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ABCRaster stands for Accuracy assessment of Binary Classified Raster. It is a package for performing validation, accuracy assessment, or comparing binary classified rasters (.tiff) versus a reference (.shp). Primary use case is to compare flood maps encoded as (1,0) in tiff file format against a reference vector from CEMS.
This project aims to predict heart failure outcomes by applying statistical learning algorithms. The goal is to improve the prediction accuracy through the SuperLearner algorithm.
A reinforcement learning model specialized in stock prediction utilizing deep learning techniques, incorporating reward mechanisms, compatible with any machine equipped with Python.
This website is an application of Machine learning classification model where the airline company can predict customer satisfaction while using their service are depending on which factors and can decide where they can invest much. It solves a real business problem (accuracy 93%)
This repository contains code for classifying galaxies into three classes: Galaxy, Quasar, and Star, using machine learning techniques. The dataset used in this project is the Sloan Digital Sky Survey (SDSS) dataset.
Modelled data on various machine learning models like Support Vector Machine, Logistic Regression, KN Neighbors, Decision Tree Classifier, Random Forest Algorithm and Naive Bayes Classifier to compare the accuracy metrics for each algorithm.
The primary objective of this project is to develop a robust system capable of accurately classifying patient conditions solely based on their reviews. By leveraging advanced NLP techniques, the project aims to streamline the categorization process and provide valuable insights into patient health status.
The folliwing ML project involves EDA analysis of Election Dataset, Data preparation for modelling, and prediction using ML models. Also Text Analysis on the inaugral corpora from nltk to analyse the most frequently used words in Presidents' Speeches.
This is a Python notebook giving an overview and implementing the accuracy assessments in *Good practices for estimating area and assessing accuracy of land change* by Olfosson et al. (2014).
Exploring Insights/Inferences by performing EDA on the given project data (50_Startups and Toyota Corolla data) . Model fitting via linear regression by Importing sklearn package. Selecting the best fitted model via python programming.
Evaluation of the performance of classification models can be facilitated through a combination of calculating certain types of performance metrics and generating model performance evaluation graphics. The purpose of this exercise is to calculate a suite of classification model performance metrics via Python code functions.