This project is designed to predict the success of an email marketing campaign based on customer engagement metrics. The model can help businesses make data-driven decisions to optimize their marketing strategies.

• app.py: The main Streamlit application that allows users to input data and receive campaign success predictions.
• stackking_classifier.pkl: Pre-trained machine learning model used to make predictions.
• requirements.txt: List of dependencies for deploying the project on Streamlit Cloud.
• EDA - (group 1).ipynb: Notebook containing exploratory data analysis (EDA) and model training process.

• Interactive Input Form: Users can input customer data through Streamlit's sidebar.
• Prediction Output: Provides a prediction on whether the email campaign is likely to succeed or not.

The project relies on several Python libraries listed in requirements.txt. Here’s a summary:

• streamlit: For deploying the interactive web app.
• scikit-learn: Used for model training and evaluation.
• xgboost: XGBoost classifier for prediction.
• pandas, numpy, matplotlib, seaborn: For data manipulation and visualization.
• imblearn: For handling class imbalances in the dataset.
• statsmodels: For calculating VIF to address multicollinearity.

To run this project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/devpkr1/Email-Marketing-Campaign-Project.git
   cd email-campaign-prediction

2. Install dependencies: Use the following command to install required packages:

   pip install -r requirements.txt

3. Run the Streamlit app:

   streamlit run app.py

4. Access the app: Open a web browser and go to http://localhost:8501 to interact with the app.

1. Input customer engagement metrics in the sidebar:

   • Emails Opened: Number of emails the customer opened.
   • Emails Clicked: Number of clicks on emails.
   • Purchase History: Total purchases made by the customer.
   • Time Spent on Website: Time spent on the website in minutes.
   • Days Since Last Open: Days since the customer last opened an email.
   • Customer Engagement Score: A score between 0.0 and 1.0 indicating engagement level.

2. Click "Predict" to generate a prediction. The app will indicate whether the campaign is likely to succeed.

The model was trained using various machine learning algorithms, with XGBClassifier selected as the final model. Training and evaluation details are in the EDA - (group 1).ipynb notebook.

This project is licensed under the MIT License.