Weather Forecasting with Machine Learning 🌦️

Welcome to the Weather Forecasting with Machine Learning repository! This project focuses on utilizing a dataset of meteorological sensors to clean data and identify the best machine learning models for accurate weather forecasting. Here, you will find everything you need to get started with your own weather prediction models.

Table of Contents

• Introduction
• Dataset
• Technologies Used
• Getting Started
• Data Cleaning
• Model Selection
• Visualizations
• Results
• Releases
• Contributing
• License

Introduction

Weather forecasting is a critical task that impacts various sectors, including agriculture, transportation, and disaster management. This repository aims to simplify the forecasting process using machine learning techniques. By analyzing historical weather data, we can predict future weather conditions with improved accuracy.

Dataset

The dataset used in this project consists of meteorological sensor readings, including temperature, humidity, wind speed, and atmospheric pressure. The data is collected from various sources and spans several years, providing a comprehensive view of weather patterns.

Technologies Used

This project employs several powerful libraries and tools, including:

• Pandas: For data manipulation and analysis.
• Matplotlib: For creating static, animated, and interactive visualizations.
• Seaborn: For statistical data visualization.
• Plotly: For interactive visualizations.
• Scikit-learn: For machine learning algorithms and metrics.
• SciPy: For scientific computing and advanced statistical analysis.
• Missingno: For visualizing missing data.
• Variance Inflation Factor: To detect multicollinearity in regression analysis.
• Gradient Boosting: For building predictive models.
• RandomizedSearchCV: For hyperparameter tuning.
• Learning Curve: To evaluate the performance of models.

Getting Started

To get started with this project, follow these steps:

1. Clone the Repository:

   git clone https://github.com/sushantdhruv2003/Weather-forecasting-with-machine-learning.git

2. Navigate to the Project Directory:

   cd Weather-forecasting-with-machine-learning

3. Install Required Packages: Ensure you have Python installed. Then, install the necessary libraries using pip:

   pip install -r requirements.txt

4. Download and Execute the Data: For the latest releases, visit Releases to download the files. Follow the instructions provided in the release notes for execution.

Data Cleaning

Data cleaning is a crucial step in the data analysis process. In this project, we handle missing values, outliers, and incorrect data types. The following techniques are applied:

• Missing Value Treatment: We use the Missingno library to visualize missing data and decide on appropriate methods for imputation.
• Outlier Detection: Statistical methods help identify and handle outliers in the dataset.
• Data Type Conversion: Ensure that all columns have the correct data types for analysis.

Model Selection

After cleaning the data, we explore various machine learning models to find the best fit for our forecasting task. We evaluate models using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The following models are considered:

1. Linear Regression
2. Decision Trees
3. Random Forests
4. Gradient Boosting Machines (GBM)

Using RandomizedSearchCV, we optimize hyperparameters for each model, ensuring the best performance.

Visualizations

Visualizations play a key role in understanding data and model performance. We create various plots, including:

• Learning Curves: To assess model performance as training data increases.
• Feature Importance Plots: To understand which features contribute most to predictions.
• Correlation Heatmaps: To visualize relationships between different variables.

These visualizations help in interpreting the results and guiding further improvements.

Results

After evaluating the models, we present the results in a clear format. The best-performing model is highlighted, and its predictions are compared against actual values. This section includes:

• Performance Metrics: MAE, RMSE, and R² scores.
• Prediction vs Actual Graphs: Visual comparisons to show model effectiveness.

Releases

For the latest releases, including updates and new features, please visit the Releases section. Download the files and follow the instructions to execute them.

Contributing

Contributions are welcome! If you would like to contribute to this project, please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Commit your changes.
4. Push to the branch.
5. Open a pull request.

Please ensure your code follows the project's style guidelines and includes appropriate tests.

License

This project is licensed under the MIT License. See the LICENSE file for details.

---

Thank you for visiting the Weather Forecasting with Machine Learning repository. We hope you find it useful for your own projects! For more information, feel free to explore the code and documentation.

Added circle CI setup to this branch