regression (anime).py

import math
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
data = pd.read_csv('anime.csv')
print(data['members'].isnull().values.any()) # Prints False
print(data['rating'].isnull().values.any()) # Prints True
members = [] # Corresponding fan club size for row
ratings = [] # Corresponding rating for row
for row in data.iterrows():
 if not math.isnan(row[1]['rating']): # Checks for Null ratings
  members.append(row[1]['members'])
  ratings.append(row[1]['rating'])
members = np.asarray(members)
ratings = np.asarray(ratings)
theta0 = 0.00001 # Random guess
theta1 = 0.00001 # Random guess
error = 0 # Overall error of our model.
def hypothesis(x, theta0, theta1):
 return  theta0 + theta1 * x
 fig = plt.figure(dpi=100, figsize=(5, 4))
plt.scatter(members, ratings)
line, = plt.plot(members, hypothesis(members,theta0, theta0))
plt.xlabel('Members')
plt.ylabel('Ratings')
plt.xlim((0, max(members)))
plt.ylim((0, max(ratings)))
plt.show()
def costFunction(x, y, theta0, theta1, m):
 loss = 0
 for i in range(m): # Represents summation
  loss += (hypothesis(x[i], theta0, theta1) - y[i])**2
 loss *= 1 / (2 * m) # Represents 1/2m
 return loss
 def gradientDescent(x, y, theta0, theta1, alpha, m,iterations=1500):
 for i in range(iterations):
  gradient0 = 0
  gradient1 = 0
  for j in range(m): # Represents summation
   gradient0 += hypothesis(x[j], theta0, theta1) - y[j]
   gradient1 += (hypothesis(x[j], theta0, theta1) - y[j]) * x[j]
  gradient0 *= 1/m
  gradient1 *= 1/m
  temp0 = theta0 - alpha * gradient0
  temp1 = theta1 - alpha * gradient1
  theta0 = temp0
  theta1 = temp1
  error = costFunction(x, y, theta0, theta1, len(y))
  print("Error is:", error)
 return theta0, theta1
 alpha = 0.000000000001 # Learning Rate
m = len(ratings) # Size of the dataset
print('Our final theta\'s', gradientDescent(members, ratings,    theta0, theta1, alpha, m))