GD53 MOD1 A1 Garrett York

sketchfab

Assignment 1 for Modeling Students - A Hands-on Approach to Mastering Machine Learning Techniques As a student of machine learning and artificial intelligence, you are expected to complete this assignment to demonstrate your understanding of fundamental concepts and apply them to real-world scenarios. This assignment is designed to be completed individually, allowing each student to showcase their problem-solving skills and creativity. **Objective:** The primary objective of this assignment is to develop a predictive model using historical data that can accurately forecast future trends. You will work with a dataset provided by the instructor, which includes various features related to sales, demographics, and market conditions. **Instructions:** 1. Download the provided dataset from the course website. 2. Explore the dataset using statistical methods and visualization tools to gain insights into the data distribution and relationships between variables. 3. Preprocess the data by handling missing values, normalizing features, and splitting it into training and testing sets. 4. Implement a machine learning algorithm of your choice (e.g., linear regression, decision trees, random forests) to build a predictive model. 5. Evaluate the performance of your model using metrics such as mean squared error, R-squared, and accuracy score. 6. Compare the results with other models implemented by your peers to identify areas for improvement. **Deliverables:** 1. A written report detailing the data exploration, preprocessing steps, and model implementation. 2. Code snippets in Python or R that demonstrate the preprocessing and modeling techniques used. 3. A presentation summarizing the key findings, model performance, and recommendations for future improvements. **Grading Criteria:** The assignment will be graded based on the following criteria: 1. Clarity and organization of the written report (30 points) 2. Effectiveness of data preprocessing and modeling techniques (25 points) 3. Accuracy and relevance of the results (20 points) 4. Quality and clarity of the presentation (15 points) 5. Adherence to the instructions and guidelines (10 points) **Deadline:** The assignment is due on [Insert Date and Time]. Late submissions will incur a penalty of 10% per day. By completing this assignment, you will demonstrate your ability to apply machine learning techniques to real-world problems and showcase your problem-solving skills. Good luck!