This repository, created by Ismail Ouahbi , contains various implementations of unsupervised machine learning algorithms. The main file in this repository is a Jupyter notebook named "Clustering algorithms (testing & comparing)".
The main objective of the analysis is to develop a customer segmentation to define marketing strategy. The sample dataset summarizes the usage behavior of about 9000 active credit card holders during the last 6 months. The file is at a customer level with 18 behavioral variables.
- Business Understanding: Define the problem and desired outcomes.
- Exploratory Data Analysis (EDA): Understand the data, find patterns, spot anomalies, test hypotheses, and check assumptions.
- Outlier Detection: Identify unusual data points in the dataset.
- Via Visualizations: Use graphical representations of the data to identify potential outliers.
- Univariate Outlier Detection: Detect outliers in one variable.
- Multivariate Outlier Detection: Detect outliers in multiple variables.
- Via Statistics: Use statistical measures to identify potential outliers.
- Via Visualizations: Use graphical representations of the data to identify potential outliers.
- Modeling part: Clustering techniques using
K-Means,Agglomerative clustering, andDBSCANfor outliers detection.
To clone and run this application, you'll need Git installed on your computer. From your command line:
# Clone this repository
$ git clone https://github.com/ismailouahbi/UnsupervisedML.git
# Go into the repository
$ cd UnsupervisedML
# Install dependencies
$ pip install -r requirements.txt
# Run the app
$ jupyter notebook
Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
Fork the Project Create your Feature Branch (git checkout -b newFeature) Commit your Changes (git commit -m 'Add some AmazingFeatures') Push to the Branch (git push origin AmazingFeature) Open a Pull Request
The code in this project is licensed under MIT license.