Project utilizing web scraping, data cleaning, sorting, database uploading, data analysis, and clustering for data processing and analysis purposes.

Car Web Ads Scraper and Data Analysis This project is designed to scrape car web ads, clean the raw data, perform data sorting, upload it to a PostgreSQL database, conduct data analysis and clustering using Scikit-learn and K-means algorithm, and finally describe the clusters. The entire project is implemented in Python, utilizing libraries such as Pandas, Requests, Beautiful Soup, Matplotlib, and Pyplot.

Project Overview The purpose of this project is to gather car-related information from various web ads, clean the obtained data, and perform in-depth analysis for insightful findings. The following steps are involved in the project workflow:

Web Scraping: Utilizing Requests and Beautiful Soup, the project scrapes car web ads to collect data.

Data Cleaning: Raw data obtained from web ads is cleaned, removing inconsistencies and ensuring uniformity in the dataset.

Data Sorting: The cleaned data is sorted based on relevant attributes, allowing for effective data analysis.

PostgreSQL Database: The sorted data is uploaded to a PostgreSQL database, ensuring seamless data management.

Data Analysis: Using Pandas and Matplotlib, the project performs data analysis, uncovering valuable insights and trends.

Clustering: The Scikit-learn library is utilized for implementing the K-means clustering algorithm, enabling the identification of distinct groups within the dataset.

Cluster Description: After clustering, the clusters are described, providing a comprehensive understanding of their characteristics.