ML Project Workspace: Churn, Loan, Banking & Flight Prediction by Gabriel AdebayoML Project Workspace: Churn, Loan, Banking & Flight Prediction by Gabriel Adebayo

ML Project Workspace: Churn, Loan, Banking & Flight Prediction

Portfolio project
Gabriel Adebayo

📊 ML Project Workspace – Churn, Loan, Banking Market & Flight Prediction

This repository contains structured work on real-world ML problems including Customer Churn, Loan Prediction, Banking Market Response, and Flight Price Prediction. It focuses on robust data pipelines, exploratory data analysis (EDA), preprocessing, model evaluation using multiple metrics with recall emphasized for imbalanced scenarios.
🛠 This workspace is designed to evolve. New datasets will follow a reusable pipeline and evaluation logic for consistency and clarity.

✅ Current Datasets

1. 📉 Customer Churn Prediction

Goal: Identify customers likely to churn.
Key Steps:
Cleaned and mapped binary features
OneHotEncoded categorical variables
Scaled features with StandardScaler
Balanced data using SMOTE
Trained:
Logistic Regression
Random Forest
AdaBoost
Gradient Boosting
XGBoost
Evaluated with:
Accuracy
Precision, Recall
F1 Score
ROC AUC
Focus: Maximize recall to catch at-risk customers

2. 💰 Loan Prediction

Goal: Predict loan approval status.
Key Steps:
EDA on categorical and numeric features
Handled missing values, label/one-hot encoding
Compared classifiers with emphasis on recall
Best Model: Logistic Regression (good balance of interpretability and performance)

3. 🏦 Banking Market Response (Term Deposit Subscription)

Goal: Predict if a customer will subscribe to a term deposit.
Key Steps:
Cleaned and encoded socio-economic features
Explored job, education, and campaign-related behaviors
Balanced the dataset using SMOTE
Focused on avoiding false negatives (missed interested clients)
Compared models using recall, F1-score, ROC AUC

4. ✈️ Flight Price Prediction (Deep Learning)

Goal: Predict flight ticket prices based on travel information using Deep Learning.
Key Steps:
Extracted features from date/time columns
Converted text data (e.g. airlines, source, destination) using encoding
Scaled continuous variables
Trained regression models including:
Linear Regression
Random Forest Regressor
XGBoost Regressor
✅ Neural Network (Deep Learning) using Keras/TensorFlow
Metrics Used:
MAE, MSE, RMSE, R² Score
Emphasis: Low RMSE and strong generalization using DL model

🔍 Evaluation Highlights

Problem Priority Metric Why It Matters Churn Recall Catch all at-risk customers Loan Recall Avoid missing eligible applicants Bank Recall Don’t miss likely subscribers Flight RMSE / R² Accurate price estimation using deep learning

🧪 How to Run

# Activate your environment
conda activate mlenv  # or virtualenv

# Run Jupyter
jupyter notebook

🧭 Project Structure

📁 project_root/
│
├── 📄 README.md <- This file
│
├── 📁 TeleCustomerChurnNotebook/ <- VScode folder with scripts, models, and EDA
│
├── 📁 LoanPredictionProblem/ <- VScode folder with scripts, models, and EDA
│
├── 📄 BankMarketing.ipynb <- Google Colab notebook (single file)
├── 📄 Flight_Prediction.ipynb <- Google Colab notebook (DL-based)
│
└── 📄 requirements.txt <- Project dependencies

🚀 What's Next

Add datasets in health, fraud detection, and education
Modularize reusable preprocessing and training pipelines
Convert selected notebooks into end-to-end ML/DL apps
Version experiments and models using MLflow
Add Docker & cloud deployment support (AWS, Azure, GCP)

🙌 Acknowledgments

This project is part of my ML Engineering journey — building toward deployable, production-ready solutions using best practices in data science and MLOps.
🔄 Feedback, suggestions, or collaborations are always welcome!

0 likes · 9 views · Sep 1, 2025

Developed ML models for churn, loan, banking, and flight prediction.

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