├── .gitignore
├── Customer_Segmentation_Kmeans_Clustering.ipynb
├── Orders - Analysis Task.csv
└── README.md


/.gitignore:
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/README.md:
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 1 | In this procject, I worked on a real-world e-commerce sales data and segmented its customer base with Python, [scikit-learn library](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html) and [Plotly](https://plot.ly/python/plotly-express/). 
 2 | 
 3 | The workflow of the project is as follows:
 4 | 
 5 | 1. Business Case
 6 | 2. Data Preparation
 7 | 3. Segmentation with K-means Clustering
 8 | 4. Hyperparameter Tuning
 9 | 5. Visualization and Interpretation of the Results
10 | 
11 | Article of the project "Customer Segmentation with Machine Learning" can be read [here](https://towardsdatascience.com/customer-segmentation-with-machine-learning-a0ac8c3d4d84?source=friends_link&sk=91a45f28699eda78766335947bed7044)!
12 | 
13 | The notebook can be viewed in the nbviewer [here](https://nbviewer.jupyter.org/github/cereniyim/Customer-Segmentation-Unsupervised-ML-Model/blob/3c4374dd16861ea365cdf468bd9b2c28a964f4e3/Customer_Segmentation_Kmeans_Clustering.ipynb) with interactive plotly plots.
14 | 


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