I am currently working as a Data Scientist Intern at WEBiSOFTECH, where I apply Machine Learning and Data Analysis techniques to solve real-world business problems. In my role, I work with Python and SQL to clean, transform, and analyze structured datasets. I perform exploratory data analysis (EDA), implement feature engineering techniques, and build predictive models for regression and classification tasks using Scikit-learn. I also develop interactive Power BI dashboards to visualize KPIs and support data-driven decision-making.
I am passionate about Artificial Intelligence and continuously enhancing my skills in Machine Learning and Deep Learning to build scalable and intelligent solutions. I am actively seeking full-time opportunities in Data Science and Machine Learning where I can contribute to impactful AI-driven projects and grow as a data professional.
Programming
Python (Pandas, NumPy, Scikit-learn), SQL
Machine Learning
Regression, Classification, Clustering
Deep Learning
ANN, CNN, NLP
Data Visualization
Power BI, Tableau, Matplotlib, Seaborn
Key Achievements
Built 3 Production ML Applications
Deployed live on Streamlit with real-world impact
30% Data Quality Improvement
Implemented advanced preprocessing and cleaning techniques
40% Dashboard Efficiency Gain
Created Power BI dashboards reducing reporting time
End-to-End Pipeline Expertise
Full experience from data cleaning to model deployment
Experience: WEBiSOFTECH
Data Scientist Intern
January 2026 – Present · 4 Months
Kolhapur, Maharashtra, India
As a Data Scientist Intern at WEBiSOFTECH, I apply Machine Learning and Data Analysis techniques to solve real-world business problems. My work spans the full data science pipeline — from data cleaning and exploratory analysis to model building and dashboard development — delivering actionable insights that support data-driven business decisions.
Data Cleaning & Processing
Cleaned and processed structured datasets using Python (Pandas, NumPy) and SQL, preparing data for downstream analysis and modeling.
Exploratory Data Analysis
Performed EDA to uncover patterns and insights within datasets, informing feature engineering and model development decisions.
Machine Learning Models
Built and evaluated ML models for regression and classification problems, applying feature engineering techniques to improve model performance.
Power BI Dashboards
Developed interactive dashboards to track KPIs and business metrics, delivering actionable insights to support data-driven decision-making.
Experience: DigiToad Technologies
Embedded Systems & AI Intern
January 2024 · 1 Month
Bengaluru
Role Overview
An internship focused on bridging embedded systems with Artificial Intelligence — gaining hands-on experience in hardware-software integration for intelligent automation.
Worked on integrating software logic with hardware components for intelligent system behavior, applying AI/ML concepts to enhance automation in embedded systems.
Key Contributions
Developed STM32-based embedded systems projects with real-time hardware integration
Applied AI/ML concepts to enhance automation in embedded systems using Python
Worked on integrating software logic with hardware components for intelligent system behavior
Gained hands-on experience in embedded programming, sensors, and microcontrollers
Projects
Production-ready data science and machine learning applications built to solve real business problems with measurable impact.
Problem: Retail businesses struggle to identify product affinities and optimize pricing and bundling strategies, leaving revenue on the table.
Solution: Built an end-to-end recommendation engine using Market Basket Analysis with the Apriori algorithm that identifies frequently co-purchased product combinations using Lift and Confidence scoring, classifies demand tiers (High/Medium/Low) for dynamic pricing optimization, generates actionable bundling and cross-sell strategies, and is deployed as a production-ready Streamlit web app with real-time BI dashboards.
Impact: Helps retailers increase average order value through intelligent product recommendations and data-driven pricing strategies.
Tech Stack: Python, Pandas, MLxtend, Scikit-learn, Streamlit, Association Rule Mining
IPL Smart Analytics & Intelligent Recommendation System
Problem: Cricket analysts and fans lack a unified platform for comprehensive match insights, player performance analysis, and intelligent match predictions.
Solution: Engineered a full-stack analytics platform featuring a scoring-based recommendation engine using contextual signals such as team rivalry, match margin, and team form; comprehensive team statistics, head-to-head analysis, and player performance metrics; venue and toss impact analysis with statistical modeling; and real-time interactive visualizations in a production-ready deployment.
Impact: Delivers actionable insights for match analysis, enabling data-driven predictions and strategic decision-making for cricket enthusiasts and analysts.
Automated Machine Learning Pipeline for Regression Analysis
Problem: Data scientists spend significant time on repetitive preprocessing, model selection, and hyperparameter tuning, slowing down model development cycles.
Solution: Developed an automated ML pipeline that streamlines the entire workflow by automating data cleaning, feature encoding, and IQR-based outlier removal; training multiple models including Linear Regression, Decision Trees, and Random Forests; automatically evaluating and selecting the best model based on R² and RMSE metrics; and providing visualization support for model comparison and performance interpretation.
Impact: Reduces manual effort in model development by 50%, enabling faster experimentation and more reliable model selection for regression tasks.
Tech Stack: Python, Scikit-learn, Pandas, NumPy, Linear Regression, Decision Trees, Random Forests
A comprehensive overview of key technical competencies, spanning programming, machine learning, deep learning, data analytics, and cloud platforms. These skills empower the development of innovative and data-driven solutions.
Languages & Libraries
Proficient in Python, SQL, and MATLAB. Experienced with Pandas, NumPy, Scikit-learn, TensorFlow, Keras, Matplotlib, Seaborn, and MLxtend for robust data manipulation and visualization.
Machine Learning
Expertise in Supervised & Unsupervised Learning, Regression, Classification, Decision Trees, Random Forests, Gradient Boosting, K-Means Clustering, Feature Engineering, Hyperparameter Tuning, and Model Evaluation (R², RMSE, Accuracy, F1-Score).
Deep Learning & AI
Skilled in ANN, CNN, NLP, TensorFlow, Generative AI, LLMs, LLM APIs, Retrieval-Augmented Generation (RAG), LangChain (Fundamentals), Information Retrieval, and Association Rule Mining (Apriori).
Data, Analytics & BI
Strong capabilities in EDA, Statistical Analysis, Data Mining, Data Preprocessing, Feature Selection, IQR Outlier Removal, Business Intelligence, KPI Tracking, Power BI, Tableau, and Streamlit for actionable insights.
Cloud, Tools & Platforms
Experienced with AWS (SageMaker, ML Learning Plan, Generative AI, No-code ML), Azure (Fundamentals), Git, GitHub, Jupyter, VS Code, Google Colab, Apache Spark (Fundamentals), and STM32 / Embedded AI.
Certifications
Recognized credentials validating expertise across Machine Learning, AI, and Data Science — spanning platforms from AWS to MATLAB.
Yuva AI for All
Certification in Artificial Intelligence fundamentals, building foundational knowledge in AI concepts and applications.
Machine Learning Learning Plan – AWS
Structured learning path covering Machine Learning concepts and tools on the AWS cloud platform.
No-code ML & Generative AI on AWS
Certification in no-code Machine Learning and Generative AI capabilities available on AWS.
Machine Learning with MATLAB
Certification demonstrating proficiency in applying Machine Learning techniques using MATLAB.
Data Science & Analytics
Certification covering Data Science methodologies and Analytics techniques for deriving insights from data.
Education
Bachelor of Technology — BTech
Electrical, Electronics and Communications Engineering
DKTE'S Textile and Engineering Institute — An Autonomous Institute
During my BTech in Electrical, Electronics and Communications Engineering, I developed a strong technical foundation that supports my work in data science and machine learning. My academic background in electronics and communications engineering provides the analytical grounding I apply to data cleaning, exploratory data analysis, and building predictive models for regression and classification tasks.
Let's turn data into decisions
I’m actively targeting full-time opportunities in Data Science, Machine Learning, and AI globally—especially roles like ML Engineer, Data Scientist, or Analytics Engineer where I can build impactful, production-ready solutions. I’m particularly interested in teams working on predictive analytics, recommendation systems, computer vision, and data-driven product experiences. If you're hiring for a role where Python, SQL, Machine Learning, and Deep Learning can make a real difference, I’d love to connect and contribute.
Let’s build something meaningful—turning ideas, data, and models into real-world impact.