🌤️ London Weather Temperature Predictor

📌 Project Overview

This project is a machine learning model that predicts the mean temperature in London based on various weather conditions. It uses Random Forest Regression for accurate predictions and is deployed as an interactive web app using Streamlit. 🚀

---

📂 Project Structure

📁 LONDON WEATHER/
│-- 📜 london_weather.py   # Machine learning model training & tuning
│-- 📜 app.py              # Streamlit web app
│-- 💜 london_weather.csv  # Dataset used for training
│-- 💜 requirements.txt    # List of dependencies
│-- 🖼️ Feature Importance in Predicting Temperature.png  # Visualization
│-- 📜 README.md           # Project documentation (this file)

---

🔧 Features & Technologies Used

• Python 🐍
• pandas, NumPy 📊 (Data Processing)
• scikit-learn 🤖 (Machine Learning)
• Matplotlib 📈 (Visualization)
• Streamlit 🎨 (Web App Deployment)

---

📊 Feature Importance

The most important factors for predicting temperature (based on model analysis): 1️⃣ Min Temperature 🌡️ (Strongest predictor) 2️⃣ Max Temperature 🔥 3️⃣ Global Radiation ☀️ 4️⃣ Sunshine ⏳ 5️⃣ Pressure 📏 6️⃣ Cloud Cover ☁️ 7️⃣ Precipitation 🌧️

---

🚀 How to Run This Project

1️⃣ Install Required Packages

Run the following command to install dependencies:

pip install -r requirements.txt

2️⃣ Run the Streamlit App

streamlit run app.py

Then, open the browser to interact with the UI and make predictions!

---

📈 Model Performance

Model	MAE (°C)	R² Score
Decision Tree	0.96°C	0.95
Random Forest	0.69°C	0.97
Optimized RF	0.68°C	0.974

✅ Random Forest performed the best!

---

🎯 Future Improvements

• ✅ Deploy the app online (Streamlit Cloud or Hugging Face Spaces)
• ✅ Enhance the UI with better visuals & graphs
• ✅ Improve model performance by adding new features

---

👨‍💻 Author

🚀 Harjot / Iris