I want to do exploratory data analysis on below dataset found on kaggle

https://www.kaggle.com/datasets/neuromusic/avocado-prices
Exploratory Data Analysis (EDA) is an essential step in understanding your dataset and uncovering insights. Below is a step-by-step guide to perform EDA on the Avocado Prices dataset from Kaggle.

### Step 1: Load the Dataset

First, you need to load the dataset into your Python environment. You can use pandas for this purpose.

```python
import pandas as pd

# Load the dataset
url = "https://raw.githubusercontent.com/neuromusic/avocado-prices/master/avocado.csv"
avocado_data = pd.read_csv(url)

# Display the first few rows of the dataset
print(avocado_data.head())
```

### Step 2: Understand the Data Structure

Examine the structure of the dataset to understand its columns, data types, and missing values.

```python
# Check the shape of the dataset
print(avocado_data.shape)

# Get a summary of the dataset
print(avocado_data.info())

# Check for missing values
print(avocado_data.isnull().sum())
```

### Step 3: Descriptive Statistics

Use descriptive statistics to get a sense of the numerical data.

```python
# Get descriptive statistics
print(avocado_data.describe())
```

### Step 4: Data Visualization

Visualizations can help you understand the data better. You can use libraries like Matplotlib and Seaborn for this purpose.

```python
import matplotlib.pyplot as plt
import seaborn as sns

# Set the
  Great! The avocado prices dataset from Kaggle is a great dataset for exploratory data analysis. Here are some steps you can follow to get started:

1. Load the dataset: You can use the `kaggle_datasets` library to easily load the dataset. You can install it by running `!pip install kaggle_datasets` in your terminal. Once you have installed the library, you can load the dataset using `kaggle_datasets.load("avocado_prices")`.
2. Visualize the dataset: Before diving into the analysis, it's a good idea to visualize the dataset to get a sense of its structure and distribution. You can use `matplotlib` or any other visualization library to visualize the dataset. For example, you can create a histogram of the prices to see if there are any patterns or outliers:
```
import matplotlib.pyplot as plt

plt.hist(df["price"], bins=20)
plt.xlabel("Price")
plt.ylabel("Count")
plt.title("Histogram of Avocado Prices")
plt.show()
```
3. Examine the data: Once you have a better understanding of the dataset's structure, you can start examining the data more closely. Here are some things you might