Data Preparation Load and clean a CSV file containing daily stock trading data. Handle missing values caused by non-trading days. Ensure consistency and proper formatting for modeling. Feature Engineering Create meaningful numerical eatures from price and volume data (e.g., returns, changes, technical indicators). Document and justify feature transformations. Supervised Learning Build and train classification models to predict next-day stock price movement (up/down/stable). Evaluate performance using appropriate classification metrics. Unsupervised Learning Apply clustering techniques to identify patterns, stock groupings, or market regimes without labels. Analyze and explain discovered clusters. Model Interpretation Analyze feature importance and explain which factors influence predictions. Provide clear interpretation of results suitable for financial analysis. Evaluation & Reporting Compare different modeling approaches and discuss strengths and limitations. Deliver well-documented code (Jupyter notebooks) and a short technical report summarizing methods and findings. Deliverables: 1. Report: -Table of Contents, List of Figures, List of Tables, Introduction, Overview of the project and its purpose, Brief summary of the dataset and tasks to be carried out in the project. -Task Sections: Supervised Learning, Unsupervised Learning, Feature Analysis, Critical Evaluation and Reflection as per the details given in the project brief. For Supervised and unsupervised tasks, include Data Preparation, Feature Development, Model Design, Evaluation, and Validation as per the details given in the project brief. 2. Codebase: Modular and well-documented implementation (e.g., .ipynb notebooks). Covers preprocessing, training, evaluation, and visualization of the given dataset. Deadline: We can agree upon that together. For more details about the project, reach out to me.