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https://github.com/chandkund/customer-segmentation-using-k-means-clustering
Implemented K-Means Clustering to segment customers based on purchasing behavior, enabling targeted marketing strategies. Analyzed data, optimized clusters using the Elbow Method, and derived insights to enhance customer engagement and retention.
https://github.com/chandkund/customer-segmentation-using-k-means-clustering
data-science kmeans-clustering machine-learning matplotlib numpy pandas python seaborn sklearn
Last synced: about 1 month ago
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Implemented K-Means Clustering to segment customers based on purchasing behavior, enabling targeted marketing strategies. Analyzed data, optimized clusters using the Elbow Method, and derived insights to enhance customer engagement and retention.
- Host: GitHub
- URL: https://github.com/chandkund/customer-segmentation-using-k-means-clustering
- Owner: chandkund
- License: mit
- Created: 2024-08-16T18:21:42.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-08-16T18:44:27.000Z (6 months ago)
- Last Synced: 2024-11-17T20:56:00.819Z (3 months ago)
- Topics: data-science, kmeans-clustering, machine-learning, matplotlib, numpy, pandas, python, seaborn, sklearn
- Language: Jupyter Notebook
- Homepage:
- Size: 126 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# CUSTOMER SEGMENTATION USING K-MEANS CLUSTERING
## Project Overview
This project focuses on customer segmentation using the K-Means Clustering algorithm. By clustering customers based on their purchasing behaviors and demographics, businesses can tailor their marketing strategies and improve customer engagement.
## Table of Contents
- [Project Overview](#project-overview)
- [Installation](#installation)
- [Usage](#usage)
- [Code Explanation](#code-explanation)
- [Model Evaluation](#model-evaluation)
- [License](#license)
## Installation
To run this project, you will need Python along with the following libraries:
- `pandas`
- `numpy`
- `matplotlib`
- `seaborn`
- `scikit-learn`
Install the required packages using `pip`:
pip install pandas numpy matplotlib seaborn scikit-learn
Clone the repository:
git clone https://github.com/chandkund/customer-segmentation-using-k-means-clustering.git
cd customer-segmentation-using-k-means-clustering
## Usage
- Prepare the Dataset:
Place your dataset (e.g., `mall_customers.csv`) in the project directory or adjust the file path in the code.
- Run the Code:
Execute the Python scripts to perform data preprocessing, clustering, and visualization.
python script.py
## Code Explanation
- **Import Relevant Libraries**:
```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import MinMaxScaler
from sklearn.cluster import KMeans
```
- **Load and Preprocess Data**:
```python
raw_data = pd.read_csv("path/to/your/dataset.csv")
df = raw_data.copy()
```
- **Visualize Data Before Normalization**:
```python
sns.scatterplot(x='Annual Income (k$)', y='Spending Score (1-100)', data=df)
plt.show()
```
- **Normalize the Data**:
```python
cols = ['Annual Income (k$)', 'Spending Score (1-100)']
scaled = MinMaxScaler()
df[cols] = pd.DataFrame(scaled.fit_transform(df[cols]), columns=cols)
sns.scatterplot(x='Annual Income (k$)', y='Spending Score (1-100)', data=df)
plt.show()
```
- **Determine Optimal Number of Clusters**:
```python
Wcss = []
for i in range(1, 12):
kmeans = KMeans(n_clusters=i, random_state=0)
kmeans.fit(df[cols])
Wcss.append(kmeans.inertia_)
plt.plot(range(1, 12), Wcss)
plt.title("The Elbow Method")
plt.xlabel("Number of Clusters")
plt.ylabel("Wcss Values")
plt.show()
```
- **Train the K-Means Model**:
```python
optimal_clusters = 5
kmeans_model = KMeans(n_clusters=optimal_clusters, random_state=0)
df['cluster'] = kmeans_model.predict(df[cols])
```
- **Visualize Clusters**:
```python
plt.scatter(df['Annual Income (k$)'], df['Spending Score (1-100)'], c=df['cluster'], cmap='rainbow')
plt.title("Clusters of Customers")
plt.xlabel("Annual Income (k$)")
plt.ylabel("Spending Score (1-100)")
plt.show()
```
## Model Evaluation
Evaluate the clustering results by analyzing the characteristics of each cluster and the distribution of data points within each cluster.
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.