Hi, I'm Manohar
Hi, I'm Manohar Mata Master’s in Applied Analytics @ Saint Louis University | Former Machine Learning Intern @ SkillVertex
Data Analyst in training, Machine Learning enthusiast, and passionate about building solutions that bridge AI and real-world impact.
Contact MeAbout Me
My Introduction
I am an M.S. candidate in Applied Analytics at Saint Louis University (GPA: 4.00), with practical experience in Machine Learning and Data Analysis. I specialize in leveraging AI and Machine Learning techniques, including predictive modeling, time series analysis, and neural networks, to solve complex, real-world problems. My expertise also extends to data visualization, ETL pipelines, and statistical analysis, enabling me to derive actionable insights from diverse datasets.
Experience
Presentations
Projects
Skills
My Technical LevelDevelopment
All About the CorePython
90%R
70%C++
40%MS Excel
70%Photoshop
40%Frameworks
Everyone Needs SupportNumPy
80%pandas
90%matplotlib
70%scikit-learn
70%Pytorch
60%Basic OpenCV
60%HuggingFace
60%NLTK
60%seaborn
70%Flask
40%Streamlit
60%ML, DL, GenAI
Theory, theory!Linear and Logistic Regression
95%Decision Trees
90%Ensemble Models
90%Clustering
65%Convolutional Neural Networks
80%Graph Neural Networks
40%Recommender Systems
75%Natural Language Processing
65%Exploratory Data Analysis
90%Large Language Models
55%Cloud and Engineering
Fly Fast & High!AWS ECR
45%AWS EC2
65%Docker
60%CI/CD Pipeline
40%Git
40%Databases and Viz
Wow! FactorMySQL
80%PostgreSQL
65%Tableau
50%Power BI
70%Qualification
My Personal JourneyResearch Assistant
Under Professor Jie Hou
Optimized GNN and LSTM models, boosting predictive accuracy.
Machine Learning Intern
SkillVertex
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Optimized algorithms for Diabetics Prediciton.
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Performed advanced data analysis for customer segmentation.
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Deployed NLP-based sentiment analysis models.
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Built robust ML pipelines to improve data quality.
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Directed a team analyzing billing and customer datasets.
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Developed ML models to optimize power usage strategies.
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Implemented data-driven energy-saving strategies.
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Enhanced scalability of energy management systems with software solutions.
Masters of Science in Applied Analytics
Saint Louis University, USAB.Tech in Computer Science
Dhanekula Institute of Engineer & Technology, IndiaSTATE>
Sri Chaitanya, India
2018 - 2020
Projects
What I Worked and WorkingOnPredicting Patient Outcomes with Graph Representation Learning
April 2025 - PresentRecent ICU outcome prediction models focus mainly on physiological time series, overlooking sparse data like diagnoses and medications. We propose LSTM-GNN, a hybrid model that combines LSTMs for temporal feature extraction with Graph Neural Networks to capture relationships between similar patients. Applied to the eICU database, LSTM-GNN outperforms LSTM-only models in predicting length of stay, highlighting the value of incorporating patient similarity via graph-based learning in EHR analysis.
Calmpanion: AI-Powered Mental Health Support Chatbot
Feb 2025 - April 2025
Built an AI-powered mental health chatbot with text input using Streamlit.
Integrated Llama 3 & Mistral AI models for flexible, multimodal conversation support.
Enabled customizable prompts and system instructions for dynamic user experiences.
Designed a responsive UI with real-time interaction and speech-to-text features.
Implemented RAG support with ChromaDB to provide contextual well-being resources.
Adventure Works Sales Analysis Dashboard
April 2025 - April 2025Developed an interactive Power BI dashboard for Adventure Works to track KPIs (sales, revenue, profit), compare regional performance, analyze product trends, and identify key customers. Used raw CSV files, built a relational data model, and implemented DAX calculations. The dashboard provides actionable insights into sales performance, product trends, and customer segments.
CNN-Based Deep Learning Approach for Early Diagnosis of Chronic Kidney Stones from MRI Images
Nov 2023 - March 2024This project utilizes a Convolutional Neural Network (CNN)-based deep learning approach to detect chronic kidney stones from MRI images at an early stage. By leveraging advanced image processing and pattern recognition capabilities of CNNs, the model analyzes MRI scans to identify stone formations with high accuracy, aiding in timely diagnosis and treatment planning.
Enhancing Network Security Through Phishing Website Detection Using Machine Learning(MLops)
Nov 2024 - Dec 2024This project aims to develop an end-to-end pipeline for detecting phishing websites by leveraging machine learning techniques. The pipeline includes multiple modular components to ensure efficient data handling, model training, and deployment.
Blood Donation Prediction with MLOps Integration
Dec 2024 - Jan2025Built an MLOps pipeline with steps including data ingestion, validation, transformation, model training, and evaluation. Achieved 80% accuracy using Logistic Regression and deployed the model using Docker and AWS ECR. Leveraged Hub Actions for CI/CD pipelines, ensuring seamless version control.