Problem Statement

Classifying whether a person is successful or not related to age and interest

Dataset

The dataset used is the Beginner's Classification (https://www.kaggle.com/sveneschlbeck/beginners-classification-dataset) from Kaggle

The class labels are

1. age
2. interest
3. success

Model used

1. Logistic Regression:

Logistic regression (LR) is basically a supervised classification algorithm. In a classification problem, the target variable(or output), y, can take only discrete values for a given set of features(or inputs),X. The model builds a regression model to predict the probability that a given data entry belongs to the category numbered as “1”. Just like Linear regression assumes that the data follows a linear function, Logistic regression models the data using the sigmoid function. The setting of the threshold value is a very important aspect of Logistic regression and is dependent on the classification problem itself. In this particular case, LR is binomial, i.e., target variable can have only 2 possible types: “0” or “1” which represent "caffeine is not present in considerable amount" for "0" and vice-versa.

2.Support Vector Machine

Support Vector Machine or SVM is one of the most popular Supervised Learning algorithms, which is used for Classification as well as Regression problems. However, primarily, it is used for Classification problems in Machine Learning. The goal of the SVM algorithm is to create the best line or decision boundary that can segregate n-dimensional space into classes so that we can easily put the new data point in the correct category in the future. This best decision boundary is called a hyperplane.