With over 5 years of experience in the automotive, manufacturing, and e-commerce industries, I have honed my analytical and problem-solving skills through roles in quality management, process improvement, and team leadership. During my transition to data science, I completed a comprehensive course at GUVI Geek Networks. Winning the AI-based Application Development Contest demonstrated my skills and unique innovative approach.
Gaining valuable real-world experience, I completed a data science internship at Indian Institute of Technology (IIT) Punjab, which refined my ability to apply data-driven techniques effectively. Leveraging quick learning, adaptability, and the ability to handle pressure, combined with my background in Mechanical Engineering, I excel at transforming complex data into actionable insights that drive impactful and strategic business decisions.
Python, SQL, Statistics, Machine Learning, Natural Language Processing (NLP), Deep Learning(DL), Artificial Intelligence (AI)
scikit-learn, Regression, Classification, Clustering, Algorithms, Neural Networks, Keras, Tensorflow, RNN, CNN, ANN, OpenCV, Computer Vision, Pillow, Hyperparameter Tuning, Pandas, NumPy, AWS, Hugging Face, Streamlit
SpaCy, NLTK, Large Language Models (LLM), OpenAI, Text Vectorization (One-Hot, Bag of Words, TF-IDF, Word Embeddings), Optical Character Recognition (OCR), MongoDB, Power BI, Tableau, Excel, ETL, Exploratory Data Analysis (EDA), Selenium, BeautifulSoup, Data Visualization (Plotly, Matplotlib, Seaborn)
Winner of AI-based Application Development Contest
GUVI Geeks Networks | Sep 2023
Microsoft Certified: Azure AI Fundamentals (AI-900)
Microsoft | Oct 2023
IIT-M Advanced Programming and Master Data Science
GUVI Geeks Networks | Oct 2023
SQL for Data Science
Great Learning | Feb 2024
Advanced SQL
Great Learning & HackerRank | Feb 2024
SQL 5-Star Gold Batch
HackerRank | Mar 2024
Developed an AI application using LLM to analyze user resumes and provided the summarization, strengths, weaknesses, suggestions, suitable job titles, and also scraping job details from LinkedIn using Selenium. This application reduces time by 30% and helps candidates tailor their resumes effectively.
Technologies Used: Python, LangChain, LLM, OpenAI, Selenium, NumPy, Pandas, Streamlit, Hugging Face, AWS.
Engineered a robust deep learning model using Convolutional Neural Networks and TensorFlow to classify 114 bird species based on audio recordings. Model achieved an impressive accuracy of 93.4%, providing valuable insights for conservationists and ecologists in the wildlife & ecological research sectors.
Technologies Used: Python, TensorFlow, CNN, Keras, scikit-learn, OpenCV, NumPy, Pandas, Matplotlib, Streamlit, Hugging Face.
Build a comprehensive management application to automate the administrative and academic processes in educational institutions. Key features included user authentication, handwriting verification, examination management, and performance tracking. The system increased overall performance by 40%, facilitating seamless communication with stakeholders.
Technologies Used: Python, TensorFlow, CNN, Keras, scikit-learn, OpenCV, Pillow, PostgreSQL, NumPy, Pandas, Streamlit.
Developed a deep learning model using TensorFlow and Convolutional Neural Networks to classify disease images of potato plants, including early blight, late blight, and overall plant health in agriculture. Model achieved an impressive accuracy of 97.8%, empowering farmers with precise treatment applications to enhance crop yield and quality.
Technologies Used: Python, TensorFlow, CNN, Keras, OpenCV, Pillow, NumPy, Matplotlib, Streamlit, Hugging Face.
Engineered an advanced deep learning model to automate the classification of financial documents, including Balance Sheets, Cash Flow and Income Statements using Bidirectional LSTM and TensorFlow. The model achieved an impressive accuracy of 96.2%, enhancing efficiency and reducing errors in document management for the finance and banking sectors.
Technologies Used: Python, TensorFlow, LSTM, Keras, spaCy, NLTK, Word2Vec, NumPy, Pandas, Matplotlib, BeautifulSoup, Streamlit, Hugging Face.
Build a machine learning model to predict weekly sales with 97.4% accuracy. Integrated Exploratory Data Analysis (EDA) tools to analyze trends, patterns, and actionable insights. The solution enables detailed sales comparisons, evaluates feature impacts and ranges, and identifies top performers, greatly enhancing decision-making in the retail industries.
Technologies Used: Python, scikit-learn, PostgreSQL, NumPy, Pandas, ETL, EDA, Plotly, Matplotlib, Seaborn, Streamlit.
Developed two machine learning models for the copper industry: a regression model to predict sales prices with 95.7% accuracy and a classification model to identify potential customers with 96.5% accuracy. These models are integrated into a comprehensive solution, leveraging advanced techniques to better optimize decision-making in the copper industry.
Technologies Used: Python, scikit-Learn, Numpy, Pandas, Matplotlib, Seaborn, Streamlit.
Engineered an Streamlit application to analyze user data and transactions from the PhonePe Pulse dataset. Conducted Exploratory Data Analysis (EDA) on states, years, quarters, districts, transaction types, and user brands. Visualized trends and patterns using plots and charts to derive actionable insights and optimize decision-making in the Fintech industry.
Technologies Used: Python, PostgreSQL, Pandas, Plotly, Streamlit, GitHub.
Built an interactive Tableau dashboard to analyze Airbnb data and developed a Streamlit application for trend analysis, pattern recognition, and data insights using exploratory data analysis. Explored variations in price, location, property type, and seasons with interactive plots and charts, greatly aiding decision-making in the hospitality and real estate industries.
Technologies Used: Python, Tableau, Pandas, MongoDB, PostgreSQL, Plotly, Streamlit.
Developed an interactive Power BI dashboard to analyze factors influencing IMDB movie success. Conducted descriptive statistical analysis of genres, language, duration, director, and budget, revealing their impact on IMDB scores. Provided valuable insights to producers, directors, and investors, enhancing decision-making in the film industries.
Technologies Used: Python, Power BI, Pandas, Pillow, GitHub.