Predicting and Forecasting Temperature using Multiple-Linear Regression in CAMANAVA

Dataset

• Title: Philippine Major Cities Weather Data ☀️
• Source: Kaggle

Preprocessing

1. Checking for Null Values:

   • Ensure there are no missing values in the dataset.

2. Getting Data from CAMANAVA:

   • Extract data specifically for the cities of Caloocan, Malabon, Navotas, and Valenzuela.

3. Setting Datetime as Index:

   • Convert the datetime column to the index of the DataFrame.

4. Encoding Categorical Data:

   • Use one-hot encoding for the weather.id column to handle categorical data.

Model Training

Separating the Features

• Dependent Variable:

  • Temperature: main.temp

• Independent Variables:

  • Atmospheric Pressure: main.pressure (hPa, on the sea level)
  • Humidity: main.humidity (%)
  • Cloudiness: clouds.all (%)
  • Weather Condition: weather.id
  • Wind Speed: wind.speed

Data Splitting

• Split the dataset into training and testing sets, preserving temporal data, with an 80/20 ratio.

Model Initialization and Training

• Initialize and train the Linear Regression model using the training dataset.

Model Testing and Evaluation

1. Testing on the Test Set:

   • Evaluate the model performance using the test dataset.

2. Evaluation Metrics:

   • Mean Squared Error (MSE):
     • Measures the average squared difference between the predicted values and the actual (observed) values.
   • Mean Absolute Error (MAE):
     • Measures the average absolute difference between predicted and actual values, providing a straightforward measure of prediction accuracy.
   • Root Mean Squared Error (RMSE):
     • Measures the square root of the average squared difference between predicted and actual values, providing a comparable metric in the original scale of the data.