Pollution is a major problem affecting the environment and human health. In this analysis project, we investigated pollution levels using three different datasets on global air pollution, shore pollution, and water potability. The aim of the project is to explore the datasets, identify trends and patterns, and draw insights into the pollution levels across the world.
We used three different datasets for this analysis project:
- Global Air Pollution: This dataset contains information on the air quality index (AQI) across different countries and cities in the world. It includes data on various air pollutants such as PM2.5, PM10, NO2, SO2, and O3.
- Shore Pollution: This dataset provides information on the pollution levels in different shorelines across the world. It includes data on various pollutants such as plastic waste, sewage waste, and chemical waste.
- Water Potability: This data set contains information on the potability of water in different countries. It includes data on various water quality parameters such as pH, hardness, solids, organic carbon, and conductivity.
We followed a standard data analysis process for this project. The steps involved in the analysis are as follows:
- Data Exploration: We began by exploring the datasets to understand the structure, variables, and data types. We used various descriptive statistics and visualization techniques to explore the datasets.
- Data Cleaning: We identified and cleaned any missing or incorrect data in the datasets to ensure data accuracy and consistency.
- Data Analysis: We performed various statistical and machine learning techniques to analyze the dataset. We identified patterns, trends, and relationships between different variables in the dataset.
- Data Visualization: We used various charts, graphs, and plots to visualize the analysis results and present the findings.
Our analysis revealed several interesting insights into the pollution levels across the world. Some of the key findings are as follows:
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Global Air Pollution: We found that countries with higher population densities have higher air pollution levels. We also identified that the levels of PM2.5 and PM10 are the most prominent air pollutants across the world.
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Shore Pollution: We found that there is a significant difference in the organic matter content between different shores, Resistance is one of the most affecting features on pollution level.
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Water Potability: We found that water potability is a significant problem in developing countries. We also identified that the presence of organic carbon and hardness are the most critical factors affecting water potability.
In conclusion, this analysis project provides valuable insights into the pollution levels across the world. By using three different datasets, we were able to identify patterns and trends that can help policymakers and environmentalists take necessary actions to reduce pollution levels. The analysis also shows the importance of data integration and the value of using multiple datasets to get a comprehensive view of the problem.
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Directed By:
- Director of Machine Learning and Training Department: Eslam Shouman
Team leader:
Team Members:
- 30 April 2023