sharmaroshan

Detecting Frauds in Online Transactions using Anamoly Detection Techniques Such as Over Sampling and Under-Sampling as the ratio of Frauds is less than 0.00005 thus, simply applying Classification Algorithm may result in Overfitting

In this Data set we are Predicting the Insurance Claim by each user, Machine Learning algorithms for Regression analysis are used and Data Visualization are also performed to support Analysis.

Social network analysis (SNA) is the process of investigating social structures through the use of networks and graph theory.[1] It characterizes networked structures in terms of nodes (individual actors, people, or things within the network) and the ties, edges, or links (relationships or interactions) that connect them. Examples of social structures commonly visualized through social network analysis include social media networks,[2] memes spread,[3] information circulation,[4] friendship and acquaintance networks, business networks, social networks, collaboration graphs, kinship, disease transmission, and sexual relationships.[5][6] These networks are often visualized through sociograms in which nodes are represented as points and ties are represented as lines. These visualizations provide a means of qualitatively assessing networks by varying the visual representation of their nodes and edges to reflect attributes of interest.

It is Based on Anamoly Detection and by Using Deep Learning Model SOM which is an Unsupervised Learning Method to find patterns followed by the fraudsters.

One of the most basic data sets to learn and implement some of the most easy and basic algorithms of machine learning and visualization

It is a Repository for Basic C++ Programs for Placements

Principal Component Analysis is One of the Most Popular Dimensionality Reduction Algorithms used in Machine Learning Which comes under Unsupervised Way of Learning. It is also Used as a way of Feature Extraction where, More Information is Extracted from all the Existing Attributes, in just some 3-4 Attributes using the Concepts of Eigen Values and Eigen Vectors.