Loaded and preprocessed the dataset, ensuring data integrity and cleanliness. Explored the dataset's statistics and visualized the correlation between study hours and scores. Split the data into training and testing sets for model evaluation. Implemented a linear regression model and trained it on the training data. Evaluated the model's accuracy, achieving an impressive accuracy score of approximately 95.66%. Visualized the actual vs. predicted scores for further analysis, demonstrating the model's effectiveness. Calculated Mean Absolute Percentage Error (MAPE) and R-squared (R2) score as performance metrics, showcasing the model's reliability.
AliN3bil2/sparks_1st_task_Lr
predict the percentage of an student based on the number of study hours
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