Predicting Student Success with Machine Learning and Python
Student success is a critical aspect of education, and being able to predict which students are at risk of struggling can help educators to provide the support that these students need to succeed. In this article, we will show you how to create a program that can predict student success using Python and Machine Learning.
Before we begin, make sure you have the following libraries installed: numpy, pandas, and scikit-learn. If you don’t have these libraries installed, you can install them by running !pip install numpy pandas scikit-learn in your command line.
The first step in creating our program is to gather data. We will use student attributes such as previous grades, attendance, and test scores to train our machine learning model. Here’s an example of the data we’ll be using:
data = pd.read_csv("students.csv")
train_data = data.sample(frac=0.8, random_state=1)
test_data = data.drop(train_data.index)
Next, we will use the DecisionTreeClassifier algorithm from the scikit-learn library to create our model. This algorithm is a good choice for this task because it can handle categorical variables and it can also handle missing data.
from sklearn.tree import DecisionTreeClassifier
# Create a decision tree classifier
clf =…
