Table of Contents
Customer segmentation is crucial for businesses aiming to boost marketing efficiency and customer satisfaction. By categorizing customers based on demographics, interests, and purchasing behavior, companies tailor marketing messages to engage each segment effectively. Our app employs advanced clustering algorithms like KMeans, DBSCAN, and AGNES to extract insights from your customer data. Whether you're a marketer targeting specific segments or a strategist refining product offerings, our tool facilitates informed decision-making. Our application consists of three modules: Dataset Overview, allowing you to gain a comprehensive understanding of your customer dataset's structure and variables; Clustering Performance Analysis, enabling you to evaluate the effectiveness of different clustering algorithms and compare performance metrics; and Individual Cluster Summary, where you can dive deep into each segmented cluster to uncover unique traits and behaviors for targeted marketing strategies.
To build this project, I've started by outlining the programming languages that form its foundation, followed by an in-depth exploration of the libraries incorporated. This deliberate documentation not only promotes transparency but also serves as a comprehensive reference to the technologies leveraged throughout the development journey.
- Programming Language : Python
- Libraries: Pandas, Numpy, Seaborn, Plotly, Matplotlib, Pickle, Plotly.graph_objects, Plotly.subplots, Plotly.express, StandardScaler, MinMaxScaler, KNNImputer, TSNE, Plotly.graph_objects, Plotly.subplots, KMeans, DBSCAN, Axes3D, silhouette_score, OPTICS, matplotlib.colors, colorsys, scipy.cluster.hierarchy, AgglomerativeClustering.
This section provides guidance on configuring this project on your local machine and running the Streamlit application locally.
To run this project, ensure that your PC/Laptop has the following prerequisites:
- Python
- Integrated Development Environment (IDE) such as PyCharm or Visual Studio Code.
To initialize this project on your local machine, kindly adhere to the outlined instructions provided herewith. By following these meticulously crafted steps, you will seamlessly configure the project environment for optimal functionality on your personal workstation. Your cooperation in adhering to these guidelines is greatly appreciated.
- Create a new virtual environment by using the command
conda create -p venv python=3.10 -y
- Activate the newly created virtual environment
conda activate venv/
- Install all the required project dependencies by executing the provided command. Subsequent to running this command, the internal setup file will be invoked, facilitating the configuration of your project by identifying and installing the necessary packages.
pip install -r requirements.txt
- After successfully installing all essential dependencies, proceed to run Streamlit locally by executing the following command:
streamlit run Strealmit/Home.py
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under the MIT License. See LICENSE.txt for more information.
- Twitter Handle: @Singh_yuvraaj1
- Linkedin Handle: Yuvraj Singh
- Mail: [email protected]