abdallasaad35/egypt-used-cars-analysis

An End-to-End Data Analytics project built with Python to clean, analyze, and visualize the used car market trends in Egypt. # Egypt Used Cars Market Data Analytics Project ## 📌 Project Overview This is an **End-to-End Data Analytics project** built with Python to simulate, clean, analyze, and visualize used car market trends in Egypt. The project demonstrates a complete data professional workflow: overcoming data acquisition constraints, cleaning unstructured text data, applying business-driven missing value imputation, conducting Exploratory Data Analysis (EDA), and creating professional data visualizations. --- ## 🏗️ Data Architecture & Lifecycle ### 1. Data Simulation & Acquisition * **Challenge:** Direct web scraping from local automotive platforms faced robust anti-scraping policies (e.g., HTTP 403 Forbidden errors due to automated environments). * **Solution:** Developed a programmatic data generation script that simulates a highly realistic dataset of **250 used car listings** matching real-world Egyptian market dynamics, brand distributions, and pricing tiers (including Hyundai, Kia, Toyota, Chevrolet, and Fiat). * **Data Anomaly Injection:** Purposefully injected missing values (`NaN` in prices) and extreme outliers (unusually high mileages) to recreate authentic, messy real-world data environments. ### 2. Data Cleaning & Preprocessing * Removed regional currency strings (`EGP`) and localized thousands separators (commas) using regular expressions. * Handled missing price entries utilizing a **Groupby Imputation Strategy** (replacing missing prices with the calculated mean price of that specific car's Brand and Model) to preserve dataset integrity and business logic. * Converted cleaned features into optimized numeric data types (`int` and `float`) for advanced computational analytics. ### 3. Exploratory Data Analysis (EDA) & Insights The dataset was aggregated to extract high-value business insights regarding the Egyptian automotive sector: * **Market Share:** Identified the most frequently listed automotive brands. * **Pricing Tiers:** Analyzed the average valuation of …