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In this study, after detailed Exploratory Data Analysis, 5 different machine learning models were tested on Titanic Data to answer the question "What sorts of people were more likely to survive?" and the best model for survival prediction was determined.
Gaussian naive Bayes classifier for digits in the MNIST dataset. Similar in nature to my other repo ("newsgroup-naive-bayes"), albeit instead of multinomial document classification, this repo explores gaussian image classification. Covariance smoothing utilized to minimize error rates to the ~4% realm.
The sinking of the RMS Titanic is one of the most infamous shipwrecks in world history. In this model, need to analyse what sorts of people were likely to survive. We also need to apply the tools of machine learning to predict which passengers survived in this tragedy.
This repository contains the implementation of Gaussian Naive Bayes from scratch in a Jupyter Notebook. Gaussian Naive Bayes is a simple and effective algorithm for classification tasks. It is based on Bayes' theorem with the assumption of independence between the features.
Develop machine learning models that detect diseases from blood test data. Utilizing machine learning, it analyzes patterns and predicts potential health conditions, offering users early insights for proactive care.
Gaussian Naive Bayes is a probabilistic classification algorithm that makes use of Bayes' theorem and assumes that features are normally distributed within each class. This description provides an overview of the algorithm's mathematical formulation and highlights its vectorized computations.
The goal of this project is to create a classifier and see how accurately it can predict song genres. Taking a dataset from Spotify [Pandya, 2022], which is al- ready using machine learning algorithms for these purposes, can help assess if the resulting model can be considered apt for a large-scale business.
Prediction of the outcome of football matches of European teams using various ML models. The features include many in-game statistics and team ratings to predict the result.