Data Science Project | Part 1 | Shipment Price Prediction

Introduction

In this tutorial, we will explore the process of predicting shipment prices using data science techniques. This step-by-step guide is designed for both beginners and those looking to enhance their data science skills. We will cover the entire workflow from data collection to model evaluation, providing you with valuable insights and practical advice on building your own predictive model.

Step 1: Understand the Project Overview

• Familiarize yourself with the objectives of the shipment price prediction project.
• Recognize the real-world applications of this model, such as helping businesses optimize their shipping costs.

Step 2: Collect the Data

• Identify the data sources relevant to shipment prices. This may include:
  • Public datasets
  • Company-specific data
• Use libraries like pandas to import your dataset in Python.

import pandas as pd

# Load the dataset
data = pd.read_csv('path_to_your_dataset.csv')

Step 3: Explore the Data

• Use exploratory data analysis (EDA) techniques to understand the dataset.
• Consider the following methods:
  • Visualizations (e.g., histograms, scatter plots) using libraries like matplotlib and seaborn.
  • Summary statistics to assess the data's central tendency and dispersion.

import matplotlib.pyplot as plt
import seaborn as sns

# Example: Histograms
data['price'].hist()
plt.title('Distribution of Shipment Prices')
plt.show()

Step 4: Preprocess the Data

• Clean the dataset by addressing missing values and outliers.
• Standardize and normalize features as necessary to prepare for modeling.
• Example techniques:
  • Fill missing values using mean or median.
  • Remove or cap outliers based on domain knowledge.

# Filling missing values
data['price'].fillna(data['price'].mean(), inplace=True)

Step 5: Engineer Features

• Create new features that could improve model accuracy, such as:
  • Combining existing features (e.g., total shipment weight).
  • Encoding categorical variables (e.g., one-hot encoding).

# Example of one-hot encoding
data = pd.get_dummies(data, columns=['category'])

Step 6: Select the Model

• Choose appropriate machine learning algorithms for predicting shipment prices.
• Consider models such as:
  • Linear Regression
  • Decision Trees
  • Random Forests
• Each model comes with its own strengths and weaknesses, so select based on your data characteristics and project goals.

Step 7: Build the Initial Model

• Set up and train your selected model using the training data.
• Use scikit-learn for building models.

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

# Split the data
X_train, X_test, y_train, y_test = train_test_split(data.drop('price', axis=1), data['price'], test_size=0.2)

# Train the model
model = LinearRegression()
model.fit(X_train, y_train)

Step 8: Evaluate Model Performance

• Assess model performance using appropriate evaluation metrics, such as:
  • Mean Absolute Error (MAE)
  • Mean Squared Error (MSE)
  • R-squared value
• Use these metrics to understand how well your model predicts shipment prices.

from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score

# Predictions
predictions = model.predict(X_test)

# Evaluate
mae = mean_absolute_error(y_test, predictions)
mse = mean_squared_error(y_test, predictions)
r2 = r2_score(y_test, predictions)

print(f'MAE: {mae}, MSE: {mse}, R2: {r2}')

Conclusion

In this first part of the shipment price prediction project, we covered the essential steps from project overview to initial model evaluation. You should now have a foundational understanding of how to collect, explore, and preprocess data, as well as how to select and evaluate a predictive model. In the next part, we will focus on optimizing and fine-tuning our model for better performance. Be sure to subscribe for updates and continue your learning journey!