Title: Prediction of Users’ Responses to Marketing Campaigns

1. Project Motivation
2. Installation
3. Data
4. Implementation
5. Results

Project Motivation

The aim of this project is to analyse user information and build a model capable of predicting users’ responses to marketing campaigns based on the features in the provided dataset. 1 means ‘Yes’, and 0 means ‘No’

Installation

Python V-3.

Libraries:

• Scikit Learn.
• Imblearn.
• Pandas.
• Numpy.
• Seaborn
• Matplotlib.

Data

The data was gotten from the private hackathon by to Data Science Nigeria for AI+ members to qualify for the 2021 AI Bootcamp. The hackathon took place on Zindi. Full description of the data is available on Zindi.

Implementation

Three classification models were run on the data, including Decision Tree, Random Forest and Logistic Regression. The target variable was made balanced using SMOTE and make_imbalanced from the Imblearn library. Details about the models’ performance on the different sets of data is available in the notebook. The models were run on selected features data with a ratio of 70:30 and the performance of the model was reviewed using the classification_report metric to display a summary of accuracy score, precision score, recall score and f1_score as shown in the notebook

Results

The output of the prediction result is in the ‘predicted_response.csv’ above and in the notebook as well