Machine Learning clasificación con SKLearn

Part of an internal project for my internship

DevStack Solution Internship Program "Data Science Internship" Task-1 on Fake news detecting system using python and machine learning

This is a simple project which trains on the datasets, based on the tweets of US President Donald Trump and Canadian Prime Minister Justin Trudeau and predicts which one belongs to whom?

This project aims to determine the likelihood of a company facing bankruptcy, a crucial aspect of financial analysis and investment decision-making.

This project involves the implementation of efficient and effective LinearSVC on MNIST data set. The MNIST data comprises of digital images of several digits ranging from 0 to 9. Each image is 28 x 28 pixels. Thus, the data set has 10 levels of classes.

Developed a project which detects the news either as fake or real. GPT2 transformer model is used to predict the sentiment and genre of news. Classifier Machine Learning models and Hugging Face Transformer-Based language models are used to classify the news

Fake news related to the coronavirus pandemic has now become a huge problem since false information can lead to worry and concerns regarding the disease. It is not possible to perfectly detect fake news unless the news has been labelled fake or real. Therefore, I have taken this issue as my problem and have developed a project that can detect fa…

Scraping data through Instagram and using the data to build a predictive model

The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 150.

Analyzing data patterns and different classification methods

Choosing the Correct Model for the our Data Set on the Basis of Scikit Learn Algorithm Map.

Implementation of Drug database with LinearSVC, BernoulliNB, MultinomialNB, LogisticRegression, Perceptron and MLPClassifier models

Erdos Institute Bootcamp project analyzing cuisines by recipe ingredient lists.

This repository contains a number of experiments with Multi Lingual Transformer models (Multi-Lingual BERT, DistilBERT, XLM-RoBERTa, mT5 and ByT5) focussed on the Dutch language.

Dartmouth COSC 274: Machine Learning models for Amazon Reviews dataset

This repository houses a Streamlit web application for fake news detection. The app allows users to input a news article and predicts whether it is likely fake or real based on its content. It provides options to select different vectorizers (TF-IDF or Bag of Words) and classifiers (Linear SVM or Naive Bayes) to customize the prediction model.

A mini ML project of feature and model selection on breast cancer data