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https://github.com/zara5961/deep-learning-challenge

This project focuses on building and optimizing a deep learning neural network to predict the success of applicants funded by Alphabet Soup Charity.
https://github.com/zara5961/deep-learning-challenge

deep-machine-learning deep-neural-networks python skicit-learn tensorflow

Last synced: about 2 months ago
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This project focuses on building and optimizing a deep learning neural network to predict the success of applicants funded by Alphabet Soup Charity.

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# Alphabet Soup Charity - Deep Learning Model

![Project](Images/deep-learning.jpg)

## Overview of the Analysis

The purpose of this analysis is to build a binary classifier using a deep learning neural network. This model predicts the success of applicants funded by Alphabet Soup, helping the organization select applicants with the highest likelihood of success in their ventures.



![Project](Images/artificial.jpeg)



I worked on developing a *Neural Network model*



### Data Preprocessing

- **Target Variable**:

  - `IS_SUCCESSFUL`



- **Features**: 

  - `APPLICATION_TYPE`

  - `AFFILIATION`

  - `CLASSIFICATION`

  - `USE_CASE`

  - `ORGANIZATION`

  - `STATUS`

  - `INCOME_AMT`

  - `SPECIAL_CONSIDERATIONS`

  - `ASK_AMT`



- **Removed (Dropped) Variables**: 

  - `EIN`: Identification column not relevant to predictions.

  - `NAME`: Identification column that may introduce noise without adding predictive value.

- **Handling Categorical Data**: Categorical variables with more than 10 unique values were grouped into an "Other" category. and One-hot encoding was applied to categorical features to convert them into numeric

format.

- **Scaling the Features**: StandardScaler was used to normalize numerical data to ensure consistency and improve model convergence.

- **Data Splitting**: The dataset was split into training (80%) and testing (20%) sets using train_test_split.



### Compiling, Training, and Evaluating the Model

- **Model Structure**:

  - **Neurons**: Initial configurations used between 30 and 200 neurons per layer.

  - **Hidden Layers**: Multiple attempts with 2-5 layers to balance complexity and training efficiency.

  - **Activation Functions**: `relu` for hidden layers and `sigmoid` for the output layer (binary classification).

![Project](Images/pro.jpg)



- **Performance**:

  - Initial attempts achieved ~73% accuracy.

  - Various techniques were used to optimize performance, including:

    - Adjusting neurons and layers.

    - Using dropout and batch normalization.

    - Adjusting learning rate and adding callbacks.



![Project](Images/Before-and-After.jpg)

### Optimization Steps

To achieve a target accuracy of 75%, the following optimizations were attempted:

1. **Data Adjustments**:

   - Encoding categorical variables with `pd.get_dummies()`.

   - Combining rare categorical values into "Other".

   - Scaling features using `StandardScaler()`.



2. **Model Adjustments**:

   - Increased the number of neurons in hidden layers.

   - Added more hidden layers for greater learning capacity.

   - Experimented with different activation functions (`relu`, `tanh`).



3. **Training Adjustments**:

   - Increased the number of epochs.

   - Used EarlyStopping and ModelCheckpoint callbacks to improve training efficiency.



### Final Model

- **Accuracy**: Despite extensive optimization, the highest accuracy achieved was ~73%.

Then I had to try different approach that is focused on identifying and handling low-frequency categories in a dataset:

* *Replacing with "Other"*: Consolidate rare categories into a single "Other" category.

* *Dropping*: Remove the data associated with these rare categories (only if justified).

* *Encoding Separately*: Treat them differently in downstream processing.



### Questions

- Which features were used as inputs?

Categorical features as application type, organization type, special considerations were utilized, transformed into numerical data using one-hot encoding.



- What was the target variable?

The target variable was *IS_SUCCESSFUL*, which represents whether the charity was successful.



- What methods were used to preprocess the data?

The data was prepared by removing non-essential columns, applying one-hot encoding for categorical variables, scaling numerical features with StandardScaler, and splitting the dataset into training and testing subsets.



- How was the model structured?

The model was a neural network with first one hidden layer then increased by 5 comprising 30 and 200 neurons, using ReLU activation for hidden layers and a Sigmoid function for the output layer.



- What were the model’s performance metrics?

The optimized model achieved an accuracy of 72%-73% and showed a reduced loss value, starting from 0.577 and improving during the optimization process.



- How was the model optimized?

Optimization included:

* Modifying the architecture by experimenting with layers and neurons.

* Tuning hyperparameters like learning rate and increasing training epochs.

* Incorporating early stopping to mitigate overfitting.



### Summary

- The deep learning model performed well but did not achieve the target accuracy of 75%, by adding hidden layers.

- Recommendation:

  - Perform feature importance analysis to focus on the most predictive variables.