Difference between revisions of "ISSS608 2016-17 T3 Assign MANISH MITTAL"

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In most of the cases, sensors 3 & 4 contribute to the maximum readings which is due to the location of sensors and the wind direction. We further dig down to figure out the different patters.
 
In most of the cases, sensors 3 & 4 contribute to the maximum readings which is due to the location of sensors and the wind direction. We further dig down to figure out the different patters.
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==Q3==
 
==Q3==
 
Q: Which factories are responsible for which chemical releases? Carefully describe how you determined this using all the data you have available. For the factories you identified, describe any observed patterns of operation revealed in the data.
 
Q: Which factories are responsible for which chemical releases? Carefully describe how you determined this using all the data you have available. For the factories you identified, describe any observed patterns of operation revealed in the data.

Revision as of 19:55, 7 July 2017

Q1

Q: Characterize the sensors’ performance and operation. Are they all working properly at all times? Can you detect any unexpected behaviors of the sensors through analyzing the readings they capture?

Number of records/day

By looking at the calendar plot we can see at some specific dates of April, August and December the number of readings recorded are less than usual. There are 3 dates in August for which the number of records are less 2nd, 4th & 7th. One thing to be noted by this graph is that on 2nd on every month the number of recordings are less. We further will dig down to find out the reason.

Records1.png
Records2.png
Records3.png

We clearly see an unusual pattern of number of records recorded by monitors 3, 4, 5, 6 and 9. The Above screenshots shows that the unusual pattern was mainly due to the chemical AGOC – 3A. The monitors are taking multiple reading where they are not supposed to be. Looking the graphs whenever there are higher number of records noted by monitors for chemical AGOC -3A, there are lower number of records recorded for chemical Methylosmolene which means monitors are confusing the AGOC – 3A chemical with Methylosmolene. This unusual pattern is shown throughout the year for all months. This can be verified by looking at the number of records for other two chemicals Appluimonia & Chlorodinine. For these two the number of records are almost constant throughout the year.



































This unusual pattern was shown for monitors 7 & 8 also for the month of April which later got fixed in August & December.

1.png

Monitor 3 & 6 shows unusual pattern in readings as well. For all the 3 months, monitors 3 & 6 are showing extremely high readings. The same pattern was observed for monitor 4 as well but for the month of December only. We can conclude that In the month of December the wind direction was towards north west where the monitor 4 exists and hence the reading was more in monitor 4 specifically.

2.png
3.png

Q2

Which chemicals are being detected by the sensor group? What patterns of chemical releases do you see, as being reported in the data?

Distribution of Readings.png

The graphs on the left hand side show the distribution of readings by different monitors for different chemicals. The few observations are - 1. Sensors 3 & 6 contribute to the high number of readings in the month of April for all the chemicals which is followed by 7 & then 8th sensor. 2. For the month of August, sensors 3 & 4 recorded the maximum readings for all 4 chemicals, which is further followed by sensor 6. 3. For the month of December, Sensor 4 shows the highest readings for all the 4 chemicals which is followed by sensor 3 & sensor 6.

In most of the cases, sensors 3 & 4 contribute to the maximum readings which is due to the location of sensors and the wind direction. We further dig down to figure out the different patters.


Q3

Q: Which factories are responsible for which chemical releases? Carefully describe how you determined this using all the data you have available. For the factories you identified, describe any observed patterns of operation revealed in the data.