Global Weather to Country Predictor 🌍🌦️

A Streamlit web application that predicts a geographic location (country) based on real-time weather measurements. The prediction engine is powered by a custom PyTorch Multi-Layer Perceptron (MLP) neural network.

📌 Project Overview

This project utilises a deep learning model to classify a country using 10 specific weather features[cite: 5]. The data is standardised before being processed by the PyTorch model, and the resulting predictions are decoded back into readable country names using a scikit-learn label encoder[cite: 5].

Live Link

Dataset

The 10 Weather Features Analysed:

• Temperature (°C)[cite: 5]
• Wind Speed (kph)[cite: 5]
• Wind Degree[cite: 5]
• Pressure (mb)[cite: 5]
• Precipitation (mm)[cite: 5]
• Humidity (%)[cite: 5]
• Cloud Cover[cite: 5]
• Visibility (km)[cite: 5]
• UV Index[cite: 5]
• Gust Speed (kph)[cite: 5]

📂 Repository Structure

• app.py: The main Streamlit web application script.
• Location_Prediction_2.py: The PyTorch training script[cite: 5].
• location_classifier.pth: Saved PyTorch model weights.
• scaler.joblib: Saved scikit-learn StandardScaler.
• label_encoder.joblib: Saved scikit-learn LabelEncoder.
• requirements.txt: Python dependencies for the server environment.

🧠 Model Architecture

The core of this application is a PyTorch neural network built with torch.nn.Sequential[cite: 5]. The architecture consists of fully connected linear layers with ReLU activations[cite: 5]:

• Input Layer: 10 features[cite: 5]
• Hidden Layer 1: 256 neurons[cite: 5]
• Hidden Layer 2: 128 neurons[cite: 5]
• Hidden Layer 3: 128 neurons[cite: 5]
• Hidden Layer 4: 64 neurons[cite: 5]
• Output Layer: Matches the number of unique countries in the dataset[cite: 5]

The model was optimised using Stochastic Gradient Descent (SGD) with a learning rate of 0.001 and a momentum of 0.9[cite: 5]. Cross-Entropy Loss was used as the criterion[cite: 5].

💻 Local Installation and Usage

To run this application locally, ensure Python 3.8+ is installed.

1. Clone the repository:

   git clone [https://github.com/your-username/your-repo-name.git](https://github.com/your-username/your-repo-name.git)
   cd your-repo-name

2. Install dependencies:

   pip install -r requirements.txt

3. Launch the Streamlit application:

   streamlit run app.py

☁️ Deployment on Streamlit Cloud

This application is configured for seamless deployment on Streamlit Community Cloud.

1. Push this repository to a public GitHub account.
2. Log in to Streamlit Cloud.
3. Click New app.
4. Select this repository, set the branch to main, and set the main file path to app.py.
5. Click Deploy!

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Created by Shanta Majumder