answer:def evaluate_model(): import numpy as np from sklearn.model_selection import validation_curve, learning_curve, ValidationCurveDisplay, LearningCurveDisplay from sklearn.datasets import load_iris from sklearn.svm import SVC from sklearn.utils import shuffle import matplotlib.pyplot as plt # Load and shuffle the dataset X, y = load_iris(return_X_y=True) X, y = shuffle(X, y, random_state=0) # Validation Curve param_range = np.logspace(-7, 3, 10) train_scores, valid_scores = validation_curve( SVC(kernel='linear'), X, y, param_name='C', param_range=param_range, cv=5) # Plot Validation Curve plt.figure(figsize=(10, 6)) ValidationCurveDisplay.from_estimator( SVC(kernel='linear'), X, y, param_name='C', param_range=param_range, cv=5) plt.title('Validation Curve') plt.xlabel('Parameter C') plt.ylabel('Score') plt.legend(['Training score', 'Validation score']) plt.show() # Learning Curve train_sizes = [50, 80, 110] train_sizes, train_scores, valid_scores = learning_curve( SVC(kernel='linear'), X, y, train_sizes=train_sizes, cv=5) # Plot Learning Curve plt.figure(figsize=(10, 6)) LearningCurveDisplay.from_estimator( SVC(kernel='linear'), X, y, train_sizes=train_sizes, cv=5) plt.title('Learning Curve') plt.xlabel('Training examples') plt.ylabel('Score') plt.legend(['Training score', 'Validation score']) plt.show() # Analysis # The validation curve helps us understand if the model is overfitting or underfitting by # comparing training and validation scores across different values of parameter C. # If both curves are low, the model is underfitting. # If the training score is high and the validation score is much lower, the model is overfitting. # If both curves converge to a high value, the model is generalizing well. # The learning curve shows how the validation and training error change with varying training set sizes. # If the training and validation scores converge to a high value as the number of training examples increases, # the model is likely generalizing well. If there is a large gap between curves, the model might be overfitting. # The analysis should provide specifics on the behavior observed in the plots related to underfitting, overfitting, or good generalization.

answer:import pandas as pd def style_and_export_sales_data(file_path: str, output_path: str) -> None: # Read the CSV file into a DataFrame df = pd.read_csv(file_path) # Function to apply styles based on conditions def highlight_sales(cell): return ['background-color: green' if v > 1000 else '' for v in cell] def highlight_revenue(cell): return ['background-color: red' if v < 5000 else '' for v in cell] # Applying styles using Styler styled_df = (df.style .apply(highlight_sales, subset=['Sales']) .apply(highlight_revenue, subset=['Revenue']) .format({'Revenue': '{:,.2f}'})) # Export the styled DataFrame to an HTML file styled_df.to_html(output_path) # Example usage (This would be handled outside this function in practice) # style_and_export_sales_data('sales_data.csv', 'styled_sales_data.html')

answer:from typing import List, Dict, Union def process_data(data: List[Dict[str, Union[int, float, str]]]) -> Dict[str, Union[int, float]]: Processes a list of dictionaries and filters out those whose status is not 'active'. Args: - data: List[Dict[str, Union[int, float, str]]]: List of dictionaries containing data records. Returns: - Dict[str, Union[int, float]]: A dictionary containing the count of active records and the total of their values. active_records = [record for record in data if record['status'] == 'active'] count = len(active_records) total = sum(record['value'] for record in active_records) return {"count": count, "total": total}

answer:import json class Person: def __init__(self, name, age, hobbies, contact_info): self.name = name self.age = age self.hobbies = hobbies self.contact_info = contact_info def __eq__(self, other): return (self.name == other.name and self.age == other.age and self.hobbies == other.hobbies and self.contact_info == other.contact_info) class PersonEncoder(json.JSONEncoder): def default(self, obj): if isinstance(obj, Person): return { 'name': obj.name, 'age': obj.age, 'hobbies': obj.hobbies, 'contact_info': obj.contact_info } return super().default(obj) class PersonDecoder(json.JSONDecoder): def __init__(self, *args, **kwargs): super().__init__(object_hook=self.object_hook, *args, **kwargs) def object_hook(self, dct): if 'name' in dct and 'age' in dct and 'hobbies' in dct and 'contact_info' in dct: return Person(name=dct['name'], age=dct['age'], hobbies=dct['hobbies'], contact_info=dct['contact_info']) return dct # Example usage: person = Person(name="Alice", age=30, hobbies=["Reading", "Hiking"], contact_info={"email": "[email protected]", "phone": "123-456-7890"}) json_str = json.dumps(person, cls=PersonEncoder) reconstructed_person = json.loads(json_str, cls=PersonDecoder) print(reconstructed_person == person) # Should print: True