reshamsai150/7.Analysis-and-Prediction-of-Electric-Vehicle-Costs-A-Machine-Learning-Based-Approach

AI Summary: Implement a light/dark mode toggle feature in an existing Flask web application. This involves adding a toggle button to the UI and using CSS to switch between light and dark themes, potentially leveraging browser system preferences.

AI Summary: Implement a fully responsive login and signup page for an existing electric vehicle cost prediction web application built with Flask, HTML, and CSS. The page should include email/password login and registration, password visibility toggles, responsive design for mobile and desktop, and basic validation and error handling.

AI Summary: Implement a new feature in an existing EV cost prediction web application to predict the driving distance an EV can travel given a specific cost and speed. This involves framing the problem as an inverse regression problem, training appropriate machine learning models (e.g., MLP, XGBoost, SVR), evaluating their performance, and integrating the prediction functionality into the existing web interface.

AI Summary: Integrate a recommendation system into an existing EV price prediction web application. The system should suggest EVs based on user preferences like range, budget, charging speed, battery capacity, and specific features. This requires expanding the application's functionality beyond price prediction to include a feature-based recommendation engine.

AI Summary: Add an open-source license (e.g., MIT, Apache 2.0, GPLv3) to the Electric Vehicle Cost Analysis & Prediction project repository. This involves choosing a suitable license, downloading the license text, and adding it as a file (e.g., LICENSE or LICENSE.txt) to the repository.