Various Algorithms

import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score
from sklearn.metrics import confusion_matrix, accuracy_score, classification_report

X = np.array(
    [
        [1, 1], [1, 2], [2, 2], [2, 3]
        ,[3, 5], [6, 4], [56, 23], [34, 56], [4, 5]
    ]
)

# y = 1 * x_0 + 2 * x_1 + 3
y = np.dot(X, np.array([1, 2])) + 3

# X_train, y_train = X, y

X_train, X_test, y_train, y_test = train_test_split(X,y,
                                               test_size=0.2,
                                               random_state=42)

reg = LinearRegression().fit(X, y)
reg.score(X, y)

reg.coef_

reg.intercept_

reg.predict(np.array([[3, 5]]))

from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier()
knn_model = knn.fit(X_train, y_train)
knn_model

from sklearn.linear_model import LogisticRegression

loj = LogisticRegression(solver = "liblinear")
loj_model = loj.fit(X,y)
# loj_model

from sklearn.svm import SVC
svm_model = SVC(kernel = "linear").fit(X_train, y_train)

from sklearn.naive_bayes import GaussianNB
nb = GaussianNB()
nb_model = nb.fit(X_train, y_train)
nb_model

from sklearn.neural_network import MLPClassifier
mlpc = MLPClassifier().fit(X_train, y_train)

from sklearn.tree import DecisionTreeClassifier

cart = DecisionTreeClassifier()
cart_model = cart.fit(X_train, y_train)

from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier().fit(X_train, y_train)

models = [
    knn_model,
    loj_model,
    svm_model,
    nb_model,
    mlpc,
    cart_model,
    rf_model
]

for model in models:
    names = model.__class__.__name__
    y_pred = model.predict(X_test)
    accuracy = accuracy_score(y_test, y_pred)
    print("-"*28)
    print(names + ":" )
    print("Accuracy: {:.4%}".format(accuracy))