SungananiM2710/Customer-Segmentation-KMeans-Selore-Nigeria

# Customer Segmentation Using K-Means Clustering - Selore Nigeria This project uses **Unsupervised Machine Learning** (specifically, the **K-Means clustering** algorithm) to segment Selore Nigeria's customers based on their demographic information and spending behaviour. The insights derived from this project can support the development of personalised marketing strategies. --- ## 🏢 About Selore Nigeria Selore Nigeria is a well-known electronics retail chain in Nigeria, offering a variety of mobile phones, tablets, laptops, and accessories. With nationwide outlets and a strong after-sales support service (repairs, maintenance, and trade-ins), Selore has become a trusted brand for electronics shoppers in Nigeria. --- ## 💼 Business Problem The company aims to understand **customer spending behaviour** to improve **targeted marketing campaigns**. The dataset includes: - Demographic features: Age, Gender - Spending features: Annual Income, Spending Score The goal is to **segment customers into distinct groups** based on these features using clustering. --- ## 🎯 Project Objectives - Implement **K-Means Clustering** to segment customers. - Explore and visualise the data to understand customer patterns. - Provide actionable insights to Selore Nigeria’s marketing team. --- ## 🧠 Methodology 1. **Data Preprocessing**: - Handle missing values - Encode categorical variables (e.g., Gender) - Standardize numerical features 2. **Exploratory Data Analysis (EDA)**: - Visualise distributions and relationships - Understand feature importance and correlation 3. **Model Building**: - Use **K-Means** for clustering - Determine optimal K using the **Elbow Method** --- ## 💻 Technologies Used - Python 🐍 - Pandas, NumPy - Scikit-learn - Matplotlib, Seaborn - Jupyter Notebook ## 🧐 Key Observations: The analysis identified four distinct customer segments based on income and spending behavior: High Income/High Spending, Low Income/Low Spending, Low Income/High Spending (as …