plushycat

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.datasets import load_breast_cancer
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.metrics import confusion_matrix, classification_report

# Load and preprocess data

plushycat

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_olivetti_faces
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix

# Load and split data
X, y = fetch_olivetti_faces(shuffle=True, random_state=42, return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

plushycat

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier, plot_tree
from sklearn.metrics import accuracy_score

# Load and split data
X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

plushycat

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures, StandardScaler
from sklearn.pipeline import make_pipeline
from sklearn.metrics import mean_squared_error, r2_score

def evaluate_and_plot(X_test, y_test, y_pred, xlabel, ylabel, title, is_poly=False):

plushycat

import numpy as np
import matplotlib.pyplot as plt

def locally_weighted_regression(x, X, y, tau):
    w = np.exp(-np.sum((X - x)**2, axis=1) / (2 * tau**2))
    W = np.diag(w)
    theta = np.linalg.pinv(X.T @ W @ X) @ X.T @ W @ y
    return x @ theta

# Data

plushycat

import numpy as np
import matplotlib.pyplot as plt
from collections import Counter

# Generate data
np.random.seed(42)  # Added for reproducibility
data = np.random.rand(40)
train_data, test_data = data[:20], data[20:]
train_labels = ["Class1" if x <= 0.5 else "Class2" for x in train_data]

plushycat

import pandas as pd

def find_s_algorithm(file_path):
    data = pd.read_csv(file_path)
    print("Training data:")
    print(data) 
    attributes = data.columns[:-1]
    class_label = data.columns[-1]
    hypothesis = ['?' for _ in attributes]
    for index, row in data.iterrows():

plushycat

import numpy as np
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt

# Load data and perform PCA
iris = load_iris()
data_reduced = PCA(n_components=2).fit_transform(iris.data)

plushycat

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_california_housing
# Step 1: Load the California Housing Dataset
california_data = fetch_california_housing(as_frame=True)
data = california_data.frame
# Step 2: Compute the correlation matrix
correlation_matrix = data.corr()

plushycat

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_california_housing

# Load dataset and select numerical features
housing_df = fetch_california_housing(as_frame=True).frame
numerical_features = housing_df.select_dtypes(include=[np.number]).columns