This repository contains a data analysis project implemented in Python. The project aims to analyze a dataset and extract meaningful insights using various data analysis techniques and Python libraries.
In this data analysis project, we analyze a dataset to gain insights and answer specific questions. The project demonstrates how to perform data cleaning, exploratory data analysis, and visualization using Python. It also showcases the usage of popular Python libraries such as Pandas, NumPy, Seaborn and Matplotlib.
Before running this project, make sure you have the following prerequisites installed on your system:
- Python 3.x: The project is implemented in Python, so you need to have Python installed. You can download Python from the official website: Python.org
To use this project, you can either clone the repository or download the ZIP file and extract it to your desired location.
To clone the repository, open a terminal and run the following command:
git clone https://github.com/shailesh2210/Data-Analysis.git
To use this project, follow these steps:
-
Navigate to the project directory:
cd data-analysis-project -
Launch Jupyter Notebook:
jupyter notebook -
Open the
data_analysis.ipynbnotebook in Jupyter. -
Run the notebook cells sequentially to perform data analysis on the provided dataset.
The project structure is as follows:
data-analysis-project/
├── data/
│ ├── dataset.csv
├── notebooks/
│ └── data_analysis.ipynb
├── README.md
-
The
datadirectory contains the dataset (dataset.csv) that will be used for analysis. -
The
notebooksdirectory contains the Jupyter Notebook (ipynb) where the data analysis is performed. -
The
README.mdfile provides an overview of the project and instructions on how to use it.
Contributions are welcome! If you find any issues or want to enhance the project, feel free to open an issue or submit a pull request.