Contact
Let’s Connect
I’m always open to new opportunities, collaborations, or discussions in data science and AI. Feel free to reach out via email or connect with me on LinkedIn or GitHub!
Turning Data Into Impactful Decisions
Solving real-world problems using machine learning, analytics, and intelligent data-driven solutions
About
I'm Meena, a mathematics postgraduate and passionate data science enthusiast. I enjoy turning data into meaningful insights through machine learning, analytics, and real-world applications.
About Me
Data Science Professional in the Making
I’m passionate about solving real-world problems using data, machine learning, and analytical techniques. My background in mathematics helps me think critically and build intelligent models that uncover insights.
From data cleaning to deploying models, I enjoy the complete data science pipeline and continuously explore new technologies to stay ahead in the AI-driven world.
Name
MEENA M
Phone
9791537311
Age
24 Years
Email
meenamgrp@gmail.com
Occupation
Data Scientist
Nationality
Indian
Skills
Python
Proficient in Python for data analysis, machine learning, and building end-to-end data science projects using libraries like Pandas, NumPy, and Scikit-learn.
90%Machine Learning
Skilled in building supervised and unsupervised machine learning models using Scikit-learn and Python. Applied techniques like regression, classification, clustering, and model evaluation in real-world projects.
90%Statistics
Strong foundation in statistical concepts such as hypothesis testing, probability, distributions, correlation, and regression — essential for data analysis and model interpretation.
90%Mysql
Experienced in writing complex SQL queries to extract, filter, join, and aggregate data from relational databases for analysis and reporting.
90%Work Experience
MASAI SCHOOL
FEB 2025 - CurrentMASTER'S IN DATA SCIENCE
Currently undergoing hands-on training in data science, covering Python, databases, Excel, machine learning, deep learning, and advanced statistics through real-world projects. Mentored by industry professionals from top MNCs, I’m building expertise in solving practical data challenges. I'm also strengthening my communication and analytical thinking to deliver impactful, data-driven decisions.
Vlippr Pvt Ltd
Dec 2023-Jun 2024Data Scientist Intern
Worked as a Data Science Intern at Vlippr Pvt. Ltd., contributing to voice-based AI systems and multilingual transcription pipelines.
- Achieved 100% voice conversion accuracy using RVC inference via Gradio; trained models in Google Colab with Ngrok integration.
- Transcribed multilingual audio using Whisper and Vasista models on GCP, achieving a 10% Word Error Rate (WER).
- Performer of the Week: Awarded for exceptional project execution.
- Star of the Month: Recognized for dedication and excellence in data science.
Upwork
Jul 2022-Nov 2024Data Scientist Freelance
Explored freelancing to understand real-world data science requirements and client expectations. Strengthened skills in data analysis, machine learning, and proposal writing through self-driven projects using Python, TensorFlow, and visualization tools.
My Education
St Joseph's college of Arts & science for Women
2020-2022Master's in Mathematics(88%)
St Joseph's college of Arts & science for Women
2017 - 2020Bachelor's in Mathematics(83%)
Projects
Amazon Product Review Sentiment Analysis
Built a sentiment classifier to label Amazon reviews as positive or negative. Preprocessed text using NLTK, TF-IDF, WordNet Lemmatizer, and regex. Generated embeddings with Word2Vec, GloVe, and FastText. Trained models like MultinomialNB, Logistic Regression, and LSTM with embedding layers. Addressed class imbalance using SMOTE and achieved 87% accuracy (NLP) and 93% accuracy (Deep Learning).
Tech Stack: Python, NLTK, Word2Vec, GloVe, FastText, TensorFlow, Keras
GitHubPlant Village Disease Prediction System
Implemented an image classification system using ResNet50 with Global Average Pooling for accurate plant leaf disease detection. Achieved 95.54% training and 94.78% test accuracy. Deployed the model as a real-time web application using Streamlit for disease prediction.
Tech Stack: OpenCV, NumPy, Pandas, TensorFlow, Keras, Streamlit, Matplotlib
GitHub