🌐 WeatherMLProject

This project demonstrates two machine learning tasks using real-time weather data fetched from an API:

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⚙️ Data Source

We use the OpenWeatherMap API to fetch current weather data (e.g., temperature, humidity, pressure) for a specific location. The data is then used in two separate machine learning tasks:

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📡 1. Weather Data Collection via API

We used the OpenWeatherMap API in a script to:

• Call the weather API using a personal API key.
• Fetch current weather data for a specific city (e.g., London).
• Extract features like:
  • temperature
  • humidity
  • pressure
  • weather condition (as a label)
• Save the collected data to a local CSV file (weather_data.csv) for further use.

💡 API used: api.openweathermap.org/data/2.5/weather?q=CityName&appid=YOUR_API_KEY

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📈 2. Temperature Prediction (Regression)

• Objective: Predict temperature using other weather features.
• Features: Humidity, Pressure
• Target: Temperature
• Model Used: LinearRegression
• Notebook: 01_Temperature_Regression.ipynb

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🧠 3. Weather Condition Classification

• Objective: Classify the type of weather (e.g., Clear, Rainy, Cloudy) using numerical weather features.
• Data Reuse: We reuse the same weather_data.csv generated in the regression task.
• Preprocessing:
  • Encode weather conditions (e.g., Rain, Clear) into numeric labels.
  • Scale the features using StandardScaler.
• Model Used: RandomForestClassifier (with baseline comparison to LogisticRegression)
• Notebook: 02_Condition_Classification.ipynb

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📁 Files

File	Description
models/temperature_model.pkl	Trained Linear Regression model
models/condition_classifier.pkl	Trained Logistic Regression/Random Forest Classifier
data/	Original
notebooks/01_Temperature_Regression.ipynb	Regression training notebook
notebooks/02_Condition_Classification.ipynb	Classification training notebook

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💾 How to Run

1. Install dependencies:

   pip install -r requirements.txt