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machine learning final year project – Student Scoring Manipulation Analysis

machine learning final year projects
machine learning final year projects

Abstract

Student Scoring Manipulation Analysis using Machine learning system developed to predict the student mean scores with the help of pre-score, mid-score and post-score by Create a list of the mean scores for each variable and Create a list of variances, which are set at .25 above and below the score using the python library pandas and Matplotlib.

Request for  Project – [email protected]

Contact Number – 7667668009 / 7667664842

For complete project lists  –  final year project for cse

For internship – internship  in chennai

machine learning final year project  – Introduction

The educational advantages of e-learning include online teaching and course delivery, which do not require physical classrooms for students. Compared to traditional modes of learning, e-learning is less expensive, and a larger number of students can register for online courses. However, in e-learning, there is no direct communication between students and teachers. Therefore, e-learning poses some challenges. First, it is difficult for instructors to assess the effectiveness of a course. Second, the dropout rate of students in e-learning courses is much higher than that in traditional modes of learning. Third, assessing student’s performance is difficult. Fourth predicting at-risk students in new courses is also difficult. Finally, teachers are interested in predicting students’ expected results on upcoming assessments (Lykourentzou et al. 2009; Pahl and Donnellan 2002; Smith-Gratto 1999; Kuzilek et al. 2015; Bakki et al. 2015). Web-based learning environments such as massive open online courses (MOOC), digital electronics education and design suite (DEEDS) and learning management systems (LMSs) allow teachers to study student performances using logged student data, but teachers may have difficulty analyzing the student logs. MOOCs and LMSs are popular types of web-based learning platforms; they provide free higher education to the entire world and offer courses from different universities. Furthermore, they provide administration, documentation, content assembly, student management and self-services (He et al. 2015). LMSs are online portals for both students and teachers that facilitate teacher-student interactions and allow them to perform their educational tasks and activities. More-over LMSs deliver courses to students, and the students can select their own courses through a course selection process (Imran et al. 2014). MOOCs are free web-based learning platforms that supply all their courses online. Students can register and attend these courses from any location (Kloft et al. 2014). These web-based learning environments affect how teachers and students think during class, and they can be used to predict a student’s performance during the next class or a student’s behavior at different times. In addition, these environments can be used to improve courserelated content (Chen et al. 2000). Predicting a student’s progress in a class or session through, for example, quizzes, assignments, exams, and session activities can provide instructors with in-depth information on the progress of students throughout the course. To achieve this goal, researchers have applied various machine learning and statistical techniques to data acquired from both traditional and online universities.

Anatomy and consequences of manipulation

The anatomy of the manipulation problem is most simply understood by contrasting “raw scores” (scores observed in the data) to “true scores” (scores that would have been observed had manipulation not taken place). Policy-relevant quantities, such as average score or percentage of students scoring at a pre-defined level, are defined using student’s true scores. When scores are not manipulated, raw scores correspond to true scores for achievement all students, and data reveal the true quantity of interest. When manipulation occurs, however, a fraction of scores is corrupted. In this case, raw scores do not equal true scores for some students and raw data do not yield the correct class aggregate (e.g. average test scores in a class). For example, if the fraction of manipulated scores is 10%, then only 90% of exams used to compute the average class score are honest. If contamination is substantial, the average score observed may be far off the real average.

machine learning final year project – HARDWARE REQUIREMENTS

  • System : Intel inside i3
  • System Type : 64-bit Operating System
  • Storage :500GB
  • RAM :4 GB

 

machine learning final year project – SOFTWARE REQUIREMENTS

  • Operating system : Windows 10
  • Software : Python
  • Python Libraries: Numpy , Matplotlib , pandas

 

machine learning final year project  – What is Machine Learning

In the real world, we are surrounded by humans who can learn everything from their experiences with their learning capability, and we have computers or machines which work on our instructions. But can a machine also learn from experiences or past data like a human does? So here comes the role of Machine Learning.

Machine Learning is said as a subset of artificial intelligence that is mainly concerned with the development of algorithms which allow a computer to learn from the data and past experiences on their own. The term machine learning was first introduced by Arthur Samuel in 1959.

Machine learning enables a machine to automatically learn from data, improve performance from experiences, and predict things without being explicitly programmed.

With the help of sample historical data, which is known as training data, machine learning algorithms build a mathematical model that helps in making predictions or decisions without being explicitly programmed. Machine learning brings computer science and statistics together for creating predictive models. Machine learning constructs or uses the algorithms that learn from historical data. The more we will provide the information, the higher will be the performance.

A machine has the ability to learn if it can improve its performance by gaining more data.

How does Machine Learning work

A Machine Learning system learns from historical data, builds the prediction models, and whenever it receives new data, predicts the output for it. The accuracy of predicted output depends upon the amount of data, as the huge amount of data helps to build a better model which predicts the output more accurately.

Suppose we have a complex problem, where we need to perform some predictions, so instead of writing a code for it, we just need to feed the data to generic algorithms, and with the help of these algorithms, machine builds the logic as per the data and predict the output. Machine learning has changed our way of thinking about the problem.

Features of Machine Learning:

  • Machine learning uses data to detect various patterns in a given dataset.
  • It can learn from past data and improve automatically.
  • It is a data-driven technology.
  • Machine learning is much similar to data mining as it also deals with the huge amount of the data.

Need for Machine Learning

The need for machine learning final year project is increasing day by day. The reason behind the need for machine learning is that it is capable of doing tasks that are too complex for a person to implement directly. As a human, we have some limitations as we cannot access the huge amount of data manually, so for this, we need some computer systems and here comes the machine learning to make things easy for us.

We can train machine learning algorithms for machine learning final year project by providing them the huge amount of data and let them explore the data, construct the models, and predict the required output automatically. The performance of the machine learning algorithm depends on the amount of data, and it can be determined by the cost function. With the help of machine learning, we can save both time and money.

The importance of machine learning can be easily understood by its uses cases, currently, machine learning is used in self-driving carscyber fraud detectionface recognition, and friend suggestion by Facebook, etc. Various top companies such as Netflix and Amazon have build machine learning models that are using a vast amount of data to analyze the user interest and recommend product accordingly.

Following are some key points which show the importance of Machine Learning:

  • Rapid increment in the production of data
  • Solving complex problems, which are difficult for a human
  • Decision making in various sector including finance
  • Finding hidden patterns and extracting useful information from data.

Classification of Machine Learning

At a broad level, machine learning final year project can be classified into three types:

  1. Supervised learning
  2. Unsupervised learning
  3. Reinforcement learning

1) machine learning final year project on Supervised Learning

Supervised learning is a type of machine learning method in which we provide sample labeled data to the machine learning system in order to train it, and on that basis, it predicts the output.

The system creates a model using labeled data to understand the datasets and learn about each data, once the training and processing are done then we test the model by providing a sample data to check whether it is predicting the exact output or not.

The goal of supervised learning is to map input data with the output data. The supervised learning is based on supervision, and it is the same as when a student learns things in the supervision of the teacher. The example of supervised learning is spam filtering.

Supervised learning can be grouped further in two categories of algorithms:

  • Classification
  • Regression

2) machine learning final year project on UnSupervised Learning

Unsupervised learning is a learning method in which a machine learns without any supervision.

The training is provided to the machine with the set of data that has not been labeled, classified, or categorized, and the algorithm needs to act on that data without any supervision. The goal of unsupervised learning is to restructure the input data into new features or a group of objects with similar patterns.

In unsupervised learning, we don’t have a predetermined result. The machine tries to find useful insights from the huge amount of data. It can be further classifieds into two categories of algorithms:

  • Clustering
  • Association

3) machine learning final year project on Reinforcement Learning

Reinforcement learning is a feedback-based learning method, in which a learning agent gets a reward for each right action and gets a penalty for each wrong action. The agent learns automatically with these feedbacks and improves its performance. In reinforcement learning, the agent interacts with the environment and explores it. The goal of an agent is to get the most reward points, and hence, it improves its performance.

The robotic dog, which automatically learns the movement of his arms, is an example of Reinforcement learning.

 

Python

Python is a general purpose, dynamic, high-level, and interpreted programming language. It supports Object Oriented programming approach to develop applications. It is simple and easy to learn and provides lots of high-level data structures.

Python is easy to learn yet powerful and versatile scripting language, which makes it attractive for Application Development.

Python’s syntax and dynamic typing with its interpreted nature make it an ideal language for scripting and rapid application development.

Python supports multiple programming pattern, including object-oriented, imperative, and functional or procedural programming styles.

Python is not intended to work in a particular area, such as web programming. That is why it is known as multipurpose programming language because it can be used with web, enterprise, 3D CAD, etc.

Sample Code for – machine learning final year project

#matplotlib inline

import pandas as pd

import matplotlib.pyplot as plt

import numpy as np

raw_data = {‘first_name’: [‘Venkat’, ‘Prabu’, ‘Santhosh’, ‘Asha’, ‘Krishiv’],

‘age’: [25, 26, 31, 32, 23],

‘female’: [0, 0, 0, 1, 0],

‘pre_score’: [14, 24, 23],

‘mid_score’: [25, 94, 57, 62, 70],

‘post_score’: [75, 43, 63, 43, 51]}

 

Request for  Project – [email protected]

Contact Number – 7667668009 / 7667664842

For complete project lists  –  final year project for cse

For internship – internship  in chennai

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