https://github.com/anas436/data-wrangling2-with-automobile-data-set-using-python
https://github.com/anas436/data-wrangling2-with-automobile-data-set-using-python
jupyter-notebook matplotlib numpy pandas python3
Last synced: 4 months ago
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- Host: GitHub
- URL: https://github.com/anas436/data-wrangling2-with-automobile-data-set-using-python
- Owner: Anas436
- Created: 2022-07-01T18:27:35.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-07-01T18:31:49.000Z (over 3 years ago)
- Last Synced: 2025-10-14T01:36:51.583Z (4 months ago)
- Topics: jupyter-notebook, matplotlib, numpy, pandas, python3
- Language: Jupyter Notebook
- Homepage:
- Size: 26.4 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Data-Wrangling2-with-Automobile-Data-Set-Using-Python
In this notebooks shown basics operations of Pandas, Numpy and Matplotlib which is so necessary for any Data Wrangling. If you does not understand data and does not perform Data Wrangling you could not find out proper result. Which will reflects your target forcasting. __After Data Wrangling we have a dataset with no missing values.__
### Data Set Information
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This data set consists of three types of entities:
(a) The specification of an auto in terms of various characteristics.
(b) Its assigned insurance risk rating.
(c) Its normalized losses in use as compared to other cars. The second rating corresponds to the degree to which the auto is more risky than its price indicates. Cars are initially assigned a risk factor symbol associated with its price. Then, if it is more risky (or less), this symbol is adjusted by moving it up (or down) the scale. Actuarians call this process "symboling". A value of +3 indicates that the auto is risky, -3 that it is probably pretty safe.
The third factor is the relative average loss payment per insured vehicle year. This value is normalized for all autos within a particular size classification (two-door small, station wagons, sports/speciality, etc...), and represents the average loss per car per year.
Note: Several of the attributes in the database could be used as a "class" attribute.