# importing packages and modules
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import r2_score
import scipy.stats as stats
  
dataset = pd.read_csv('ball.csv')
sns.scatterplot(data=dataset, x='time', 
                y='height', hue='time')
  
plt.title('time vs height of the ball')
plt.xlabel('time')
plt.ylabel('height')
plt.show()
  
# degree 2 polynomial fit or quadratic fit
model = np.poly1d(np.polyfit(dataset['time'],
                             dataset['height'], 2))
  
# polynomial line visualization
polyline = np.linspace(0, 10, 100)
plt.scatter(dataset['time'], dataset['height'])
plt.plot(polyline, model(polyline))
plt.show()
  
print(model)
  
# r square metric
print(r2_score(dataset['height'], 
               model(dataset['time'])))

       2
2.008 x - 21.69 x + 121.7
0.8133556222534604