To reproduce the works here. clone the repo. and run the following command:
git clone https://www.github.com/pacificg/CustomerClustering.git
make sure you have virtualenv installed.
virtualenv --version
if not installed, run the following command:
pip install virtualenv
then run the following command to create a virtualenv:
virtualenv venv
activate the virtualenv(for ubuntu):
source venv/bin/activate
activate the virtualenv(for windows):
venv\Scripts\activate
then run the following command to install the dependencies:
pip install -r requirements.txt
then run the following command to run do the preprocessing:
python preprocess.py \n
--pathToCSV data/marketing_campaign.csv \n
--pathToSaveProcessedCSV data/processed_marketing_campaign.csv
fireup the jypyter notebook:
jupyter notebook
check the KMeans notebook:
jupyter notebook EDAClustering.ipynb
Now we will use the K-Means algorithm to cluster our customers into groups. I created some clusters for you to see how the algorithm works. The algorithm is very simple: it takes a set of points and then assigns each point to the cluster with the closest centroid.
Run the EDAClustering notebook:
jupyter notebook EDAClustering.ipynb
Run the BinomialClassification notebook:
jupyter notebook CustomerPersonalityAnalysis.ipynb
Thank you for your time!