Sahil Ruhil

Bahadurgarh

Summary

Aspiring Data Scientist with hands-on experience in data preprocessing, model development, and visualization. Skilled in Python, SQL, and cloud platforms, with a strong foundation in machine learning and deep learning. Passionate about leveraging data to derive actionable insights, and deliver impactful solutions.

Overview

year of professional experience

Certification

Work History

Application Support Engineer

MIPL Solutions

11.2024 - Current

Experienced in SQL and banking operations, specializing in the Cheque Truncation System (CTS).
Skilled in troubleshooting and assisting bank staff with CTS issues, ensuring smooth system operations and efficient query resolution.

Data Science Intern

AI Variant

10.2024 - 07.2025

Developed a comprehensive knowledge of data and a deep understanding of business problems.
Conducted thorough data cleaning and preprocessing, as well as exploratory data analysis.
Built predictive models using machine learning algorithms, assessed model performance, and provided clear documentation for optimal outcomes.
Documented methodologies and results for reproducibility and future reference.

Education

Bachelor of Engineering - Electronics and Telecommunication engg.

MITWPU

Pune

07-2022

Skills

Programming and libraries: Python (NumPy, Pandas, Scikit-learn, XGBoost, LightGBM), R
Databases: BigQuery and MySQL
Machine learning: regression, classification, clustering, feature engineering, hyperparameter tuning

Visualization: Tableau, Power BI, Matplotlib and Seaborn
Cloud and tools: AWS, Azure, Vertex AI, JupyterLab, Excel, GitHub, PowerPoint

Certification

Masters program in data science (Gold category), NASSCOM, 28/03/2025
Data Science Certification, ExcelR Solutions, 11/23/24
Python certification, Infosys

Projects

• Sentiment Analysis of Product Reviews : Developed an NLP pipeline to classify customer sentiment from Amazon product reviews. Performed web scraping, text preprocessing (tokenization, lemmatization), and exploratory data analysis. Built and compared multiple sentiment classifiers (Logistic Regression, Naive Bayes). Deployed the final model via Streamlit for real-time review sentiment prediction. This project focused on capturing customer feedback trends and improving user experience insights.

• Fraud Detection in Financial Transactions :Built a machine learning model to proactively identify fraudulent transactions from a dataset with over 6 million records. Applied extensive data cleaning, handled missing values, outliers, and multicollinearity. Used SMOTE for class imbalance and implemented ensemble models including Random Forest and XGBoost. Evaluated using ROC-AUC and accuracy. Extracted key predictors of fraud and proposed actionable prevention strategies. Final insights were delivered through visual dashboards

• Song Recommendation System : Developed a personalized song recommendation system using collaborative (SVD, ALS), content-based (TF-IDF, cosine similarity), and hybrid filtering Evaluated performance with precision, recall, and F1-score, and deployed the system using Streamlit.

• Clustering of Global Development Measurement : Implemented a clustering model to analyze global development metrics, identifying patterns among countries based on socioeconomic indicators. Performed data preprocessing, feature selection, and dimensionality reduction using PCA. Applied K-Means and Hierarchical clustering to segment countries and evaluated cluster quality using silhouette scores. Visualized insights through data-driven dashboards and deployed the model using Streamlit for interactive exploration.

Timeline