Marketing Campaign Prediction

This repository contains a machine learning project aimed at predicting the success of marketing campaigns. By analyzing past campaign data, we develop models that can forecast the effectiveness of future campaigns, helping businesses optimize their marketing strategies.

Workflow to follow

1. Update config.yaml
2. Update schema.yaml
3. Update params.yaml
4. Update the config_entity.py
5. Update the configuration manager in src/config
6. Update the components
7. Update the pipeline
8. Update main.py

Table of Contents

Project Overview Dataset Installation Usage Model Building Evaluation Contributing License

Project Overview

Marketing campaigns are essential for businesses to promote their products and engage customers. Predicting the success of these campaigns can save resources and improve targeting strategies. This project leverages machine learning techniques to predict the outcome of marketing campaigns based on historical data.

Data Source

The dataset can be downloaded from [This Link]https://www.kaggle.com/datasets/sujithmandala/marketing-campaign-positive-response-prediction.

Clone the Repository

git clone https://github.com/Somtochukwu-Achikanu/ML-project.git

Run the following commands

bash init_setup.sh

Run the following commands

python main.py

Run the Dvc Command

dvc.init dvc.repro

Feature Engineering

We perform feature engineering to enhance the predictive power of our models:

• Encoding categorical variables
• Scaling Numerical features

Model Building

The project uses several machine learning algorithms to build predictive models, including:

• Logistic Regression
• K Nearest neighbors
• Gradient Boosting
• Support Vector Machines (SVM)
• Decision Trees

Evaluation

The performance of the models is evaluated using the following metrics:

• Accuracy
• Precision
• Recall
• F1 Score
• ROC AUC

Contributing

We welcome contributions to improve the project. Here are some ways you can contribute:

• Fix bugs and issues
• Add new features or models
• Improve documentation

To contribute, please fork the repository, create a new branch, and submit a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Feel free to customize this README.md file as per your specific project requirements. This template should give you a solid starting point for documenting your marketing campaign prediction use case using machine learning.