The goal is to understand and effectively market to the target customers of the mall. By segmenting customers based on their spending score, the business can identify those who are most likely to make purchases and develop targeted marketing campaigns to reach them. This approach allows for tailoring marketing and sales efforts to specific customer groups, resulting in more effective communication and a better customer experience. Exploratory data analysis and predictive modeling techniques such as linear regression, decision trees, and random forests are employed to forecast customer spending behavior, optimize inventory, and enhance customer satisfaction, resulting in increased revenue for the mall.

The dataset from Kaggle will be utilized, containing basic data about customers such as Customer ID, age, gender, annual income, and spending score. The dataset can be accessed here: https://www.kaggle.com/datasets/vjchoudhary7/customer-segmentation-tutorial-in-python

1. Data Cleaning and Formatting
2. Exploratory Data Analysis (EDA)
3. Clustering (K-means)
4. Predictive Model ( Linear Regression, Decision Tree, Random Forest)