Project: Predicting Basketball Hall of Fame Induction Using Player Statistics
In this project, I aim to predict whether a basketball player would be inducted into the Hall of Fame based on their career statistics. By leveraging machine learning techniques, we can identify key performance indicators and develop a predictive model. This case study will walk through the entire process, from data collection and cleaning to model development and evaluation, showcasing my quantitative skills in data analysis and machine learning.
Data Collection and Cleaning:
Steps:
Loading Data:
Imported multiple datasets: Player Career Info, Player Per Game, Advanced Statistics, and Player Totals.
Used libraries:
dplyr,data.table,tidyverse.
Cleaning Data:
Converted character variables to factors and ensured consistent data formats.
Merged datasets to create a comprehensive dataset of player statistics.
Handled missing values and removed unnecessary columns.
Exploratory Data Analysis (EDA):
Steps:
Exploring Data:
Examined data structure and summary statistics.
Identified trends and patterns in the data.
Key Insights:
Post-1979 data (modern era) is more reliable due to the introduction of the three-point line.
Data Preprocessing:
Steps:
Handling Missing Data:
Dropped columns with excessive missing values.
Filtered data to include only post-1979 statistics.
Feature Engineering:
Applied one-hot encoding to categorical variables.
Scaled and centered numerical features.
Model Development:
Neural Network Model:
Architecture:
Input layer, hidden layer with ReLU activation, and output layer with sigmoid activation.
Training:
Used binary cross-entropy loss and Adam optimizer.
Trained the model with 70% of the data, using 30% for validation.
Naive Bayes Model:
Implementation:
Used
caretpackage for model training with cross-validation.Evaluated model using custom evaluation metrics.
Model Evaluation:
Metrics:
Accuracy, AUC, precision, recall, and F1-score.
Visualized feature importance using variable importance plots and partial dependence plots.
Applications in UX Research:
User Segmentation: Similar techniques can be applied to segment users based on behavior and preferences.
Predictive Modeling: Predict user engagement or satisfaction based on interaction data.
Feature Importance Analysis: Identify key features that drive user engagement and improve product design.
This case study illustrates the potential for integrating quantitative analysis and machine learning into UX research, enhancing our ability to make data-driven decisions.