https://github.com/ali-jalil88/mlflow-bank-marketing

Bank-Marketing dataset using Mlflow
https://github.com/ali-jalil88/mlflow-bank-marketing

decisiontreeclassifier dessio gaussiannb kneighborsclassifier-model labelencoder logistic-regression mlflow pandas random-forest sklearn-model svc-model

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Bank-Marketing dataset using Mlflow

• Host: GitHub
• URL: https://github.com/ali-jalil88/mlflow-bank-marketing
• Owner: Ali-jalil88
• Created: 2024-11-18T13:32:08.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-11-18T14:10:42.000Z (7 months ago)
• Last Synced: 2025-01-30T22:47:38.937Z (5 months ago)
• Topics: decisiontreeclassifier, dessio, gaussiannb, kneighborsclassifier-model, labelencoder, logistic-regression, mlflow, pandas, random-forest, sklearn-model, svc-model
• Language: Jupyter Notebook
• Homepage:
• Size: 542 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Bank Marketing Dataset - MLflow Project

    1-Table of Contents

    2-Introduction

    3- Dataset Description

    4- Features

    5- Setup and Installation

    6- Project Workflow

    7- MLflow Integration

    8- Results and Evaluation

    9- Contributing

    10- License

![Machine Learning Models in Bank Marketing Project](https://raw.githubusercontent.com/Ali-jalil88/Mlflow-Bank-Marketing/refs/heads/main/DALL%C2%B7E%202024-11-18%2015.08.36%20-%20An%20infographic%20displaying%20different%20machine%20learning%20models%20used%20in%20a%20Bank%20Marketing%20Project.%20Include%20models%20such%20as%20Logistic%20Regression%2C%20Random%20Fores.webp)

# Introduction

The Bank Marketing Dataset MLflow Project is a machine learning project that predicts whether a client will subscribe to a term deposit (deposit as target variable) based on their demographic and interaction data. This repository utilizes MLflow to streamline experiment tracking, reproducibility, and model deployment.

# Dataset Description

The dataset used in this project is from the Bank Marketing Dataset on Kaggle.

- Source: UCI Machine Learning Repository

- Size: ~45,000 rows and 17 features

- Objective: Predict the outcome of the marketing campaign (deposit: yes/no).

# Features

Key features include:

- Demographic Information: age, job, marital, education.

- Campaign Details: campaign, pdays, previous, poutcome.

- Financial Data: balance, loan, housing.

- Date Information: month, day_of_week.

# Setup and Installation

Prerequisites

- Python 3.8 or later

- MLflow installed

- Libraries: pandas, scikit-learn, xgboost, seaborn, matplotlib

# Project Workflow

1. Exploratory Data Analysis (EDA):

- Visualize distributions, correlations, and outliers.

- Tools: seaborn, matplotlib.

2. Preprocessing:

- Handle missing data, encode categorical features, and scale numerical ones.

- Techniques: LabelEncoding, OneHotEncoding, StandardScaler.

3. Model Training:

- Models used: Logistic Regression, Random Forest, XGBoost.

- Feature selection and hyperparameter tuning.

4. Evaluation:

- Metrics: Accuracy, Precision, Recall, ROC-AUC.

5. Experiment Tracking:

  - Log parameters, metrics, and artifacts in MLflow.

6. Model Deployment:

- Save the best-performing model for deployment.

# MLflow Integration

What is MLflow?

MLflow is an open-source platform for managing the end-to-end machine learning lifecycle:

- Tracking: Log metrics, parameters, and models.

- Projects: Reproducible packaging of code.

- Models: Deployment and sharing of models.

- Registry: Centralized model store.

## Links:

- **[Project Notebook](https://www.kaggle.com/code/alialarkawazi/bn-marketing-ml)**

- **[Dataset](https://www.kaggle.com/datasets/janiobachmann/bank-marketing-dataset)**