Data Science Trainee
During my internship, I had the opportunity to independently design and implement an end-to-end data science project. From data collection and cleaning to building and deploying a machine learning model, I handled the entire workflow on my own. I used Python and Pandas for data manipulation and analysis and applied machine learning algorithms using Scikit-learn to build predictive models.To present my findings and make the model accessible, I developed a web application using Flask for the backend and integrated HTML and CSS to create an intuitive, user-friendly interface. The application allowed users to interact with the model in real-time, inputting data and receiving predictions based on the trained model.This hands-on project gave me valuable experience in both the technical aspects of data science—such as model development, feature engineering, and evaluation—and in building full-stack applications for deploying data-driven solutions. It also helped me hone my problem-solving skills and ability to work independently, as I was responsible for all aspects of the project, from start to finish.Programming Languages: Python, HTML, CSSData Science & Machine Learning: Pandas, NumPy, Scikit-learn, Matplotlib, SeabornWeb Development: Flask, HTML, CSSDatabases: MongoDB, SQL (basic queries for data extraction)Data Preprocessing: Data cleaning, feature engineering, missing value handlingModeling: Classification, regression, model evaluation (accuracy, precision, recall, etc.)Version Control: Git, GitHubDeployment: Model deployment using Flask