text: # importing libraries 
  import pandas as pd 
  import numpy as np 
  import sklearn as sk 
  from sklearn.linear_model import LinearRegression 
  import matplotlib.pyplot as plt 
  
  # read the cleaned data 
  data = pd.read_csv("austin_final.csv") 
  
  # the features or the 'x' values of the data 
  # these columns are used to train the model 
  # the last column, i.e, precipitation column 
  # will serve as the label 
  X = data.drop(['PrecipitationSumInches'], axis = 1) 
  
  # the output or the label. 
  Y = data['PrecipitationSumInches'] 
  # reshaping it into a 2-D vector 
  Y = Y.values.reshape(-1, 1) 
  
  # consider a random day in the dataset 
  # we shall plot a graph and observe this 
  # day 
  day_index = 798
  days = [i for i in range(Y.size)] 
  
  # initialize a linear regression classifier 
  clf = LinearRegression() 
  # train the classifier with our 
  # input data. 
  clf.fit(X, Y) 
  
  # plot a graph of the precipitation levels 
  # versus the total number of days. 
  # one day, which is in red, is 
  # tracked here. It has a precipitation 
  # of approx. 2 inches. 
  print("the precipitation trend graph: ") 
  plt.scatter(days, Y, color = 'g') 
  plt.scatter(days[day_index], Y[day_index], color ='r') 
  plt.title("Precipitation level") 
  plt.xlabel("Days") 
  plt.ylabel("Precipitation in inches") 
  
  
  plt.show() 
  inpt=[]
  inpt=input("enter the input").split()
  for i in range(len(inpt)):
      inpt[i]=float(inpt[i])
  print(inpt)
  inpt=np.array(inpt)
  inpt = inpt.reshape(1, -1) 
  print('The precipitation in inches for the input is:', clf.predict(inpt)) 
  
  