Trip based fuel consumption modelling using Machine Learning Project built a website from scratch 😁👍
Summary
TLDRThe project FP presents a fuel consumption prediction model using machine learning. It utilizes various regression methods, with the decision tree model achieving 98% accuracy. The model considers parameters like distance, speed, temperature, and weather conditions to predict fuel consumption. The project includes a user-friendly website with a predictor feature, providing drivers with cost-efficient and environmentally sustainable fuel usage insights.
Takeaways
- 📊 **Data-Driven Model**: The project utilizes a dataset named 'measurements.csv' for fuel consumption prediction.
- 🌐 **Web Integration**: A web application is developed to interface with the prediction model using Python.
- 🔍 **Data Visualization**: The team performed data visualization as part of their analysis process.
- 🤖 **Machine Learning Models**: Various regression models were tested including Linear Regression, Lasso Regression, Decision Tree, Random Forest, and SVM.
- 🏆 **Best Model Chosen**: The Decision Tree model achieved the highest accuracy at 98% and was selected for deployment.
- 📝 **Model Persistence**: The trained Decision Tree model is saved using a 'pickle' file for later use.
- 💻 **Web Development**: The project features a web application with HTML pages connected via Python.
- 🔗 **Live Website**: A live website is demonstrated, accessible through a provided link.
- 📈 **Input Parameters**: Users can input parameters such as distance, speed, temperature, and weather conditions to predict fuel consumption.
- 🌍 **Global Impact**: The project aids drivers and travel managers in planning fuel needs and promoting cost efficiency and environmental sustainability.
- 📦 **Deployment**: The model is deployed through a command-line interface, showcasing its practical application.
Q & A
What is the main focus of the project FP?
-The main focus of the project FP is to develop a fuel consumption prediction model using machine learning.
What type of data file is used for the project?
-The project uses a CSV file named 'measurements' for the dataset.
What are the different machine learning models explored in the project?
-The project explored various regression models including linear regression, Lasso regression, decision tree model, random forest model, and SVM.
Which evaluation metric was used to compare the models?
-The R-squared score was used as the evaluation metric to compare the models.
Which model provided the best accuracy according to the R-squared score?
-The decision tree model provided the best accuracy with an R-squared score of 98%.
How is the decision tree model's performance utilized in the project?
-The decision tree model is used to predict fuel consumption, and the model is dumped into a pickle file for later use.
What is the programming language used to connect the web pages with the Python language?
-The project uses Flask, a micro web framework written in Python, to connect the web pages.
How is the project deployed and accessed?
-The project is deployed by running a Python script that starts a web server, and it can be accessed through a provided link in a web browser like Microsoft Edge.
What are the parameters required for the fuel consumption prediction model?
-The parameters required include distance, speed, temperature inside and outside the vehicle, and whether the weather is rainy or sunny.
What is the unit of fuel consumption predicted by the model?
-The model predicts fuel consumption in liters per 100 kilometers.
How does the project help drivers or travel managers?
-The project helps drivers or travel managers by predicting the fuel needed for a trip, suggesting cost-efficient methods, and promoting environmental sustainability.
What additional features are available on the project's website?
-The website includes pages for the home, predictor, about us, team details, and a contact us section.
Outlines
🚀 Project FP: Fuel Consumption Prediction Model
This paragraph introduces a machine learning-based fuel consumption prediction model named 'Project FP'. The data set, named 'measurements.csv', is used to train various regression models such as linear regression, lasso regression, decision tree, random forest, and SVM. The model evaluation is based on the R2 Square score, and the decision tree model is chosen for its highest accuracy of 98%. The model is then saved as a pickle file. The project also includes a Flash code written in Python to connect HTML web pages, allowing users to interact with the website. The website has several pages including home, predictor, about us, and contact us. The 'predict' button on the home page allows users to input parameters such as distance, speed, temperature, and weather conditions to predict fuel consumption in liters per 100 kilometers.
🌐 Project Impact and Conclusion
The second paragraph concludes the project by emphasizing its utility in predicting fuel consumption for trips, which assists drivers and travel managers in planning fuel needs and understanding cost-efficient methods. The project also promotes environmental sustainability by monitoring fuel consumption over time through the website. The speaker expresses gratitude and concludes the presentation of the project.
Mindmap
Keywords
💡Machine Learning
💡Fuel Consumption Prediction Model
💡Dataset
💡Regression Methods
💡Decision Tree Model
💡R2 Score
💡Pickle File
💡Web Pages
💡Parameters
💡Predictor
💡Cost Efficiency
💡Environment Sustainability
Highlights
Project FP is a fuel consumption prediction model based on machine learning.
The data set is named 'measurements.csv'.
The project involves data visualization and testing.
Various regression methods were explored, including linear regression, lasso regression, decision tree, random forest, and SVM.
The R2 Square score was used as an evaluation metric to compare models.
The decision tree model achieved the best accuracy at 98%.
The model is saved as a pickle file.
Flash code is used to connect HTML pages to Python.
The project is deployed using a command line interface.
The website is accessible through a provided link.
The website features pages like home, predictor, about us, and contact us.
The predictor page allows users to input parameters for fuel consumption prediction.
Parameters include distance, speed, temperature, and weather conditions.
The model predicts fuel consumption in liters per 100 kilometers.
The project helps drivers and travel managers estimate fuel needs for trips.
The website promotes cost efficiency and environmental sustainability.
The project can monitor fuel consumption on a regular basis.
The project concludes that it accurately predicts fuel consumption needed for trips.
Transcripts
let me demonstrate my project our
project FP based fuel consumption
prediction model using machine learning
this is our source
file we have the data set name
measurements.
CSV performing the data from performing
the data visualization to
testing we have worked on very various
models
and we have um train our models using
various regression mod regression
methods such as linear
regression lasso regression decision
tree model random forest model and also
svm so uh by with using uh various
evaluation metrics We compare the model
you based on the R2 Square R2 Square
score we have B uh we have got the best
accuracy for decision Tre as you can see
it is of
98% so we go with the decision tree and
dump our pickle
file so let me drive
you let me dve you to my uh flash
code so as you can see this is our flash
code which is us to connect the uh HTML
Pages web pages through Python language
uh it is written in Python language it
can drive us through our
website let us
run
this let me go to the anop the Navigator
or unop the prompt to deploy this
project I'm using un open account
so let me copy
the
this so let
me P this
part is in the command python
app do
app1 we done
this so here's here is
the link for our website let me copy
this and paste this in a
using Microsoft
Edge so here is our our
website that is based on this logo
indicates that AB FCP is transfer trip
based fuel consumption
prediction and U as you can see this is
our
homepage and we have uh four
different other web web pages such as
home here predict
predictor and about us this is about us
this is our team and teammate details
contact
us they can also contact
us so when we go to the
home we have a button predict
now so here are the parameters such as
uh for a trip we need a distance to be
CED speed it
maintains the temperature inside the
vehicle and out the
vehicle and air condition in the vehicle
whether uh it is rainy or sunny these
are the parameters we give as a input
for our model predict
so here it predicts our fuel
consumption vehicle fuel consumption
which Le uh liter per 100
kilm so here is the F
consumption as you can see here it it
has got some output uh while we train
the model it while we give the input it
gives gives us the best accurate fuel
prediction for a trip based on this uh
project we
can conclude that
the the our project is predicting a fuel
consumption that is needed for the trip
which U which which helps
the drivers or the travel managers to
know the fuel which is needed for the
trip and
also know the cost efficient methods and
uh environment
sustainability through our uh website
and it can monitor on time to time basis
so this is our project thank you
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