📊 Data Science Internship Projects — ShadowFox (June–July 2025) This repository contains the projects completed during my Data Science internship at ShadowFox between June and July 2025. These projects focused on applying data analysis, visualization, and machine learning techniques to real-world datasets and problems. The tools used include Python, Google Colab, Pandas, Plotly, Bokeh, and others.
🧠Projects Overview
- Visualization Library Documentation Objective: Compare two major Python visualization libraries — Plotly and Bokeh.
Key Features: Library overviews, use cases, and suitability for various scenarios. Detailed documentation of 5 core graph types (Line, Scatter, Bar, Histogram, Pie). Code examples and comparative analysis across interactivity, performance, and ease of use.
Output Format: Technical documentation (Word)
File: Visualization Library Documentation.docx
- Analysis of Air Quality Index (AQI) in Delhi Objective: Perform an in-depth analysis of AQI trends in Delhi using real-time and historical data.
Key Tasks:
Data preprocessing and cleaning of AQI datasets. Calculation of AQI using CPCB guidelines. Visualizations: Trend lines, heatmaps, and bar charts using Bokeh. Insights on pollution patterns, temporal variations, and threshold exceedances.
Tech Stack: Python, Pandas, Matplotlib, Bokeh
File: analysis of the Air Quality Index (AQI) in Delhi.ipynb
- Students’ Social Media Addiction Analysis Objective: Explore the patterns and impact of social media usage among students.
Key Tasks: Statistical summary and visualization of survey responses. Correlation analysis between variables like screen time, academic performance, and sleep. Clustering and classification for identifying risk groups.
Tech Stack: Python, Pandas, Seaborn, Matplotlib
File: Students' Social Media Addiction.ipynb
🛠️ Tools & Technologies Used Languages: Python Libraries: Pandas, NumPy, Matplotlib, Seaborn, Plotly, Bokeh Platform: Google Colab
Tools: Microsoft Word (for documentation), GitHub
đź“Ś Learnings & Contributions Applied data wrangling techniques for real-world datasets. Developed clear, interactive, and insightful data visualizations. Conducted statistical analysis and feature engineering. Gained hands-on experience in creating professional-grade documentation.
đź”— Acknowledgment These projects were completed as part of the Data Science Internship at ShadowFox, under the mentorship and guidance of the internship team. I am thankful for the opportunity to apply my learning in practical, impactful ways.