📊 Analysis and Clustering of Business Revenue Factors

📌 Overview

This project focuses on analyzing key business factors that influence revenue and applying clustering techniques to identify patterns and group similar business profiles. It helps in understanding customer behavior and optimizing revenue strategies.

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🎯 Objective

To analyze business data and use clustering algorithms to group entities based on revenue-related factors, enabling better decision-making and targeted business strategies.

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🚀 Features

• Data preprocessing and cleaning
• Exploratory Data Analysis (EDA)
• Feature selection and scaling
• Clustering using K-Means
• Visualization of clusters
• Insights generation for revenue optimization

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🤖 Clustering Model

Algorithm Used:

• K-Means Clustering

Workflow:

1. Data Collection
2. Data Cleaning & Preprocessing
3. Feature Scaling
4. Finding Optimal K (Elbow Method)
5. Model Training
6. Cluster Visualization

The optimal number of clusters is determined using the Elbow Method, and the model groups data points based on similarity in revenue-related features.

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📊 Visualization

• Elbow curve to determine optimal clusters
• Scatter plots to visualize grouped data

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🛠️ Tech Stack

• Python
• Pandas
• NumPy
• Scikit-learn
• Matplotlib

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