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How to Predict the Traffic Volume Using Machine Learning

In this project, we’ll learn to explore and visualize traffic data and also create multiple machine learning models to predict the estimation of traffic data based on different parameters.

How to Predict the Traffic Volume Using Machine Learning

You will learn to:

Explore and visualize a dataset using seaborn and Matplotlib.

Build different machine learning models using scikit-learn.

Create and save trained models as .pkl files.

Make predictions using trained models.


Machine Learning

Deep Learning

Data Visualisation


Intermediate knowledge of Python

Intermediate knowledge of machine learning models

Familiarity with Python and machine learning libraries







Project Description

In this data-driven project, we aim to predict traffic volume on a given roadway by leveraging machine learning techniques and various environmental factors.

First, we’ll preprocess the data, address the missing values, and convert categorical variables into numerical representations for seamless processing. Next, we’ll harness the visualization capabilities of seaborn to gain valuable insights into the data. We’ll utilize historical data, including date and time, weather conditions, and holidays, to predict the traffic volume on the roads. We’ll employ three regression models: linear regression, decision tree regressor, and random forest regressor. Finally, we’ll save all three models using joblib for future deployment, allowing us to make real-time traffic volume predictions.

Project Tasks


Get Started

Task 0: Introduction

Task 1: Import Libraries and Modules

Task 2: Load the Dataset


Explore the Dataset

Task 3: Remove Duplicate Values

Task 4: Create a Histogram of Traffic Volume


Preprocess the Dataset

Task 5: Get the Date and Time

Task 6: Convert Categorical Columns to Numerical

Task 7: Create Input and Output Parameters

Task 8: Split the Training and Testing Data


Build, Train and Validate the Model

Task 9: Build the Models

Task 10: Train the Models

Task 11: Evaluate the Models

Task 12: Save the Models

Task 13: Load and Use the Models