This project analyzes air quality data for Delhi, India in January 2023. Key findings include:
Predominantly Poor Air Quality:
- Over 97% of the time, Delhi's air quality fell into the "Very Poor" or "Severe" categories.
- This highlights concerning pollution levels with potential health risks.
- Nighttime AQI Slightly Higher: On average, nighttime AQI was marginally higher than daytime AQI.
- Strong Correlations Between Pollutants: The analysis revealed strong correlations between various pollutants, suggesting shared sources or dispersion patterns.
This project provides valuable insights into Delhi's air quality and can be a template for analyzing environmental data from other cities or pollutants.
Seaborn:
Strengths:Simplicity, integration with statistical analysis, and ease of use for basic plots.
Weaknesses:Limited interactivity and customization for complex visualizations.
Plotly:
Strengths:Interactivity, customization, and support for advanced visualizations like 3D plots.
Weaknesses:Steeper learning curve, complexity for beginners, and potential overkill for simple plots.
Overview: This project, conducted during my internship at ShadowFox as a Data Science intern, focuses on analyzing fielding performances in the RCB vs KKR IPL 2024 match.
Objective: The goal is to provide insights into fielding strategies and their impact on the game through meticulous data collection and analysis.
Data Collection: Assisted by Kevin, we collected and analyzed fielding events to derive meaningful insights into key moments and player contributions.
Deliverables The project includes analysis and performance metrics.