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
In this repository I have uploaded all my machine learning Assignments
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.
Analysis of Contraceptive Discontinuation using machine learning
Predicting house prices in California using machine learning techniques.
A Machine Learning Project that uses Random Forest Regressor model to predict used cars price
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)
Machine learning model that predicts which passengers survived the Titanic shipwreck.
Developed a Machine Learning Model that tries to Predict the Price of an apartment based on parameters as given in the dataset obtained from Kaggle. This project is implemented using the machine learning algorithm (Linear Regression) and accuracy score is 84%
Prediction of optimum number of clusters using K-Means Clustering on Iris dataset
Study exercises for learning machine learning algorithms
using machine learning regression algorithms to train model for predictions