Weather Data Analysis Project

Overview

This project involves analyzing weather data using Python and SQL to answer various questions about the dataset. The goal is to perform data manipulation, quality checks, and statistical analysis.

Objectives

• Identify records with specific weather conditions.
• Count occurrences of specific wind speeds.
• Check for missing values in the dataset.
• Rename columns for clarity.
• Calculate mean visibility and other statistical measures.
• Filter and aggregate data based on various conditions.

Setup

Prerequisites

• Python: Ensure Python is installed on your system. You can download it from python.org.
• SQL: Install a SQL database system like MySQL, PostgreSQL, or SQLite.
• Required Python Libraries:
  • pandas
  • sqlalchemy (for SQL operations)

Install the required Python libraries using pip:

pip install pandas sqlalchemy

Dataset

The dataset used for this analysis is a CSV file named weather_data.csv. It contains columns such as Date, Weather Condition, Temperature, Relative Humidity, Wind Speed, and Visibility.

Steps to Follow

1. Load the Dataset:

   • Use Pandas to read the CSV file into a DataFrame.

2. Filter Records:

   • Identify records based on specific weather conditions and criteria (e.g., clear weather, wind speed).

3. Count Occurrences:

   • Count occurrences of specific values (e.g., wind speed of 4 km/hr).

4. Check for NULL Values:

   • Determine if there are any NULL values present in the dataset.

5. Rename Columns:

   • Rename columns for better readability and consistency.

6. Calculate Statistics:

   • Compute mean visibility and other statistical measures for the dataset.

7. Filter and Aggregate Data:

   • Filter records based on multiple conditions and compute aggregated values (e.g., mean values for each weather condition).

8. Analyze Weather Conditions:

   • Find instances where weather conditions and other parameters meet specific criteria (e.g., snow conditions).

SQL Analysis

If you wish to perform similar analyses using SQL, you can write SQL queries to:

• Filter records based on weather conditions.
• Count specific occurrences.
• Check for NULL values.
• Calculate means and aggregates.
• Join tables if needed.

Refer to the provided SQL files for examples of the queries used in the SQL-based analysis.

Contributing

If you would like to contribute to this project, please fork the repository and submit a pull request with your changes.

License

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

Acknowledgements

• The dataset used for this analysis was obtained from Kaggle.
• Special thanks to the community for support and collaboration.

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