This project focuses on analyzing oil and gas data to extract meaningful insights and trends. The analysis includes data cleaning, exploratory data analysis, and visualizations to understand the underlying patterns in the dataset.
oil_gas.ipynb: The Jupyter notebook containing the complete analysis and visualizations.data/: Directory containing the dataset used for the analysis.images/: Directory for storing images and visualizations generated during the analysis.
The dataset used in this project includes information about oil and gas production. The data includes various features such as production volume, well locations, and other relevant attributes.
The analysis is divided into several key sections:
- Data Cleaning: Handling missing values, outliers, and data transformations.
- Exploratory Data Analysis (EDA): Initial exploration of the dataset to identify trends and patterns.
- Visualizations: Creating visual representations of the data to better understand the relationships between different variables.
- Modeling: Applying machine learning models to predict future trends or classify data.
- Python 3.x
- Jupyter Notebook
- Pandas
- NumPy
- Matplotlib
- Seaborn
You can install the required packages using the following command:
pip install pandas numpy matplotlib seaborn