A walk through to Neo4j

Neo4j Concepts 1. What is index-free adjacency? Index-free adjacency (IFA) is a property of Neo4j’s native graph engine that eliminates the need for complex joins and enables real-time traversals. With IFA, Neo4j stores nodes and relationships as objects that are linked to each other. Conceptually, the graph looks as follows: Using IFA, the Neo4j query engine starts with the anchor of the query which is the Group node with the id of 3.

SQL Normalizations

Normalization Normalization in SQL refers to the process of organizing data in a relational database to minimize data redundancy and improve data integrity. This is done by dividing larger tables into smaller, more manageable ones and establishing relationships between them. There are several normal forms, with the most common being first normal form (1NF), second normal form (2NF), and third normal form (3NF). Each normal form has specific rules that must be followed to ensure that the data is properly organized.

Sentiment Analysis with Logistic Regression

1 Introduction Sentiment analysis refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications tha range from marketing to customer service to clinical medicine [1]. This blog explains the sentiment analysis with logistic regression with real twitter dataset.

KNN and Naïve Bayes - Data Science

1 Introduction Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. The machine learning is classified into different categories viz. supervised machine learning, unsupervised learning, semi-supervised learning, and reinforcement machine learning. The supervised learning algorithm takes features as input, maps to a mapping function and approximates a result. The goal is to approximate the mapping function so well that when it gets new input that it can predict the output variables for that data.