Drop-in replacement of sklearn's Linear Regression with coefficients constraints
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Updated
May 5, 2024 - Python
Drop-in replacement of sklearn's Linear Regression with coefficients constraints
This project develops a machine learning model to predict the salaries of baseball players based on their past performance.
Algoritmos de regressão na linguagem python utilizando bibliotecas como sklearn e pandas.
The main objective of this project is to forecast the Customer Lifetime Value (CLTV) using user and policy data.
In this project I built machine learning models using Multiple Linear Regression, Ridge regression, Lasso regression, Elasticnet regression and then created a pickle file of the regression model which gave best accuracy
This model trains according to the data and makes a Polynomial Regression curve of degree 16. The model is regularized using ElasticNet regression of l1_ratio 0.5. It also compares the predicted values with original outputs and for different alphas.
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