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I have developed a comprehensive data preprocessing and manipulation application that streamlines the data preparation phase before applying machine learning models. This application provides a range of essential data preprocessing functionalities, including handling duplicate values and missing data.
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One of the key features of the application is the utilization of advanced techniques like k-nearest neighbors (KNN) and rule-based methods (RL) to address missing values. These methods intelligently impute missing data, ensuring the integrity and quality of the dataset.
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Furthermore, the application offers a user-friendly interface that allows users to interactively explore and manipulate their data. It provides intuitive options for data cleaning, feature selection, and transformation, empowering users to optimize their datasets for better model performance.
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With this data preprocessing application, users can efficiently handle duplicate values, identify and handle missing data, and perform various data manipulation tasks. By automating these processes, it accelerates the data preprocessing phase and enables users to focus more on building robust machine learning models.
5- or we can choose to use the advanced model such as Random forest or KNN to handle our data from encoding to filling the missing value
6- here are the result of the trained model we can train many model and compare between them to choose the best one
the main object of this project is enables users to focus more on building robust machine learning models .
PS : just download the code and excute it , you will need an IDE such as spyder (i reccomend this one)
If this project inspired you, gave you ideas or helped you, please consider giving me a Star and share the project with others ❤️.