Within the context of this internship, my role as a Data Science Intern involved developing predictive models for estimation of student's academic performance using Supervised Machine Learning predictive models.Key Responsibilities:Data Collection: Collect and preprocess pertinent data, including student study hours and academic scores.Data Exploration: Analyze the dataset to uncover patterns and relationships between study hours and academic performance.Feature Engineering: Craft meaningful features to augment predictive model accuracy, encompassing study habits, subject preferences, and study environment.Model Selection: Choose suitable supervised machine learning algorithms (e.g., Linear Regression, Decision Trees, Support Vector Machines) for constructing predictive models.Model Training: Train and fine-tune selected models with cross-validation and hyperparameter optimization techniques.Visualization: Create insightful visualizations (e.g., scatter plots, regression lines) to effectively convey the study hours-academic performance relationship.Deployment: Develop a user-friendly interface for educators and students to input study hours and receive percentage predictions.Documentation: Maintain thorough documentation of data preprocessing, model development, and evaluation procedures.