Link to deployed model: https://connect-midhunr-email-campaign-effectiveness-predict-app-e58c8b.streamlit.app/
In this project, I have attempted to analyze the data on e-mail marketing campaign and build a machine learning model to predict the mail that is ignored, read, or acknowledged by the reader. No personal information of recipient is provided in this dataset.
This project contains an executable iPython Notebook, a presentation and source as follows:
- Email_Campaign_Effectiveness_Prediction_Capstone_Project.ipynb - Google Colab notebook containing data summary, exploration, visualisations, feature engineering, modelling, performance evaluation and conclusion.
- Presentation PDF - Supervised Machine Learning - Classification - Email Campaign Effectiveness Prediction - Capstone Project.pdf - Presentation slideshow of the project.
- data_email_campaign.csv - Includes e-mail marketing campaign data.
Most of the small to medium business owners are making effective use of Gmail-based e-mail marketing strategies for offline targeting of converting their prospective customers into leads so that they stay with them in business. The main objective is to create a machine learning model to characterize the mail and track the mail that is ignored; read; acknowledged by the reader.
- Understanding the business task.
- Import relevant libraries and define useful functions.
- Reading data from files given.
- Data pre-processing, which involves inspection of both datasets and data cleaning.
- Exploratory data analysis, to find which factors affect sales and how they affect it.
- Feature engineering, to prepare data for modelling.
- Modelling data and comparing the models to find out most suitable one for classification.
- Conclusion.
The following insights were gained from EDA:
Link to deployed model: https://connect-midhunr-email-campaign-effectiveness-predict-app-e58c8b.streamlit.app/
An interactive dashboard was also created with Tableau to display charts associated with the analysis.
Click here to interact with the data visualization.
The following conclusions were drawn from Modelling:
Midhun R | Avid Learner | Data Analyst | Data Scientist | Machine Learning Enthusiast
Contact me for Data Science Project Collaborations
Towards Data Science, 'Having an Imbalanced Dataset? Here Is How You Can Fix It.'. [Online].
Machine Learning Mastery, 'Multinomial Logistic Regression With Python'. [Online].
Available: https://machinelearningmastery.com/multinomial-logistic-regression-with-python/
Kaggle, 'How to Choose Right Metric for Evaluating ML Model'. [Online].
Available: https://www.kaggle.com/code/vipulgandhi/how-to-choose-right-metric-for-evaluating-ml-model/
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