Question

Consider the table given below. It contains the movie ratings of a single user. There are 15 movies and we also have four movie features (romance, adventure, historical, horror). Each feature numerically represents how much the movie belongs to that genre. For example, Ml is a historical romance movie; there is almost no horror and adventure. However, the user has not rated some of the movies yet (shown with the question marks). In this task, you will be learning the preferences of a user and predict the rating that the user would give to unrated movies.

I need a code written on PYTHON LANGUAGE, you can use any class in scikit. Below, you can see the "Movie Rate.txt" provided. Can you solve this question? I will give thumbs up

Answer 1

**I HAVE ANSWER THE QUESTION FIRST AND THEN GIVE THE CODE. IF YOU DON'T NEED THE CODE YOU CAN SKIP IT.

1. It is a regression problem because we have to predict the movie rating which can be in range 0-5 and in any fractional value.

2. Learned parameters

 [-0.88396343 -3.3650339 -0.12833597 -2.67356581]

3.

Rating prediction for M2: 1.97688151

Rating prediction for M7: 1.39717038

Rating prediction for M11: 3.9678044

PYTHON CODE:

#import libaries
import pandas as pd
from sklearn import linear_model

#load the data into datframe
df = pd.read_csv('Movie Rate.txt',sep='\t',header=None)
print(df)

#row 2 7 11 has the rating value ?
#except these all are the train set
train_set = df.drop([2,7,11])
#we have to preicct the rating
#i.e column 0 it is Y
Y_train = train_set.iloc[:,0]
#other rows are X
X_train = train_set.iloc[:,1:5]
X_train

#row 2 7 11 has the rating value ?
#thse are test value we have to predict
test_set = df.iloc[[2,7,11],:]
X_test = test_set.iloc[:,1:5]
#No Y_test we have to predict it

#simple linear regression model
reg = linear_model.LinearRegression()
reg.fit(X_train,Y_train)

#learnt paramenters
print(reg.coef_)

#predction for M2,M7,M11
Y_pred = reg.predict(X_test)
print(Y_pred)

File Edit View Insert Cell Kernel Help Trusted Python 3 Run + e Code In [82]: #import Libaries import pandas as pd from sklearn import linear_model In [83]: #Load the data into datframe df = pd.read_csv('Movie Rate.txt', sep='\t', header=None) print(af) @ 1 2 3 + 5 1 0.800 -1 0.780 0.300 2.1 0.881 4.3 0.280 5 0.500 -1 0.882 0.280 5 0.990 3.4 0.001 -1 0.900 0.500 1 0.821 2 0.480 5 0.880 2 3 0.060 0.960 0.600 0.070 2.880 0.eei 8.670 0.001 2.600 0.940 e.200 0.940 0.700 0.001 2.300 0.001 0.020 0.eei 0.002 0.880 2.500 0.880 e.200 0.940 a. see 0.300 2.500 0.001 0.600 0.700 4 0.0810 8.4eae e.ceei 8.9820 .0010 8.02ee 2.9500 0.9860 .0010 8.0881 0.0001 0.0001 8.8eae 2.8900 0.0020 7 8 9 1e 11 12 13 14 In [84]: #row 2 7 11 has the rating value ? #except these all are the train set train set = df.drop ([2,7,11]) #iwe have to preicct the rating #i.e column @ it is Y Y_train = train_set.iloc[:,] #other rows are X X_train = train_set.iloc[:,1:5] In [85]: #row 2 7 11 has the rating value ? #thse are test value we have to predict test_set = df.iloc[[2,7,11),:] X_test = test_set.iloc[:,1:5] #No Y_test we have to predict it In [86]: #simple Linear regression model reg = linear_model.Linear regression() reg.fit(x_train, Y_train) Out[36]: Linear regression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False) In [88]: #Learnt paramenters print (reg.coef_) [-6.88396343 -3.365339 -0.12833597 -2.67356581] In [89]: #predction for M2, M7, M11 Y_pred = reg.predict(x_test) print(Y_pred) [1.97688151 1.39717038 3.9678844 ]

*IF YOU GET ANY ERROR READING THE FILE PLEASE INSERT A TAB BETWEEN THE DATA IN Movie Rate.txt FILE