<jupyter_start><jupyter_text>Preprocessing<jupyter_code># Import our dependencies
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn import svm
from sklearn.preprocessing import StandardScaler, MinMaxScaler
import matplotlib.pyplot as plt
from sklearn.neighbors import KNeighborsClassifier
import os
import numpy as np
from sklearn.metrics import confusion_matrix, classification_report
#  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, 'EIN' and 'NAME'.
#  YOUR CODE GOES HERE
application_df = application_df.drop(["EIN","NAME"],1)
application_df.head(2)
# Determine the number of unique values in each column.
#  YOUR CODE GOES HERE
application_df.nunique()

# Look at APPLICATION_TYPE value counts for binning
#  YOUR CODE GOES HERE
application_counts = application_df["APPLICATION_TYPE"].value_counts()
application_counts

#Suggested cutoff will be 500, anything less will be binned into 'other'
# Choose a cutoff value and create a list of application types to be replaced
# use the variable name `application_types_to_replace`
#  YOUR CODE GOES HERE
application_types_to_replace = list(application_counts[application_counts < 500].index)
# Replace in dataframe
for app in application_types_to_replace:
    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
#  YOUR CODE GOES HERE
class_counts = application_df["CLASSIFICATION"].value_counts()
class_counts<jupyter_output><empty_output><jupyter_text>Suggested cutoff will be everywhere where the classification counts are >1<jupyter_code># You may find it helpful to look at CLASSIFICATION value counts >1
#  YOUR CODE GOES HERE
class_counts[class_counts > 1]<jupyter_output><empty_output><jupyter_text>suggested cutoff is 1000 for the classification counts<jupyter_code># Choose a cutoff value and create a list of classifications to be replaced
# use the variable name `classifications_to_replace`
#  YOUR CODE GOES HERE
classifications_to_replace = list(class_counts[class_counts<1000].index)
# Replace in dataframe
for cls in classifications_to_replace:
    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`
#  YOUR CODE GOES HERE
application_with_dummies_df = pd.get_dummies(application_df)
application_with_dummies_df.head(3)
# Split our preprocessed data into our features and target arrays
#  YOUR CODE GOES HERE
X = application_with_dummies_df.drop(['IS_SUCCESSFUL'], axis = 'columns').values
y = application_with_dummies_df['IS_SUCCESSFUL'].values

# Split the preprocessed data into a training and testing dataset
#  YOUR CODE GOES HERE
X_train, X_test, y_train, y_test = train_test_split(X, y)

# 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.
#  YOUR CODE GOES HERE
number_input_features = len(X_train[0])
hidden_nodes_layer1 =  69
hidden_nodes_layer2 = 29

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"))

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

# Check the structure of the model
nn.summary()
# Compile the model
#  YOUR CODE GOES HERE
nn.compile(loss = "binary_crossentropy", optimizer="adam",metrics=["accuracy"])
# Train the model
#  YOUR CODE GOES HERE
fit_model = nn.fit(X_train_scaled,y_train,epochs=100, verbose=4)
# 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
#  YOUR CODE GOES HERE
nn.save("ASCharity.h5")<jupyter_output><empty_output>
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