EXPERIENCE

PROJECTS

PUBLICATIONS

SKILLS & TOOLS

Programming: Python (Base, Pandas, Numpy, Matplotlib, Seaborn, Scikit-Learn, TensorFlow, Keras), SQL, R, JMP Machine Learning: Linear Regression, Logistic Regression, Decision Trees, Random Forest, KNN, k-means, PCA, Association Rule Learning, Causal Impact Analysis, Support Vector Machine, Anomaly Detection, Naive Bayes, Regularization (Ridge and Lasso Regression)

Deep Learning: ANN, CNN (Transfer Learning, Hyperparameters Tuning) Visualization: PowerBI, Tableau

Languages: English, Hindi, Punjabi

Other: Statistics, Github, Spark, FASTAPI, AWS, Google Cloud Platform, MS Excel, MS PowerPoint Collaborative work with clients, Statisticians to plan, and execute clinical research besides evaluating and reporting studies starting from research design and data analysis.

Utilized different statistical and machine learning models to enhance the predictive and/or inferential accuracy based on client requirements.

Facilitated and lead an interactive brainstorming day for students studying Data Analytics at Louisiana State University. Graduate Statistical Consultant (Data Analyst), Louisiana State University (2019 - Present) Used SQL & PowerBI to automate the extraction of phenotypic data, and create a dynamic weekly report that helped graduate advisors to understand and investigate trends over time, and diagnose potential issues Graduate Research Assistant, Louisiana State University (2018 - 2022) Used k-means clustering on grocery transaction data to split out customers into distinct "shopper types" that could be used to better understand customers over time, and to more accurately target customers Deployed the model to production (AWS) using FLASK API

"You Are What You Eat" Customer Segmentation (Jan - April, 2022) EDUCATION

adx6rl@r.postjobfree.com

www.linkedin.com/in/lovepreet-singh-lsu

LOVEPREET SINGH

+1-217-***-**** github.com/lovepreetkhandal

Baton Rouge, LA, 70820

Used Causal Impact Analysis to understand and analyze the sales uplift of customers that joined the new "Delivery Campaign" Saw 41.1 % uplift in sales for customers that joined the campaign. Quantifying Sales Uplift With Causal Impact Analysis (Sept - Oct, 2022) Compared the predictive accuracy of Linear Regression, Decision Tree and Random Forest models. Deployed the model to Heroku using Docker and Github action to provide an application to the clint for future use. Predicting Customer Loyalty using Regression Models (Feb, 2021) Compressed a wide dataset of historical listening time allocated to 100 artists to 24 features using PCA to make the data more manageable for classification.

Used Random Forest Classifier to predict the sales of Ed Sheeran's new album with 93% of accuracy. Compressing Feature Space for Classification using Principal Component Analysis (July, 2021) Build an image search engine using VGG16 model to help customers to find the products on the website that they are after. An Image Search Engine using Transfer Learning (Ongoing) Used Association Rule Learning (Apriori algorithm) to build a recommender system for improving the sales of alcohol products. Understanding Alcohol Product Relationships (Ongoing) Build a classification model for accurate prediction of consumer signup for delivery offer. Compared the Accuracy of Logistic Regression, Decision Tree, Random Forest and KNN models and deployed the best model to production (Heroku).

Enhancing Target Accuracy using Classification Models (May - Sept, 2021) Build a Convolutional Neural Network model for fruit classification to assist a robotic sort arm that could pick up, and move the particular fruit into a designated bin using a camera to see and classify the product based on built model. Deployed the model to Google Cloud Platform using FastAPI. Fruit Classification using Deep Learning (April - Oct, 2021) Extracted data using the SQL query, transformed, and loaded in Power BI for performance assessment Built a dashboard and report to identify the potential and challenging market segments by analyzing 4-years historical data Sales Insights Analysis using SQL and PowerBI (June, 2022) https://lovepreetkhandal.github.io/pf.github.io/

Dual Degree: MS Statistics / Ph.D. Quantitative Genetics Louisiana State University, Baton Rouge, GPA: 3.8 ( 2018 -2023) Relevant Coursework: Machine Learning, Regression Analysis, Multivariate Analysis, Experimental Design, Categorical Analysis, Non-Parametric Statistics, Probability and Statistics, Linear Algebra, Calculus, Statistical Techniques B.S Agriculture -Quantitative Genetics

Punjab Agricultural University, India, GPA: 4.0 ( 2014 -2018) AFFILIATIONS

CERTIFICATIONS

Advanced Learning Algorithms (Coursera), Data Science Professional (DSI), Power BI Essential (LinkedIn Learning), Supervised Machine Learning (Coursera), SQL for Data Science (Coursera), Python for Data Science, AI & Development (Coursera) Vice-president of Indian Student Association, LSU (2020), Institute of Mathematical Statistics (Active member), Rice Technical Working Group (Active member)

Molecular breeding for improving salinity tolerance in rice: Recent Progresses and Prospects Novel Breeding approaches to develop climate resilient Rice