Data Analysis and Visualisation Report:

An extensive report with visually appealing graphs and charts that summarise significant findings and patterns from data analysis.

Predictive Models:

Forecasting and trend detection are made possible by the machine learning models that have been developed for predictive analytics.

Documentation for Data Preprocessing:

Comprehensive report detailing the methods used for data cleaning, preprocessing, and feature engineering.

Model Performance Metrics:

F1-score, accuracy, precision, recall, and other performance metrics are used in the evaluation and analysis of machine learning models.

Model Deployment Guidelines:

Suggestions for implementing machine learning models in real-world settings, taking monitoring and scalability into account.

Interactive Dashboards:

Real-time access to data insights and model projections is made possible by dynamic dashboards, which help make well-informed decisions.

Codebase and Algorithms:

Documentation for future reference and reproducibility, together with source code and algorithms used in data analysis, preprocessing, and machine learning model creation.

Post-Project Support:

Ongoing assistance with model upkeep, debugging, and optimisation to guarantee continued functionality and applicability.