TARANVEER SINGH
Linkedin | Github | Singhtaranveer094@gmail.com | +91-9315642730
Education
Vivekananda Institute of Professional Studies affiliated by GGSIPU, New Delhi 2021-2025
Branch: IIOT | CGPA: 8.66/10 (till 7th semester) (expected)
Hansraj Model School Punjabi Bagh, New Delhi 2009-2021
● AISSCE (Class XII), Aggregate: 85%
● AISSE (Class X), Aggregate: 88%
Skills
● Programming: Python, SQL, C, VBA
● Analytics & Visualization: PowerBI, Tableau
● Data Science Libraries: pandas, NumPy, Matplotlib, TensorFlow, scikit-learn, OpenCV, NLP
● Web: HTML, CSS, Django, Bootstrap
Work Experience
IBM and CSRBOX | Data Analyst Intern June’24 - August’24
● Performed in-depth analysis of crime data using Python and SQL, identifying key trends to support data-driven decision-
making.
● Developed an interactive crime analysis dashboard in PowerBI with visual insights, ensuring accessibility and clarity for
stakeholders.
● Achieved 85% accuracy in predicting crime hotspots through data preprocessing and statistical analysis, enhancing
reliability of insights.
ERASMITH TECHNOLOGIES | Python Developer Intern Jan’25 - Present
● Developed FastAPI microservices to aggregate server utilization data into hourly, daily, weekly, and monthly summaries,
optimizing performance with in-memory computations.
● Implemented efficient data processing pipelines for large-scale server monitoring, enhancing real-time analytics and
reporting.
Projects
HR ANALYTICS DASHBOARD USING POWERBI | Link
Built a Power BI dashboard to analyze key HR metrics and provide strategic insights.
● Used DAX functions to evaluate employee performance, retention, and satisfaction.
● Integrated data sources and visualized HR KPIs to deliver insights on employee turnover and demographics.
PREDICTIVE ANALYSIS OF AIR QUALITY INDEX (AQI) | Link
Leveraged machine learning to predict AQI values, focusing on model optimization and trend visualization to aid environmental agencies in
proactive public health measures.
● Developed and optimized predictive models using Random Forest and Support Vector Machine algorithms, achieving
85% accuracy through careful model tuning.
● Visualized AQI trends through data preprocessing and feature engineering with Scikit-Learn, Pandas, Matplotlib,
providing actionable insights for urban planning.
UNDERWATER IMAGE ENHANCEMENT PROJECT | Link
Developed AI-powered solutions to improve image quality, supporting marine researchers and underwater photographers.
● Applied machine learning, neural networks, and image processing techniques like Histogram Equalization, CLAHE,
and White Balance to enhance image clarity.
● Statistical analysis and entropy visualization graphs to assess and showcase enhancement performance.
WEATHER APP PROJECT | Link
Developed a weather app to provide real-time weather information through a clean, responsive interface .
● Built with Django, integrating a weather API to display real-time temperature and humidity data.
● Designed a responsive interface using HTML, CSS, and Bootstrap, ensuring compatibility across devices.
Publication
PREDICTIVE ANALYSIS OF AIR QUALITY INDEX (AQI) USING MACHINE LEARNING
Co-authored a research article that was presented at the 2024 International Conference on Advanced Materials for Sustainable Innovation (IC-AMSI 2024).
● Developed predictive models using Random Forest and Support Vector Machine with 85% accuracy
● Applied data preparation techniques like missing value handling, normalization, and feature engineering to improve
model performance.
● Evaluated models using RMSE, MAE, and R-squared, with findings published in a peer-reviewed publication.
