Data Science Lab Project: Performance of Predictive Models (Supervised Learning Analysis) - The Interpretability and Explainability Across Multi-Domains: Health, Environment, and Business (Classification Problems)

Objective:

Evaluated the performance of various supervised learning algorithms in predicting binary target variables and addressed overfitting issues across three diverse datasets from Kaggle.

Domains & Datasets:

• Health: Cardiovascular Dataset

• Environment: Weather in Australia

• Business: Hotel Reservations

Key Questions:

• Impact of sophisticated modeling methods

• Model transferability across datasets

• Effects of standardization techniques

• Handling imbalanced datasets

• Identifying optimal hyperparameters

• Mitigating overfitting issues

Methodology:

• Preprocessing: Data cleaning, handling missing values, and feature selection.

• Exploratory Data Analysis (EDA): Identified key patterns and insights.

• Modeling: Evaluated various supervised learning algorithms.

• Performance Metrics: Accuracy, precision, recall, F1-score, ROC AUC.

Techniques & Tools:

• Python (Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn)

• R (tidyverse, caret)

• Machine Learning Models: Random Forest, Gradient Boosting, Logistic Regression, Decision Tree, KNN

• Additional Techniques: Feature Engineering, Hyperparameter Tuning, Cross-Validation

Results:

Business Field - Hotel Reservations

• Analyzed booking status data to predict cancellations.

• Key Techniques: Random Forest Classifier, Learning curves, Overfitting analysis.

• Achievements: Achieved high performance with a balanced model (max depth 10-15), ensuring robust generalization.

Environmental Domain - Weather in Australia

• Processed weather data to predict rainfall.

• Key Techniques: Missing value handling, Feature selection, Random Forest Classifier.

• Achievements: Optimal performance with 100 estimators and max depth of 6, with diminishing returns beyond this setup.

Health Sector - Cardiovascular Dataset

• Cleaned and analyzed health data to predict cardiovascular diseases.

• Key Techniques: Outlier removal, Class imbalance handling, Gradient Boosting.

• Achievements: Balanced class distribution via undersampling, best performance using Gradient Boosting with resampling. The optimal max depth appears to be around 12.

Impact:

• Algorithm Performance: Provided insights into the behavior of different algorithms across various domains.

• Generalization: Ensured models generalized well to unseen data, mitigating overfitting.

• Trade-offs: Highlighted the trade-offs involved in model complexity and performance.

Key Achievements:

• Developed robust frameworks for handling missing values and imbalanced datasets.

• Demonstrated model stability and effective generalization through cross-validation and learning curve analysis.

• Presented findings through performance metrics tables and visualizations, guiding future model selection.

• Compiled comprehensive reports and delivered presentations to stakeholders, effectively communicating complex technical concepts and findings.

Skills Demonstrated:

• Data Collection & Preprocessing: Mastered data cleaning, feature selection, and handling missing values.

• Advanced Analytics: Applied and evaluated various machine learning models, ensuring optimal performance and generalization.

• Technical Proficiency: Utilized Python and R for in-depth data analysis and model development.

• Communication: Effectively communicated complex technical concepts and findings to non-technical audiences through reports and presentations.

Key Achievements:

• High-Impact Insights: Identified and validated the best imputation technique and model combination for each dataset.

• Model Stability: Ensured model stability and effective generalization through cross-validation and learning curve analysis.

• Innovative Approaches: Employed advanced imputation and machine learning techniques to solve real-world data challenges.