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A Practical Guide to Machine Learning with Python

SVM Implementation Steps: 1 to 7

Explore the step-by-step process to implement Support Vector Machines in Python using Scikit-learn. Learn to prepare data by encoding variables, set up features and targets, apply the SVC algorithm, and evaluate model performance with classification reports and confusion matrices. Understand key practical steps to optimize SVM models for better classification outcomes.

We'll cover the following...

1) Import libraries

You will be using SVC (Support Vector Classifier) from the Scikit-learn library to implement this model and evaluating the predictions using a classification report and confusion matrix. You will later optimize the model using grid search.

Python 3.5
#1- Import libraries
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.model_selection import GridSearchCV

Note: Codes of further steps won’t ...