Hi There !

I'm Ipsita Mishra

A Final Year Computer Science Student

About Me

I am Ipsita Mishra, a final year student pursuing BTech at GIET University, Gunupur, Odisha and my major field of Study is Computer Science and Engineering. Currently, Working as a Google DSC Campus Lead. I have a strong proficiency in AIML, Data Science, and Cloud technology. I'm an enthusiast in these fields and have actively engaged in various projects and internships to deepen my expertise.

Skills

Programming Languages

Python

C

Java

SQL

Frameworks

Tensorflow

Keras

Scikit-learn

Web Development

HTML

CSS

JavaScript

Data Visualization

Matplotlib

Excel

Tableau

Google Sheets

Data Science

Data Analysis

Natural Language Processing

Computer Vision

Core Subjects

DSA

OOPS

DBMS

Statistics

OS

Education

BTech in CSE

GIET University, Gunupur, Odisha
2021 - 2025

Higher Secondary

Sri Chaitanya Techno School, Visakhapatnam, AP
2019 - 2021

Matriculation

S.V.B.V, Koraput, Odisha
2009 - 2019

Experience

URSC, ISRO

Data Analyst Intern
View more
    • Conducted comprehensive analysis of telemetry data to identify patterns and insights, enhancing data-driven decision-making processes.
    • Built and validated predictive models using telemetry data, achieving a 15% improvement in prediction accuracy.
    • Streamlined and optimized data preprocessing pipelines, resulting in a 20% reduction in data processing time.
    • Documentation and Reporting,
    • Continuous Learning.
    • Created detailed reports and documentation on data analysis findings and suggested actionable improvements.

IIT Indore

Data Science Intern
View more
    • Developed and fine-tuned deep learning models for image and text data, improving system accuracy by 20%.
    • Collaborated with a cross-functional team to implement scalable deep learning solutions, reducing processing time by 25%.
    • Designed and optimized data preprocessing pipelines, enhancing model performance and data integrity by 30%.
    • Conducted research on advanced deep learning techniques, contributing to a project that achieved a 92% accuracy rate in predictive analytics.

Google DSC

Campus Lead
View more
    • Selected among India's top 700 students as GDSC Lead.
    • Trained by Google in Data Science, ML, and Google Cloud Platform.
    • Addressed students globally as a speaker at over 15 events.
    • Selected as Google Cloud Facilitator, trained and guided 150+ students, with 100% achieving certification.
    • Also selected as Google Gen AI Facilitator, trained and guided 150+ students, with 80% achieving certification.

Projects

Optical Character Recognition Model

Deep Learning , Computer Vision , Neural Networks

  • Developed a deep learning OCR model using Tensorflow and Keras that recognizes handwritten alphabets.
  • Used a dataset consisting of 28x28 pixel images of handwritten alphabets, with a total of 372,037 entries.
  • Evaluated the OCR model on a test set, achieving a accuracy of 98.16%.
Source Code

Book Recommendation System

Collaborative Filtering , Popularity Based

  • Implemented an item-based collaborative filtering method for the recommendation system and used cosine similarity to calculate item-item similarities and generate accurate recommendations.
  • Developed a Popularity Based Recommender System that handpicked the top 50 books with the highest average ratings, considering only those that received a minimum of 250 ratings.
Source Code

Deepfake Detection

Deep Learning

  • This Projects Aims In Detection Of Video Deepfakes Using Deep Learning Techniques Like ResNext And LSTM.
  • We Have Achived Deepfake Detection By Using Transfer Learning Where The Pretrained ResNext CNN Is Used To Obtain A Feature Vector, Further The LSTM Layer Is Trained Using The Features.
Source Code

Criminal Detection

Python, Open CV

  • This project aims to identify Criminal and Missing People to enhance and upgrade the Criminal distinguishing into a more effective and efficient approach.
  • System Will Detect The Face And Recognize The Criminal.
Source Code

Sentiment Analysis Model

Natural Language Processing , Sentiment Analysis , Machine Learning

  • Trained and developed a Sentiment Analysis model using Machine Learning techniques to analyze the sentiments of tweets about National Education Policy 2020.
  • Used the TextBlob library to label and classify tweets as positive, negative or neutral based on their sentiment.
  • Successfully implemented and tested four supervised learning algorithms including Bernoulli Naive Bayes, XGBoost, Logistic Regression, and Decision Tree to improve the accuracy of the model.
  • Achieved an accuracy of 91.2% with the Decision Tree algorithm, which was the best-performing model in the study.
Source Code

Hey devs!! Feeling lost in the Matrix of code?

PRs, Data chats, or existential dread? I got you.

Contact me

Contact me

Call me

+91 8658838323

E-mail

mishraaipsitaa702@gmail.com

Location

Odisha, India