This data set consists of the marks secured by the students in Maths, Writing and Reading and background of the students from which they belong. We are trying to predict Writing Score from Reading Score.
The dataset used is the StudentsPerformance.csv(https://www.kaggle.com/spscientist/students-performance-in-exams).
The target variable is Writing score. Writing Score is scored by students in writing test. So, it can be predicting if Reading score is given.
Target Variable: Writing Score
Mean Writing Score: 68.054
Max Writing Score: 10
Min Writing Score: 100
1. Linear Regression: Linear Regression is a machine learning algorithm based on supervised learning. It performs a regression task. Regression models a target prediction value based on independent variables. It is mostly used for finding out the relationship between variables and forecasting. Linear regression performs the task to predict Writing score based on Reading Score. So, this regression technique finds out a linear relationship between Reading Score and Writing Score.
2. Decision Tree: Decision tree builds regression in the form of a tree structure. It breaks down a dataset into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. The final result is a tree with decision nodes (Reading Score) and leaf nodes (Writing Score).
3. Random Forest: Random forest consists of a large number of individual decision trees that operate as an ensemble. Each individual tree in the random forest spits out a class prediction and the class with the most votes becomes our model’s prediction.
I could have added other variable in account to predict writing score using Multiple regression.