Machine Learning Data Science

Niteesh Vanama

Overland Park, Kansas, USA

913-***-**** # *****************@*****.*** ï https://www.linkedin.com/in/niteesh-vanama-6179a11bb/ § https://github.com/VNITEESH

Education

University Of Central Missouri Jun 2023 – May 2025 Master’s in Computer Science Lees-summit, Kansas

Parul University Jun 2019 – Apr 2023

Bachelor’s in Computer Science and Engineering Vadodara, Gujart Semester Exchange School Program

ITMO University, Russia Jun 2022 – August 2022

Exchange school program Saints Petersberg, Russia

• During this semester exchange program I had completed subjects like.

Competitive Program

Python programming with data science

Machine Learning

Experience

Trainty Tech Jan 2021 — Jun 2021

Data Science Intern Hyderabad, Telengana

Developed and deployed machine learning models to solve business challenges, improving operational efficiency by 95 to 97 percent. Conducted exploratory data analysis (EDA) and feature engineering on structured/unstructured datasets to extract actionable insights.

Built and optimized predictive models like Regression Models, Classification Models and Clustering using Python, Scikit-learn, and TensorFlow/PyTorch. Collaborated with cross-functional teams to present findings via dashboards Power BI and Tableau, enhancing data-driven decision-making. Projects

Diabetes Prediction Python, Machine Learing Algorithms, Flask December 2023

· Developed a predictive model to identify the likelihood of diabetes in patients using machine learning techniques. Pre-processedd and analyzed clinical data sets that were taken from Kaggle, by handling missing values, scaling features, and performing exploratory data analysis (EDA). Trained and evaluated multiple algorithms (Logistic Regression, Random Forest, SVM, Decision Tree, K Nearest Neighbor, etc.) to determine the best-performing model. Achieved an accuracy of 98 percent using Decision Tree, optimized via hyperparameter tuning. The model was deployed as a web application using Flask for real-time predictions. SMS Spam Detection Python, NLP, Machine Learning November 2022

· Built a text classification model to detect spam messages using Natural Language Processing (NLP) and machine learning. SMS data pre-processed by removing stop words, performing tokenization, and applying TF-IDF/Word2Vec for feature extraction. Trained and evaluated models like Naive Bayes, Logistic Regression, K Nearest Neighbor, Decision Tree, and Random Forest, achieving 99 percent accuracy. Optimized performance using hyperparameter tuning and deployed the model via Flask/Streamlit for real-time spam detection. Loan Price Prediction Python, Machine Learning March 2023

· Developed a predictive model to assess loan eligibility based on applicant data (income, credit score, loan amount, etc.). Performed exploratory data analysis (EDA) to identify key trends and handled missing values, outliers, and categorical encoding. Engineered relevant features and trained models like Logistic Regression, Decision Trees, XGBoost, Random Forest, K Nearest Neighbor and Naive Bayes achieving 98 percent accuracy from K Nearest Neighbor. Flight Price Prediction Python, Machine Learning October 2022

· Developed a predictive model to estimate dynamic flight prices based on historical data (airline, route, departure time, etc.). Basically this is a regression problem I used some of the regression algorithms like Linear Regression, Mutiple Linear Regression, Polynomial Regression, Random Forest Regressor, XGBoost, ADABoost and Decision Tree Regressor and optimized performance using hyperparameter tuning, achieving 97.5 percent RMSE by Decision Tree Regressor. Deployed the model as a web application using Django for real-time price forecasting. Laboratory Management System MangoDB, HTML,CSS, Flask, Python may 2024

· In this project, i had developed a website for patients to book a slot for specific test.

· By using this project we connected patient and doctor according to their times and their availability. Technical Skills

Languages: Python, Java, C, HTML/CSS, JavaScript, SQL AI ML: Machine Learning, Deep Learning, Image Processing, Natural Language Processing, Computer Vision, Time Series Analysis.

Relevant Libraries: NumPy, Opencv, Scikit- Learn, Matplotlib, Keras, SpaCy, Tensorflow, NLTK, PyTorch, Pandas, Seaborn.

Technologies/Frameworks: AWS, Excel, GitHub, Tableau. Developer Tools: VS Code, Eclipse, Azure Devops.

Certifications

Introduction to Supervised and Unsupervised Machine Learning.

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