Merge pull request #279 from alo7lika/main

Customer Clust - Customer Segmentation Tool

Customer Clust - Customer Segmentation Tool

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+

Machine_Learning/Customer Clust - Customer Segmentation Tool/Mall_Customers.csv

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+CustomerID,Genre,Age,Annual Income (k$),Spending Score (1-100)
+0001,Male,19,15,39
+0002,Male,21,15,81
+0003,Female,20,16,6
+0004,Female,23,16,77
+0005,Female,31,17,40
+0006,Female,22,17,76
+0007,Female,35,18,6
+0008,Female,23,18,94
+0009,Male,64,19,3
+0010,Female,30,19,72
+0011,Male,67,19,14
+0012,Female,35,19,99
+0013,Female,58,20,15
+0014,Female,24,20,77
+0015,Male,37,20,13
+0016,Male,22,20,79
+0017,Female,35,21,35
+0018,Male,20,21,66
+0019,Male,52,23,29
+0020,Female,35,23,98
+0021,Male,35,24,35
+0022,Male,25,24,73
+0023,Female,46,25,5
+0024,Male,31,25,73
+0025,Female,54,28,14
+0026,Male,29,28,82
+0027,Female,45,28,32
+0028,Male,35,28,61
+0029,Female,40,29,31
+0030,Female,23,29,87
+0031,Male,60,30,4
+0032,Female,21,30,73
+0033,Male,53,33,4
+0034,Male,18,33,92
+0035,Female,49,33,14
+0036,Female,21,33,81
+0037,Female,42,34,17
+0038,Female,30,34,73
+0039,Female,36,37,26
+0040,Female,20,37,75
+0041,Female,65,38,35
+0042,Male,24,38,92
+0043,Male,48,39,36
+0044,Female,31,39,61
+0045,Female,49,39,28
+0046,Female,24,39,65
+0047,Female,50,40,55
+0048,Female,27,40,47
+0049,Female,29,40,42
+0050,Female,31,40,42
+0051,Female,49,42,52
+0052,Male,33,42,60
+0053,Female,31,43,54
+0054,Male,59,43,60
+0055,Female,50,43,45
+0056,Male,47,43,41
+0057,Female,51,44,50
+0058,Male,69,44,46
+0059,Female,27,46,51
+0060,Male,53,46,46
+0061,Male,70,46,56
+0062,Male,19,46,55
+0063,Female,67,47,52
+0064,Female,54,47,59
+0065,Male,63,48,51
+0066,Male,18,48,59
+0067,Female,43,48,50
+0068,Female,68,48,48
+0069,Male,19,48,59
+0070,Female,32,48,47
+0071,Male,70,49,55
+0072,Female,47,49,42
+0073,Female,60,50,49
+0074,Female,60,50,56
+0075,Male,59,54,47
+0076,Male,26,54,54
+0077,Female,45,54,53
+0078,Male,40,54,48
+0079,Female,23,54,52
+0080,Female,49,54,42
+0081,Male,57,54,51
+0082,Male,38,54,55
+0083,Male,67,54,41
+0084,Female,46,54,44
+0085,Female,21,54,57
+0086,Male,48,54,46
+0087,Female,55,57,58
+0088,Female,22,57,55
+0089,Female,34,58,60
+0090,Female,50,58,46
+0091,Female,68,59,55
+0092,Male,18,59,41
+0093,Male,48,60,49
+0094,Female,40,60,40
+0095,Female,32,60,42
+0096,Male,24,60,52
+0097,Female,47,60,47
+0098,Female,27,60,50
+0099,Male,48,61,42
+0100,Male,20,61,49
+0101,Female,23,62,41
+0102,Female,49,62,48
+0103,Male,67,62,59
+0104,Male,26,62,55
+0105,Male,49,62,56
+0106,Female,21,62,42
+0107,Female,66,63,50
+0108,Male,54,63,46
+0109,Male,68,63,43
+0110,Male,66,63,48
+0111,Male,65,63,52
+0112,Female,19,63,54
+0113,Female,38,64,42
+0114,Male,19,64,46
+0115,Female,18,65,48
+0116,Female,19,65,50
+0117,Female,63,65,43
+0118,Female,49,65,59
+0119,Female,51,67,43
+0120,Female,50,67,57
+0121,Male,27,67,56
+0122,Female,38,67,40
+0123,Female,40,69,58
+0124,Male,39,69,91
+0125,Female,23,70,29
+0126,Female,31,70,77
+0127,Male,43,71,35
+0128,Male,40,71,95
+0129,Male,59,71,11
+0130,Male,38,71,75
+0131,Male,47,71,9
+0132,Male,39,71,75
+0133,Female,25,72,34
+0134,Female,31,72,71
+0135,Male,20,73,5
+0136,Female,29,73,88
+0137,Female,44,73,7
+0138,Male,32,73,73
+0139,Male,19,74,10
+0140,Female,35,74,72
+0141,Female,57,75,5
+0142,Male,32,75,93
+0143,Female,28,76,40
+0144,Female,32,76,87
+0145,Male,25,77,12
+0146,Male,28,77,97
+0147,Male,48,77,36
+0148,Female,32,77,74
+0149,Female,34,78,22
+0150,Male,34,78,90
+0151,Male,43,78,17
+0152,Male,39,78,88
+0153,Female,44,78,20
+0154,Female,38,78,76
+0155,Female,47,78,16
+0156,Female,27,78,89
+0157,Male,37,78,1
+0158,Female,30,78,78
+0159,Male,34,78,1
+0160,Female,30,78,73
+0161,Female,56,79,35
+0162,Female,29,79,83
+0163,Male,19,81,5
+0164,Female,31,81,93
+0165,Male,50,85,26
+0166,Female,36,85,75
+0167,Male,42,86,20
+0168,Female,33,86,95
+0169,Female,36,87,27
+0170,Male,32,87,63
+0171,Male,40,87,13
+0172,Male,28,87,75
+0173,Male,36,87,10
+0174,Male,36,87,92
+0175,Female,52,88,13
+0176,Female,30,88,86
+0177,Male,58,88,15
+0178,Male,27,88,69
+0179,Male,59,93,14
+0180,Male,35,93,90
+0181,Female,37,97,32
+0182,Female,32,97,86
+0183,Male,46,98,15
+0184,Female,29,98,88
+0185,Female,41,99,39
+0186,Male,30,99,97
+0187,Female,54,101,24
+0188,Male,28,101,68
+0189,Female,41,103,17
+0190,Female,36,103,85
+0191,Female,34,103,23
+0192,Female,32,103,69
+0193,Male,33,113,8
+0194,Female,38,113,91
+0195,Female,47,120,16
+0196,Female,35,120,79
+0197,Female,45,126,28
+0198,Male,32,126,74
+0199,Male,32,137,18
+0200,Male,30,137,83

Machine_Learning/Customer Clust - Customer Segmentation Tool/README.md

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+# 🛍️ Customer Clust - Customer Segmentation Tool
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/alo7lika/PyVerse/refs/heads/main/Machine_Learning/Customer%20Clust%20-%20Customer%20Segmentation%20Tool/Customer%20Clust%20-%20Segmentation%20Tool.png" alt="Customer Clust Segmentation Tool" width="600"/>
+</p>
+
+
+## 📚 Table of Contents
+1. [Overview](#-overview)
+2. [Features](#-features)
+3. [How It Works](#-how-it-works)
+4. [Tech Stack](#-tech-stack)
+5. [Installation](#-installation)
+6. [Usage](#-usage)
+7. [Visualizations](#-visualizations)
+8. [Machine Learning Models](#-machine-learning-models)
+9. [Goals](#-goals)
+10. [License](#-license)
+11. [Contact](#-contact)
+
+
+## 📋 Overview
+Customer Clust is a powerful customer segmentation tool designed to categorize customers based on their purchasing behavior, preferences, and demographic characteristics. By leveraging advanced data analytics and machine learning techniques, this tool helps businesses:
+
+- 📈 Enhance marketing strategies
+- 🧠 Improve customer understanding
+- ⚙️ Optimize resource allocation
+- 🚀 Drive business growth
+- 💡 Foster a data-driven culture
+
+## 🔍 Features
+- **Segmentation**: Classifies customers into distinct groups for targeted marketing.
+- **Behavioral Insights**: Provides valuable insights into customer preferences and purchasing habits.
+- **Visualization**: Interactive charts and graphs for easy interpretation of customer segments.
+- **Advanced Metrics**: Incorporates KPIs to measure the impact of different segments on business growth.
+
+## 🧑‍💻 How It Works
+1. **Data Collection**: Input customer purchase history, preferences, and demographic data.
+2. **Data Preprocessing**: Clean and preprocess the data for machine learning models.
+3. **Modeling**: Apply clustering algorithms like K-Means or Hierarchical Clustering to identify customer groups.
+4. **Evaluation**: Analyze the results using metrics like silhouette score or within-cluster sum of squares (WCSS).
+5. **Visualization**: Visualize the segmentation results using intuitive dashboards.
+
+## 🛠️ Tech Stack
+- **Languages**: Python 🐍
+- **Libraries**: 
+  - pandas 📊
+  - numpy 🔢
+  - scikit-learn 📚
+  - matplotlib 📉
+  - seaborn 📈
+
+## 🚀 Getting Started
+
+### Prerequisites
+- Python 3.8+
+- Jupyter Notebook
+- Required libraries in `requirements.txt`
+
+### Installation
+Clone this repository:
+```bash
+git clone https://github.com/yourusername/Customer_Clust.git
+cd Customer_Clust
+```
+Install the necessary dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+### Usage
+Run the Jupyter notebook to explore the data and generate customer segments:
+
+```bash
+jupyter notebook notebooks/Customer_Segmentation.ipynb
+```
+To run the segmentation pipeline as a script:
+
+```bash
+python scripts/segment_customers.py
+```
+
+## 📊 Visualizations
+The tool provides insightful visualizations to help you understand customer clusters and trends, such as:
+
+- 📉 **Purchase trends over time**
+- 🧩 **Segmented customer behavior**
+- 🗺️ **Demographic distribution maps**
+- 🎯 **Targeted marketing groupings**
+
+## 🧠 Machine Learning Models
+Customer Clust uses unsupervised learning techniques, primarily focusing on:
+
+- **K-Means Clustering**: For grouping customers into meaningful clusters.
+- **Hierarchical Clustering**: To provide more granular segmentation if needed.
+
+## 🏆 Goals
+- Improve customer retention and acquisition.
+- Maximize marketing campaign efficiency.
+- Tailor product recommendations to specific customer segments.
+
+## 🛡️ License
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+
+## 💬 Contact
+For more information or queries, feel free to contact the project maintainers at: [alolikabhowmik72@gmail.com]
+
+Happy clustering! 🎉
+