Name: Ganga Dhwaj Lingden

Profile: Aspiring Data Scientist

Email: lingden.ganga@gmail.com

Phone: +358-442005386

Tech Stack

Data Science: Python, Sckit-Learn, Pandas, Numpy, Pytorch, Keras/Tensorflow

Web Development: HTML5, CSS, Javascript, React

Cloud Platform: AWS

About Me

👋 Hello there!, I'm Ganga, pursuing my M.Sc.(Tech) in Machine Learning at Tampere University. I am a data science enthusiast with a strong interest in the application of machine learning and deep learning techniques. I am passionate about unlocking insights from data and using these insights to drive decision-making.

Through my experience working on projects involving diverse data types, such as text, image, time series, and audio, I have developed an interest in the potential of data science and machine learning techniques across different domains. I aspire to apply these skills and knowledge to solve the real-world problems.

Project

You can view projects.

Multi Task Audio Classification

Project for Advanced Audio Processing Course at Tampere University. In this project, we explored the effectiveness of multi-task learning for audio classification tasks.

Time Series Analysis and Forecast

In this project, the univaritate and multi-variate time sereies forecast is perfomed using statsmodel alogorithms.

Country App

This web app provides information about countries worldwide, including population, languages, and borders. Developed using React.js, React hooks, Redux, and Material UI.

Sentiment Analysis

In this project, employed multiple word embedding techniques, such as CountVectorizer, TF-IDF, Word2Vec, and Dov2Vec for performing sentiment classification: negative, neutral, and positive.

Protein-Protein Interaction Analysis with Networkx

In this analysis, examine the behaviors of protein-protein interactions to identify the protein whose status change has the greatest potential effect on the rest of the network.

Customer Churn Prediction

The aim of this project is to build customer churn prediction models using different algorithms and determine the best one. Additionally, a detailed exploratory data analysis (EDA) is conducted on the dataset to gain insights.