Students Performance Prediction using Machine Learning

Student performance Prediction is a method for foreseeing an understudy's presentation dependent on his/her past marks. This additionally makes the student know whether he/she is in a situation to arrive at his/her normal or expected marks or not.  On the off chance that this model shows that he/she needs to improve then that student can get ready more for that semester with the goal that he/she can arrive at their normal score. This venture helps the students in improving their exhibition. Presently, Machine learning is one of the most developing and creating dialects nowadays and in this task, we went to utilize ML, some classification algorithms and python.

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Project description:

The essential ideas utilized in the Students’ Performance Prediction model are:

1. Python as a Programming language: Python is a broadly utilized universally useful, elevated level programming language. It permits programming in Object-Oriented and Procedural ideal models. In this venture, we're going to utilize diverse Python libraries like Numpy, Pandas, and so on.
2. Machine learning: It is one of the most powerful and emerging technologies, here we are going to use machine learning to improve the precision of the model.
3. Database: Any Database to store the informational collection.
4. This project involves a different strategy which comprises

• Data collection- To collect and store the data
• Data preprocessing- It includes data cleaning, transformation, and filtering.
• Generating training and testing dataset-Some algorithms used for testing for the assessment of input variables: LMT, Decision tree, Naive Bayesian and Support Vector Machine.
• Model generation
• Prediction & Result

Project Implementation:

Steps to achieve a Students’ Performance Prediction model using machine learning are:

1. At first, we need the gathered dataset from the web or from your school/university (if you're utilizing school/college information kindly do this with consent as it were).
2. Presently we have to pre-process the information like Data cleaning, transformation, and filtering, etc.
3. Subsequent to preprocessing, the following stage is to Generating the Training and Test Dataset, for this procedure, we can utilize algorithms like LMT, Naive Bayesian, Decision tree and Support Vector Machine.
4. Now we have the Model Generation stage, modeling is a simplified, scientifically formalized approach to rough reality and on the other hand to make figures from this estimation.
5. The last step is the Prediction and results from analysis.
6. Hence you get your result.

Requirement -

-Programming language -Python

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Students Performance Prediction using Machine Learning