Indeed, first of all import the required libraries:
```python
from seaborn import load_dataset, pairplot
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
```
where:
- The seaborn library is used to provide the example dataset the `penguins`. Function `pairplot()` is used to better understand the data.
- `matplotlib.pyplot` to show and modify visualizations
- Pandas is used to manipulate data via DataFrame methods
- The `SVC` class is used to create the classification model
- The `train_test_split()` function is used to split data into training and testing data
- The `accuracy_score()` function allows to evaluate the performance of the model

Next step, dealing with pandas dataframe and visualizing features
```python
# Loading and exploring dataset
df = load_dataset('penguins')
print(df.head())

# Dropping missing records
df = df.dropna()
print(len(df))

# Plotting penguin species features
pairplot(df, hue='species')
plt.show()
```
Where you can make additional step of e.g. limiting classes used
```python
df = df[df['species'] != 'Gentoo']    # This limits us to two classes
print(len(df))

# Choose the features and target
X = df[['bill_length_mm', 'bill_depth_mm']]
y = df['species']
```

Then split data and instantiate classifier. Constructor has multiple parameters to choose from, you can later investigate them:
```python
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=100)

# The SVC Class from Sklearn
sklearn.svm.SVC(*, 
    C=1.0,                          # The regularization parameter
    kernel='rbf',                   # The kernel type used 
    degree=3,                       # Degree of polynomial function 
    gamma='scale',                  # The kernel coefficient
    coef0=0.0,                      # If kernel = 'poly'/'sigmoid'
    shrinking=True,                 # To use shrinking heuristic
    probability=False,              # Enable probability estimates
    tol=0.001,                      # Stopping crierion
    cache_size=200,                 # Size of kernel cache
    class_weight=None,              # The weight of each class
    verbose=False,                  # Enable verbose output
    max_iter=- 1,                   # Hard limit on iterations
    decision_function_shape='ovr',  # One-vs-rest or one-vs-one
    break_ties=False,               # How to handle breaking ties
    random_state=None               # Random state of the model
)
```
the most important for you, would be those three:
 - `kernel=`, which defines what type of function is used to transform the dataset
- `C=`, which defines the regularization of the error.
- `gamma=` defines how loosely the model will fit the training data, allowing you to prevent overfitting

Training and making predictions is then straightforward. Here I used linear kernel for non-default parameter
```python
# Build and train model
clf = SVC(kernel='linear')
clf.fit(X_train, y_train)

# Making predictions
predictions = clf.predict(X_test)
print(predictions[:5])
```
In the end, it is advised to have fancy visualization for your audience and tutor!
```python
# Visualizing the linear function for our SVM classifier
import numpy as np
from seaborn import scatterplot
w = clf.coef_[0]
b = clf.intercept_[0]
x_visual = np.linspace(32,57)
y_visual = -(w[0] / w[1]) * x_visual - b / w[1]

scatterplot(data = X_train, x='bill_length_mm', y='bill_depth_mm', hue=y_train)
plt.plot(x_visual, y_visual)
plt.show()
```
Feel free to explore every aspect of the code and reach me again!
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