This project involves creating a house price prediction model using XGBoost. Factors to consider include average income, hospital count, school count, crime rate, etc.
This project is focused on developing a machine-learning model that analyzes the chemical properties of wines to predict quality using wine quality dataset.
A stock price predictor is a fascinating ML project idea for the financial sector. It determines a company's performance and predicts future prices.
Machine learning datasets based on Iris Flowers are among the easiest to learn. ML beginners will need to comprehend how to handle and load numeric traits in the dataset.
This project uses apriori algorithm to predict consumer purchasing behaviors. According to it, if consumers purchase one group of items, they are likely to purchase similar items.