Customer Segmentation using K-Means Clustering

Overview 📊:

This project aims to segment customers based on their income 💰 and spending score 💳 using the K-Means clustering algorithm. Customer segmentation helps businesses understand their customers better and tailor marketing strategies to different segments effectively.

Dataset 📝:

The dataset consists of customer information from a mall, including their income and spending score. The income represents the annual income of the customer, while the spending score is assigned based on customer behavior and spending patterns.

Implementation 🛠️:

1. Data Preprocessing: The dataset is preprocessed to handle missing values, scale the features, and remove any outliers that might affect the clustering process.

2. Model Selection: K-Means clustering is chosen as the clustering algorithm due to its simplicity and effectiveness in segmenting data points into clusters based on their similarity.

3. Feature Selection: Only the income and spending score features are used for clustering, as these are the most relevant features for segmenting customers.

4. Model Training: The K-Means algorithm is trained on the preprocessed dataset to cluster customers into groups based on their income and spending score. The number of clusters is determined based on domain knowledge or using techniques such as the elbow method.

5. Evaluation: Since K-Means is an unsupervised learning algorithm, there is no direct evaluation metric. However, the quality of the clusters can be assessed visually by plotting the clusters and analyzing their characteristics.

6. Interpretation: Once the clusters are formed, businesses can interpret the characteristics of each cluster to gain insights into customer behavior and preferences. This information can then be used to tailor marketing strategies and improve customer satisfaction.

Dependencies 🛠️:

• Python 3.x
• scikit-learn
• Pandas
• Matplotlib and/or Seaborn

Usage 💻:

1. Clone the repository or download the project files.
2. Install the required dependencies using pip or conda.
3. Run the provided Jupyter Notebook or Python script to preprocess the data, train the K-Means model, and analyze the clusters.
4. Interpret the results and use them to inform business decisions.

Contributing 🤝:

Contributions to the project are welcome! Whether it's improving the clustering algorithm, enhancing data preprocessing techniques, or adding visualizations, feel free to submit pull requests.