Regression on large dataset: Why does accuracy drop? - matplotlib

Question

I am trying to predict the views on olx's ads. I write a scraper to scrape all the data(50000) ads. When I perform linear regression (on 1400 samples) I got 66% accuracy.But after that I perform on 52000 samples it dropped to 8%. Here is the Imgcount vs Views and Price vs Views stats.
Is there any problem with my data? or How can I perform regression on this. I know that this data is very polarized.
I wanted to know what's the problem why my accuracy dropped when I used large dataset.
Thank you for the help.`
CODE:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import MinMaxScaler
from sklearn import linear_model
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn import preprocessing
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import PolynomialFeatures
import seaborn as sns
url = '/home/msz/olx/olx/with_images.csv'
df = pd.read_csv(url, index_col='url')
df['price'] = df['price'].str.replace('.', '')
df['price'] = df['price'].str.replace(',', '')
df['price'] = df['price'].str.replace('Rs', '')
df['price'] = df['price'].astype(int)
df['text'] = df['text'].str.replace(',', ' ')
df['text'] = df['text'].str.replace('\t', '')
df['text'] = df['text'].str.replace('\n', '')
X = df[['price', 'img']]
y = df['views']
print ("X is like ", X.shape)
print ("Y is like ", y.shape)
df.plot(y='views', x='img', style='x')
plt.title('ImgCount vs Views')
plt.xlabel('ImgCount')
plt.ylabel('Views')
plt.show()
df.plot(y='views', x='price', style='x')
plt.title('Price vs Views')
plt.xlabel('Price')
plt.ylabel('Views')
plt.show()
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.451, random_state=0)
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
score = regressor.score(X_test, y_test)
print('Accuracy is : ',score*100)

Answer 1

Regression is the basic algorithm which works on linear datasets mostly but if you have a large and non liner dataset you have to use another algorithm like k-nearest neighbour or may be decision tree. But I prefer to use Naives Bayes classifier and others.