I am excited to apply for the Time Series Forecasting Model Development project, bringing my expertise in machine learning, AI, and data analysis to build a robust forecasting solution. With a strong background in Python, TensorFlow, PyTorch, and statistical modeling, I have experience in time series analysis and have worked with models such as ARIMA, Prophet, LSTMs, and XGBoost for predictive analytics. My past projects, including an AI-powered Assistive System for Blind People and a Face Recognition-Based Attendance System, showcase my ability to handle complex datasets, optimize algorithms, and deliver scalable AI-driven solutions. I will begin by cleaning and preprocessing historical data, conducting an exploratory analysis to identify trends and seasonality, and testing multiple forecasting models to determine the most accurate approach. Using error metrics like RMSE and MAPE, I will validate the model’s performance and provide insightful visualizations with confidence intervals. The final deliverables will include a clean dataset, fully documented model code, and a comprehensive report detailing methodology, results, and deployment recommendations. I am eager to contribute my expertise to this project and ensure an accurate and actionable forecasting system that supports business decision-making.