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
Let's dive into exploratory data analysis (EDA) on the Avocado Prices dataset from Kaggle.

**Loading the dataset**

First, let's load the dataset into a Pandas DataFrame using the `pandas` library:
```python
import pandas as pd

url = "https://www.kaggle.com/neuromusic/avocado-prices/download"
df = pd.read_csv(url)
```
**Initial Data Inspection**

Let's take a look at the first few rows of the dataset:
```python
print(df.head())
```
This will give us an idea of the column names, data types, and a glimpse of the data.

**Data Summary**

Next, let's get a summary of the dataset using the `info()` and `describe()` methods:
```python
print(df.info())
print(df.describe())
```
The `info()` method will show us the number of non-null values, data types, and memory usage for each column. The `describe()` method will provide summary statistics for each column, such as mean, median, min, max, and quartiles.

**Data Visualization**

Now, let's create some visualizations to get a better understanding of the data. We'll use the `matplotlib` and `seaborn` libraries for this.

**1. Distribution of Avocado Prices**

Let's create a histogram to visualize the distribution of avocado prices:
```python
import matplotlib.pyplot as plt

plt.hist(df['AveragePrice'], bins