Clothing Aanlysis

I began by reading the raw data and understanding its structure and the relationships between sections, categories, and reviews, progressing through the following stages: 1. Reading the Data and Understanding the Relationships: Exploring the fundamentals and understanding how subsections (sections) relate to actual categories (chapters). Analyzing the demographic distribution (age) and its relationship to review and rating behavior. 2. Processing and Cleaning the Data (Cleaning and Preprocessing): Managing the price tags in category and rating names. Preparing the digital data for statistical analysis. Making clean, organized tables ready for analysis. 3. Integrating the Data and Extracting Insights: After preparing the data, I linked it to cigarette devices to answer essential business questions, such as: What is the average age of the customer interested in each section (adult vs. young)? Which categories (product categories) would you like to see the largest share of positive reviews? Is there a relationship between age group and the type of clothing purchased? Identify the most in-demand categories (category names) in the market. Analyze the variation in the number of reviews (number of comments) and the level of experience. 4. Analysis Phase Using SQL: After completing data exploration with Python, SQL was used to simulate a real-world business environment by: Writing queries to generate aggregate reports (groupings) for each category. Grouping smart price and age categories with accurate figures. Calculating the metrics for the driver evaluation variances for each product category. 5. Final Phase: Building the Dashboard (Power BI): I developed an interactive dashboard based on: Key Performance Indicators (KPIs): such as pros, cons, and average customer ages. Smart visualizations: Pie charts and bar charts were used to facilitate comparing the performance of different categories (e.g., T-shirts vs. dresses). Providing a specific visual perspective helps innovation decision-makers identify the "most successful" products and the most popular age groups loyal to the brand.