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Different modeling techniques like multiple linear regression and random forest, etc. will be used for predicting the cement compressive strength. A comparative analysis will be performed to identify the best model for our prediction in terms of accuracy.

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linear-regression mean-absolute-error pandas-library seaborn-pairplot train-test-split train-test-using-sklearn python-library

regression-model-to-predict-cement-compressive-strength's Introduction

Regression-Model-to-Predict-Cement-Compressive-Strength

This project will be based on a dataset obtained from the UCI Repository. The dataset consists of 1030 observations under 9 attributes. The attributes consist of 8 quantitative inputs and 1 quantitative output. The dataset does not contain any missing values. The dataset is focused on the compressive strength of a cement.

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