entropy_weight_method.py

import pandas as pd
import numpy as np
from scipy.stats import entropy

def calculate_entropy(data, n):
    return 1/np.log(n)*entropy(data)

def calculate_weights(entropy_list):
    weight_list = [1 - entropy_i for entropy_i in entropy_list]
    return weight_list / sum(weight_list)

def calculate_scores(data, weight_list):
    scores = []
    for i in range(len(data)):
        score_i = sum(weight_list[j] * data[i,j] for j in range(len(weight_list)))
        scores.append(score_i)
    return scores

# Load data
df = pd.read_excel('data.xlsx')
df.set_index('Name', inplace=True)

indicators = df.columns.tolist()
roller_coasters = df.index.tolist()
data = df.values
n = data.shape[0]

# Calculate Entropy
entropy_list = [calculate_entropy(data[:,i], n) for i in range(len(indicators))]

# Print Entropy weights
for i, entropy_i in enumerate(entropy_list):
    print(f"Entropy weight for {indicators[i]}: {entropy_i}")

# Calculate and print overall scores
weight_list = calculate_weights(entropy_list)
scores = calculate_scores(data, weight_list)
for i, score_i in enumerate(scores):
    print(f"Scores for {roller_coasters[i]}: {score_i}")

# Get and print top ten roller coasters
best_roller_coasters_indices = np.argsort(scores)[-10:][::-1]
best_roller_coasters = [roller_coasters[i] for i in best_roller_coasters_indices]
print("Top 10:：", best_roller_coasters)

predict_linear_regression.py

import numpy as np
from sklearn.linear_model import LinearRegression
import pandas as pd

file_path = 'data.xlsx'
df = pd.read_excel(file_path)

# Y
y_column_data = df['Drop (feet)']
y_data_list = y_column_data.tolist()

# X
column_names = ['Height (feet)', 'Speed (mph)', 'Length (feet)']
x_data_list = []

for column_name in column_names:
    x_data_list.append(df[column_name].tolist())

# Model
x_data_list, y_data_list = np.array(x_data_list), np.array(y_data_list)

model = LinearRegression().fit(x_data_list, y_data_list)

r_sq = model.score(x, y)

sklearn.py

import pandas as pd
from sklearn.linear_model import LinearRegression

# Data
df = pd.read_excel('data.xlsx')
df_clean = df.dropna(subset=['Drop (feet)'])
X = df_clean[['Height (feet)', 'Speed (mph)', 'Length (feet)']]
y = df_clean['Drop (feet)']

# Model
regression = LinearRegression()
regression.fit(X.values, y)

# Test
height_input = 98.4
speed_input = 45
length_input = 2788.8

predicted_drop = regression.predict([[height_input, speed_input, length_input]])[0]
print(predicted_drop)