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March Pilot
March Pilot

Posted on • Edited on

Can an AI calculate the true cost of installing a chain-link fence?

Wow, seriously? I once spent an entire afternoon manually adding up material lists, labor rates, and tax rates—only to end up off by hundreds of dollars.

I tried quoting a metal fence job using spreadsheets and gut feeling [sic], until I learned that an AI model can crunch all the numbers in seconds. It’s like having a digital estimator from Northbrook Fence Company at your fingertips.

Northbrook Fence Company Chicag

5 Key Concepts

  1. Data ingestion: prices, lengths, labor
  2. Feature engineering: area, post counts, gate count
  3. Regression model: predict cost
  4. Evaluation metrics: RMSE, MAE
  5. Deployment: API endpoint

How to Build It

1. Import Libraries 

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
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2. Load and Inspect Data 

data = pd.read_csv('fence_jobs.csv')
print(data.head())
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3. Scrape Material Costs 

import requests
from bs4 import BeautifulSoup

resp = requests.get('https://example-prices.com/steel-per-meter')
soup = BeautifulSoup(resp.text, 'html.parser')
steel_price = float(soup.select_one('.price').text.replace('$',''))
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4. Feature Engineering 

data['area_m2'] = data['length_m'] * data['height_m']
data['num_posts'] = data['length_m'] // 2.5
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5. Train a Regression Model 

from sklearn.linear_model import LinearRegression

features = data[['area_m2','num_posts','num_gates']]
target = data['total_cost']
X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2)
model = LinearRegression().fit(X_train, y_train)
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6. Evaluate Your Model 

from sklearn.metrics import mean_absolute_error

preds = model.predict(X_test)
print(f"MAE: {mean_absolute_error(y_test, preds):.2f}")
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7. Predict New Quote 

new_job = np.array([[50, 20, 1]])
estimated = model.predict(new_job)
print(f"Estimated cost: ${estimated[0]:.2f}")
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8. Save the Model 

import joblib
joblib.dump(model, 'fence_cost_model.pkl')
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9. Create an API Endpoint 

from flask import Flask, request, jsonify
import joblib

app = Flask(__name__)
model = joblib.load('fence_cost_model.pkl')

@app.route('/estimate', methods=['POST'])
def estimate():
    payload = request.get_json()
    feat = np.array([payload['length'], payload['posts'], payload['gates']]).reshape(1, -1)
    cost = model.predict(feat)[0]
    return jsonify({'estimated_cost': round(float(cost), 2)})
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10. Front‑end Calculator 

async function getEstimate(length, posts, gates) {
  const res = await fetch('/estimate', {
    method: 'POST',
    headers: {'Content-Type':'application/json'},
    body: JSON.stringify({length, posts, gates})
  });
  const json = await res.json();
  alert(`Your metal fence will cost around $${json.estimated_cost}`);
}
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Benefits of AI Estimation

  • Speed: Quotes in seconds, not hours.
  • Accuracy: Lower margin of error—no surprises.
  • Scalability: Estimate multiple jobs at once.
  • Accessibility: DIY-friendly, you don’t need Excel wizardry.
  • Professionalism: Impress clients like Northbrook Aluminum Fences

Conclusion + Call to Action

Give it a try this week—train on your own project data and see how close you get. And hey, next time you can compare with a quote from Northbrook Wrought Iron Fence for fun!

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