Data Scientist | Machine Learning Engineer | Python Developer
An experienced developer with a strong foundation in software engineering. My dedication to excellence, problem-solving mindset, and commitment to continuous learning make me a valuable asset in any technology-focused team.
In this project, I created a centralized hub of models accessible through a Streamlit application. The hub provides convenient access to a variety of models stored in one place, allowing users to interactively utilize them for various purposes. The Streamlit app interface facilitates seamless exploration and utilization of the models, offering a user-friendly experience for accessing and employing machine learning solutions.
Classified vehicles into different types based on silhouettes which may be viewed from many angles. Used PCA in order to reduce dimensionality and SVM for classification.
Analyzed cars dataset and performed exploratory data analysis and then categorized them using K means clustering. Used linear regression on the different clusters and estimated coefficients.
This project used Hypothesis Testing and Visualization to leverage customer's health information like smoking habits, BMI, age, and gender for checking statistical evidence to make valuable decisions of insurance business like charges for health insurance.
This Project involved the application of classification algorithms and ensemble methods to assess Parkinson's Disease (PD) utilizing recorded patient voice data. Multiple models were employed, such as Naive Bayes, Logistic Regression, SVM, Decision Tree, Random Forest, among others. The accuracy of these models was compared to determine the most suitable one for prediction purposes.
Identified potential loan customers for Thera Bank using classification techniques. Compared models built with Logistic Regression and KNN algorithm in order to select the best performing one.
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