This is a project based on ML model that simply predicts laptop price according to provided user configuration.
To develop a machine learning model that can accurately predict the price of a laptop based on its configuration, including features such as CPU, GPU, RAM, storage, display size, and brand.
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Developed a laptop price prediction model using the Random Forest algorithm and performed EDA on 1100 rows of data from Kaggle.
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Conducted extensive data manipulation and feature engineering to prepare the dataset for modelling.
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Achieved an impressive accuracy rate of over 88.7% through rigorous testing and validation, demonstrating the effectiveness of the model.
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Accurately forecasted laptop prices based on key features, helping users make informed purchasing decisions.