Try SVM Algorithm with different kernels on same dataset, tabulate and compare the results

Sure, here's a step-by-step guide on how to use SVM algorithm with different kernels on the same dataset, tabulate and compare the results:

1. Import necessary libraries: You'll need libraries such as pandas for data manipulation, sklearn for the SVM algorithm, and matplotlib for visualizations.

import pandas as pd
from sklearn import svm, metrics
import matplotlib.pyplot as plt

2. Load your dataset: You can use pandas to load your dataset. For example, if your dataset is a CSV file:

df = pd.read_csv('your_dataset.csv')

3. Preprocess your data: This includes handling missing values, converting categorical data to numerical data, and splitting your data into features (X) and target (y).

4. Split your data into training and testing sets: You can use sklearn's train_test_split function for this.

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

5. Train your SVM models with different kernels: The most common kernels are 'linear', 'poly', 'rbf', and 'sigmoid'. You can train a model for each kernel like this:

kernels = ['linear', 'poly', 'rbf', 'sigmoid']
models = []
for kernel in kernels:
    model = svm.SVC(kernel=kernel)
    model.fit(X_train, y_train)
    models.append(model)

6. Evaluate your models: You can use sklearn's accuracy_score or classification_report to evaluate your models. You can also use a confusion matrix for a more detailed evaluation.

for i, model in enumerate(models):
    predictions = model.predict(X_test)
    print(f"Kernel: {kernels[i]}")
    print("Accuracy:", metrics.accuracy_score(y_test, predictions))
    print("Classification report:")
    print(metrics.classification_report(y_test, predictions))

7. Tabulate and compare the results: You can use pandas to create a DataFrame to tabulate your results. You can then use matplotlib to visualize your results.

results = pd.DataFrame()
for i, model in enumerate(models):
    predictions = model.predict(X_test)
    accuracy = metrics.accuracy_score(y_test, predictions)
    results = results.append({'Kernel': kernels[i], 'Accuracy': accuracy}, ignore_index=True)

print(results)

results.plot(x='Kernel', y='Accuracy', kind='bar', title='Comparison of SVM Kernels')
plt.show()

Remember to replace 'your_dataset.csv' with the path to your actual dataset. Also, the preprocessing steps will depend on your specific dataset.