Module 13 Challenge
For this challenge I work as a risk management associate at Alphabet Soup, a venture capital firm. Alphabet Soup’s business team receives many funding applications from startups every day. This team has asked me to help them create a model that predicts whether applicants will be successful if funded by Alphabet Soup.
The data we're analyzing comes from a jupyter notebook that we'll create and import files to. We'll be using Python to run and read our data.
- [jupyter] - (https://github.com/jupyter/notebook) - Helps us run our code and get the information we need from the data listed in csv files.
In order for us to get the data we need we must import pandas, plots and the csv files we want to observe.
# Imports
import pandas as pd
from pathlib import Path
import tensorflow as tf
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler,OneHotEncoder- With each, try to improve on your first model’s predictive accuracy.
# First alternative layer
nn_A1.add(Dense(units=hidden_nodes_layer1, input_dim=number_input_features, activation="relu"))
# Output layer
nn.add(Dense(units=1, activation="sigmoid"))
# Check the structure of the model
nn_A1.summary()# Second alternative layer
nn_A2.add(Dense(units=hidden_nodes_layer1_A2, input_dim=number_input_features, activation="relu"))
# Output layer
nn_A2.add(Dense(units=number_output_neurons, activation="linear"))
# Check the structure of the model
nn_A2.summary()# Third alternative layer
nn_A3.add(Dense(units=hidden_nodes_layer1_A3, input_dim=number_input_features, activation="relu"))
# Output layer
nn_A3.add(Dense(units=number_output_neurons, activation="linear"))
# Check the structure of the model
nn_A3.summary()Brought to you by Elgin Braggs Jr.
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