Traffic Prediction Using Deep Learning / College mini Project
Summary
TLDRThis project focuses on predicting traffic volume using deep learning techniques. The dataset includes various weather-related features, and traffic volume is the target variable. The team used an MLP regressor, mapping string-type data to numerical values for better processing. They scaled the data, visualized feature relationships, and trained the model using a 500-iteration approach. With a mean absolute error of 7%, the model provides reasonably accurate predictions. The project also involves deploying the model through Flask, allowing users to input features and predict traffic conditions via a web interface.
Takeaways
- 📅 The project focuses on traffic prediction using deep learning techniques.
- 📊 The dataset includes features such as date, holiday status, air pollution, humidity, wind speed, wind direction, visibility, and weather conditions, with traffic volume as the target feature.
- 🔢 Both numerical and string-type data are present in the dataset. The string-type data (e.g., weather conditions) was mapped to numerical values for model training.
- 📈 The data was scaled using Min-Max Scaler to a range of -1 to 1 to standardize different ranges of the data.
- 🧠 The team used an MLP (Multi-Layer Perceptron) regressor, a supervised learning technique, with the ReLU activation function and Adam optimizer.
- 💡 Mean Absolute Error (MAE) was calculated as 0.07, or 7%, indicating the performance of the traffic prediction model.
- 🖥️ For deployment, the project was implemented using Flask on the Visual Studio platform, enabling a web-based interface for interaction.
- 📱 The front end allows users to input features like date, temperature, holiday status, and weather conditions to predict traffic volume.
- 🚦 The model predicts traffic conditions (e.g., heavy traffic) based on the input features provided by the user.
- 🔧 Additional features include routes for training the model and testing it through manual input via a user interface.
Q & A
What is the target feature in the dataset used for traffic prediction?
-The target feature in the dataset is traffic volume.
Which supervised learning technique was used for traffic prediction?
-The MLP Regressor from neural networks, a supervised learning technique, was used for traffic prediction.
How was string data in the dataset handled for the MLP regressor?
-String data in the dataset was mapped to different numerical values because MLP regressor cannot handle string data directly.
What technique was used to scale the dataset values?
-The Min-Max Scaler was used to scale the dataset values between -1 and 1.
What error metric was used to evaluate the model performance, and what was the error value?
-The mean absolute error (MAE) was used to evaluate the model performance, and the error value was 0.07, or 7%.
How were weather-related string features such as weather description handled?
-Weather-related string features like weather description were mapped to numerical values to be processed by the MLP regressor.
What optimization function was used in the MLP regressor?
-The Adam optimizer was used as the optimization function for the MLP regressor.
What front-end framework was used for the deployment of the model?
-The Flask framework was used for the front-end deployment of the model.
What happens when the 'Click me to train model' button is pressed on the webpage?
-When the 'Click me to train model' button is pressed, the model is trained and the corresponding code is executed.
How does the input form on the webpage interact with the prediction model?
-The input form allows users to provide features like date, temperature, climate conditions, and weather description. After inputting the data, users can click 'Predict,' and the model will predict the traffic condition based on the provided features.
Outlines
🚦 Overview of Traffic Prediction Project
This mini project focuses on predicting traffic volume using deep learning. The dataset contains features such as date, holiday, air pollution, humidity, wind speed, wind direction, visibility, and various weather conditions. The main target is traffic volume. The dataset includes both numeric and string data, with string data mapped to numerical values for machine learning compatibility. The coding section includes importing essential libraries and using the MLP Regressor from neural networks. After preprocessing, the data is scaled using MinMaxScaler, and the MLP regressor model is trained and tested to predict traffic volumes, with a 7% mean absolute error achieved.
🖥️ Model Deployment and Prediction Interface
The second part describes the deployment of the model using Flask in Visual Studio. A web interface is created where users can input features like date, time, temperature, holiday status, climate conditions, and weather description to predict traffic conditions. After submitting the inputs, the model provides traffic predictions (e.g., heavy traffic). The flow of execution is described: the server is started, the model is trained, and predictions are made through the input form on the webpage. The result and model analysis are presented, showing the relationship between features and predictions.
Mindmap
Keywords
💡Traffic Prediction
💡Deep Learning
💡MLP Regressor
💡Feature Mapping
💡Normalization
💡Mean Absolute Error (MAE)
💡Flask
💡Data Scaling
💡Training
💡Test Dataset
Highlights
The project involves traffic volume prediction using deep learning methods.
The dataset includes features like date, holiday status, air pollution, humidity, wind speed, wind direction, visibility, and other weather details.
Traffic volume is the target feature, with string-type data for weather conditions and types being processed.
The team used MLP Regressor from neural networks, a supervised learning technique, for model training.
Data preprocessing involved mapping string-type features to numerical labels to make them compatible with the MLP Regressor.
Min-Max scaling was applied to normalize feature values between -1 and 1.
Visualization of the correlation matrix was done using C library, showing relationships between features.
The model was trained using 500 iterations with the ReLU activation function and Adam optimizer.
The model's performance was evaluated using Mean Absolute Error (MAE), resulting in an error of 7% (0.07 MAE).
A test dataset was used to further evaluate the model’s performance, showing similar results to the training set.
Manual testing with sample input resulted in predicted traffic volume values, which were compared with actual values.
The model was deployed using Flask on Visual Studio for frontend integration.
The frontend allowed users to manually input features like date, time, weather, and climate conditions to predict traffic volume.
Flask was used to create routes for training the model and predicting traffic based on user inputs.
The project successfully demonstrated a functional deep learning model for traffic prediction, deployed with a user-friendly interface.
Transcripts
mini project about traffic prediction
using deep learning this is our Pro and
we have collected the data set and these
are the features which are in our data
set date holay air pollution humidity
wind speed wind direction visibility
view points and all other weather
details and this is a traffic volume
which is the target feature in our data
set and these are the string type data
data set which weather conditions and
the type of weather which is present and
actually we have this this is the
original data set which we have
collected from this data set we have
extracted the required data for our
project and coming to our coding
part in this we have done our coding
here
and we have imported all the libraries
which are use used in our project and we
have used MLP regor from neur networks
which is a supervised learning technique
and we have imported a data set train.
CSV which is the data set which we have
collected and here we have taken all the
attributes into our data set which which
are required and store them in the
traffic volume data set and from
there yes and we have collected the data
set these are all the data which have
which are value stored Fe features which
are of integer type and these data of
string type and we
have we have mapped them with different
for different labels and after
that for the data which is the data
which is of string type we have mapped
the data for different values as we all
know the string data is not trained by
the trained by our MLP regressor for for
that we have mapped our data for
different values for that we have used
this fun tools which is used for mapping
the string data to the different values
and here is the mapping procedure which
is done in our project and before
mapping these are all the data string
data which is in present in those
features and after mapping we can see
here the weather type and weather
description they are mapped to different
values
and this this is a relation Matrix
between the all the features which are
present in our data
data set and here we have done our
scaling part and the we the data which
we have collected it is of different
range values so we have mapped this by
using the mid Max scaler between minus1
to 1 and after scaling we have got these
features as these values
and this is a metrics we have visualized
using the C library
and and here comes we have used our MLP
regressor which is a multi-layer
perception regressor and we have try our
model using 500x and we have used the ra
by default we have used the Rao function
and AD
them for our optimization in our project
and we have predicted the values and
these are all the these are the
predicted values and these are the
actual values which are there in our
project and for the we have another data
set which is
test data set and we have tested our
data and calculated the error value as
it is a regressor regression we have
calculated as a mean absolute error and
we have got
0.07 as an error which is of 7% for
overall the project
and and here I have tested the data by
giving a manually some input to our
model and I have got the output 171 it
is a scaled value and after iners
scaling we have got this range traffic
volume as this
value based on this traffic volume we
have predictor we have mapped our data
and
predictor our data and the condition of
the traffic and coming to the deployment
part we have done our deployment in this
Visual Studio platform by using the
flask here we can see we have imported
to the flask
library for the front end part we have
used the flask uh
uh here are the routes you can see when
we the homepage is home.
HTML when we run the
server the the test. P the server has
started this is the landing page which
we have created when we click on this
button click me to train model the model
will be
trained uh after clicking the train here
you can see this code this code will be
executed and the model will be trained
and uh you can see the terminal that the
code has been
executed and next this page is re
redirected here you can see the input
text
boxes uh using this uh text boxes you
can see the input uh the day feature
date feature the day feature the
time the temperature and is holiday or
not the climate conditions
and the weather
description rainy and the you can see
there are many
features um we can give anyone turn from
result when we click on predict here you
can see the output heavy traffic uh
according to our given features the
output is predicted
uh these are the routes when we click on
predi this this code is exed and when we
click on
train when you click on train button
this code is
executed and this is the
output and these are the analysis of our
model on the features how the data is
[Music]
dependent thank you sir
تصفح المزيد من مقاطع الفيديو ذات الصلة
Machine Learning & Data Science Project - 1 : Introduction (Real Estate Price Prediction Project)
Machine Learning Tutorial Python - 3: Linear Regression Multiple Variables
Plant Leaf Disease Detection Using CNN | Python
Linear Regression, Cost Function and Gradient Descent Algorithm..Clearly Explained !!
Building a Plagiarism Detector Using Machine Learning | Plagiarism Detection with Python
Trip based fuel consumption modelling using Machine Learning Project built a website from scratch 😁👍
5.0 / 5 (0 votes)