In this program, I have implemented the K-Nearest Neighbors (KNN) model to estimate the entrance exam (konkur) rank for Iranian universities. Additionally, I have developed a Telegram bot that allows users to conveniently utilize this functionality. The KNN algorithm, a popular machine learning technique, utilizes the concept of proximity to classify or estimate unknown data points based on the characteristics of their neighboring points. By training the model with a dataset comprising relevant features and corresponding entrance exam ranks, it becomes capable of predicting ranks for new instances. The Telegram bot serves as a user-friendly interface, enabling users to input their specific data and receive estimated entrance exam ranks promptly. This project aims to provide a valuable tool for Iranian students to gauge their performance and make informed decisions regarding university admissions. These are two picture of the ranking telegram bot:
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In this program, I used the KNN model to estimate Iranian universities' entrance exam (konkur) rank, and I also developed a telegram bot so users could use it.
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hassanzadehmahdi/Rank-estimation
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In this program, I used the KNN model to estimate Iranian universities' entrance exam (konkur) rank, and I also developed a telegram bot so users could use it.
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