march_madness_ml

A fun collab project to try and predict march madness results. More for learning ML rather than an accurate algorithm.

Stuff we will need to do (Or at least, this is what ChatGPT scaffolded for me)...

Collect Data:

Collect data on past March Madness tournaments, including team rankings, game scores, and other relevant features such as team statistics, player statistics, etc. There are many sources for this data, including Kaggle, sports data APIs, and web scraping techniques.

Data Preprocessing:

After collecting the data, clean it, and preprocess it by removing any irrelevant or missing data, handling categorical data, normalizing the data, and splitting it into training and testing sets.

Feature Engineering:

Create new features from the existing data that can potentially improve the accuracy of the model. For example, you might want to calculate the average number of points scored per game, or the average number of rebounds per game for each team.

Model Selection:

Select an appropriate machine learning algorithm to train the model. This could be a decision tree, random forest, or neural network model. You can use libraries such as Scikit-learn, TensorFlow or PyTorch to build and train the model.

Model Training:

Train the selected model using the preprocessed data. Use techniques such as cross-validation and hyperparameter tuning to optimize the model's performance.

Model Evaluation:

Evaluate the model's performance using metrics such as accuracy, precision, recall, F1 score, etc. Test the model on the testing set and assess the model's generalization performance.

Predictions:

Use the trained model to predict the outcome of the March Madness bracket for the current year. Provide probabilities for each team to win each game and then simulate the entire tournament to obtain the probability of each team winning the championship.

Model Deployment:

Finally, deploy the model in a user-friendly interface that can take inputs from users, generate predictions, and display the results in a visually appealing format.