# 描述性、诊断性、预测性和规范性数据分析的全面概述以及……

## 法典

``````import pandas as pd
import matplotlib.pyplot as plt

# Hypothetical dataset of student exam scores
data = {'StudentID': [1, 2, 3, 4, 5],
'ExamScore': [85, 78, 90, 82, 92]}
df = pd.DataFrame(data)

# Calculate mean and standard deviation
mean_score = df['ExamScore'].mean()
std_dev = df['ExamScore'].std()

print("Mean Exam Score:", mean_score)
print("Standard Deviation:", std_dev)

# Create a histogram to visualize the distribution of scores
plt.hist(df['ExamScore'], bins=10)
plt.title("Distribution of Exam Scores")
plt.xlabel("Exam Score")
plt.ylabel("Frequency")
plt.show()
``````

``````import pandas as pd

# Hypothetical dataset of student exam scores and study hours
data = {'StudentID': [1, 2, 3, 4, 5],
'ExamScore': [85, 78, 90, 82, 92],
'StudyHours': [4, 3, 5, 3, 6]}
df = pd.DataFrame(data)

# Calculate correlation between exam scores and study hours
correlation = df['ExamScore'].corr(df['StudyHours'])
print("Correlation between Exam Scores and Study Hours:", correlation)
Correlation between Exam Scores and Study Hours: 0.9575129564099746
``````

``````import pandas as pd
from sklearn.linear_model import LinearRegression

# Hypothetical dataset of student exam scores and study hours
data = {'StudentID': [1, 2, 3, 4, 5],
'ExamScore': [85, 78, 90, 82, 92],
'StudyHours': [4, 3, 5, 3, 6]}
df = pd.DataFrame(data)

# Prepare data for modeling
X = df[['StudyHours']]
y = df['ExamScore']

# Create and train a linear regression model
model = LinearRegression()
model.fit(X, y)

# Predict exam scores for new study hours
new_study_hours = [7, 8, 9]
predicted_scores = model.predict(pd.DataFrame(new_study_hours, columns=['StudyHours']))

print("Predicted Exam Scores for New Study Hours:")
for i in range(len(new_study_hours)):
print(f"Study Hours: {new_study_hours[i]}, Predicted Score: {predicted_scores[i]}")
Predicted Exam Scores for New Study Hours:
Study Hours: 7, Predicted Score: 97.17647058823529
Study Hours: 8, Predicted Score: 101.38235294117646
Study Hours: 9, Predicted Score: 105.58823529411765
``````

``````import pandas as pd
from scipy.optimize import minimize

# Hypothetical dataset of student exam scores and study hours
data = {'StudentID': [1, 2, 3, 4, 5],
'ExamScore': [85, 78, 90, 82, 92],
'StudyHours': [4, 3, 5, 3, 6]}
df = pd.DataFrame(data)

# Define the objective function to maximize exam scores
def objective_function(x):
return -(df['ExamScore'] * x).sum()

# Define the constraint function for study hours (total study hours <= 20)
def constraint_function(x):
return 20 - (df['StudyHours'] * x).sum()

# Initial guess for the optimization
x0 = [1, 1, 1, 1, 1]

# Define the constraints
constraint = {'type': 'ineq', 'fun': constraint_function}

# Perform optimization
result = minimize(objective_function, x0, constraints=constraint)

# Extract the optimal study hour allocation
optimal_study_hours = result.x

print("Optimal Study Hour Allocation:")
for i, student_id in enumerate(df['StudentID']):
print(f"Student {student_id}: {optimal_study_hours[i]} hours")
Optimal Study Hour Allocation:
Student 1: -4.4565814352035854e+24 hours
Student 2: -5.0932735987608005e+28 hours
Student 3: -6.576581741048669e+29 hours
Student 4: 1.0960680991161268e+30 hours
Student 5: -3.7006777519270405e+24 hours
``````

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