machine learning to predict who passed a course

Machine learning techniques can be utilized for students' grades prediction in different courses. Such techniques would help students to improve their performance based on predicted grades and would enable instructors to identify such individuals who might need assistance in the courses.

Full Answer

Can a machine learning algorithm predict a student's academic success?

In this work, we will use linear regression, a machine learning algorithm, to predict a student's academic success. Index Terms- Component,formatting,style,styling,insert. I. INTRODUCTION The Internet has opened the door to a new way of learning. The amount of information available therein exceeds that of any physical library.

What is machine learning and how does it work?

M achine learning is the study of computer algorithms that improve automatically through experience and by the use of data. It is a subset of Artificial Intelligence, based on the ideology that a system can not only learn from data, but identify hidden trends and patterns, and further make decisions with little human intervention.

Can machine learning take the mean value of a numerical value?

In case of numerical values we can find mean, clean missing values and perform other mathematical operation. Machine learning can only read and understand numerical value. But you can’t take the mean value of “pass”, “redo”, “retake”.

How important is it to prepare the dataset for machine learning?

For any Machine learning mode, its really important to prepare the dataset. If you haven't cleaned and prepossessed your datasets your model will not -work.

Does machine learning make predictions?

Businesses use machine learning to recognize patterns and then make predictions—about what will appeal to customers, improve operations, or help make a product better.

What can machine learning be used to predict?

Machine learning model predictions allow businesses to make highly accurate guesses as to the likely outcomes of a question based on historical data, which can be about all kinds of things – customer churn likelihood, possible fraudulent activity, and more.

How do you predict using ML?

StepsStep 1: Prepare Your Data.Step 2: Create a Training Datasource.Step 3: Create an ML Model.Step 4: Review the ML Model's Predictive Performance and Set a Score Threshold.Step 5: Use the ML Model to Generate Predictions.Step 6: Clean Up.

Why is machine learning better at prediction?

More Data – Better Accuracy Machine Learning Forecasting allow for more data to be fused into the forecast. The forecast is augmented at the level of the distinct product, including what is known about evaluating history, rebates, and other issues that may be under administrative control.

Which ML algorithm is best for prediction?

Naive Bayes is a simple but surprisingly powerful algorithm for predictive modeling. The model is comprised of two types of probabilities that can be calculated directly from your training data: 1) The probability of each class; and 2) The conditional probability for each class given each x value.

Which algorithm is best for prediction in machine learning?

Random Forest is perhaps the most popular classification algorithm, capable of both classification and regression. It can accurately classify large volumes of data. The name “Random Forest” is derived from the fact that the algorithm is a combination of decision trees.

Is Arima machine learning?

ARIMA is an acronym that stands for AutoRegressive Integrated Moving Average. This is one of the easiest and effective machine learning algorithm to performing time series forecasting. This is the combination of Auto Regression and Moving average.

What is predict () in Python?

Python predict() function enables us to predict the labels of the data values on the basis of the trained model. Syntax: model.predict(data) The predict() function accepts only a single argument which is usually the data to be tested.

How do you make predictions based on data?

Predictive analytics is the process of using data analytics to make predictions based on data. This process uses data along with analysis, statistics, and machine learning techniques to create a predictive model for forecasting future events.

Which model can be used for prediction?

There are many different types of predictive modeling techniques including ANOVA, linear regression (ordinary least squares), logistic regression, ridge regression, time series, decision trees, neural networks, and many more.

Introduction

M achine learning is the study of computer algorithms that improve automatically through experience and by the use of data. It is a subset of Artificial Intelligence, based on the ideology that a system can not only learn from data, but identify hidden trends and patterns, and further make decisions with little human intervention.

Application

Research on the educational field involving machine learning techniques has recently taken a steep growth trajectory. A new term called “Educational Data Mining” has come into existence, i.e., the application of data mining techniques in an educational background aiming to discover hidden trends and patterns about student’s performance.

Building a Prediction Model

Supervised Machine Learning will be applied to predict and analyze a student’s marks. For this task, we begin our pursuit by approaching the problem using a technique called the “ simple linear regression model ”.

Results & Discussion

The project’s main goal was to determine whether a relationship between the two quantitative measures existed. If that was the case, then we had to develop a prediction model for students’ academic performance.

Conclusion

Deciding and dedicating the best practice and environment to uplift a student’s academic portfolio can be challenging and quite an uphill task due to many uncertainties.

Abstract

Students’ success has recently become a primary strategic objective for most institutions of higher education. With budget cuts and increasing operational costs, academic institutions are paying more attention to sustaining students’ enrollment in their programs without compromising rigor and quality of education.

1. Introduction

Student retention is a pressing issue for academic institutions around the globe, given tight budgets and limited resources [1]. The average dropout rate in Organization for Economic Co-operation and Development (OECD) countries is around 45% [2].

2. Theoretical background

The topic of predicting student performance in academic institutions has attracted the attention of researchers and academic administrators for the past two decades [10].

3. Methodology

In this study, we rely on AutoML to derive the best classification model and corresponding hyper-parameters. Amongst the most popular tools that offer AutoML features are Auto-Weka [28] and Auto-sklearn [29]. We chose to run the Auto-Weka search algorithm with the hyper-parameter optimization option. Fig.

4. Results

We used a 10 folds cross-validation to test the accuracy of the resulting Ensemble Model. The model is trained on 90% of the points and tested with 10% over 10 different runs. It is important to note that the data points that are allocated for testing as part of the 10% split are different each time. Fig.

5. Conclusion

The reported work in this paper contributes to the body of knowledge in the field of predicting student academic success. Specifically, it relies on AutoML to increase the prediction accuracy of student performance using data features available prior to the students starting their new academic program, i.e. pre-start data.

Author statement

Hassan Zeineddine: Conceptualization, Methodology, Software, Investigation, Visualization, Writing.

Introduction

Application

Building A Prediction Model

• A. Analytical Approach
  Supervised Machine Learning will be applied to predict and analyze a student’s marks. For this task, we begin our pursuit by approaching the problem using a technique called the “simple linear regression model”. It is a statistical model commonly used to estimate the relationship between …
• B. Data Analysis
  I. Importing Libraries & Loading Data We will import the libraries involved. Please note that Scikit-Learn will be imported later on. The next step is to load the given data into the Python Interpreter I used on Jovian, to proceed with the training of the model. Pandas are used to load the CSV file a…

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Results & Discussion

Conclusion