Vaishnavi Nailadi

Project #1: Aerospace/PMT Revenue and Margin

Tools: Tableau,Snowflake,Excel

Client: Honeywell Inc

Overview: Collaborated with business users and subject-matter experts to establish technical vision and analysis for business usability and performance requirements. Developed reports and other tools to deliver information enabling business users and leaders to make informed decisions.

Responsibilities:

• Understand Requirement, Analyzing Systems and Source Database

• Designed, developed, and implemented Tableau Business Intelligence reports.

• Create basic calculations including string manipulation, basic arithmetic calculations, custom aggregations and date math, logic statements and quick table calculations.

• Representing data using the Pie Charts, Bar Charts, Tree Maps.

• Use groups, bins, hierarchies, sorts, sets and filters to create focused and effective visualizations.

• Build advanced chart types and visualizations such as Dynamic Colum names change when change in filter. Also, Dynamic value changes from thousand to million, billion etc.

• Validate the filters, formats, cascading filters, Data blending, and make visualizations perform as well as possible by using the extracts and using connection methods correctly.

Project #2: CD2 Decomm Analytics

Client: Honeywell Inc.

Tools: PowerBi,Snowflake,MS Office

Overview: Honeywell International Inc. is an American multinational conglomerate company that produces commercial and consumer products, engineering services and aerospace systems. The company operates four business units, known as Strategic Business Units – Honeywell Aerospace, Home and Building Technologies (HBT), Safety and Productivity Solutions (SPS), and Honeywell Performance Materials and Technologies.

Responsibilities:

• Used Power BI to collaborate with different users and stakeholders to work on live data sources.

• Was responsible for performing data analytics and generating insights as per business requirements.

• Reduced ETL job failures by 90% through code optimizations and error handling improvements.

• Gathered requirements to do analysis,design,development,and testing.

•Perform troubleshooting analysis and resolution of critical issues.

Client: Best Buy (US)

Tools: Python, R, SQL

Project: Mining Online Reviews

Responsibilities:

•Understanding business requirement and develop data driven solution to address the business problem.

•Business Requirement was to enable consumers to quickly extract the key topics covered by the reviews without having to go through all of them and help the sellers/retailers get consumer feedback in the form of topics (extracted from the consumer reviews)

• Data preprocessing and cleaning by removing the punctuations, stop words and normalize the reviews as much as possible.

• Created an object for LDA model and trained it on Document-Term matrix

•Generated Business Requirement Use case hypothesis to find the sales of each product at a particular store.

• Help Business to find out the properties of any product or store, which play a key role in increasing the overall sales.·

•Exploratory data analysis to identify essential factors for sales.

•Built Random Forrest to identify the most important variable for predicting the target variable.

•Feature engineering played a crucial role in predictive modeling for the overall sales report.

Model: Random Forrest Project: Forecast Store Sales Report. Business Requirement use case was to provide the forecast report of the sales using store, promotion, and competitor data to the Business.

• Created a robust prediction model, which has helped store managers to stay focused on what’s most important to them: their customers and their teams.

• Analysis per store type and correlation analysis of stores activity.· Performed extensive Time Series Analysis (seasonal decomposition, trends, autocorrelation).

Client: Specsavers (UK)

Tools: JAVA,SQL

Responsibilities:

• Deliver critical problem and changes fixes with short time lines requested by Business as urgent changes to the application.

• Regular interaction with Business Analyst on getting requirements for business process changes in application and validations of problem fixes.

• Prepare Low level design and High level design documentation for fixes/changes as part of release preparation.

• Reviewed code to check for errors and sub-optimal coding practices.

• Mentored and assisted software team with Java-specific technical issues.

MISCELLANEOUS PROJECTS

Project: Twitter Sentiment Analysis

The task was to classify racist or sexist tweets from other tweets. •Tweets Preprocessing and Cleaning· Extracting Features from Cleaned Tweets

• Story Generation and Visualization from Tweets

• Model Building: Sentiment Analysis: We have build the predictive models on the dataset using the two feature set — Bag-of-Words and TF-IDF. We have used logistic regression to build the models

• The evaluation metric from this practice problem is F1-Score.

Project: Bike Sharing Demand using R (Kaggle Project):The participants were asked to forecast bike rental demand of Bike sharing program in Washington, D.C based on historical usage patterns in relation with weather, time and other data.

• Data Cleaning: Worked on missing values, Outliers, Feature Engineering, Encoding Categorical Variable.

• Exploratory Data Analysis: Univariate and Bivariate Analysis.

• Model & Prediction: Used decision tree, Random Forest for model creation then doing prediction on test data set and found that random forest is performing the best.

Project: Basic Recommendation Engine :A recommendation engine filters the data using different algorithms and recommends the most relevant items to users. It first captures the past behavior of a customer and based on that, recommends products which the users might be likely to buy

• Data collection

• Data storage

• Filtering the data

1. Content based filtering

2. Collaborative filtering