<jupyter_start><jupyter_text>OPTIMIZE MODEL Preprocessing<jupyter_code># Import our dependencies
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler,OneHotEncoder
import pandas as pd
import tensorflow as tf
#  Import and read the charity_data.csv.
import pandas as pd 
application_df = pd.read_csv("charity_data.csv")
application_df.head()
# Drop the non-beneficial ID columns
application_df = application_df.drop(["EIN"],1)
application_df.head()
# Determine the number of unique values in each column.
application_df.nunique()
namecounts = application_df['NAME'].value_counts()
namecounts
names_replace = list(namecounts[namecounts<5].index)
names_replace
for name in names_replace:
    application_df['NAME'] = application_df['NAME'].replace(name,"Other")

application_df['NAME'].value_counts()
# Look at APPLICATION_TYPE value counts for binning
applicationcount = application_df.APPLICATION_TYPE.value_counts()
applicationcount
# Choose a cutoff value and create a list of application types to be replaced
# use the variable name `application_types_to_replace`
replaceapplication = list(applicationcount[applicationcount < 600].index)

# Replace in dataframe
for app in replaceapplication:
    application_df['APPLICATION_TYPE'] = application_df['APPLICATION_TYPE'].replace(app,"Other")

# Check to make sure binning was successful
application_df['APPLICATION_TYPE'].value_counts()
# Look at CLASSIFICATION value counts for binning
classcount = application_df.CLASSIFICATION.value_counts()
classcount
# Choose a cutoff value and create a list of classifications to be replaced
# use the variable name `classifications_to_replace`
replaceclass = list(classcount[classcount < 500].index)

# Replace in dataframe
for cls in replaceclass:
    application_df['CLASSIFICATION'] = application_df['CLASSIFICATION'].replace(cls,"Other")
    
# Check to make sure binning was successful
application_df['CLASSIFICATION'].value_counts()
# Convert categorical data to numeric with `pd.get_dummies`
application_cat = application_df.dtypes[application_df.dtypes == "object"].index.tolist()
print(application_cat)
application_df = pd.get_dummies(application_df, dtype=float)
application_df.head()
# Split our preprocessed data into our features and target arrays
y = application_df['IS_SUCCESSFUL'].values
y
X = application_df.drop('IS_SUCCESSFUL', axis=1).values
X
# Split the preprocessed data into a training and testing dataset
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=78)

# Create a StandardScaler instances
scaler = StandardScaler()

# Fit the StandardScaler
X_scaler = scaler.fit(X_train)

# Scale the data
X_train_scaled = X_scaler.transform(X_train)
X_test_scaled = X_scaler.transform(X_test)<jupyter_output><empty_output><jupyter_text>Compile, Train and Evaluate the Model<jupyter_code># Define the model - deep neural net, i.e., the number of input features and hidden nodes for each layer.
number_input_features = len( X_train_scaled[0])
hidden_nodes_layer1=90
hidden_nodes_layer2=50
hidden_nodes_layer3=25

nn = tf.keras.models.Sequential()

# First hidden layer
nn.add(tf.keras.layers.Dense(units=hidden_nodes_layer1, input_dim=number_input_features, activation="relu"))

# Second hidden layer
nn.add(tf.keras.layers.Dense(units=hidden_nodes_layer2, activation="relu"))

#Added Thrid hidden layer
nn.add(tf.keras.layers.Dense(units=hidden_nodes_layer3, activation="relu"))

# Output layer
nn.add(tf.keras.layers.Dense(units=1, activation="sigmoid"))

# Check the structure of the model
nn.summary()
# Compile the model
nn.compile(loss="binary_crossentropy", optimizer="adam", metrics=["accuracy"])
# Train the model
fit_model = nn.fit(X_train_scaled,y_train,validation_split=0.15, epochs=100)
# Evaluate the model using the test data
model_loss, model_accuracy = nn.evaluate(X_test_scaled,y_test,verbose=2)
print(f"Loss: {model_loss}, Accuracy: {model_accuracy}")
# Export our model to HDF5 file
nn.save("AlphabetSoupCharityOptimized.h5")<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.09553196741545297, 'fraction_numerical': 0.008392989385336953, 'mean_word_length': 7.49475890985325, 'pattern_counts': {'":': 0, '<': 12, '<?xml version=': 0, '>': 9, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '552482', 'license': 'ADSL', 'n_tokens_mistral': 1300, 'n_tokens_neox': 1255, 'n_words': 321, 'path': 'AlphabetSoupCharityOptimization.ipynb', 'repo_name': 'andersonigben/DeepLearning-Challenge', 'size': '4051'}