We then use Artificial Neural Network (ANN) and Support Vector Machine (SVM) to classify the characters. To increase the recognition rate of the characters, we have introduced a fuzzy unsupervised feature learning algorithm to learn features of individual characters. Unsupervised feature learning becomes a powerful tool in machine learning nowadays. Our novel character segmentation procedure extracts characters maintaining 97.5% accuracy. To address the segmentation problem we have proposed an algorithm namely Blob-Labeled character Segmentation (BLCS) that initiates with an extensive preprocessing to extract the characters from text. However, Bangla text recognition is highly challenging job for its complicated writing style, compound characters and highly diversified fonts. Researchers already proposed many machine learning algorithms to address this problem. Optical character recognition (OCR) is a widely used technology to convert text images to editable text. It was discovered that there is a positive correlation between co-curricular activities and academic performance using Logistic Regression using Python and Google Colab. This study's objective is to ascertain the actual effect of co-curricular activities on pupils. One of the primary reasons is because many believe these activities would jeopardize a student's academic performance.
However, a significant proportion of pupils in Bangladesh do not participate in such activities. They are a vital component of educational institutions' attempts to help students develop their personalities and improve classroom learning. Co-curricular activities are extracurricular activities that support and enhance the academic or core curriculum.
The purpose of this study is to determine the relationship between extracurricular activities and student performance. The study project named "Effects of Co-Curricular Activities on Student's Academic Performance Through Machine Learning" examines the effect of co-curricular activities on a student's academic performance.
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