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One of the factors is study hours. In this mini analysis project, there are 3 models that will learn and predict the relation between study hours of students and their scores in an exam/test. This project will result the best ML model to solve the problem. \n\n**Goals :**\n-  Range of best number of study hours to get a high score\n-  Minimum and maximum number of study hours that are good for learning\n-  Range of study hours that are less suitable for getting good scores or grades\n-  Finding the best Machine Learning model from [Scikit-learn](https://scikit-learn.org) library to predict and answer the three goals above\n\nWith this analysis, the students are able to find the perfect range of study hours for getting good scores.\n\n**Insights :**\n- Study Hours and Scores are strongly related\n- Studying for 12 hours and above per day can increase the chances of getting high scores\n- Approximately 4 hours of study time and 2 hours or less is the ideal time to study\n\n**Advices :**\n- Some students should not study for 3 hours, 6 hours, or 10 hours per day\n- Because the dataset is still very small, which only amounts to 2 columns and 25 rows, the results of the conclusions obtained may not be valid or not representative of the existing sample\n- The size of the dataset should be further expanded\n\nFor a more interactive explanation of the project, you can read and/or download this presentation on the repository files menu or [here](https://drive.google.com/drive/folders/1UTuSdZ-Li9S4rIOFBMjAXfCzkEq9D7YK).\n\nIf you have any suggestions or feedback, please don't hesitate to contact to me in direct message on [LinkedIn](https://linkedin.com/in/raulahmadm) or [Email](mailto:raul.maulidhino@gmail.com)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fraulmaulidhino-dev%2Fml_modelling_regression","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fraulmaulidhino-dev%2Fml_modelling_regression","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fraulmaulidhino-dev%2Fml_modelling_regression/lists"}