This repository contains a comprehensive analysis of house sale prices using decision tree models. The aim is to predict house sale prices accurately while understanding regional differences in real estate values. The analysis explores various predictor variables such as the number of bedrooms, bathrooms, square footage, and age of the house. Through exploratory data analysis, feature engineering, and model development, actionable insights are provided for buyers, sellers, and real estate professionals to make informed decisions in the housing market. Additionally, insights into the importance of location, property size, age, and other factors are highlighted to guide investment strategies and enhance understanding of regional real estate trends.
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This repository contains a Decision Tree Regression model developed to predict house sale prices based on various predictor variables, aiming to provide accurate predictions and insights into regional differences in real estate values.
srimallipudi/House-Price-Prediction-Using-Decision-Tree-ML-Algorithm
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This repository contains a Decision Tree Regression model developed to predict house sale prices based on various predictor variables, aiming to provide accurate predictions and insights into regional differences in real estate values.
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