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 repository for machine learning projects learned from ineuron.ai uploaded along with the resources.
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.
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 the machine learning I have done in The University of British Columbia
This is a machine learning model to predict the survival in titanic disaster.
Helps to predict the rent of a house in indian metro cities using a machine learning model called Random Forest Regression
Developed ML based models as Machine Learning enthusiast. Individual academic work on Machine Learning.
This is Machine Learning Beginner level Project. In this Project We can Predict fire in forest based on some features.
Final Project for CS-577 Principles and Techniques of Data Science at San Diego State University
Analysis of Contraceptive Discontinuation using machine learning
In this repository I have uploaded all my machine learning Assignments
Projects on Machine learning using classification and regression techniques
Predicting house prices in California using machine learning techniques.
A house price prediction project is a data-driven approach to estimating the future value of a residential property using statistical and machine learning techniques with the goal of providing insight and forecasting capabilities.
Predicting energy prices using various machine learning techniques. Specifically, I have implemented and compared three different regression models.
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.
The goal of this project is to obtain a classifier that can automatically classify environmental sounds according to their category. This can be implemented on both transport vehicles and wearable devices to improve road safety.
Using patient data as a csv file, I have built machine learning models to predict heart disease. Predictions involve: