Varshitha Gudimalla

Motivated data science enthusiast with a solid educational foundation in mathematics and computer science, currently pursuing a Master's in Data Science at the University at Albany, SUNY. Proficient in Python, SQL, and skilled in data analysis, manipulation, and visualization tools like Matplotlib and Seaborn. Experienced in leading collaborative teams and applying expertise to real-world projects, including skin disease prediction, movie recommendation systems, weather prediction, and bird species identification. Certified in Python, Numpy, and Deep Learning, with a passion for staying at the forefront of technology. Eager to contribute as a Data Scientist, aiming to leverage strong problem-solving skills and knowledge to drive organizational success. Excited about the endless possibilities in the field of data science and committed to creating substantial value and impact.

• Machine Learning using Python
• Database and SQL for Data Science with Python
• Machine and AI Using Python

Skin Disease Detection

• Designed and implemented a machine learning project focused on the detection of various skin diseases using Convolutional Neural Networks (CNN).
• Developed a robust classifier capable of recognizing skin diseases from input images, achieving accurate predictions across a set of five chosen diseases.
• https://github.com/varshitha-g/Skin-Disease-Prediction

Movie Recommendation System 

• Engineered a machine learning solution for personalized movie recommendations, utilizing a straightforward KNN model.
• Implemented content-based recommendation strategies by merging two distinct datasets and creating a new dataset for model training.
• https://github.com/varshitha-g/Movie-recommendation-System

Weather Prediction

• Utilized the "weatherHistory" dataset to conduct comprehensive Data Cleaning and Data Analysis, extracting meaningful insights and transforming data into categories for enhanced clarity.
• Applied regression techniques to predict weather patterns. Successfully deployed the model locally using the Flask framework.
• https://varshithagudimalla.medium.com/weather-prediction-1-deploying-machine-learning-locally-using-flask-5afbbe5214a5

Boston House Price Prediction

• Executed a comprehensive analysis on the "Boston House Price Prediction" dataset, involving meticulous Data Cleaning and the application of advanced Text Analysis techniques, including NLP (Natural Language Processing) and Sentiment Analysis on text columns.
• Transformed extracted insights into meaningful categories. Implemented various regression techniques, such as Linear Regression, Random Forest Regressor and Decision Tree, to minimize MSE (Mean Square Error).
• https://varshithagudimalla.medium.com/how-is-a-machine-learning-project-done-19ae24f78632