<jupyter_start><jupyter_text>Preprocessing<jupyter_code># Import our dependencies
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import tensorflow as tf
import numpy as np

#  Import and read the charity_data.csv.
import pandas as pd 
application_df = pd.read_csv("../Resources/charity_data.csv")
application_df.head()
# Drop the non-beneficial ID columns, 'EIN' and 'NAME'.
drop_df = application_df.drop(columns=["EIN", "NAME"])
drop_df.info()
# Determine the number of unique values in each column.
print(drop_df.nunique())
# Look at APPLICATION_TYPE value counts for binning
app_cnt = drop_df["APPLICATION_TYPE"].value_counts()
app_cnt
# Choose a cutoff value and create a list of application types to be replaced
# use the variable name `application_types_to_replace`
application_types_to_replace = list(app_cnt[app_cnt < 528].index)

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

# Check to make sure binning was successful
drop_df['APPLICATION_TYPE'].value_counts()
# Look at CLASSIFICATION value counts for binning
class_cnt = drop_df["CLASSIFICATION"].value_counts()
class_cnt
# create a Series of class_cnt with list and set as index
list(class_cnt[class_cnt < 75].index)
# Choose a cutoff value and create a list of classifications to be replaced
# use the variable name `classifications_to_replace`
classifications_to_replace = list(class_cnt[class_cnt < 75].index)

# Replace in dataframe
for cls in classifications_to_replace:
    drop_df['CLASSIFICATION'] = drop_df['CLASSIFICATION'].replace(cls,"Other")
    
# Check to make sure binning was successful
drop_df['CLASSIFICATION'].value_counts()
# Convert categorical data to numeric with `pd.get_dummies`
predict_dummies = pd.get_dummies(drop_df, dtype=float)
predict_dummies
# Split our preprocessed data into our features and target arrays
X = predict_dummies.drop(columns=["IS_SUCCESSFUL"])
y = predict_dummies["IS_SUCCESSFUL"]

# 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=50)
X_train = np.asarray(X_train)
y_train = np.asarray(y_train)
X_test = np.asarray(X_test)
y_test = np.asarray(y_test)
# 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.
nn = tf.keras.models.Sequential()

# First hidden layer
nn.add(tf.keras.layers.Dense(units = 51, activation = 'relu' , input_dim = 51 )) 

# Second hidden layer
nn.add(tf.keras.layers.Dense(units = 43, 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, 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('Models/AlphabetSoupCharity.h5')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.09869909502262443, 'fraction_numerical': 0.006221719457013574, 'mean_word_length': 6.462025316455696, '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': '167193', 'license': 'ADSL', 'n_tokens_mistral': 1201, 'n_tokens_neox': 1153, 'n_words': 320, 'path': 'Starter_Code.ipynb', 'repo_name': 'tommyjohnny36/charity-fund-predictor', 'size': '3536'}