Solution-Attrition Prediction and Control
Description
Methodology
Benefits from the Project
Tools Used
Description
A leading MNC came to us with a problem of high rate of employees leaving their company.
Methodology
Analysed the reasons that contribute most to an employee quitting the company by using feature selection.
Applying statistical models to predict an employee’s intention of quitting.
Applying statistical models to predict an employee’s intention of quitting.
Benefits from the Project
Suggested factors that affect the behaviour of current employee to reduce the attrition in the company from 16% down to 4 %.
Tools Used
R
Tableau
Descriptive Statistical Techniques
Tableau
Descriptive Statistical Techniques
Offer Renege for a conglomerate
Description
Methodology
Benefits from the Project
Tools Used
Description
A very large conglomerate was struggling with offer renege and we solved there problem using analytics.
Methodology
Predicted the probability in buckets ranging from 5% to 95% of an individual to renege on an offer based on the machine learning algorithms.
Benefits from the Project
Reduced offer renege from 30% to 12%. Also helped the organization to focus on more suitable candidates.
Tools Used
Python
Tableau
Insights based on customer reviews
Description
Methodology
Benefits from the Project
Tools Used
Description
By the help of user reviews we need to find insights which can help retailers and manufacturers.
Methodology
Using SQL we created reports which can help to take business decisions
Using Tableau we created effective visualisations
Word cloud of all the negative comments to understand the major issues
Using Tableau we created effective visualisations
Word cloud of all the negative comments to understand the major issues
Benefits from the Project
Recommended factors that needs to be worked on for manufacturer and retailers and thereby improve the business
Tools Used
SQL
Tableau
Excel
Tableau
Excel
Sentiment Analysis
Description
Methodology
Benefits from the Project
Tools Used
Description
Sentiment of general public regarding two recent economic changes i.e. Demonetisation and GST are to be observed on the social networking platform Twitter
Methodology
Data is collected using flume by hitting the right APIs using the desired keywords
Using AFINN dictionary, the tweets are assigned positive/negative scores and then exported to excel for analysis.
Benefits from the Project
The public sentiment regarding various policies and changes can be determined.
This kind of sentiment analysis helps in better policy making.
Targeting the right groups for election campaigns, advertisements etc
This kind of sentiment analysis helps in better policy making.
Targeting the right groups for election campaigns, advertisements etc
Tools Used
Hadoop
Pig
Flumel
Sea Route Cost Optimization
Description
Methodology
Benefits from the Project
Tools Used
Description
To decide the cost optimal location among various ports to bring the materials from for a construction project. Factors such as tidal data, material cost, distance, barge size, ship size, fuel cost etc. are considered.
Methodology
Considered the factors which play critical role in determination of the overall costs of a construction project in port areas.
Cost function is developed to find the minimum cost location with maximum available resources for the construction to be done.
Cost function is developed to find the minimum cost location with maximum available resources for the construction to be done.
Benefits from the Project
For a construction project material may be available from multiple sites but finding the optimal one keeping many costs and constraints is the key task which has been accomplished in this project.
Predicting maximum time for the construction material ships to transit during appropriate tide levels.
Minimising the cost
Predicting maximum time for the construction material ships to transit during appropriate tide levels.
Minimising the cost
Tools Used
Python
Tableau
Airlines Delay and Cancellation Patterns
Description
Methodology
Benefits from the Project
Tools Used
Description
Flight delay and cancellation data for the past few years in the US is given. Objective is to find trends and patterns with focus on various airlines, days, seasons etc.
Methodology
Data cleaning and pre-processing was the first step.
Using EDA various graphs were plotted to find various trends such as correlations, heat maps etc.
Using EDA various graphs were plotted to find various trends such as correlations, heat maps etc.
Benefits from the Project
FWe can find out which airlines should be booked on what days and in what seasons so as to reduce the probability of delays and cancellations.
Find the overall general attributes to be improved from airline’s point of view.
Find the overall general attributes to be improved from airline’s point of view.
Tools Used
Python
R
Heart Disease Prediction
Description
Methodology
Benefits from the Project
Tools Used
Description
The Department of Cardiology at a very renowned Indian hospital plans to predict severity of heart disease among patients so as to reduce the death rate.
Methodology
Design an UI to predict accuracy of severity level.
Applying statistical models to predict the accuracy of severity level.
Applying statistical models to predict the accuracy of severity level.
Benefits from the Project
It can help doctors to attend patients having high severity of heart disease. This resulted in reduction in causality rate by 10%.
Tools Used
Python
Tableau
Descriptive Statistical Techniques
Tableau
Descriptive Statistical Techniques
Analysis of Nifty-50 shares on NSE
Description
Methodology
Benefits from the Project
Tools Used
Description
This project will help the investors to decide when to buy a particular share and will predict the possibility of 10% increase in share price in next 6 months based on various ratio analysis.
Methodology
Extracted the last 5 years data of al 50 shares.
Did 6 major Ratio Analysis with peer companies.
Did 6 major Ratio Analysis with peer companies.
Benefits from the Project
Helps investors to make right decisions through technical analysis.
Tools Used
Python
Sentimental Analysis of YouTube comments
Description
Methodology
Helps investors to make right decisions through technical analysis
Tools Used
Description
Understand the public response to a particular video in YouTube by performing sentiment analysis of the comments using Big Data tools.
Methodology
Extract the comments of the YouTube video.
Analyse the polarity of each comment by lexicographic method.
Analyse the polarity of each comment by lexicographic method.
Helps investors to make right decisions through technical analysis
Determine the overall positive, neutral and negative response of the viewers for a video using big data tools.
Tools Used
Python
Flume
Pig
Hive
Flume
Pig
Hive
SIG Members
