https://github.com/lkethridge/supervised_learning
Supervised Learning project from TripleTen
https://github.com/lkethridge/supervised_learning
class-imbalance-handling confusion-matrix data-upload downsampling f1-score feature-prep feature-scaling fpr imbalanced-classification label-encoding one-hot-encoding ordinal-encoding pr-curve precision recall regression-metrics roc-curve supervised-learning tpr upsampling
Last synced: about 2 months ago
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Supervised Learning project from TripleTen
- Host: GitHub
- URL: https://github.com/lkethridge/supervised_learning
- Owner: LKEthridge
- License: cc0-1.0
- Created: 2025-01-20T20:11:31.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-01-20T22:51:21.000Z (4 months ago)
- Last Synced: 2025-02-02T05:28:58.616Z (4 months ago)
- Topics: class-imbalance-handling, confusion-matrix, data-upload, downsampling, f1-score, feature-prep, feature-scaling, fpr, imbalanced-classification, label-encoding, one-hot-encoding, ordinal-encoding, pr-curve, precision, recall, regression-metrics, roc-curve, supervised-learning, tpr, upsampling
- Language: Jupyter Notebook
- Homepage:
- Size: 324 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Supervised_Learning
## *This was a Supervised Learning project for TripleTen. 👩🏽💻*
This project developed a Random Forest Classifier to predict customer churn for Beta Bank, achieving an F1 score of 0.61 and a strong AUC-ROC score despite class imbalance. By targeting likely-to-leave customers, the model provides a tool for optimizing retention strategies and aligning predictions with actual churn trends. This approach offers Beta Bank a data-driven solution to reduce customer attrition and secure its future.
## Skills Highlighted
👀 Supervised Learning
🧼 Feature Prep including One-Hot, Label, and Ordinal Encoding
⚖️ Feature Scaling & Class-Imbalance Handling
🤔 Confusion Matrices, Precision, Recall, and F1 Score
↕️ Imbalanced Classification with Upsampling or Downsampling
🪨 ROC-Curve, PR Curve, True Positive Rate, and False Positive Rate
💯 Regression Metrics
## Installation & Usage
* This project uses pandas, numpy, train_test_split, DecisionTreeClassifier, RandomForestClassifier, LogisticRegression, f1_score, roc_auc_score, accuracy_score, matplotlib.pyplot, shuffle, and StandardScaler. It requires python 3.9.6.