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polynomial-regression

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Predicting Amsterdam house / real estate prices using Ordinary Least Squares-, XGBoost-, KNN-, Lasso-, Ridge-, Polynomial-, Random Forest-, and Neural Network MLP Regression (via scikit-learn)

  • Updated Apr 9, 2019
  • Python

Built house price prediction model using linear regression and k nearest neighbors and used machine learning techniques like ridge, lasso, and gradient descent for optimization in Python

  • Updated Dec 8, 2023
  • Jupyter Notebook

pwtools is a Python package for pre- and postprocessing of atomistic calculations, mostly targeted to Quantum Espresso, CPMD, CP2K and LAMMPS. It is almost, but not quite, entirely unlike ASE, with some tools extending numpy/scipy. It has a set of powerful parsers and data types for storing calculation data.

  • Updated Jul 30, 2024
  • Python
synthetic_well-log_polynomial_regression

This project attempts to construct a missing well log from other available well logs, more specifically an NMR well log from the measured Gamma Ray (GR), Caliper, Resistivity logs and the interpreted porosity from a well.

  • Updated Sep 21, 2020
  • Jupyter Notebook

This is a Statistical Learning application which will consist of various Machine Learning algorithms and their implementation in R done by me and their in depth interpretation.Documents and reports related to the below mentioned techniques can be found on my Rpubs profile.

  • Updated Apr 7, 2018
  • R
mida

Different modeling techniques like multiple linear regression, decision tree, and random forest, etc. will be used for predicting the concrete compressive strength. A comparative analysis will be performed to identify the best model for our prediction in terms of accuracy. The best model will be helpful for civil engineers in choosing the approp…

  • Updated Jul 28, 2020
  • Jupyter Notebook

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