<![CDATA[A place to live or a house is one of the many primary needs for humans. Therefore it is very important to make a plan so that each family can have a private residence. This planning requires a prediction or forecast of future prices. So, the aim of this study is to create a house price prediction model using machine learning methods while the algorithm is linear regression. By doing web scraping to collect data, through several websites that are involved in the sale and purchase of houses. Meanwhile, according to home developers who were successfully asked in the field related to variables that affect house prices, including land area, standing building area, number of bedrooms, number of bathrooms, and the availability of a car park. To get a high predictive value, research is carried out repeatedly but the largest predictive value is using 80% of the dataset for training and 20% of the dataset is used for testing to produce an output value with an accuracy level of predicting 88%.]]>
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