Predictive machine learning model with 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa.
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Updated
Mar 17, 2023 - Python
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.
Predictive machine learning model with 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa.
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.
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.
This is the machine learning I have done in The University of British Columbia
Predicting breast cancer in women in the next five years
Analysis of Contraceptive Discontinuation using machine learning
Predicting house prices in California using machine learning techniques.
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.
Football Prediction Model
In this project, five different machine learning (ML) models are trained and compared in term of predicting the early-stage diabetes. A data collected in hospital Frankfurt, Germany containing 2000 patients’ information have been used in this study. RF, NB, SVM, KNN, and LR are the five models used for predicting the diabetes.
Practical Approach to AI (example testing)
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.
Prediction of optimum number of clusters using K-Means Clustering on Iris dataset
This is a machine learning model to predict the survival in titanic disaster.
This is Machine Learning Beginner level Project. In this Project We can Predict fire in forest based on some features.