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Data Science with less than a year in Machine Learning, Deep Learning, and NLP.
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Mrunali Ganesh Wande is an aspiring Data Scientist with a strong academic background in Data Science and Computer Science & Engineering. She possesses a comprehensive skill set in programming (Python, SQL), web technologies (HTML, CSS, Javascript), machine learning (Supervised, Unsupervised, XGBoost, Classification, Regression, Random Forest, SVM), deep learning & NLP (PyTorch, Tensorflow), and visualization tools (Power BI, Streamlit, Tableau). Her project experience includes developing intelligent systems for resume analysis, crop yield prediction, and TV show popularity analysis, demonstrating her ability to apply advanced analytics and machine learning to solve real-world problems.
Savitribai Phule Pune University, Pune
M.Tech · Data Science
August 1, 2024 – May 1, 2026
Sipna College of Engineering and Technology, Amravati
B.E · Computer Science & Engineering
July 1, 2020 – May 1, 2024
Vivekananda Junior College,Jamtha,Nagpur
HSC
N/A – May 31, 2020
New Orange City Convent, Warud
SSC
N/A – May 31, 2018
Smart Resume Analysis Ranking Using NLP and ML
June 24, 2026 – Present
Developed a hybrid multimodal system for analyzing PDF resumes and voice introductions using Streamlit and Sentence Transformers. Designed and implemented HALA (Hybrid Acoustic-Linguistic Assessment) algorithm to evaluate communication quality and keyword relevance from voice input. Built ATS compatibility scoring with fuzzy skill extraction, along with personalized course recommendations based on skill gaps.
Crop Yield Prediction Using Climate, Soil, and Historical Agricultural Time Series Data with Bidirectional LSTM
June 24, 2026 – Present
Designed and implemented a Bidirectional LSTM-based time series model for accurate crop yield forecasting in Maharashtra by integrating multi-source agricultural data (climate, soil, and historical records), achieving R2 = 0.5754.
TV Show Popularity Analysis Using Data Mining (IMDb Dataset)
June 24, 2026 – Present
Performed data mining and feature analysis on large-scale IMDb dataset to uncover patterns affecting TV show popularity and audience engagement. Developed predictive models to forecast TV show success based on genre, cast, runtime, and historical rating trends using Python.
Optical Character Recognition(OCR) Text Detection Using Tessaract
June 24, 2026 – Present
Used for classification of optical pattern in digital images corresponding to alphanumeric or other characters using (CNN,Tessaract, ICR)
Tours and Travel Management System
June 24, 2026 – Present
It provides convenient way for a customer to book packages with all facilities using (HTML,CSS,SQL,PHP,Javascript).
Smart Resume Analysis and Ranking Using NLP and Machine Learning
International Journal of Innovative Science and Research Technology (IJISRT)
May 1, 2026 – Present
Optical Character Recognition(OCR)Text Detection using Tesseract
International Research Journal of Engineering and Technology(IRJET)
April 1, 2024 – Present
Cultural Fit Analysis
The candidate's project portfolio demonstrates a strong interest and foundational skills in Data Science, aligning well with the target role. The diversity of projects, ranging from crop yield prediction to resume analysis and TV show popularity, indicates a broad curiosity and ability to apply data science techniques across different domains. The academic focus of all projects and lack of professional experience means cultural fit in a corporate environment is yet to be proven. The candidate is pursuing a Master's degree, which shows a commitment to continuous learning and specialization.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on complex, multi-faceted problems. The academic nature of all projects suggests a strong foundation in theoretical concepts and problem-solving within a structured environment. However, there is no information regarding collaboration, leadership, or communication in a professional team setting, which are crucial for operational fit in a senior role. The candidate's experience level is 0, indicating a lack of professional work experience.