Logistic regression model focusing on significant features extraction using different methods
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Updated
Sep 2, 2022 - Jupyter Notebook
Logistic regression model focusing on significant features extraction using different methods
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.
Customer Attrition Prediction with Python
Supervised Classfication models - Logistic Regression & Decision Tree, AUC-ROC Curve
Predictive model that tells important factors(or features) affecting the demand for shared bikes
Boston house price prediction using Linear Regression.
Prevendo Customer Churn em Operadoras de Telecom
isye6414_group_project
Verificar Hipóteses da Regressão Linear
'21 한국통신학회 동계종합학술발표회 투고 논문, "XGBoost기반 당뇨병 예측 알고리즘 연구:2016~2018을 이용하여" 연구 과정 전반의 Open Archive입니다.
Supervised-ML---Multiple-Linear-Regression---Cars-dataset. Model MPG of a car based on other variables. EDA, Correlation Analysis, Model Building, Model Testing, Model Validation Techniques, Collinearity Problem Check, Residual Analysis, Model Deletion Diagnostics (checking Outliers or Influencers) Two Techniques : 1. Cook's Distance & 2. Levera…
Lead_Scoring Case Study using Logistic Regression
an R project of manipulating and fittingdata into regression with 95.5% R-Square, involving Automated Selection, detecting outliers, influential observations and multicollinearity
Statistical study and analysis of Multiple linear regression model using Scalation tool
Linear Regression to identify the important physicochemical properties of the substrate that influence the aerial biomass production in the Cape Fear Estuary.
This project uses the Reaction Time Survey dataset to develop a linear regression model for accurately predicting student reaction times based on various predictors. Tech: R (RStudio)
This is a Linear Regression Project, we have created multiple models using different feature selection techniques to predict the future demands for a bike company.
The objective of this project is to find the variables which most affect in predicting the price of a property from the data.
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