TUSHAR JITENDRA BHADANE

Aspiring Data Scientist | Machine Learning Engineer

Vapi, IN.

About

Highly analytical Bachelor of Technology student specializing in Computer Science with Cyber Security, possessing a robust foundation in Data Science, Machine Learning, and Natural Language Processing. Proven ability to develop and deploy high-accuracy predictive models, demonstrated by achieving 99.96% accuracy in credit card fraud detection and competitive results in sentiment analysis. Eager to leverage expertise in data analysis, machine learning, and problem-solving to contribute to innovative technical challenges within a dynamic technology environment.

Education

VIT Bhopal University

Bhopal, Madhya Pradesh, India
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Bachelor of Technology Computer Science with Cyber Security Grade: CGPA: 8.18

Awards

Qualified till Round 2 of national-level coding competition TCS CodeVita

TCS CodeVita

Achieved qualification through the initial rounds of a prestigious national coding competition, demonstrating strong algorithmic and problem-solving skills.

Languages

English

Certificates

NPTEL Cyber Physical System

NPTEL

The Bits and Bytes of Computer Networking

Coursera (Google)

Data Science Specialization

Coursera

AWS Cloud Practitioner Certificate

Amazon Web Services (AWS)

Google Data Analytics Professional Certificate

Google

Skills

Programming Languages

Python, SQL, Java, C++.

Data Science & Machine Learning

Statistical Analysis, Predictive Modeling, Data Mining, Scikit-learn, TensorFlow, Data Visualization, Matplotlib, Seaborn, Tableau, Power BI.

Data Handling & Tools

Pandas, NumPy, Jupyter Notebook, Excel, Database Management, MySQL, A/B Testing, Exploratory Data Analysis (EDA), Feature Engineering, Data Cleaning.

Natural Language Processing (NLP)

Text Preprocessing, Sentiment Analysis, NLTK.

Deep Learning

Neural Networks, CNN, RNN, Transfer Learning, TensorFlow.

Soft Skills

Critical Thinking, Problem-Solving, Communication, Collaboration, Adaptability.

Projects

Twitter Sentiment Analysis – NLP

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Summary

Performed sentiment classification of tweets to identify positive, negative, or neutral opinions using Python on the Sentiment140 dataset, applying various NLP techniques.

Credit Card Fraud Detection – Machine Learning

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Summary

Developed a machine learning model to detect fraudulent transactions using a dataset of 284,807 entries with 31 features, addressing class imbalance and visualizing results.