#### Summary
* **Lending Club 2007-2011** dataset with **42,538** entries and **115** features.
* Tools Used: **NumPy** | **pandas** | **Matplotlib** | **Seaborn** | **Scikit-Learn** | **XGBoost** | **Imbalanced-Learn**
* Constructed a **binary classifier** based on financial attributes of loan applicants to differentiate unprofitable loans from the rest.
* **1**: Good/Profitable Loan | Majority Class
* **0**: Bad/Unprofitable Loan | Minority Class
* Addressed the issue of imbalanced data (6:1 class ratio) by oversampling the minority class using **SMOTE** from **Imbalanced-Learn**.
* Models Trained/Evaluated: **Logistic Regression** | **Random Forest** | **Support Vector Machine** | **eXtreme Gradient Boosting**
* Optimized models by tuning their hyperparameters with **RandomizedSearchCV**.
* Best Model: **eXtreme Gradient Boosting**
* Best Model Performance: **Minority F1** = **0.56** | **Majority F1** = **0.89** | **ROC AUC** = **0.80** | **Accuracy** = **83%**
#### Summary
* Datasets provided by **Machine Hack: Melanoma Tumor Size Prediction**
* Training Set: **9,146** entries, **10** features
* Test Set: **36,584** entries, **10** features
* Tools Used: **NumPy** | **pandas** | **Matplotlib** | **Seaborn** | **Scikit-Learn** | **Keras**
* Constructed a regressor to predict the numerical value of melanoma tumor size based on relevant attributes.
* Models Trained/Evaluated: **Multiple Linear Regression** | **Random Forest** | **Support Vector Machine** | **Multi-Layer Perceptron** | **Keras Regression**
* Optimized models by tuning their hyperparameters with **RandomizedSearchCV**.
* Best Model: **Random Forest**
* Best Model Performance: **MSE** = **8.25** | **R2** = **0.23**