Final Project

Final Project

Applying Machine Learning in a Domain of Your Choice

Overview: 

In this project, you will explore the use of machine learning in a domain of your choice. You will identify a problem that can be solved using machine learning, gather data related to the problem, and apply appropriate machine learning techniques to build a model that can predict or classify the outcomes.

Steps:

1. Choose a domain of your interest: Identify a domain that interests you and where machine learning can be applied. Some examples could be healthcare, finance, marketing, sports, or social media.
2. Identify a problem: Within the domain, identify a problem that can be solved using machine learning. The problem could be related to predicting a certain outcome, identifying patterns or trends, or classifying data.
3. Gather data: Gather data related to the problem from reliable sources. The data should be appropriate for the problem and have enough examples to train a machine learning model.
4. Preprocess data: Clean and preprocess the data by removing duplicates, filling missing values, and removing outliers if necessary.
5. Feature engineering: Extract meaningful features from the data that can be used by the machine learning model. You may also consider feature scaling or normalization.
6. Train the model: Select an appropriate machine learning algorithm and train the model using the preprocessed and engineered data. Evaluate the performance of the model using appropriate metrics.
7. Fine-tune the model: Fine-tune the model by adjusting hyperparameters or trying different algorithms to improve its performance.
8. Interpret results: Interpret the results and provide insights based on the predictions or classifications made by the model.

Deliverables:

1. A report detailing the problem, data collection process, data preprocessing, feature engineering, machine learning model selection, and performance evaluation.
2. A Jupyter notebook or code file containing the code for the data preprocessing, feature engineering, machine learning model training, and performance evaluation.
3. A presentation summarizing the findings and insights.

Grading Criteria:

1. Problem selection: The chosen problem should be relevant to the chosen domain and should have a clear problem statement.
2. Data collection: The collected data should be appropriate for the problem and should have enough examples to train a machine learning model.
3. Data preprocessing: The data should be cleaned, preprocessed, and feature engineered appropriately.
4. Machine learning model selection: The appropriate machine learning algorithm should be selected for the problem.
5. Performance evaluation: The performance of the model should be evaluated using appropriate metrics.
6. Results interpretation: The results should be interpreted, and insights should be provided based on the predictions or classifications made by the model.
7. Code quality: The code should be well-documented and should follow best practices.
8. Presentation: The presentation should effectively summarize the findings and insights of the project.