K-Nearest Neighbor

KNN

Work in Progress…. šŸ™‚

IMDB

IMDb is the world’s most popular and authoritative source for movie, TV and celebrity content. Find ratings and reviews for the newest movie and TV shows.

drawing

Objectives

  • Perform Feature Engineering, clean, wraggling and tidy then save the new dataset in a .csv file. The new file will be use in machine learning model, KNN and Decision Tree.

Data Dictionary:

  • Poster_Link – Link of the poster that imdb using
  • Series_Title = Name of the movie
  • Released_Year – Year at which that movie released
  • Certificate – Certificate earned by that movie
  • Runtime – Total runtime of the movie
  • Genre – Genre of the movie
  • IMDB_Rating – Rating of the movie at IMDB site
  • Overview – mini story/ summary
  • Meta_score – Score earned by the movie
  • Director – Name of the Director
  • Star1,Star2,Star3,Star4 – Name of the Stars
  • Noofvotes – Total number of votes
  • Gross – Money earned by that movie

Import Packages and Load Data

In [2]:
from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor
from sklearn.model_selection import train_test_split, cross_val_predict
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import mean_squared_error, r2_score
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings

warnings.filterwarnings("ignore")
In [3]:
# define training points and training labels
df_new = pd.read_csv('imdb_top_1000_clean.csv')
print(df_new.shape)
df_new.head(2)
(1000, 15)
Out[3]:
Unnamed: 0 Series_Title Released_Year Certificate Runtime Genre IMDB_Rating Meta_score Director Star1 Star2 Star3 Star4 No_of_Votes Gross
0 0 Dark Waters 2019.0 PG-13 126 Biography, Drama, History 7.6 73.0 Todd Haynes Mark Ruffalo Anne Hathaway Tim Robbins Bill Pullman 60408 6.212517e+07
1 1 Searching 2018.0 U/A 102 Drama, Mystery, Thriller 7.6 71.0 Aneesh Chaganty John Cho Debra Messing Joseph Lee Michelle La 140840 2.602096e+07

K Nearest Neighbor

We will classify our movie if it’s good or bad.

First let’s create a new column that has as a 1 value if IMDB_Rating is equal or higher than 8 otherwise 0.

In [4]:
df_new['good_bad'] = 0
# df_new['good_bad'][df_new['IMDB_Rating'] > 8.0 ] = 1

# use this code for 1's
df_new['good_bad'][df_new.IMDB_Rating.isin([8. , 8.1, 8.2, 8.3, 8.4, 8.5, 8.6, 8.7, 8.8,
                                            8.9, 9. , 9.2, 9.3])] = 1
In [5]:
# checking proportion of good and bad movies
df_new['good_bad'].value_counts()
Out[5]:
0    537
1    463
Name: good_bad, dtype: int64
In [6]:
# classifier instance
classifier = KNeighborsClassifier(n_neighbors = 5)
In [7]:
#Ā defineĀ trainingĀ pointsĀ andĀ trainingĀ labels
training_points = df_new[['Gross', 'Runtime', 'Released_Year', 'Meta_score', 'IMDB_Rating', 'No_of_Votes']]
training_labels = df_new['good_bad']
In [8]:
# Training data
x_train, x_test, y_train, y_test = train_test_split(training_points, training_labels,  test_size = 0.7, random_state = 42)
In [9]:
# Normalize our training point(x)
scaler = StandardScaler()
x_train = scaler.fit_transform(x_train)
x_test = scaler.transform(x_test)
In [10]:
# fitting training data
classifier.fit(x_train, y_train)
Out[10]:
KNeighborsClassifier()
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Visialize K-value’s

In [11]:
# show graph
accuracies = []
k_list = range(1,101)  

for k in range(1,101):
   classifier = KNeighborsClassifier(n_neighbors = k)
   classifier.fit(x_train, y_train)
   accuracies.append(classifier.score(x_test, y_test))

plt.figure(figsize=(15, 5)) 

ax1 = plt.subplot(1,2,1)
plt.plot(k_list, accuracies)
plt.title('K Values Accuracies')
plt.xlabel('K values range')
plt.ylabel('Accuracy')

ax2 = plt.subplot(1,2,2)
plt.plot(k_list, accuracies)
plt.axvline(5, color = 'r', linestyle = '--', label = 'K peak value')
plt.legend()
plt.xlim(0,15)
plt.title('Zoom in')
Out[11]:
Text(0.5, 1.0, 'Zoom in')

Testing our model

In [14]:
#Ā makeĀ sureĀ itĀ isĀ notĀ inĀ ourĀ dataset,Ā weĀ dontĀ wantĀ theĀ neighborĀ toĀ beĀ itself
# Movies sample
Spiderman = [[1800000000, 148, 2021, 71, 83, 697614]]
badmovie_sample = [[180000, 3, 1920, 7, 7, 5000]]

# Normalize our movie sample
Spiderman = scaler.transform(Spiderman)
badmovie_sample  = scaler.transform(badmovie_sample)
In [24]:
# Test result
print(classifier.predict(Spiderman))
print(classifier.predict(badmovie_sample))
[1]
[0]

K-NEAREST NEIGHBOR REGRESSOR

Instead of classifying a new movie as either good or bad, we are now going to predict its IMDb rating as a real number.

In [25]:
# KNN Regression instance
# weight distance is more accurate
regr = KNeighborsRegressor(n_neighbors = 13, weights = 'distance')
In [26]:
# Set IMDB Rating as our new training label
training_labels2 = df_new['IMDB_Rating']

# Training data
x_train2, x_test2, y_train2, y_test2 = train_test_split(training_points, training_labels2,  test_size = 0.2, random_state = 42)
In [27]:
# Fit training data
regr.fit(x_train2, y_train2)
Out[27]:
KNeighborsRegressor(n_neighbors=13, weights='distance')
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Testing

regression for bad movies seems off. Can we do better?

Pending

  • Use feature selection to improve our regression model
In [28]:
# Note: Spiderman values are normalized
print(regr.predict(Spiderman))
print(regr.predict(badmovie_sample))
[7.95370096]
[7.95369507]

Model Evaluation

  • Mean Squared Error: Averaged of the squared error of the difference between the actual and predicted values (higher = better)
  • R2: The corelation between the dependent variable and the set of independent variables (higher = better)

Classification

In [29]:
y_predict = cross_val_predict(classifier, x_test, y_test, cv=5)
In [30]:
from math import sqrt
sqrt(mean_squared_error(y_test, y_predict))
Out[30]:
0.37984959429452375
In [31]:
r2_score(y_test, y_predict)
Out[31]:
0.4159727729315359

Regression

In [32]:
y_predict2 = cross_val_predict(regr, x_test2, y_test2, cv=5)
# note: y_test2 is the actual rating while y_predict2 is our predicted IDMB rating
In [33]:
sqrt(mean_squared_error(y_test2, y_predict2))
Out[33]:
0.2976213235335613
In [34]:
r2_score(y_test2, y_predict2)
Out[34]:
-0.08154398317300093

Can we do better?

Both Classification and Regression has very lower score, lets do some feature engineering to select better training labels for both models

Work in Progess…

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