House Price Prediction Using Linear Regression
تفاصيل العمل
The dataset containing 21,613 records and 20 variables was cleaned and verified to ensure there were no missing values. Irrelevant features were removed to improve model performance. Exploratory Data Analysis (EDA) was performed to understand price distribution and relationships between variables. Results showed that living area, bathrooms, and location strongly influence house prices. The data was split into training and testing sets, and a Linear Regression model was built to predict house prices. Model evaluation showed good agreement between predicted and actual values.
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