The Mall Customer Segmentation Machine Learning Project aims to build a predictive model that can accurately segment customers of a mall based on various demographic and behavioral factors. The project will leverage unsupervised machine learning algorithms, such as K-Means clustering, to group customers into different segments based on their similarities in various factors such as age, gender, income, spending habits, and other related features.
The project will involve several key steps, including data cleaning and preprocessing, feature engineering, model selection, and model training and evaluation. The dataset will be sourced from various mall management systems and will contain several features such as customer demographic information, purchase history, and spending patterns, among others.
The dataset will be cleaned and preprocessed to remove any missing values, outliers, or irrelevant features. The dataset will be split into training and testing sets to train and evaluate the model’s performance.
The project will leverage the K-Means clustering algorithm to segment customers into different groups based on their similarities in various factors. The optimal number of clusters will be determined using the elbow method or silhouette method. The model’s performance will be evaluated based on the within-cluster sum of squares (WCSS) and silhouette scores.
The project will also involve visualization techniques to explore the dataset and gain insights into the relationship between different features and customer segments. The visualization techniques will include scatter plots, histograms, and box plots, among others.
The final output of the project will be a web-based application that can take in data on various customer demographic and behavioral factors and provide a segmentation of the customer into different groups based on their similarities in various factors. The Mall Customer Segmentation Machine Learning Project is aimed at providing valuable insights into the customer base of a mall and providing an accurate segmentation of customers based on various factors. The project can be useful for mall management in designing marketing strategies, optimizing store layouts, and targeting specific customer segments.
