Revolutionize Your Construction Business with Neural Networks: QuickBooks Online and Excel Combined
- Charles Stoy
- Jan 17
- 3 min read
Managing a construction business can feel like juggling a hundred moving parts. Between tracking expenses, managing multiple projects, and forecasting profitability, the work never stops. But what if I told you there’s a way to use your data from QuickBooks Online (QBO) and Excel to make smarter, faster decisions? Enter the world of Neural Networks—a tool that learns from your data and predicts outcomes to keep your business ahead of the curve.
In this blog, I’ll show you how to set up a neural network using Python to analyze your QBO and Excel data. Even if you’re new to this, I’ll break it down so anyone can understand. I used Jupyter Notebooks to create and run the Python code. Ready to dive in? Let’s build something amazing.
What’s a Neural Network?
Think of a neural network as a digital brain. Just like your brain learns from experience, a neural network learns from data. It takes input (your QBO and Excel data), processes it through layers (kind of like decision-making), and gives an output (like predicting which projects will be most profitable).
What You’ll Need
Before we get started, here’s a quick checklist:
Data from QBO:
Export data like revenue, expenses, assets, and equity to a CSV file.
Excel for Preprocessing:
Clean your data and organize it into columns like “Income,” “Expenses,” and “Cash Flow.”
Python:
Install the following Python libraries:
Pandas: For data manipulation.
Numpy: For numerical calculations.
Scikit-Learn: For splitting data and scaling.
TensorFlow or PyTorch: For building and training the neural network.
Matplotlib: For visualizing results.
How to Prepare Your Data
Your data is the foundation of the neural network. Start by exporting reports from QBO (e.g., Profit and Loss, Balance Sheet) and importing them into Excel.
Organize Your Data:
Columns you’ll need:
Revenue: Combine Construction Income and Change Orders Income.
Expenses: Sum up Labor Costs, Materials, Subcontractor Payments, etc.
Assets: Include Cash, Accounts Receivable, Retainage Receivable, and Construction in Progress.
Equity: Use Owner’s Equity from your Balance Sheet.
Example:RevenueExpensesAssetsEquity250000150000172000100000
Save as CSV:
Once the data is clean, save it as a CSV file (e.g., construction_data.csv).
Building Your Neural Network
Here’s where the magic happens. We’ll use Python to build a simple neural network that predicts your Return on Equity (ROE) based on your input data.
Load the Data in Python:
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler import tensorflow as tf
# Load the CSV file
data = pd.read_csv('construction_data.csv')
# Separate inputs (X) and output (y)
X = data[['Revenue', 'Expenses', 'Assets', 'Equity']].values
y = data['ROE'].values # Assume ROE is calculated in Excel
Prepare the Data:
# Scale the data for the neural network
scaler = MinMaxScaler()
X_scaled = scaler.fit_transform(X)
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)
Build the Neural Network:
# Define the model
model = tf.keras.Sequential([ tf.keras.layers.Dense
(8, activation='relu', input_shape=(4,)), #Input layer
tf.keras.layers.Dense(16, activation='relu'), # Hidden layer
tf.keras.layers.Dense(1, activation='linear') # Output layer
])
# Compile the model
model.compile(optimizer='adam', loss='mean_squared_error', metrics=['mae'])
# Train the model
model.fit(X_train, y_train, epochs=100, validation_split=0.2)
Evaluate the Model:
# Evaluate on the test data
loss, mae = model.evaluate(X_test, y_test) print(f"Mean Absolute Error: {mae}")
Make Predictions:
# Predict ROE for new projects
new_data = np.array([[300000, 180000, 200000, 120000]]) # Example inputs
new_data_scaled = scaler.transform(new_data)
predicted_roe = model.predict(new_data_scaled)
print(f"Predicted ROE: {predicted_roe[0][0]:.2f}")
Where Do Excel and QBO Fit In?
Inputs: The data from Excel and QBO (Revenue, Expenses, Assets, Equity) feed into the neural network.
Outputs: The neural network predicts:
Return on Equity (ROE).
Profitability trends for upcoming projects.
Recommendations for which projects to prioritize.
Why This Matters
Using neural networks to analyze your QBO and Excel data means you’re no longer guessing which projects will be the most profitable. Instead, you’re making decisions backed by data and powered by advanced technology. This gives you a competitive edge, improves efficiency, and ensures long-term growth.
Call to Action
Are you ready to harness the power of neural networks for your construction business? Let me help you set up your system, clean your data, and build a neural network tailored to your needs. Together, we’ll turn your QuickBooks and Excel data into actionable insights. Contact me today to get started!
Comments