Data Analysis & SQL Exploration Project 📈

📝 Project Overview

This project focuses on performing a complete data analysis workflow on a given dataset. The process includes data cleaning, exploratory data analysis (EDA) to uncover trends, and SQL for efficient data exploration and pattern identification. The goal is to derive meaningful insights from the raw data using Python libraries and SQL queries.

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🛠️ Technologies Used

• Python: For data manipulation and analysis.
  • Pandas: For data cleaning and manipulation.
  • Matplotlib & Seaborn: For data visualization.
• SQL: For efficient data querying and pattern identification.
• Development Environment:
  • Jupyter Notebook: For interactive data analysis and visualization.
  • SQL Workbench: For executing and testing SQL queries.

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📋 Project Workflow

1. Data Cleaning: The initial dataset was processed to handle inconsistencies, such as missing values and incorrect data types, ensuring data quality for analysis.
2. Exploratory Data Analysis (EDA): Using Pandas, Matplotlib, and Seaborn, I conducted a deep dive into the data. This involved creating various visualizations to understand variable distributions, relationships, and outliers.
3. SQL Exploration: SQL queries were written and executed in SQL Workbench to perform efficient data exploration, aggregate data, and identify patterns that were not immediately obvious.
4. Statistical Analysis: Basic statistical methods were applied to understand the relationships and correlations between key variables.

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🚀 How to Get Started

To replicate this analysis on your local machine, follow these steps:

1. Clone the repository:

   git clone [https://github.com/your-username/your-repository-name.git](https://github.com/your-username/your-repository-name.git)

2. Navigate to the project directory:

   cd your-repository-name

3. Install the required libraries:

   pip install -r requirements.txt

4. Run the analysis:
   • Open the analysis.ipynb file in Jupyter Notebook to see the Python-based EDA.
   • The SQL queries used for exploration can be found in the /sql_queries folder and can be run using an environment like SQL Workbench.