# Importing all the required libraries import numpy as np import pandas as pd from sklearn.preprocessing import LabelEncoder from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score, confusion_matrix from sklearn.metrics import precision_recall_fscore_support, f1_score from sklearn.svm import SVC

# Loading the csv that contains the sample dataset data = pd.read_csv("apples_and_oranges.csv") data.head()

# Seperating labels from ther dataset for sending as input to the model y = np.array(data["Class"]) x = np.array(data.drop("Class", axis = 1))

# Doing the train and test split for evaluating the performace of the model X_train, X_test, y_train, y_test = train_test_split(x, y, test_size = 0.2 , random_state = 42)

# Doing Lbel Encoding as class column in categorical and contains textual data # which needs to be converted to numeric form for input on to the model le = LabelEncoder() y = le.fit_transform(y) np.unique(y, return_counts=True)

# Initializing the model (SVM), fitting aka training it, and then running predictions on test set # and then finding some of the metrics for the models performance model = SVC(C = 5) model.fit(X_train, y_train) preds = model.predict(X_test) print(accuracy_score(y_test, preds), "\n") print("confusion_matrix -\n", confusion_matrix(preds, y_test)) print("precision_recall_fscore_support -\n" , precision_recall_fscore_support(preds, y_test))