Artificial intelligence (AI) has the potential to revolutionize pathology. AI refers to the application of modern machine learning techniques to digital tissue images in order to detect, quantify, or characterize specific cell or tissue structures. By automating time‑consuming diagnostic tasks, AI can greatly reduce the workload and help to remedy the serious shortage of pathologists. At the same time, AI can make analyses more sensitive and reproducible and it can capture novel biomarkers from tissue morphology for precision medicine. So, we created a Machine Learning model for Customer Segmentation Analysis of E-Commerce Industry. .
- Table of Contents
⚠️ Frameworks and Libraries- 📖 Data Preprocessing
- 🔗 Download
- 🔑 Prerequisites
- 🚀 Installation
- 💡 How to Run
- 🔑 Results
- 👏 And it's done!
- 🙋 Citation
- 🔰 Future Goals
- ❤️ Owner
- 👀 License
- SKLearn: Simple and efficient tools for predictive data analysis
- Seaborn: Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.
- Plotly: The plotly Python library is an interactive, open-source plotting library that supports over 40 unique chart types covering a wide range of statistical, financial, geographic, scientific, and 3-dimensional use-cases.
- Matplotlib : Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.
- Numpy: Caffe-based Single Shot-Multibox Detector (SSD) model used to detect faces
- Pandas: pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.
Data pre-processing is an important step for the creation of a machine learning model. Initially, data may not be clean or in the required format for the model which can cause misleading outcomes. In pre-processing of data, we transform data into our required format. It is used to deal with noises, duplicates, and missing values of the dataset. Data pre-processing has the activities like importing datasets, splitting datasets, attribute scaling, etc. Preprocessing of data is required for improving the accuracy of the model.
The dataset is now available here !
All the dependencies and required libraries are included in the file requirements.txt See here
- Clone the repo
$ git clone https://github.com/Chaganti-Reddy/AI-Prototype-Customer-Segmentation.git
- Change your directory to the cloned repo
$ AI-Prototype-Customer-Segmentation
- Now, run the following command in your Terminal/Command Prompt to install the libraries required
$ pip3 install -r requirements.txt
- Open terminal. Go into the cloned project directory and type the following command:
$ python3 Customer-Segmentation.py
• Silhouette intra-cluster score:
• Word Cloud Analysis:
• Decomposed Data:
• Report via PCA:
• Customers Morphology
• Confusion Matrix:
Check out the report here
Feel free to mail me for any doubts/query :email: chagantivenkataramireddy1@gmail.com
You are allowed to cite any part of the code or our dataset. You can use it in your Research Work or Project. Remember to provide credit to the Maintainer Chaganti Reddy by mentioning a link to this repository and her GitHub Profile.
Follow this format:
- Author's name - Chaganti Reddy
- Date of publication or update in parentheses.
- Title or description of document.
- URL.
This study endeavoured to present Customer Segmentation of E-Commerce Industry using an a-priori approach to categorize potential buyers into sub-segments of old and new customers and their purchases.
Made with ❤️ by Chaganti Reddy
MIT © Chaganti Reddy