Student Performance Using Machine Learning Model

Github Link:

In this blog, I am gonna share my experience of building a Machine Learning Model for Predicting marks.

Machine learning plays a major role from past years in normal speech command, spam reorganization, image detection, product recommendation and medical diagnosis. Present Machine learning algorithm helps us in enhancing security alerts, ensuring public safety and improve medical enhancements. Machine learning system also provides better customer service and safer automobile systems. When I started experimenting with machine learning, I wanted to come up with an application that would solve a real-world problem but would not be too complicated to implement. I also wanted to practice working with regression algorithms.

Machine Learning is the process of learning a set of rules from instances or more generally speaking creating a classifier that can be used to generalize from new instances. Creation of a classifier is a two step process. In the first step the classifier model is constructed using a given data set. This step is called training. In this step classification rules are constructed. The second step called testing determines the accuracy of the classification rules. If the accuracy of the classifier is above an acceptable limit then the classifier model constructed in the first step may be used for classification of new data records. Training the classifier requires the use of MLAs. Choice of algorithm is critical because the accuracy of prediction is often dependent on these algorithms. Over the years several researchers [3,4,5,6,7,8, 9] have used varied MLAs to perform classification in various knowledge domain. In this study five classes of MLAs has been chosen for classification with a representative algorithm from each class. MLAs from five classes are chosen so as to introduce a certain degree of variation among the classifiers, i.e. they should not make identical or correlated errors.


Python IDLE

jupyter notebook or Google Colab


Exploratory data analysis

Import the necessary libraries.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

Load dataset

Let’s see how the dataset actually looks. To do so, we can use the “head()” function as shown below:

Discover And Visualiz The Data To Gain Insights

Data Cleaning

Select Model and Train It

# y = m * x + c

from sklearn.linear_model import LinearRegression

lr = LinearRegression()

Tune Model

Deployment Using Flask on Local Machine

In this project we have deployed a machine learning model on the local machine(laptop/PC) using the python flask framework.



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