AI-Based Weather Recommendation System

Inspiration : The project was inspired by the need for personalized activity recommendations based on real-time weather conditions. By integrating weather data with AI, the system aims to enhance decision-making for daily activities.

What it Does : The AI-Based Weather Recommendation System provides users with real-time weather updates and suggests suitable activities based on current conditions. It utilizes the OpenWeatherMap API to fetch weather data and a Decision Tree Classifier to recommend activities.

How We Built It : Data Retrieval: Used the OpenWeatherMap API to obtain real-time weather data. AI Model: Implemented a Decision Tree Classifier using Scikit-learn to map weather conditions and temperatures to recommended activities. Visualization: Incorporated Matplotlib to visualize simulated temperature trends throughout the day.

Challenges We Ran Into : API Key Issues: Encountered difficulties with API key generation and authentication. Weather Condition Recognition: Initial challenges in mapping weather conditions correctly led to unrecognized inputs. Accomplishments That We're Proud Of Successfully integrated real-time weather data and AI-driven recommendations. Developed a user-friendly interface that prompts users for their city and provides instant feedback. Enhanced the model to recognize a broader range of weather conditions and corresponding activities.

What We Learned : The importance of proper data mapping and error handling when integrating APIs. Insights into machine learning model training and prediction processes. Enhanced skills in data visualization to communicate insights effectively.

Built With

• api
• matplotlib
• openweathermap
• python
• scikit-learn
• vscode