title: Regression plot is wrong (python)
body: <p>So my program reads MPG vs weight relationship and draws a graph of what it is suppose to look like but as you can see the graph is not looking right. </p>
  
  <pre><code>import numpy as np
  import pandas as pd
  import matplotlib.pyplot as plt
  
  #read txt file
  dataframe= pd.read_table('auto_data71.txt',delim_whitespace=True,names=['MPG','Cylinder','Displacement','Horsepower','Weight','acceleration','Model year','Origin','Car Name'])
  dataframe.dropna(inplace=True)
  
  #filter the un-necessary columns
  X = dataframe.iloc[:,4:5].values
  Y = dataframe.iloc[:,0:1].values
  
  
  #scale data
  from sklearn.preprocessing import StandardScaler
  sc_X = StandardScaler()
  sc_Y= StandardScaler()
  X = sc_X.fit_transform(X)
  Y = sc_Y.fit_transform(Y)
  
  #split data into train and test set
  from sklearn.model_selection import train_test_split
  x_train,x_test,y_train,y_test = train_test_split(X,Y,test_size=0.2)
  
  #create model
  from sklearn.preprocessing import PolynomialFeatures
  from sklearn.linear_model import LinearRegression
  poly_reg = PolynomialFeatures(degree=2)
  poly_X = poly_reg.fit_transform(x_train)
  poly_reg.fit(poly_X,y_train)
  regressor2= LinearRegression()
  regressor2.fit(poly_X,y_train)
  
  
  #graph
  result = regressor2.predict(poly_X)
  plt.scatter(x_train,y_train,color='red')
  plt.plot(x_train, result,color='blue')
  plt.show()
  </code></pre>
  
  <p>the output is this:
  As you can see the regression line does not look right. Any help will be much appreciated.</p>
  
  <p><img src="https://i.stack.imgur.com/K8SRE.png" alt="as you can see the regression line does not look right. Any help will be much appreciated"></p>
  
  <pre><code>#auto_data.txt(part of data...)
  </code></pre>
  
  <p>****NOTE:i am only using weight and mpg column for this code
  file(mpg,cylinder,distance,horsepower,weight,acceleration,year,origin,name)</p>
  
  <pre><code>27.0   4.   97.00      88.00      2130.      14.5   71.  3. "datsun pl510"
  28.0   4.   140.0      90.00      2264.      15.5   71.  1. "chevrolet vega 2300"
  25.0   4.   113.0      95.00      2228.      14.0   71.  3. "toyota corona"
  25.0   4.   98.00      NA         2046.      19.0   71.  1. "ford pinto"
  NA     4.   97.00      48.00      1978.      20.0   71.  2. "volkswagen super beetle 117"
  19.0   6.   232.0      100.0      2634.      13.0   71.  1. "amc gremlin"
  16.0   6.   225.0      105.0      3439.      15.5   71.  1. "plymouth satellite custom"
  17.0   6.   250.0      100.0      3329.      15.5   71.  1. "chevrolet chevelle malibu"
  19.0   6.   250.0      88.00      3302.      15.5   71.  1. "ford torino 500"
  18.0   6.   232.0      100.0      3288.      15.5   71.  1. "amc matador"
  14.0   8.   350.0      165.0      4209.      12.0   71.  1. "chevrolet impala"
  14.0   8.   400.0      175.0      4464.      11.5   71.  1. "pontiac catalina brougham"
  14.0   8.   351.0      153.0      4154.      13.5   71.  1. "ford galaxie 500"
  14.0   8.   318.0      150.0      4096.      13.0   71.  1. "plymouth fury iii"
  12.0   8.   383.0      180.0      4955.      11.5   71.  1. "dodge monaco (sw)"
  13.0   8.   400.0      170.0      4746.      12.0   71.  1. "ford country squire (sw)"
  13.0   8.   400.0      175.0      5140.      12.0   71.  1. "pontiac safari (sw)"
  18.0   6.   258.0      110.0      2962.      13.5   71.  1. "amc hornet sportabout (sw)"
  </code></pre>