
User: pramodini18
rfe,A US-based housing company named Surprise Housing has decided to enter the Australian market. The company uses data analytics to purchase houses at a price below their actual values and flip them on at a higher price.
User: priya-aggarwal27
rfe,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.
User: rakibhhridoy
Home Page: https://rakibhhridoy.github.io
rfe,[Codenation] Feature Selection w/ Recursive Feature Elimination (aka RFE) and Dimensionality Reduction using Principal Component Analysis (aka PCA)
User: renatokano
rfe,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.
User: rimtouny
rfe,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.
User: rushhemant
rfe,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.
User: sakusuma
rfe,Objective: To reduce customer churn, telecom companies need to predict which customers are at high risk of churn.
User: sharmashubh08
rfe,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.
User: shrawan-kumar
rfe,We are required to build a regression model using regularization in order to predict the actual value of the prospective properties and decide whether to invest in them or not.
User: shubhanshu1995
rfe,This assignment is a programming assignment wherein we have to build a multiple linear regression model for the prediction of demand for shared bikes.
User: shubhanshu1995
rfe,This is a project demonstrating Logistic Regression method using Python. An education company named X Education sells online courses to industry professionals. On any given day, many professionals who are interested in the courses land on their website and browse for courses.
User: smartnamdevoloper
rfe,Machine Learning Project
User: smquadri
rfe,Data warehouse and analytics project to predict bike theft prediction from TPS data
User: sneha-santhosh
rfe,A comprehensive ML framework to detect heart disease using the Cleveland dataset
User: vayuputra2401
rfe,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.
User: vikrantarora25
rfe,project for the practice of webscraping, APIs, machine learning, feature selection
User: xamweis
rfe,Student grade prediction using different machine learning models
User: xyrusgallito
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