📊 Classic Models Data Analysis Project

📝 Overview

The Classic Models Data Analysis Project provides insights into sales performance, customer behavior, inventory trends, and customer segmentation of Classic Models using SQL databases, Python, and NLP techniques.
It processes data from the Classic Models database to generate visualizations and reports that aid in decision-making.

Data Source: MySQL Sample Database

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🚀 Key Features

📈 Sales Performance Analysis

• Tracks monthly revenue and sales trends.
• Analyzes top-selling products and high-revenue regions.

🧑‍💼 Customer Segmentation (with LDA)

• Uses Latent Dirichlet Allocation (LDA) for topic modeling and customer segmentation.
• Groups customers based on spending patterns, purchase history, and behavioral trends.
• Identifies key clients and growth opportunities.

📦 Inventory Management Insights

• Evaluates stock levels and product turnover rates.
• Recommends strategies for inventory optimization.

📊 Data Visualization

• Generates interactive charts and graphs.
• Provides dashboard-ready insights.

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🛠 Tech Stack

• 💾 Database: MySQL for data storage and querying.
• 🐍 Programming: Python with Pandas, Matplotlib, Seaborn, and NumPy.
• 🧠 NLP: Latent Dirichlet Allocation (LDA) for topic modeling and segmentation.
• 📊 Tools: Jupyter Notebooks for interactive data analysis.

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📌 Usage

• 💼 Sales Teams: Forecast revenue and identify growth opportunities.
• 📦 Inventory Managers: Optimize stock levels based on sales patterns.
• 📊 Marketing Teams: Target promotions based on customer segments derived from LDA analysis.