BoysHostelAQ.txt

Missing file

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.

The scatter matrix plot provides a visual representation of the relationships between the different variables in the dataset. Here are some key observations:

**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.**CO2 vs. Temperature**: There is no clear correlation between CO2 levels and temperature based on the scatter plot.**Light Intensity vs. Other Variables**: Light intensity does not show a strong correlation with any of the other variables.

- 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?

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.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.

**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?

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.

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 1 | Principal 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?