AhmedEssamElDien/Smartphone-Market-Analytics-Egypt

A full-stack analytics project exploring smartphone trends in Egypt using synthetic sales data. Includes data cleaning, segmentation, anomaly detection, and a dynamic Power BI dashboard. \# 📱 Smartphone Market Analytics – Egypt This project explores smartphone sales data in Egypt, focusing on cleaning, transformation, and preparation for analytics workflows. The goal is to produce a high-quality, reproducible dataset ready for advanced analysis and dashboarding. --- \## Tools Used \- \*\*Python\*\* \- \*\*pandas\*\* for data cleaning and transformation \- \*\*CSV\*\* as the final export format --- \## Dataset Overview The raw dataset contains product-level information scraped from e-commerce platforms, including: \- Product identifiers and titles \- Pricing details (original and discounted) \- Ratings and review counts \- Offer counts and seller flags \- Brand and product type \- Sales volume (textual) --- \## 🧹 Data Cleaning Steps The cleaning pipeline includes: \### 1. \*\*Column Pruning\*\* \- Removed irrelevant or redundant columns not useful for analysis. \### 2. \*\*Duplicate Handling\*\* \- Dropped exact duplicate rows to ensure uniqueness. \### 3. \*\*Type Conversion\*\* \- Converted `product\_price` and other numeric fields to proper `float` types. \- Ensured all numeric columns are free of non-numeric characters. \### 4. \*\*Missing Value Imputation\*\* \- Imputed missing numeric values using \*\*median\*\* strategy for robustness. \### 5. \*\*Sales Volume Normalization\*\* \- Created a new column `sales\_volume\_clean` to standardize textual sales volume into a consistent format. --- Author Ahmed — Data Analyst Focused on building reproducible analytics workflows.