Classification-Algorithms---ML

Goals

• Implement Two Classification Algorithms

1. Naïve Bayesian
2. Logistic Regression

• Using the sklearn Python package for support vector machines (SVM)
• Compare the performance of several classification methods by conducting an empirical comparative study.

Dataset

Mobile Price Classification https://www.kaggle.com/iabhishekofficial/mobile-priceclassification?select=train.csv

Requirements and Key Points

Data Splitting

• Split the original ‘train.csv’ into ‘train.csv’, ‘valid.csv’ and ‘test.csv’ with the ratio of 0.8 :0.1 : 0.1, respectively.

Data Preprocessing

• Convert labels into to two classes: low (0, 1) and high (2, 3)
• For Naïve Bayes, you may need to:

1. discretize continuous attributes into intervals
2. split large number into ranges

• You may need data normalization (i.e., scaling values of attributes tothe same level, e.g., [0, 1])

Model Implementation

Implement Naïve Bayes and Logistic Regression using Python’s primitive classes and functions. Calling third-party libraries (such as sklearn) for model building (except for SVM) will get 0 points.