Portfolio - Hemalatha Sundaramurthy

Python Project

Harvard University Fee Predictor

In this project we have used Pandas , Scikit Learn , Seaborn libraries to predict the fee using Linear Regression. We have checked whether the data has satisfied the assumptions and have used Grid search to find the best model. We have deployed the model using flask server.

View Project
on GitHUB

In this project, we check the linear regression assumptions and use gridsearch to find the best model.

This is our final screen where we can enter the year and predict the fee for that particular year.

Project Details

• Developed the model which aims to predict Harvard University's fee using linear regression in Python, leveraging historical data Enabled informed HR decisions and resource allocation, fostering workforce stability and performance enhancement.
• Implementation involves data preprocessing, model training, and evaluation to provide accurate fee predictions for prospective students.
• Used Flask server to deploy the model.

Python Project

Channel Analysis using Youtube API

This project uses youtube API to scrape and analyze the data of the youtube channel "Code Basics". Packages Used: Pandas,Seaborn,Requests. In this project we analyse the videos , view counts, comments and how they are performing.

View Project
Engagement

View Project
on GitHUB

Here we can see the best performing videos with the view count.

In this screenshot we can see the not so great performing videos.

Project Details

In this project we have analysed the data to answer the following:

Best performing videos
Worst performing videos
Distribution of video duration
Distribution of views
Top 3 most liked video
Top 3 videos with the highest comments
Wordcloud containing most used words in the title of the video

Python Project

Customer Segmentation

In this project , RFM Customer Segmentation Analysis we are using Kmeans, TSNE, DBSCAN to segment customers in Gerrmany based on Recency, Frequency of shopping and monetary (how much they spent)

View Project
on GitHUB

In the above we can see how we have used the elbow method to find the number of clusters.

We have used TSNE to visualise the clusters and it shows the 2 distinct clusters of customers.

Project Details

RFM Analysis helps in ---

Targeted Marketing: Incorporated RFM segmentation for personalized campaigns, boosting conversion rates and ROI.
Customer Retention: Identify and nurture high-value customers through tailored strategies and loyalty programs.
Resource Optimization: Allocate resources effectively by pinpointing low-value segments, reducing costs, and enhancing profitability.

Excel Project

Identify sales patterns and track key performance indicators (KPIs) using Excel

This will be helpful to determine effective customer discounts, facilitate negotiations with consumers, and identify potential business expansion opportunities in promising countries.

View Project
on GitHUB

Identify sales patterns and track key performance indicators (KPIs) using Excel

Profit and Loss (P&L) reports by _[Fiscal Year]

Profit and Loss (P&L) reports by _[Markets]

Project Details

Create Profit and Loss (P&L) reports by _[Fiscal Year]
Create Profit and Loss (P&L) reports by _[Markets]

PowerBI Project

Headcount Analysis

• Developed a dynamic Power BI dashboard, analyzing employee headcount dynamics, starters, leavers, and turnover rates by employee type and by function. We have also analysed new joinees and resignees in each employee type and by function.

View Project
on GitHUB

- We have analysed the data to find headcount by employee type and headcount by function.

-- In this dashboard we have analysed the turnover rates in depth, by function and employment type.

Project Details

• Enables informed HR decisions and resource allocation, fostering workforce stability and performance enhancement.
• Helps in estimated 50% turnover reduction within a year, with a focus on research team retention and overall performance improvement.

Live Interactive Dashboard

PowerBI Project

Attendance Analytics

Attendance analytics analysing real time data for insights into in-office, work from home and sick leave % in an organisation. We have analysed the data employeewise, monthwise and by week.

View Project
on GitHUB

This shows the in-office, work from home and sick leave % by month.

Analysis of in-office, work from home and sick leave % employee wise by month and by week.

Project Details

This dashboard helps to :
• Use insights from the report to enhance workforce productivity through optimized scheduling.
• Make data-driven decisions for resource efficiency by recognizing remote work trends and optimizing office resources.
• Implement wellness programs based on sick leave patterns to boost employee well-being.

Hello, I am

Hemalatha Sundaramurthy

1

2

3

About

Technical

My

Harvard University Fee Predictor

Channel Analysis using Youtube API

Customer Segmentation

Identify sales patterns and track key performance indicators (KPIs) using Excel

Headcount Analysis

Attendance Analytics

Awards

Contact Me

Email

Github

Linkedin

Call

Hemalatha Sundaramurthy

Download Resume