House Price Prediction is a Machine Learning project aimed at predicting the prices of residential properties based on various features. The goal is to develop a model that can accurately estimate the selling price of a house given its characteristics. 💰
House price prediction is a crucial task in real estate and property valuation. It helps buyers and sellers make informed decisions about purchasing or selling a property. For buyers, accurate price predictions ensure they are not overpaying for a house. For sellers, it helps in setting a competitive and fair price for their property. 🏡
The dataset used in this project contains various attributes of residential properties, such as the size of the property, location, number of bedrooms and bathrooms, year built, and other relevant features. The dataset also includes the selling prices of the properties, which serve as the target variable for prediction. 📋
House Price Prediction typically involves the following steps:
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Data Collection: Gathering data from various sources including real estate listings, public databases, and property websites.
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Data Preprocessing: Cleaning the data by handling missing values, encoding categorical variables, and scaling numerical features.
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Feature Engineering: Creating new features or transforming existing ones to improve the model's predictive performance.
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Model Selection: Choosing a suitable machine learning algorithm and training it on the prepared data to predict house prices accurately.