Support Vector Regression to Estimate parcel delivery time from number of drop points and distance between stops

Project info

• Field of studyLogistics and Supply Chain
• LevelMaster's thesis (S2)
• Method typeRegression
• Estimated build time± 5 working days
• Toolsscikit-learn SVR, GridSearchCV, StandardScaler, Pipeline
• PriceUS$22 - US$141

Research goal

Estimate parcel delivery time from number of drop points and distance between stops with Support Vector Regression and assess the effect of the kernel on error.

Variables

Example result

• 0.848R2 (test)
• 11.038RMSE (test)
• 8.514MAE (test)
• 149Support vectors
• 0.651SV share
• 30C

An RBF-kernel SVR models parcel delivery time with test R2 0.848 and RMSE 11.038; 149 of 229 training points are support vectors.

Computed from illustrative synthetic data, not from a real study.

About Support Vector Regression

Support Vector Regression finds a function that deviates at most epsilon from the data while staying flat, with kernels for nonlinear patterns. It suits small to medium datasets with smooth nonlinear relations.

Steps

1. 1Standardise features (and the target if needed)
2. 2Split into training and test sets
3. 3Choose an RBF kernel and tune C, epsilon, gamma by cross-validation
4. 4Fit SVR with the best hyperparameters
5. 5Evaluate R2 and RMSE on the test set
6. 6Plot the prediction curve against the data

Assumptions to check

• Features must be on comparable scales
• C, epsilon, and gamma strongly affect results and must be tuned
• Training time grows quickly on very large data

Reading and reporting the results

Report the kernel and best hyperparameters, R2, RMSE, and the number of support vectors. Stress that the model is hard to interpret, so the focus is predictive accuracy.

Example Python code

A short starting script with this project's variable names. The delivered version is adapted to your data and fully commented.

import numpy as np
import matplotlib.pyplot as plt
from sklearn.svm import SVR
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline
from sklearn.model_selection import GridSearchCV, train_test_split
from sklearn.metrics import r2_score, mean_squared_error

rng = np.random.default_rng(5)
n = 250
number_of_drop_points = np.sort(rng.uniform(0, 10, n))
parcel_delivery_time = np.sin(number_of_drop_points) + 0.3 * number_of_drop_points + rng.normal(0, 0.2, n)

X = number_of_drop_points.reshape(-1, 1)
X_tr, X_te, y_tr, y_te = train_test_split(X, parcel_delivery_time, test_size=0.2, random_state=5)
pipe = make_pipeline(StandardScaler(), SVR(kernel="rbf"))
grid = {"svr__C": [1, 10, 100], "svr__epsilon": [0.05, 0.1, 0.2], "svr__gamma": ["scale", 0.1, 1]}
gs = GridSearchCV(pipe, grid, cv=5, scoring="neg_root_mean_squared_error")
gs.fit(X_tr, y_tr)
best = gs.best_estimator_
pred = best.predict(X_te)
print("Best params:", gs.best_params_)
print("Test R2:", round(r2_score(y_te, pred), 3))
print("Test RMSE:", round(mean_squared_error(y_te, pred) ** 0.5, 3))
print("Support vectors:", len(best[-1].support_))

xs = np.linspace(0, 10, 300).reshape(-1, 1)
plt.scatter(X_tr, y_tr, s=10, alpha=0.5, label="Train")
plt.plot(xs, best.predict(xs), "r", label="SVR fit")
plt.legend()
plt.show()

Packages and prices

Choose how much help you need. Each higher package includes everything in the lower ones. Prices exclude 11% VAT.

• Payment: 50% down payment before work starts, balance after the deliverables are handed over
• Tax: Prices exclude 11% VAT
• Data and academic integrity: Results are computed from your own research data; you write and stand behind your manuscript, we help with the analysis and your understanding of it
• Revisions: Up to 2 revisions are included in the price (Package 3: 3 rounds)

Frequently asked questions