Predicting Wine Quality with Machine Learning

The given python code gives the data modeling and consists the following methods used: 1) Up sampling 2) Down sampling 3) Gridsearch for the selection of optimal combination of parameters 4) Application of Random Forest classifier 5) Dimensionality reduction using PCA

Created machine learning models capable of classifying candidate exoplanets from a raw dataset.

Hack attempt analysis

This repository contains my work on Machine Learning with Python using scikit-learn library

The ML task in this assignment is binary classification, stock market prediction, based on daily news dataset.

Hyperparameter tuning using gridsearchCV and randomizedsearchCV

Model Machine Learning for dataset mushroom using scikit learning

MACHINE LEARNING AND PYTHON PROJECT

Segmentação dos principais assuntos das reviews utilizando Latent Dirichlet Allocation (LDA). Pandas, Spacy, Deep Translator, GoogleTranslator, googletrans, NLTK, LDA, WordCloud, Pandarallel, pyLDAvis, sklearn, GridSearchCV.

Predicting cement strength

GUI for hyperparameter optimizer and plotting experiment results

This project aims at predicting the sale price of the Ames_Housing raw data. The final model was developed using ElasticNet regression and GridSearch approach

Hyperparameter tuning is the process of finding the optimal hyperparameters for a machine learning model. Hyperparameters are values that are set prior to training a model and affect its performance, but cannot be learned from the data. Some common examples of hyperparameters include the learning rate, regularization strength.

machine learning projects based on classification and regression algoithms

This notebook demonstrates how to do cross-validation (CV) with linear regression as an example (it is heavily used in almost all modelling techniques such as decision trees, SVM etc.). We will mainly use sklearn to do cross-validation.

Finding Donor for CharityML - Machine Learning Nanodegree from Udacity