Welcome to my Data Science Portfolio. Here, you'll find a collection of my projects related to Data Analysis and Data Science. Each project includes a brief description, technologies used, and a link to the GitHub repository.

A UI interface allows users to input the channel ID of a YouTube channel of interest. Using the Google API key, the project retrieves information about the channel, playlists, videos, and comments, storing the data in a backend database. The project also includes a visualization module where users can select from 10 questions, with data being retrieved and displayed from the database.

Technologies used: Pandas, logging, exception handling, sqlconnector, mysqlworkbench, streamlit
Link to access the Project: GitHub Repository

This project involves cloning PhonePe's transaction and user data from a GitHub repository. The data is preprocessed into Python dataframes and stored in a backend database. An interactive geovisualization dashboard provides insights into transactions and user activity over different years and quarters, along with additional insights.

Technologies used: Gitcloning, Pandas, logging, exception handling, sqlconnector, plotly, mysqlworkbench, streamlit
Link to access the Project: GitHub Repository

This project extracts customer details from business cards using a UI that allows users to upload the card. EasyOCR is used to extract the required information, which can then be stored, updated, or deleted in a database as needed.

Technologies used: easyOCR, Pandas, logging, exception handling, sqlconnector, mysqlworkbench, streamlit
Link to access the Project: GitHub Repository

Analyzing various Airbnb properties worldwide, this project offers insights into property prices, review scores, amenities, and occupancy over different years and months. A map visualization displays property details globally, with options to filter by country, city, and property type.

Technologies used: Pandas, plotly, streamlit, powerbi
Link to access the Project: GitHub Repository

Addressing issues like skewness and noisy data in the copper industry, this project involves creating machine learning models for regression (predicting selling price) and classification (predicting status). Advanced techniques like data normalization and outlier detection are used to improve prediction accuracy.

Technologies used: Pandas, numpy, stats, scipy, plotly, sklearn, streamlit
Link to access the Project: GitHub Repository

Developing a machine learning model to predict the resale prices of flats in Singapore, this project deploys a user-friendly web application based on historical resale data. It assists buyers and sellers by estimating resale values considering factors like location, flat type, floor area, and lease duration.

Technologies used: Pandas, numpy, stats, scipy, plotly, sklearn, streamlit, aws
Github Link to access the Project: GitHub Repository Application Link: Render Application Link

This project involves creating a machine learning model to help banks and financial institutions assess the risk of potential borrowers defaulting on loans, aiding in loan approval decisions.

Technologies used: Pandas, numpy, stats, scipy, plotly, sklearn, streamlit, aws
Link to access the Project: GitHub Repository

Creating a chatbot to resolve bank customer queries, this project uses bank documents like statements and policy documents to train the bot to respond accurately to customer questions.

Technologies used: Pandas, numpy, stats, scipy, plotly, sklearn, streamlit
Link to access the Project: GitHub Repository