Module Overview: This module provides an in-depth introduction to the data mining process and its applications in the fields of business and management. Students will learn a range of techniques and tools for collecting, accessing, and analysing data. Special attention will be given to text mining and web analytics. Additionally, the module explores the practical use of data mining models in real-world scenarios. Assignment Description: For this assignment, you will work with a comprehensive dataset comprising real data collected from road traffic accidents in the UK. This dataset includes detailed information about personal injury road collisions in the Surrey area during the year 2022. To assist you in your analysis, a data dictionary file, "RoadAccident-2022-Guide.xlsx" is provided, offering in-depth definitions for all fields. Task 1 – Data Exploration and Cleaning [20 marks] The objective of this task is to enhance your skills in data exploration, visualization, summary statistics generation, and data cleaning. You will: • Load the dataset “RoadAccident-2022-Surrey.csv” and conduct an exploratory data analysis of the dataset to gain insights into its structure, content, and quality. • Generate summary statistics for key variables to understand the data's central tendencies and dispersion. • Visualize the data using appropriate plots and charts to identify patterns, outliers, and potential relationships between variables. • Identify any data quality issues, including missing values, incorrect data, and outliers. • Develop a data cleaning strategy to address the identified issues. • Execute the data cleaning process, which may involve imputing missing values and addressing outliers. Write a comprehensive report for this task, including clear explanations of the steps taken. Task 2 – Predicting Accident Severity [30 marks] In this task, you will apply machine learning techniques to predict accident severity using the dataset. You should: • Develop a scenario and select appropriate variables for predicting accident severity. • Explore the use of at least two predictive models (e.g., neural networks, decision trees, logistic regression, etc.). • Provide a comparative analysis of the performance of these models, discussing their strengths and weaknesses. • Interpret the results obtained from both models and draw insights from their outputs. • Analyse the importance of different features in predicting accident severity for each model. • Summarize your findings and provide a conclusion regarding the effectiveness of each model. • Offer recommendations or insights for improving road safety based on your analysis. Write a comprehensive report for this task, including clear explanations of the modelling process and results. Task 3 – Text Analysis of Tweets [20 marks] For this task, you will work with a dataset containing text data collected from tweets related to road traffic accidents in the Surrey area. Your tasks include: • Loading and exploring the dataset to familiarize yourself with the data. • Performing text preprocessing tasks such as removing special characters, punctuation, tokenization, and handling start and stop words. • Conducting an exploratory analysis of the text data, which may involve calculating word frequency and visualizing word clouds. • Performing sentiment analysis on the tweets to determine overall sentiment (positive, negative, neutral) and providing visualizations or summary statistics to illustrate sentiment distribution. • Summarizing key insights and findings from your text analysis. Write a comprehensive report for this task, including clear explanations of the text analysis process and results. Task 4 – Decision-Maker's Summary and Recommendations [20 marks] Based on the results from the previous tasks, you will write a concise summary intended for decision-makers. This report should provide an explanation of the dataset, the insights gained, and offer recommendations or suggestions related to road traffic safety or public awareness. Ensure that the report is presented professionally, includes clear explanations, and incorporates visualizations to support your recommendations. Avoid technical jargon. General Assessment Criteria [10 marks] The overall layout, storytelling, professionalism, and Harvard Referencing will be assessed. Make sure your assignment adheres to appropriate formatting and citation standards. | 
