collinearity-diagnostics
There are 0 repository under collinearity-diagnostics topic.
- olsrr
rsquaredacademy / olsrr
Tools for developing OLS regression models
Language:R 100 - Language:R 4
vaitybharati / Assignment-05-Multiple-Linear-Regression-2
Assignment-05-Multiple-Linear-Regression-2. Prepare a prediction model for profit of 50_startups data. Do transformations for getting better predictions of profit and make a table containing R^2 value for each prepared model. R&D Spend -- Research and devolop spend in the past few years Administration -- spend on administration in the past few years Marketing Spend -- spend on Marketing in the past few years State -- states from which data is collected Profit -- profit of each state in the past few years.
Language:Jupyter Notebook 3Supervised-ML---Multiple-Linear-Regression---Toyota-Cars. 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. Leverage value, Improving the Model, Model - Re-build, Re-check and Re-improve - 2, Model - Re-build, Re-check and Re-improve - 3, Final Model, Model Predictions.
Language:Jupyter Notebook 3favstats / multicol_sim
Analyzing Multicollineaerity with a little simulation
Language:HTML 1- Language:R 1
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. Leverage value, Improving the Model, Model - Re-build, Re-check and Re-improve - 2, Model - Re-build, Re-check and Re-improve - 3, Final Model, Model Predictions.
Language:Jupyter Notebook 1karthikvadlamani / doordash-delivery-predictions
Prediction of delivery times for DoorDash deliveries. Performed feature engineering (creation, encoding), feature selection using (multi)collinearity analysis, Gini importance and PCA. Applied 6 ML models to perform regression analysis on delivery time prediction.
Language:Jupyter Notebook 0PatilSukanya / Assignment-05.-Multiple-Linear-regression-Q2
Used libraries and functions as follows:
Language:Jupyter Notebook 0
