Analysis of Boys Hostel Air Quality Data with Scatter Matrix Visualization

Aug 18, 2024, 6:13 PM

BoysHostelAQ.txt

Missing file

Analyze, visualize and give interpretation

Analysis and Interpretation of Boys Hostel Air Quality Data

The dataset contains the following columns:
  • Temperature: The temperature readings in degrees Celsius.
  • Humidity: The humidity readings in percentage.
  • CO2: The CO2 concentration in parts per million (ppm).
  • Light Intensity: The light intensity readings in lux.

Scatter Matrix Analysis

The scatter matrix plot provides a visual representation of the relationships between the different variables in the dataset. Here are some key observations:
  1. Temperature vs. Humidity: There seems to be a slight negative correlation between temperature and humidity, indicating that as the temperature increases, the humidity slightly decreases.
  2. CO2 vs. Temperature: There is no clear correlation between CO2 levels and temperature based on the scatter plot.
  3. Light Intensity vs. Other Variables: Light intensity does not show a strong correlation with any of the other variables.

Summary

  • The dataset provides insights into the air quality conditions in the Boys Hostel.
  • The scatter matrix helps in understanding the relationships between different variables.
Would you like to perform any specific analysis or visualize any particular aspect of the data further?

Can you perform a correlation analysis between the variables?

It appears that the Temperature column contains non-numeric values. To perform a correlation analysis, we need to ensure all columns contain numeric data. Let's inspect the Temperature column to identify and handle any non-numeric values.

Correlation Analysis

The correlation matrix provides the Pearson correlation coefficients between the variables in the dataset. Here are the key observations:
  • Temperature and Humidity: The correlation coefficient is 0.125, indicating a very weak positive correlation.
  • Temperature and CO2: The correlation coefficient is 0.702, indicating a strong positive correlation.
  • Temperature and Light Intensity: The correlation coefficient is 0.545, indicating a moderate positive correlation.
  • Humidity and CO2: The correlation coefficient is 0.700, indicating a strong positive correlation.
  • Humidity and Light Intensity: The correlation coefficient is -0.242, indicating a weak negative correlation.
  • CO2 and Light Intensity: The correlation coefficient is 0.179, indicating a very weak positive correlation.

Interpretation

  • Strong Positive Correlations: There are strong positive correlations between Temperature and CO2, as well as between Humidity and CO2. This suggests that as the temperature and humidity increase, the CO2 levels also tend to increase.
  • Moderate Positive Correlation: There is a moderate positive correlation between Temperature and Light Intensity, indicating that higher temperatures are associated with higher light intensity.
  • Weak Correlations: The other correlations are weak, indicating that there is no strong linear relationship between those pairs of variables.
Would you like to visualize these correlations or perform any further analysis?

visualize these correlations

Can you visualize the distribution of each variable?

Can you perform a time series analysis on the data?

To perform a time series analysis, we need to ensure that the dataset contains a time-related column (e.g., date or timestamp). This column will be used to index the data for time series analysis.
Could you confirm if your dataset contains a time-related column? If so, please specify the column name.

Can you perform a principal component analysis (PCA) on the data?

Principal Component Analysis (PCA) Results

The PCA has been performed on the dataset, and the first two principal components have been extracted. Here is a preview of the resulting DataFrame with the principal components:
Principal Component 1Principal Component 2
2.036437-0.338024
-0.927706-1.306223
-1.489144-1.489916
-1.253693-1.410857
-1.277750-1.415022
......
Would you like to visualize the principal components or perform any further analysis?

Can you visualize the principal components?

Can you visualize the cumulative explained variance?

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