Readme

# Exploratory Data Analysis (EDA) Repository

Welcome to my Exploratory Data Analysis (EDA) repository! This repository contains a Jupyter Notebook that demonstrates how to use Pandas for data analysis. The notebook covers various EDA techniques like data cleaning, transformation, visualization, and insight generation using Python and Pandas.

## Contents 

- **Introduction to Pandas**: Overview of the Pandas library and its core data structures.
- **Data Loading**: Instructions on how to load datasets using Pandas (e.g., from CSV, Excel).
- **Data Exploration**: Techniques for examining the structure and contents of a dataset.
- **Data Cleaning**: Identifying and handling missing data, duplicates, and data type conversions.
- **Data Transformation**: Using Pandas functions for data manipulation and transformations.
- **Visualization**: Plotting and visualizing data with Pandas and Matplotlib.

## How to Access

You can view the Jupyter Notebook from the following link:

[Exploring Data with Pandas in Jupyter Notebook](https://github.com/Thirunavukarasu11/Exploratory-Data-Analysis-EDA-/blob/main/Exploring%20Data%20with%20Pandas%20in%20Jupyter.ipynb)

## How to Use

1. Clone or download this repository to your local machine.
2. Install the necessary Python libraries by running the following command:

   ```bash
   pip install pandas matplotlib seaborn

3. Open the Jupyter Notebook by running the command:
```bash
jupyter notebook

4. Open the Exploring Data with Pandas in Jupyter.ipynb notebook in Jupyter and follow along with the EDA steps.

## Contribution
If you have any suggestions, improvements, or issues, feel free to open an issue or submit a pull request. Contributions are always welcome!
``` bash
You can copy this code into your `README.md` file, and it will render correctly on GitHub when viewed.