Trip based fuel consumption modelling using Machine Learning Project built a website from scratch 😁👍

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23 Jul 202405:50

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

00:00

🚀 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.

05:03

🌐 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

Machine learning is a subset of artificial intelligence that provides systems the ability to learn from data, identify patterns, and make decisions with minimal human intervention. In the context of the video, machine learning is used to develop a fuel consumption prediction model. The script mentions using various regression methods, which are machine learning techniques, to train models on the dataset.

💡Fuel Consumption Prediction Model

A fuel consumption prediction model is a system designed to estimate the amount of fuel a vehicle will consume based on certain parameters. The video's project, 'FP based fuel consumption prediction model,' uses machine learning to predict fuel usage for trips, which is central to the video's theme of helping drivers and travel managers optimize fuel efficiency.

💡Dataset

A dataset is a collection of data, typically used in machine learning to train models. The script refers to a 'CSV file' containing data that is used for training various models to predict fuel consumption. The dataset is crucial as it provides the raw information needed for the machine learning algorithms to learn from.

💡Regression Methods

Regression methods are statistical techniques used to determine the relationship between variables. In the video, different regression methods like linear regression, Lasso regression, and decision tree regression are used to train models. These methods help in understanding the relationship between input parameters and fuel consumption.

💡Decision Tree Model

A decision tree model is a flowchart-like structure in which each internal node represents a 'test' on an attribute, each branch represents the outcome of the test, and each leaf node represents a class label (decision taken after computing all attributes). The script highlights that the decision tree model achieved the best accuracy of 98%, making it the chosen model for predicting fuel consumption.

💡R2 Score

The R2 score, also known as the coefficient of determination, is a statistical measure that represents the proportion of the variance for a dependent variable that's explained by an independent variable or variables in a regression model. The script mentions using the R2 score to evaluate and compare the performance of different models.

💡Pickle File

A pickle file is a file format used for serializing and de-serializing a Python object structure. In the video, the 'pickle file' is mentioned in the context of saving the trained decision tree model so that it can be reused for predictions without the need to retrain it.

💡Web Pages

Web pages are accessed through the internet and are used to display information in a web browser. The video describes a website with HTML pages that are connected through Python code, allowing users to interact with the fuel consumption prediction model. The web pages include a homepage, a predictor page, and an 'about us' page.

💡Parameters

Parameters are variables used in a function or model to control the behavior of the function or model. In the context of the video, parameters such as distance, speed, temperature, and weather conditions are inputs to the model to predict fuel consumption. These parameters are essential for the model to make accurate predictions.

💡Predictor

A predictor is a function or model that estimates an unknown value based on other related information. In the video, the 'predictor' refers to the web page where users can input parameters to receive predictions on fuel consumption for their trips. This is a key component of the project as it allows users to apply the model in a practical setting.

💡Cost Efficiency

Cost efficiency refers to achieving the best output for the least cost. The video mentions that the project helps drivers and travel managers know the fuel needed for a trip, which can lead to cost-efficient methods. By predicting fuel consumption accurately, the model can assist in planning trips to minimize fuel costs.

💡Environment Sustainability

Environment sustainability is the ability to maintain or improve environmental quality over time. The video suggests that by predicting fuel consumption, the project can contribute to environmental sustainability by helping drivers and travel managers make decisions that reduce fuel usage and, consequently, reduce environmental impact.

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.