PacificG/CustomerClustering

CustomerClustering

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

Task 1: Customer Clustering with K-Means

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

Task 2: Binomial Classificaiton with XGBoost

Run the BinomialClassification notebook:

jupyter notebook CustomerPersonalityAnalysis.ipynb

Thank you for your time!