Predicting breast cancer in women in the next five years
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Updated
Apr 28, 2023
Machine learning is the practice of teaching a computer to learn. The concept uses pattern recognition, as well as other forms of predictive algorithms, to make judgments on incoming data. This field is closely related to artificial intelligence and computational statistics.
Predicting breast cancer in women in the next five years
App to count and identify crops in a raster with machine learning
This project uses machine learning classifier algorithms to predict whether the patient is suffering from cancer or not.
Predictive machine learning model with 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa.
Projects from the Post Graduate Program in ML/AI @ UT Austin
Using Machine Learning to predict the likelihood of a loan default using a loan data set obtainable from Kaggle. I employed Classification for the building the machine learning models as the target variables were binary, 0 and 1 representing no default and defaulted.
This is a machine learning model to predict the survival in titanic disaster.
This Projects creates a model that predicts Google Play Store Apps Rating based on parameters like No. of Installs, reviews, size, category , genres etc. It compares several classification model like Xgboost(booster ensembler), Random Forest(bagger ensembler), Logistic regression, Support Vector Machine(SVC) and Bayesian Classifier.
Helps to predict the rent of a house in indian metro cities using a machine learning model called Random Forest Regression
This repository for machine learning projects learned from ineuron.ai uploaded along with the resources.
This is Machine Learning Beginner level Project. In this Project We can Predict fire in forest based on some features.
A curated list of my machine learning projects. Machine learning (ML) is the study of computer algorithms that improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence.
Projects on Machine learning using classification and regression techniques
Developed a robust brain stroke prediction model leveraging machine learning techniques. The Random Forest classifier has 90% classification accuracy, which was the highest (among all the machine learning classifiers).
Diabetes Prediction
This is a project where use the Random Forest Classifier and XGBoost Machine Learning Techniques to held predict what passengers survived the sinking of the Titanic.
This is the machine learning I have done in The University of British Columbia