Project 30: Regional Sales Report

Scientific Computing

Current Task

Objective

Capstone: combine statistics, spatial queries, and optimization — the three pillars of this track — into one real admin report.

Task: test whether two regions' sales differ significantly, find the store nearest a target location, and fit a target sales value — printing all three results.

index.py

from scipy.optimize import minimize
from scipy.stats import ttest_ind
from scipy.spatial import KDTree
import numpy as np

region_a_sales = [500, 520, 480, 510]
region_b_sales = [600, 615, 590, 605]

significance = ttest_ind(region_a_sales, region_b_sales)

store_locations = np.array([[0, 0], [10, 10], [5, 5], [2, 8]])
tree = KDTree(store_locations)
nearest_distance, nearest_index = tree.query([4, 4])

def target_cost(x):
    return (x[0] - 550) ** 2

result = minimize(target_cost, x0=[0])

print(round(significance.pvalue, 5))
print(nearest_distance, nearest_index)
print(result.x.round(2))

* Hint: Correct characters turn green, incorrect ones turn red.

Live Preview

🖼️

Verify your code to see the preview

← Previous