AI Engineer | AI Agents | Automation | Cloud | Data Analytics | Open to AI Startups

Magudapathi R  • Peerlist

This project aims to analyze Airbnb data using MongoDB Atlas, perform data cleaning and preparation, develop interactive geospatial visualizations, and create dynamic plots to gain insights into pricing variations, availability patterns, and location-based trends

Airbnb Analysis

The application would also allow users to save the extracted information into a database along with the uploaded business card image. The database would be able to store multiple entries, each with its own business card image and extracted information. The final application would have a simple and intuitive user interface that guides users through the process of uploading the business card image and extracting its information. The extracted information would be displayed in a clean and organized manner, and users would be able to easily add it to the database with the click of a button.

BizCardX: Extracting Business Card Data with 

The Phonepe pulse Github repository contains a large amount of data related to various metrics and statistics. The goal is to extract this data and process it to obtain insights and information that can be visualized in a user-friendly manner. The solution includes the following steps: 1. Extract data from the Phonepe pulse Github repository through scripting and clone it.. 2. Transform the data into a suitable format and perform any necessary cleaning and pre-processing steps. 3. Insert the transformed data into a MySQL database for efficient storage and retrieval. 4. Create a live geo visualization dashboard using Streamlit and Plotly in Python to display the data in an interactive and visually appealing manner. 5. Fetch the data from the MySQL database to display in the dashboard. 6. Provided with 10 different dropdown options for users to select different facts and figures to display on the dashboard. The solution is secure, efficient, and user-friendly. The dashboard must be easily accessible and provide valuable insights and information about the data in the Phonepe pulse Github repository.

Phonepe Pulse Data Visualization and Explorat

This project focuses on harvesting YouTube data using the YouTube API, storing it in MongoDB, converting to SQL for analysis. Utilizes Streamlit, Python, and various methods for ETL. Expertise includes Python, MongoDB, SQL, API integration, and data management tools . This project mainly reduces 80% percentage of manually data processing and data storing work effectively.