vivekangadi1 Goto Github PK

advancedanalytic

amexpert

AmEx Hackathon

angular-2-template

Basic template for getting started with Angular 2 projects.

angular2

some basics of angular2..

angular2-components

av-amex19

Analytics Vidhya's ML competittion ~~ AMEX - 19

case-study---time-series

“Global Mart” is an online store super giant having worldwide operations. It takes orders and delivers across the globe and deals with all the major product categories - consumer, corporate & home office. Now as a sales/operations manager, you want to finalise the plan for the next 6 months. So, you want to forecast the sales and the demand for the next 6 months, that would help you manage the revenue and inventory accordingly. The store caters to 7 different market segments and in 3 major categories. You want to forecast at this granular level, so you subset your data into 21 (7*3) buckets before analysing these data. But not all of these 21 market buckets are important from the store’s point of view. So you need to find out 2 most profitable (and consistent) segment from these 21 and forecast the sales and demand for these segments.

decisiontree

demo

Demo Website

dependecyinjection

Advanced Dependency Injection concepts

eda_on_bank_loan

hackcode

demoproject

hr-analytics-case

You are required to model the probability of attrition using a logistic regression. The results thus obtained will be used by the management to understand what changes they should make to their workplace, in order to get most of their employees to stay.

linear_regression_formulation

deriving linear regression algorithm starting from simple linear regression to multi linear regression using numpy

logistic_regression_formulation

formulating a logistics regression and its gradients

meantodos

An angular 2 mean todos application with crud operation

mlsql

just basic operation and transformation on the data frames

nyc-parking-case-study

New York City is a thriving metropolis. Just like most other metros that size, one of the biggest problems its citizens face is parking. The classic combination of a huge number of cars and a cramped geography is the exact recipe that leads to a huge number of parking tickets. In an attempt to scientifically analyse this phenomenon, the NYC Police Department has collected data for parking tickets. Out of these, the data files from 2014 to 2017 are publicly available on Kaggle. We will try and perform some exploratory analysis on this data. Spark will allow us to analyse the full files at high speeds, as opposed to taking a series of random samples that will approximate the population. For the scope of this analysis, we wish to compare the phenomenon related to parking tickets over three different years - 2015, 2016, 2017. All the analysis steps mentioned below should be done for three different years. Each metric you derive should be compared across the three years. Use the Fiscal years as per the files. You can use calendar year if you like - you will not lose any marks for performing the analysis this way.

predictibe_analytics

projects on predictive analytics

r-machine-learming

Learning the basics of r

streaming-kafka-structured-spark

Experimenting streaming with kafka and apache spark

telecom_churn

In the telecom industry, customers are able to choose from multiple service providers and actively switch from one operator to another. In this highly competitive market, the telecommunications industry experiences an average of 15-25% annual churn rate. Given the fact that it costs 5-10 times more to acquire a new customer than to retain an existing one, customer retention has now become even more important than customer acquisition. For many incumbent operators, retaining high profitable customers is the number one business goal. To reduce customer churn, telecom companies need to predict which customers are at high risk of churn.

uber-supply-demand

The aim of analysis is to identify the root cause of the problem (i.e. cancellation and non-availability of cars) and recommend ways to improve the situation. As a result of your analysis, you should be able to present to the client the root cause(s) and possible hypotheses of the problem(s) and recommend ways to improve them.