This device is not compatible.
PROJECT
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.
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.
Skills
Machine Learning
Deep Learning
Data Visualisation
Prerequisites
Intermediate knowledge of Python
Intermediate knowledge of machine learning models
Familiarity with Python and machine learning libraries
Technologies
Pandas
Python
seaborn
Matplotlib
Scikit-learn
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
1
Get Started
Task 0: Introduction
Task 1: Import Libraries and Modules
Task 2: Load the Dataset
2
Explore the Dataset
Task 3: Remove Duplicate Values
Task 4: Create a Histogram of Traffic Volume
3
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
4
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
Congratulations!