Students Marks Prediction Using Linear Regression

Abstract:

Education institutions use new technologies to improve the quality of education but most of the applications which are used in colleges are related to service and development there are web applications that are helping students to take online training and tests. There are very few methods that can help teachers to know about student’s performance. Considering this problem machine learning techniques are used to predict students’ marks based on previous marks and predict results. Linear regression models are used to predict student performance and predict the next subject marks.

Problem statement:

Education institutions use web applications for training students and checking performance based on marks but there are no specific steps followed for prediction of students performance and take measures to improve performance.

Objective:

Design a machine learning model for the prediction of students marks and take measures to improve student performance. Liner regression algorithm is used to train model and prediction.

Existing system:

Researches had done work on the automation of grading techniques in which previous marks were used to give grades to students.

Algorithms like association rule mining and apriori algorithms are used for classifying students marks.

Disadvantages:

Existing methods mostly work based on marks obtained from exams.

Algorithms are used for classifying students based on marks. 

Proposed system:

Dataset of other subject marks are taken as input and data set is processed with labels and features and then test split is performed on the dataset and then machine learning model is applied to dataset then the prediction is performed.

Advantages:

Before the final marks of all subjects are evaluated prediction can be performed.

Using a machine learning process automation of marks prediction can be done. 

SOFTWARE REQUIREMENTS:

  • Operating system: Windows XP/7/10
  • Coding Language: python  
  • Development environment: anaconda, Jupiter 
  • Dataset: students marks dataset
  • IDE: Jupiter notebook

COVID-19 Data Analysis And Cases Prediction Using CNN

ABSTRACT:

Corona virus ( COVID-19 ) is creating panic all over the world with fast growing cases. There are various datasets available which provides information of world-wide effected information. Covid has affected all counties with large number of cases with variation of numbers under death, survived, effected. In this project we are using data set which has county wise details of cases with various combined features and labels. Covid data analysis and case prediction project provide solution for data analysis of various counties on various time and data factors and creating model for survival and death cases and prediction cases in future. Machine learning provides deep learning methods like Convolution neural network which is used for model creation and prediction for next few months are done using this project.  

PROBLEM STATEMENT:

      With the increase of COVID 19 cases all over the world daily predictions and analysis is required for effective control of pandemic all over the world

OBJECTIVE:

    By collecting data from Kaggle and new York dataset data preprocessing is performed and data analysis is performed on dataset and machine learning model is generated for future prediction of cases.

EXISTING SYSTEM:

  • Prediction was performed on COVID 19 cases based on different machine learning techniques which are based on x ray data set collected from COVID 19 patients.
  • Disease prediction from x ray images is done using deep learning techniques.

Disadvantages:

  • Data set used for predicting disease is different compare to one we are using for this project.
  • Image processing techniques are used.

PROPOSED SYSTEM:

Using data set pre-processing is performed on the collected data set and various steps for deep learning model is performed and prediction of cases is done then data analysis is done on various factors.

Advantages:

  • Data analysis and prediction is performed on textual data
  • Deep learning models are generated for predicting future cases.
  • Data analysis is performed for various factors.

SOFTWARE REQUIREMENTS

  • Operating system                   :           Windows XP/7/10
  • Coding Language              :           python
  • Development Kit                  :        anaconda 
  • Programming language: Python
  • IDE                                               :           Anaconda prompt