Marketing-Campaign-Machine-Learning

A Predictive Model for Marketing Campaigns

The goal of this project is to accurately predict who will participate in the next marketing campaign using historical data. We started by exploring, cleaning, and preparing the raw data for model training. Using feature engineering methods, we created new features and transformed existing ones to increase the predictive power of the dataset. We then experimented with various machine learning algorithms to find the best fit for our dataset. Finally, we selected the best-performing model and fine-tuned it by defining the optimal hyper-parameters.

Table of Contents

• 1. Data Exploration

• 2. Data Cleaning

• 3. Feature Engineering

• 4. Model Training

• 5. Model Evaluation and Tuning(Hyper-parameter tuning(HPs-T))

📚 SOURCES :

https://towardsdatascience.com/5-smote-techniques-for-oversampling-your-imbalance-data-b8155bdbe2b5

https://towardsdatascience.com/top-10-model-evaluation-metrics-for-classification-ml-models-a0a0f1d51b9

https://www.analyticsvidhya.com/blog/2021/03/7-popular-feature-selection-routines-in-machine-learning/

Contributions are welcome here! ✨💪