ritesh-negi Goto Github PK

analyzing-online-job-postings

Business Context: The project seeks to understand the overall demand for labour in the Armenian online job market from the 19,000 job postings from 2004 to 2015 posted on CareerCenter, an Armenian human resource portal. Through text mining on this data, we will be able to understand the nature of the ever-changing job market, as well as the overall demand for labour in the Armenia economy. The data was originally scraped from a Yahoo! Mailing group.

bank-review-and-complain-analysis

BUSINESS CONTEXT: This case requires trainees to develop a customer segmentation to define marketing strategy. The sample dataset summarizes the usage behavior of about 9000 active credit card holders during the last 6 months. The file is at a customer level with 18 behavioral variables.

interview-prepartion-data-science

network-intrusion-detection-system

BUSINESS CONTEXT: With the enormous growth of computer networks usage and the huge increase in the number of applications running on top of it, network security is becoming increasingly more important. All the computer systems suffer from security vulnerabilities which are both technically difficult and economically costly to be solved by the manufacturers. Therefore, the role of Intrusion Detection Systems (IDSs), as special-purpose devices to detect anomalies and attacks in the network, is becoming more important. The research in the intrusion detection field has been mostly focused on anomaly-based and misusebased detection techniques for a long time. While misuse-based detection is generally favored in commercial products due to its predictability and high accuracy, in academic research anomaly detection is typically conceived as a more powerful method due to its theoretical potential for addressing novel attacks. Conducting a thorough analysis of the recent research trend in anomaly detection, one will encounter several machine learning methods reported to have a very high detection rate of 98% while keeping the false alarm rate at 1%. However, when we look at the state of the art IDS solutions and commercial tools, there is no evidence of using anomaly detection approaches, and practitioners still think that it is an immature technology. To find the reason of this contrast, lots of research was done done in anomaly detection and considered various aspects such as learning and detection approaches, training data sets, testing data sets, and evaluation methods. BUSINESS PROBLEM: Your task to build network intrusion detection system to detect anamolies and attacks in the network. There are two problems. 1. Binomial Classification: Activity is normal or attack 2. Multinomial classification: Activity is normal or DOS or PROBE or R2L or U2R Please note that, currently the dependent variable (target variable) is not definied explicitly. However, you can use attack variable to define the target variable as required.

predicting-credit-card-spend-identifying-key-drivers

One of the global banks would like to understand what factors driving credit card spend are. The bank want use these insights to calculate credit limit. In order to solve the problem, the bank conducted survey of 5000 customers and collected data. The objective of this case study is to understand what's driving the total spend (Primary Card + Secondary card). Given the factors, predict credit limit for the new applicants

python-cheatsheet

Comprehensive Python Cheatsheet

segmentation-of-credit-card-customers

BUSINESS CONTEXT: This case requires trainees to develop a customer segmentation to define marketing strategy. The sample dataset summarizes the usage behavior of about 9000 active credit card holders during the last 6 months. The file is at a customer level with 18 behavioral variables.

walmart-store-sales-forecasting

Business Context: The objective is predicting store sales using historical markdown data. One challenge of modelling retail data is the need to make decisions based on limited history. If Christmas comes but once a year, so does the chance to see how strategic decisions impacted the bottom line. Data Availability & Business Problem: You are provided with historical sales data for 45 Walmart stores located in different regions. Each store contains a number of departments, and you are tasked with predicting the department-wide sales for each store. In addition, Walmart runs several promotional markdown events throughout the year. These markdowns precede prominent holidays, the four largest of which are the Super Bowl, Labour Day, Thanksgiving, and Christmas. The weeks including these holidays are weighted five times higher in the evaluation than non-holiday weeks. Part of the challenge presented by this competition is modelling the effects of markdowns on these holiday weeks in the absence of complete/ideal historical data.