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README.md

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-# Unsupervised_ML
-The Goal is to segment customers based in their behaviors and attributes, providing valuable insights for marketing strategies. Although customer segmentation is a typical application clustering, the data set chosen here offers a  fresh perspective beyond the standard academic dataset. 
+# Mall Customer Segmentation
+The Goal is to segment customers based in their behaviors and attributes, providing valuable insights for marketing strategies. Although customer segmentation is a typical application clustering, the data set chosen here offers a  fresh perspective beyond the standard academic dataset.
+
+# Features
+. CustomerID : Unique identifier for the customer
+. Gender : Customer's gender
+. Age : Customer's age
+. Annual Income (k$) : Customer's annual income in thousand dollars
+. Spending Score (1-100) : Score assigned by the mall based on customer behavior and spending nature
+
+# Task 1: Data Loading and Preprocessing
+. Load the dataset. The dataset can be sourced from Kaggle: Mall Customer Segmentation Data Link:
+https://www.kaggle.com/datasets/vjchoudhary7/customer-segmentation-tutorial-in-python.
+. Conduct necessary preprocessing steps, including handling missing values if any and encoding categorical variables.
+
+# Task 2: Exploratory Data Analysis (EDA)
+
+. Perform an EDA to understand the dataset's characteristics, focusing on the distribution of key features like Age , Annual Income (k$) ,
+and Spending Score .
+. Visualize the relationships between Annual Income (k$) and Spending Score, as these are crucial for understanding customer behavior.
+
+# Task 3: Preparing Data for Clustering
+. Decide which features to use for clustering. While CustomerID should be excluded, consider whether Gender should be included and how it
+might affect the clustering process.
+. Normalize the data if necessary to ensure that the scale of the features does not unduly influence the clustering.
+
+# Task 4: Applying K-Means Clustering
+. Apply K-Means clustering to the dataset. Start with a range of cluster numbers (e.g., 1 to 10) and use the elbow method to determine the optimal
+number of clusters.
+· Analyze the characteristics of each cluster. What does each cluster represent in terms of customer segmentation?
+
+# Task 5: Evaluation and Interpretation
+
+. Evaluate the clustering result. Discuss how well the segmentation aligns with your expectations and any insights gained from this exercise.
+. Visualize the clusters in a 2D plot, using Annual Income (k$) and Spending Score for the axes, to illustrate the customer segments.
+
+.