This is walkthrough of a Python Data Capstone Project based on 911 calls data pulled from the Kaggle website. Here, we explored and analyzed the dataset to understand the density of the calls made during different hours of the day and different days of the week and months, where those calls were coming from and for what reasons, to help improve organizational efficiency. The python libraries used for this project were numpy, pandas, matplotlib and seaborn to combine data analysis and data visualization features for exploratory data analysis.
911 calls Dataset Download:
https://jumpshare.com/b/KHXPS6LU4GC6oDp0ZT81
If you want to code along to practice your python skills, you can download this dataset using the link and put it in the same folder as the Jupyter notebook you would be using.
Chapters:
0:00 - Intro & Imports
2:20 - Data Check
4:25 - Basic Data Analysis
7:10 - Analysing Reasons for the call
15:43 - Converting timeseries data into DateTime object
23:52 - Analysing TimeSeries Data
34:58 - Estimating Missing Data (Regression Plot)
38:52 - Extracting Date( ) from TimeStamp
41:04 - Continuing with TimeSeries Data Analysis
46:20 - TimeSeries plots for different Reasons for the 911 calls
51:58 - Pivot Table, Groupby & Unstack( ) to change data into matrix format
58:20 - Heatmaps( ) and Clustermaps( )
1:07:48 - Conclusion
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