Hands-On Project 2: Classification Algorithms in Google Colab
Objective:
In this assignment, you will work with a classification problem using machine learning techniques, specifically focusing on classification algorithms such as Logistic Regression, K-Nearest Neighbors (KNN), and Support Vector Machines (SVM). You will apply these algorithms to a dataset, evaluate their performance, and analyze the results. This will help you understand the key concepts in Module 3, such as data mining methods, algorithms, and model evaluation.
Learning Outcomes:
- Understand and apply classification algorithms to real-world datasets.
- Evaluate the performance of different classification models.
- Understand the application of machine learning in predictive analytics and its integration in business contexts.
- Gain experience in model evaluation metrics such as accuracy, precision, and recall.
Instructions:
Access .
- Open Google Colab and run the code provided directly from the .
- Open the notebook for Chapter 3: Classification and run the code as provided in the Colab interface.
- Follow the instructions in the notebook and execute each step of the classification process.
Follow the Steps in the Notebook:
- Step 1: Load and explore a classification dataset (e.g., Iris dataset or MNIST dataset).
- Step 2: Preprocess the data (e.g., handling missing values, scaling the data).
- Step 3: Train classification models (Logistic Regression, KNN, SVM).
- Step 4: Evaluate model performance using appropriate metrics such as accuracy, precision, recall, and F1-score.
Ensure you run all the code and take notes on your observations, as this will form the basis of your report.
Write a Report:
After executing the code and analyzing the results, write a 1-2 page report with the following structure:
- Introduction (100 words):
- Briefly describe the dataset you used and the classification task. Summarize the algorithms you will apply.
- Process (300 words):
- Explain the steps taken during the assignment, including data exploration, preprocessing, model training, and model evaluation. Include which classification models you applied and the reasoning behind your choices.
- Findings (200 words):
- Discuss the performance of each classification model. Which model performed best? What did the evaluation metrics (accuracy, precision, recall) indicate about the models?
- Conclusion (100-150 words):
- Conclude by reflecting on the application of classification in real-world scenarios. What key insights did you gain from applying machine learning algorithms to classification tasks?
Deliverables:
- Report (2-3 pages, 1000-1200 words)- ensure you have a cover page, no specific format is required, you are free to use APA or any professional format.
Requirements: i need video recording how excuting this one
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