numpy Semi main (1/4/24)

# Function to calculate mean
def calculate_mean(data):
    return sum(data) / len(data)

# Function to calculate median
def calculate_median(data):
    sorted_data = sorted(data)
    n = len(sorted_data)
    if n % 2 == 0:
        return (sorted_data[n // 2 - 1] + sorted_data[n // 2]) / 2
    else:
        return sorted_data[n // 2]

# Function to calculate mode
def calculate_mode(data):
    frequency = {}
    for value in data:
        frequency[value] = frequency.get(value, 0) + 1

    max_frequency = max(frequency.values())
    mode = [key for key, val in frequency.items() if val == max_frequency]
    return mode

# User input for data
data = []
while True:
    value = input("Enter a number (or 'done' to finish): ")
    if value.lower() == 'done':
        break
    data.append(int(value))

# Calculate mean
mean = calculate_mean(data)
print("Mean:", mean)

# Calculate median
median = calculate_median(data)
print("Median:", median)

# Calculate mode
mode = calculate_mode(data)
print("Mode:", mode)





#Liner regression
import numpy as np
from sklearn.linear_model import LinearRegression

years = np.array([[1], [2], [3], [4], [5]])
speeds = np.array([30, 45, 45, 55, 65])


model = LinearRegression()
model.fit(years, speeds)

x= 15
predicted_speed = model.predict([[x]])

print(f"Predicted speed after {x} years:", predicted_speed[0], "km/h")