G22:
• Gustavo Costa - up202004187
• João Oliveira - up202004407
• Ricardo Cavalheiro - up202005103
This project focuses on using supervised learning techniques to analyze and predict which teams will qualify for the playoofs in the Women's National Basketball Association (WNBA). The work is divided into two notebooks: one for Exploratory Data Analysis (EDA) and the other for the Pipeline, where data preparation, pre-processing and machine learning models are implemented.
Purpose: Explore and understand the WNBA dataset, uncover patterns, and gain insights into the key features that influence player performance.
Data Loading: Importing the dataset and understanding its structure.
Data Exploration: Statistical analysis, visualizations, and summary statistics to grasp the distribution and characteristics of the data.
Feature Analysis: Identify relevant features and their potential impact on player performance.
Outlier Detection: Identify outliers in the dataset.
Purpose: Prepare the data for machine learning models, perform pre-processing tasks, and implement supervised learning algorithms for performance prediction.
Data Preparation & Pre-Processing: Handle missing values, address outliers and ensure data consistency.
Feature Engineering: Create new features or transform existing ones to enhance model performance.
Feature Selection: Choosing a subset of relevant features from the dataset that significantly contribute to predicting the teams' performance. (Removing redundant ones)
Model Selection: Choose suitable machine learning models based on the nature of the prediction task.
Model Training: Train the selected models using the training dataset.
Model Evaluation: Evaluate the model's performance using the testing dataset and appropriate metrics (Accuracy, AUC, Precision, Recall & Time).
Hyperparameter Tuning: Fine-tune model parameters for optimal results.
| Project | |
|---|---|
| Grade | 16.00 |