Day 30 of 30 days of Data Analytics with Projects Series — Classification

Welcome back peep. Hope all’s well. This is Day 30 of 30 days of data analytics where we will be covering Classification with a project.

Naive Bayes

Random Forest

Missing Value Analysis

Unique Value Analysis

Let’s cover the most important concepts in brief —

• Naive Bayes is a probabilistic machine learning algorithm based on Bayes’ theorem, which is used for classification tasks.
• Random Forest is an ensemble machine learning algorithm that creates multiple decision trees (forest) and combines the predictions made by each tree to make a final prediction.
• Missing Value Analysis is the process of identifying and handling missing values in a dataset. This can be done by imputing missing values or removing the rows or columns with missing values.
• Unique Value Analysis is a process of identifying and analyzing unique values in a dataset. This can be useful for identifying outliers or understanding the distribution of values in a dataset.

Example Code Implementation —

import numpy as np
import pandas as pd
from…