Weather Trend Forecasting Project

This project analyzes the Global Weather Repository dataset to forecast weather trends using both basic and advanced techniques.

Project Structure

• data_loader.py: Handles data loading, cleaning, and preprocessing
• exploratory_analysis.py: Contains EDA functions and visualizations
• forecasting_models.py: Implements various forecasting models
• main.py: Main script that orchestrates the analysis

Features

Basic Assessment

• Data cleaning and preprocessing
• Missing value handling
• Outlier detection and treatment
• Basic EDA with visualizations
• Temperature trend analysis
• Basic forecasting model

Advanced Assessment

• Multiple forecasting models (Linear Regression, Random Forest, XGBoost)
• Ensemble modeling
• Geographical pattern analysis
• Correlation analysis
• Feature importance analysis

Requirements

• Python 3.8+
• pandas
• numpy
• scikit-learn
• matplotlib
• seaborn
• xgboost

Usage

1. Install required packages:

pip install pandas numpy scikit-learn matplotlib seaborn xgboost

2. Place the "Global Weather Repository.csv" file in the project directory

3. Run the analysis:

python main.py

Results

The analysis includes:

• Visualization of temperature trends
• Correlation analysis of weather parameters
• Geographical weather patterns
• Model performance metrics
• Ensemble predictions

Model Evaluation

The project evaluates multiple models:

• Linear Regression
• Random Forest
• XGBoost
• Ensemble of all models

Metrics used:

• Mean Squared Error (MSE)
• Mean Absolute Error (MAE)
• R-squared Score# weather-trend-forecasting Advanced analysis of global weather trends using Python.