https://github.com/poboisvert/neural_network_charity_analysis
[Python] Using Python's TensorFlow library, the purpose of this project is to use deep learning neural networks to analyze and classify the success of charitable donations
https://github.com/poboisvert/neural_network_charity_analysis
Last synced: 5 months ago
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[Python] Using Python's TensorFlow library, the purpose of this project is to use deep learning neural networks to analyze and classify the success of charitable donations
- Host: GitHub
- URL: https://github.com/poboisvert/neural_network_charity_analysis
- Owner: poboisvert
- Created: 2021-05-08T16:41:48.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2021-05-09T23:53:58.000Z (about 4 years ago)
- Last Synced: 2025-01-07T11:25:18.481Z (6 months ago)
- Language: Jupyter Notebook
- Homepage: https://bs-uploads.toptal.io/blackfish-uploads/blog/post/seo/og_image_file/og_image/15899/REDESIGN-TensorFlow-Python-Tutorial-Luke_Social-2cf55c3af1af1358481739320f86dce0.png
- Size: 19.9 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
## Project Overview
Using Python's TensorFlow library, the purpose of this project is to use deep learning neural networks to analyze and classify the success of charitable donations. The main goal is to predict which organizations are worth donating (approving) to and which are too high risk (declining).
## Resources
- Data Source: Resources/charity_data.csv
- Software: Python 3.9, Anaconda Navigator 2.0.1, Conda 4.10, Jupyter Notebook 6.2
## Results
- The target variable for my model is the "Is_Successful" column which tells us whether the money was used successfully. Below is the code
> X = application_df.drop(['IS_SUCCESSFUL'],1).values
### Preprocessing
- The features of this model are:
```
APPLICATION_TYPE object
AFFILIATION object
CLASSIFICATION object
USE_CASE object
ORGANIZATION object
STATUS int64
INCOME_AMT object
SPECIAL_CONSIDERATIONS object
ASK_AMT int64
IS_SUCCESSFUL int64
dtype: object
```
The steps taken to optimize the model included removing the noisy variables ("EIN", "ORGANIZATION", "SPECIAL_CONSIDERATIONS, and "NAME").
> application_df = application_df.drop(columns=["EIN", "NAME","ORGANIZATION","SPECIAL_CONSIDERATIONS"], axis=1)
We added additional neurons and hidden layers (layer3 and layer4)
### Compiling, Training, and Evaluating
> Accuracy: 0.72
## Summary
By following the steps below, we were able to have a working model that had poor performance.
- 1. Preprocess Data for a Neural Network Model
- 2. Compile, Train, & Evaluate the Model
- 3. Optimize the Model
In order to have a result that could be useful, I removed uneccessary variables and increasing additional neurons to each hidden layers.