https://github.com/riju18/bangladesh_malnourished_child_identification

According to WHO(world health organization) survey in 2014, the dataset contains nourished and malnourished child information (under 5). The job is to find out whether a child is malnourished or not when a new data will come applying machine learning algorithm.
https://github.com/riju18/bangladesh_malnourished_child_identification

anaconda artificial-neural-network backward-elimination jupyter-notebook machine-learning-algorithms python3

Last synced: 2 months ago
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According to WHO(world health organization) survey in 2014, the dataset contains nourished and malnourished child information (under 5). The job is to find out whether a child is malnourished or not when a new data will come applying machine learning algorithm.

• Host: GitHub
• URL: https://github.com/riju18/bangladesh_malnourished_child_identification
• Owner: riju18
• Created: 2019-04-20T08:45:00.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2019-10-10T20:29:33.000Z (over 5 years ago)
• Last Synced: 2025-01-28T12:44:27.472Z (4 months ago)
• Topics: anaconda, artificial-neural-network, backward-elimination, jupyter-notebook, machine-learning-algorithms, python3
• Language: Jupyter Notebook
• Homepage:
• Size: 855 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# the dataset is confidential, that's why it was not uploaded

# bd_malnourished_child_identification

# data: 6965

# survey organization: WHO

# survey year: 2014

# features:

         age at first birth : mother's age at first birth

         

         

         Child Age          : child age ( in month )

         

         

         diar               : yes or no	

         

         

         fever	          : yes or no

         

         

         ari                : yes or no

         

         

         Mother_BMI	 : (in Kg)

         

         

         Birth_Order	 : how many children

         

         

         Mother_edu         : mother educated or not

         

         

         wealth_index_cat	 : poor or rich family

         

         

         Father_Edu         : father educated or not

         

         

         residence	 : rural or urban

         

         

         sex                : male or female

         

         

         currently_working_mot: mother works outside or not

         

         

         Breastfeeding      : yes or no

         

         

         household_no       : how many family members 

# decision class:

         

         stunting    :  child is stunting or not

         

         underweight : child is underweight or not

         

         wasting     : child is wasting or not

# statistical analysis:

# dataset & implementation overview

 ![newplot (6)](https://user-images.githubusercontent.com/18087611/58347883-d2db1e80-7e80-11e9-95e0-54514a4987a6.png)

# number of district:

 ![district](https://user-images.githubusercontent.com/18087611/58347885-d373b500-7e80-11e9-8e94-4e208aafe55f.png)

# accuracy of stunting child:

 ![acc_stun](https://user-images.githubusercontent.com/18087611/58347884-d373b500-7e80-11e9-8aba-beb2e9c168c9.png)

# accuracy of underweight child:

  ![acc_under](https://user-images.githubusercontent.com/18087611/58347876-d1115b00-7e80-11e9-9895-ee66d5f4bfb0.png)

# accuracy of wasting child:

  ![acc_was](https://user-images.githubusercontent.com/18087611/58347877-d1a9f180-7e80-11e9-89c8-d7847150de99.png)

# stunting child with respect to mother education:

 ![newplot (3)](https://user-images.githubusercontent.com/18087611/58347878-d2428800-7e80-11e9-973b-f01e53ca3e61.png)

# underweight child with respect to mother education:

 ![newplot (4)](https://user-images.githubusercontent.com/18087611/58347880-d2428800-7e80-11e9-95e1-6e0bf62adb6a.png)

# wasting child with respect to mother education:

 ![newplot (5)](https://user-images.githubusercontent.com/18087611/58347881-d2428800-7e80-11e9-8515-b9879c059c69.png)