To solve the question, we will go through each step using Python and the Pandas library to perform the required tasks on the Iris dataset.

Here's the complete code for each step:

# Import necessary libraries
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Load the Dataset
# Assuming the file is named 'iris.csv' and located in the same directory
file_path = '/mnt/data/file-k36L5PBV7Jk1HiqbR7D9dzF3'
df = pd.read_csv(file_path)

# Step 2: Data Cleaning
# Check for any missing values and handle them
print("Missing values in each column:")
print(df.isnull().sum())

# Drop or fill missing values (for demonstration, we'll drop them)
df.dropna(inplace=True)

# Ensure all variety names are consistent (e.g., no leading/trailing whitespace)
df['variety'] = df['variety'].str.strip()

# Step 3: Correlation Analysis
# Calculate the correlation matrix for numerical columns
correlation_matrix = df.corr()
print("\nCorrelation Matrix:")
print(correlation_matrix)

# Identify highest and lowest correlation pairs
highest_corr = correlation_matrix.unstack().sort_values(ascending=False)
highest_corr = highest_corr[highest_corr < 1].reset_index()
highest_corr.columns = ['Feature1', 'Feature2', 'Correlation']

print("\nHighest Correlation Pair:")
print(highest_corr.iloc[0])

print("\nLowest Correlation Pair:")
print(highest_corr.iloc[-1])

# Step 4: Data Visualization
# Create a scatter plot for sepal.length and sepal.width, colored by species
plt.figure(figsize=(10, 6))
sns.scatterplot(data=df, x='sepal.length', y='sepal.width', hue='variety')
plt.title("Relationship between Sepal Length and Sepal Width by Species")
plt.xlabel("Sepal Length (cm)")
plt.ylabel("Sepal Width (cm)")
plt.legend(title='Species')
plt.show()

Explanation:

1. Load the Dataset: Reads the iris.csv file into a DataFrame using pd.read_csv.


2. Data Cleaning:

Checks for missing values and removes them with dropna().

Removes any leading or trailing whitespace in the variety column to ensure consistency.



3. Correlation Analysis:

Calculates the correlation matrix for numeric columns.

Finds the highest and lowest correlated pairs among features, ignoring self-correlations (1).



4. Data Visualization:

Creates a scatter plot for sepal.length vs. sepal.width, colored by variety.




Run this code in a Python environment with the pandas, seaborn, and matplotlib libraries installed to get the desired outputs. Let me know if you need further assistance!