recom_medium.md

Movie Recommender System🎬 BY Prasant Poudel

Importing modules

import numpy as np 
import pandas as pd

Reading the dataset

movie=pd.read_csv('tmdb_5000_movies.csv')
credit=pd.read_csv('tmdb_5000_credits.csv')

movie.head()

credit.head()

Merging the two dataset using title column

movies=movie.merge(credit,on='title')

movies.head()

movies.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 4809 entries, 0 to 4808
Data columns (total 23 columns):
 #   Column                Non-Null Count  Dtype  
---  ------                --------------  -----  
 0   budget                4809 non-null   int64  
 1   genres                4809 non-null   object 
 2   homepage              1713 non-null   object 
 3   id                    4809 non-null   int64  
 4   keywords              4809 non-null   object 
 5   original_language     4809 non-null   object 
 6   original_title        4809 non-null   object 
 7   overview              4806 non-null   object 
 8   popularity            4809 non-null   float64
 9   production_companies  4809 non-null   object 
 10  production_countries  4809 non-null   object 
 11  release_date          4808 non-null   object 
 12  revenue               4809 non-null   int64  
 13  runtime               4807 non-null   float64
 14  spoken_languages      4809 non-null   object 
 15  status                4809 non-null   object 
 16  tagline               3965 non-null   object 
 17  title                 4809 non-null   object 
 18  vote_average          4809 non-null   float64
 19  vote_count            4809 non-null   int64  
 20  movie_id              4809 non-null   int64  
 21  cast                  4809 non-null   object 
 22  crew                  4809 non-null   object 
dtypes: float64(3), int64(5), object(15)
memory usage: 901.7+ KB

movies.shape

(4809, 23)

Taking only the feature that contain the meaning full keyword that help for model creation

movies=movies[['movie_id','title','overview','genres','keywords','cast','crew']]

Checking the null or missing value

movies.isnull().sum()

movie_id    0
title       0
overview    3
genres      0
keywords    0
cast        0
crew        0
dtype: int64

The size of NAN row is 3 so droping all that rows

movies.dropna(inplace=True)

movies.isnull().sum()

movie_id    0
title       0
overview    0
genres      0
keywords    0
cast        0
crew        0
dtype: int64

movie.duplicated().sum()

0

movies.iloc[0].genres

'[{"id": 28, "name": "Action"}, {"id": 12, "name": "Adventure"}, {"id": 14, "name": "Fantasy"}, {"id": 878, "name": "Science Fiction"}]'

convert the string to list type

import ast

def convert(obj):
    L=[]
    for i in ast.literal_eval(obj):
        L.append(i['name'])
    return L

movies['genres']=movies['genres'].apply(convert)

movies['keywords'] = movies['keywords'].apply(convert)

movies.head()

movies['cast'][0]

'[{"cast_id": 242, "character": "Jake Sully", "credit_id": "5602a8a7c3a3685532001c9a", "gender": 2, "id": 65731, "name": "Sam Worthington", "order": 0}, {"cast_id": 3, "character": "Neytiri", "credit_id": "52fe48009251416c750ac9cb", "gender": 1, "id": 8691, "name": "Zoe Saldana", "order": 1}, {"cast_id": 25, "character": "Dr. Grace Augustine", "credit_id": "52fe48009251416c750aca39", "gender": 1, "id": 10205, "name": "Sigourney Weaver", "order": 2}, {"cast_id": 4, "character": "Col. Quaritch", "credit_id": "52fe48009251416c750ac9cf", "gender": 2, "id": 32747, "name": "Stephen Lang", "order": 3}, {"cast_id": 5, "character": "Trudy Chacon", "credit_id": "52fe48009251416c750ac9d3", "gender": 1, "id": 17647, "name": "Michelle Rodriguez", "order": 4}, {"cast_id": 8, "character": "Selfridge", "credit_id": "52fe48009251416c750ac9e1", "gender": 2, "id": 1771, "name": "Giovanni Ribisi", "order": 5}, {"cast_id": 7, "character": "Norm Spellman", "credit_id": "52fe48009251416c750ac9dd", "gender": 2, "id": 59231, "name": "Joel David Moore", "order": 6}, {"cast_id": 9, "character": "Moat", "credit_id": "52fe48009251416c750ac9e5", "gender": 1, "id": 30485, "name": "CCH Pounder", "order": 7}, {"cast_id": 11, "character": "Eytukan", "credit_id": "52fe48009251416c750ac9ed", "gender": 2, "id": 15853, "name": "Wes Studi", "order": 8}, {"cast_id": 10, "character": "Tsu\'Tey", "credit_id": "52fe48009251416c750ac9e9", "gender": 2, "id": 10964, "name": "Laz Alonso", "order": 9}, {"cast_id": 12, "character": "Dr. Max Patel", "credit_id": "52fe48009251416c750ac9f1", "gender": 2, "id": 95697, "name": "Dileep Rao", "order": 10}, {"cast_id": 13, "character": "Lyle Wainfleet", "credit_id": "52fe48009251416c750ac9f5", "gender": 2, "id": 98215, "name": "Matt Gerald", "order": 11}, {"cast_id": 32, "character": "Private Fike", "credit_id": "52fe48009251416c750aca5b", "gender": 2, "id": 154153, "name": "Sean Anthony Moran", "order": 12}, {"cast_id": 33, "character": "Cryo Vault Med Tech", "credit_id": "52fe48009251416c750aca5f", "gender": 2, "id": 397312, "name": "Jason Whyte", "order": 13}, {"cast_id": 34, "character": "Venture Star Crew Chief", "credit_id": "52fe48009251416c750aca63", "gender": 2, "id": 42317, "name": "Scott Lawrence", "order": 14}, {"cast_id": 35, "character": "Lock Up Trooper", "credit_id": "52fe48009251416c750aca67", "gender": 2, "id": 986734, "name": "Kelly Kilgour", "order": 15}, {"cast_id": 36, "character": "Shuttle Pilot", "credit_id": "52fe48009251416c750aca6b", "gender": 0, "id": 1207227, "name": "James Patrick Pitt", "order": 16}, {"cast_id": 37, "character": "Shuttle Co-Pilot", "credit_id": "52fe48009251416c750aca6f", "gender": 0, "id": 1180936, "name": "Sean Patrick Murphy", "order": 17}, {"cast_id": 38, "character": "Shuttle Crew Chief", "credit_id": "52fe48009251416c750aca73", "gender": 2, "id": 1019578, "name": "Peter Dillon", "order": 18}, {"cast_id": 39, "character": "Tractor Operator / Troupe", "credit_id": "52fe48009251416c750aca77", "gender": 0, "id": 91443, "name": "Kevin Dorman", "order": 19}, {"cast_id": 40, "character": "Dragon Gunship Pilot", "credit_id": "52fe48009251416c750aca7b", "gender": 2, "id": 173391, "name": "Kelson Henderson", "order": 20}, {"cast_id": 41, "character": "Dragon Gunship Gunner", "credit_id": "52fe48009251416c750aca7f", "gender": 0, "id": 1207236, "name": "David Van Horn", "order": 21}, {"cast_id": 42, "character": "Dragon Gunship Navigator", "credit_id": "52fe48009251416c750aca83", "gender": 0, "id": 215913, "name": "Jacob Tomuri", "order": 22}, {"cast_id": 43, "character": "Suit #1", "credit_id": "52fe48009251416c750aca87", "gender": 0, "id": 143206, "name": "Michael Blain-Rozgay", "order": 23}, {"cast_id": 44, "character": "Suit #2", "credit_id": "52fe48009251416c750aca8b", "gender": 2, "id": 169676, "name": "Jon Curry", "order": 24}, {"cast_id": 46, "character": "Ambient Room Tech", "credit_id": "52fe48009251416c750aca8f", "gender": 0, "id": 1048610, "name": "Luke Hawker", "order": 25}, {"cast_id": 47, "character": "Ambient Room Tech / Troupe", "credit_id": "52fe48009251416c750aca93", "gender": 0, "id": 42288, "name": "Woody Schultz", "order": 26}, {"cast_id": 48, "character": "Horse Clan Leader", "credit_id": "52fe48009251416c750aca97", "gender": 2, "id": 68278, "name": "Peter Mensah", "order": 27}, {"cast_id": 49, "character": "Link Room Tech", "credit_id": "52fe48009251416c750aca9b", "gender": 0, "id": 1207247, "name": "Sonia Yee", "order": 28}, {"cast_id": 50, "character": "Basketball Avatar / Troupe", "credit_id": "52fe48009251416c750aca9f", "gender": 1, "id": 1207248, "name": "Jahnel Curfman", "order": 29}, {"cast_id": 51, "character": "Basketball Avatar", "credit_id": "52fe48009251416c750acaa3", "gender": 0, "id": 89714, "name": "Ilram Choi", "order": 30}, {"cast_id": 52, "character": "Na\'vi Child", "credit_id": "52fe48009251416c750acaa7", "gender": 0, "id": 1207249, "name": "Kyla Warren", "order": 31}, {"cast_id": 53, "character": "Troupe", "credit_id": "52fe48009251416c750acaab", "gender": 0, "id": 1207250, "name": "Lisa Roumain", "order": 32}, {"cast_id": 54, "character": "Troupe", "credit_id": "52fe48009251416c750acaaf", "gender": 1, "id": 83105, "name": "Debra Wilson", "order": 33}, {"cast_id": 57, "character": "Troupe", "credit_id": "52fe48009251416c750acabb", "gender": 0, "id": 1207253, "name": "Chris Mala", "order": 34}, {"cast_id": 55, "character": "Troupe", "credit_id": "52fe48009251416c750acab3", "gender": 0, "id": 1207251, "name": "Taylor Kibby", "order": 35}, {"cast_id": 56, "character": "Troupe", "credit_id": "52fe48009251416c750acab7", "gender": 0, "id": 1207252, "name": "Jodie Landau", "order": 36}, {"cast_id": 58, "character": "Troupe", "credit_id": "52fe48009251416c750acabf", "gender": 0, "id": 1207254, "name": "Julie Lamm", "order": 37}, {"cast_id": 59, "character": "Troupe", "credit_id": "52fe48009251416c750acac3", "gender": 0, "id": 1207257, "name": "Cullen B. Madden", "order": 38}, {"cast_id": 60, "character": "Troupe", "credit_id": "52fe48009251416c750acac7", "gender": 0, "id": 1207259, "name": "Joseph Brady Madden", "order": 39}, {"cast_id": 61, "character": "Troupe", "credit_id": "52fe48009251416c750acacb", "gender": 0, "id": 1207262, "name": "Frankie Torres", "order": 40}, {"cast_id": 62, "character": "Troupe", "credit_id": "52fe48009251416c750acacf", "gender": 1, "id": 1158600, "name": "Austin Wilson", "order": 41}, {"cast_id": 63, "character": "Troupe", "credit_id": "52fe48019251416c750acad3", "gender": 1, "id": 983705, "name": "Sara Wilson", "order": 42}, {"cast_id": 64, "character": "Troupe", "credit_id": "52fe48019251416c750acad7", "gender": 0, "id": 1207263, "name": "Tamica Washington-Miller", "order": 43}, {"cast_id": 65, "character": "Op Center Staff", "credit_id": "52fe48019251416c750acadb", "gender": 1, "id": 1145098, "name": "Lucy Briant", "order": 44}, {"cast_id": 66, "character": "Op Center Staff", "credit_id": "52fe48019251416c750acadf", "gender": 2, "id": 33305, "name": "Nathan Meister", "order": 45}, {"cast_id": 67, "character": "Op Center Staff", "credit_id": "52fe48019251416c750acae3", "gender": 0, "id": 1207264, "name": "Gerry Blair", "order": 46}, {"cast_id": 68, "character": "Op Center Staff", "credit_id": "52fe48019251416c750acae7", "gender": 2, "id": 33311, "name": "Matthew Chamberlain", "order": 47}, {"cast_id": 69, "character": "Op Center Staff", "credit_id": "52fe48019251416c750acaeb", "gender": 0, "id": 1207265, "name": "Paul Yates", "order": 48}, {"cast_id": 70, "character": "Op Center Duty Officer", "credit_id": "52fe48019251416c750acaef", "gender": 0, "id": 1207266, "name": "Wray Wilson", "order": 49}, {"cast_id": 71, "character": "Op Center Staff", "credit_id": "52fe48019251416c750acaf3", "gender": 2, "id": 54492, "name": "James Gaylyn", "order": 50}, {"cast_id": 72, "character": "Dancer", "credit_id": "52fe48019251416c750acaf7", "gender": 0, "id": 1207267, "name": "Melvin Leno Clark III", "order": 51}, {"cast_id": 73, "character": "Dancer", "credit_id": "52fe48019251416c750acafb", "gender": 0, "id": 1207268, "name": "Carvon Futrell", "order": 52}, {"cast_id": 74, "character": "Dancer", "credit_id": "52fe48019251416c750acaff", "gender": 0, "id": 1207269, "name": "Brandon Jelkes", "order": 53}, {"cast_id": 75, "character": "Dancer", "credit_id": "52fe48019251416c750acb03", "gender": 0, "id": 1207270, "name": "Micah Moch", "order": 54}, {"cast_id": 76, "character": "Dancer", "credit_id": "52fe48019251416c750acb07", "gender": 0, "id": 1207271, "name": "Hanniyah Muhammad", "order": 55}, {"cast_id": 77, "character": "Dancer", "credit_id": "52fe48019251416c750acb0b", "gender": 0, "id": 1207272, "name": "Christopher Nolen", "order": 56}, {"cast_id": 78, "character": "Dancer", "credit_id": "52fe48019251416c750acb0f", "gender": 0, "id": 1207273, "name": "Christa Oliver", "order": 57}, {"cast_id": 79, "character": "Dancer", "credit_id": "52fe48019251416c750acb13", "gender": 0, "id": 1207274, "name": "April Marie Thomas", "order": 58}, {"cast_id": 80, "character": "Dancer", "credit_id": "52fe48019251416c750acb17", "gender": 0, "id": 1207275, "name": "Bravita A. Threatt", "order": 59}, {"cast_id": 81, "character": "Mining Chief (uncredited)", "credit_id": "52fe48019251416c750acb1b", "gender": 0, "id": 1207276, "name": "Colin Bleasdale", "order": 60}, {"cast_id": 82, "character": "Veteran Miner (uncredited)", "credit_id": "52fe48019251416c750acb1f", "gender": 0, "id": 107969, "name": "Mike Bodnar", "order": 61}, {"cast_id": 83, "character": "Richard (uncredited)", "credit_id": "52fe48019251416c750acb23", "gender": 0, "id": 1207278, "name": "Matt Clayton", "order": 62}, {"cast_id": 84, "character": "Nav\'i (uncredited)", "credit_id": "52fe48019251416c750acb27", "gender": 1, "id": 147898, "name": "Nicole Dionne", "order": 63}, {"cast_id": 85, "character": "Trooper (uncredited)", "credit_id": "52fe48019251416c750acb2b", "gender": 0, "id": 1207280, "name": "Jamie Harrison", "order": 64}, {"cast_id": 86, "character": "Trooper (uncredited)", "credit_id": "52fe48019251416c750acb2f", "gender": 0, "id": 1207281, "name": "Allan Henry", "order": 65}, {"cast_id": 87, "character": "Ground Technician (uncredited)", "credit_id": "52fe48019251416c750acb33", "gender": 2, "id": 1207282, "name": "Anthony Ingruber", "order": 66}, {"cast_id": 88, "character": "Flight Crew Mechanic (uncredited)", "credit_id": "52fe48019251416c750acb37", "gender": 0, "id": 1207283, "name": "Ashley Jeffery", "order": 67}, {"cast_id": 14, "character": "Samson Pilot", "credit_id": "52fe48009251416c750ac9f9", "gender": 0, "id": 98216, "name": "Dean Knowsley", "order": 68}, {"cast_id": 89, "character": "Trooper (uncredited)", "credit_id": "52fe48019251416c750acb3b", "gender": 0, "id": 1201399, "name": "Joseph Mika-Hunt", "order": 69}, {"cast_id": 90, "character": "Banshee (uncredited)", "credit_id": "52fe48019251416c750acb3f", "gender": 0, "id": 236696, "name": "Terry Notary", "order": 70}, {"cast_id": 91, "character": "Soldier (uncredited)", "credit_id": "52fe48019251416c750acb43", "gender": 0, "id": 1207287, "name": "Kai Pantano", "order": 71}, {"cast_id": 92, "character": "Blast Technician (uncredited)", "credit_id": "52fe48019251416c750acb47", "gender": 0, "id": 1207288, "name": "Logan Pithyou", "order": 72}, {"cast_id": 93, "character": "Vindum Raah (uncredited)", "credit_id": "52fe48019251416c750acb4b", "gender": 0, "id": 1207289, "name": "Stuart Pollock", "order": 73}, {"cast_id": 94, "character": "Hero (uncredited)", "credit_id": "52fe48019251416c750acb4f", "gender": 0, "id": 584868, "name": "Raja", "order": 74}, {"cast_id": 95, "character": "Ops Centreworker (uncredited)", "credit_id": "52fe48019251416c750acb53", "gender": 0, "id": 1207290, "name": "Gareth Ruck", "order": 75}, {"cast_id": 96, "character": "Engineer (uncredited)", "credit_id": "52fe48019251416c750acb57", "gender": 0, "id": 1062463, "name": "Rhian Sheehan", "order": 76}, {"cast_id": 97, "character": "Col. Quaritch\'s Mech Suit (uncredited)", "credit_id": "52fe48019251416c750acb5b", "gender": 0, "id": 60656, "name": "T. J. Storm", "order": 77}, {"cast_id": 98, "character": "Female Marine (uncredited)", "credit_id": "52fe48019251416c750acb5f", "gender": 0, "id": 1207291, "name": "Jodie Taylor", "order": 78}, {"cast_id": 99, "character": "Ikran Clan Leader (uncredited)", "credit_id": "52fe48019251416c750acb63", "gender": 1, "id": 1186027, "name": "Alicia Vela-Bailey", "order": 79}, {"cast_id": 100, "character": "Geologist (uncredited)", "credit_id": "52fe48019251416c750acb67", "gender": 0, "id": 1207292, "name": "Richard Whiteside", "order": 80}, {"cast_id": 101, "character": "Na\'vi (uncredited)", "credit_id": "52fe48019251416c750acb6b", "gender": 0, "id": 103259, "name": "Nikie Zambo", "order": 81}, {"cast_id": 102, "character": "Ambient Room Tech / Troupe", "credit_id": "52fe48019251416c750acb6f", "gender": 1, "id": 42286, "name": "Julene Renee", "order": 82}]'

def convert3(obj):
    L=[]
    counter=0
    for i in ast.literal_eval(obj):
        if counter!=5:
            L.append(i['name'])
            counter+=1
        else:
            break
    return L

movies['cast']=movies['cast'].apply(convert3)

movies['crew'][0]

'[{"credit_id": "52fe48009251416c750aca23", "department": "Editing", "gender": 0, "id": 1721, "job": "Editor", "name": "Stephen E. Rivkin"}, {"credit_id": "539c47ecc3a36810e3001f87", "department": "Art", "gender": 2, "id": 496, "job": "Production Design", "name": "Rick Carter"}, {"credit_id": "54491c89c3a3680fb4001cf7", "department": "Sound", "gender": 0, "id": 900, "job": "Sound Designer", "name": "Christopher Boyes"}, {"credit_id": "54491cb70e0a267480001bd0", "department": "Sound", "gender": 0, "id": 900, "job": "Supervising Sound Editor", "name": "Christopher Boyes"}, {"credit_id": "539c4a4cc3a36810c9002101", "department": "Production", "gender": 1, "id": 1262, "job": "Casting", "name": "Mali Finn"}, {"credit_id": "5544ee3b925141499f0008fc", "department": "Sound", "gender": 2, "id": 1729, "job": "Original Music Composer", "name": "James Horner"}, {"credit_id": "52fe48009251416c750ac9c3", "department": "Directing", "gender": 2, "id": 2710, "job": "Director", "name": "James Cameron"}, {"credit_id": "52fe48009251416c750ac9d9", "department": "Writing", "gender": 2, "id": 2710, "job": "Writer", "name": "James Cameron"}, {"credit_id": "52fe48009251416c750aca17", "department": "Editing", "gender": 2, "id": 2710, "job": "Editor", "name": "James Cameron"}, {"credit_id": "52fe48009251416c750aca29", "department": "Production", "gender": 2, "id": 2710, "job": "Producer", "name": "James Cameron"}, {"credit_id": "52fe48009251416c750aca3f", "department": "Writing", "gender": 2, "id": 2710, "job": "Screenplay", "name": "James Cameron"}, {"credit_id": "539c4987c3a36810ba0021a4", "department": "Art", "gender": 2, "id": 7236, "job": "Art Direction", "name": "Andrew Menzies"}, {"credit_id": "549598c3c3a3686ae9004383", "department": "Visual Effects", "gender": 0, "id": 6690, "job": "Visual Effects Producer", "name": "Jill Brooks"}, {"credit_id": "52fe48009251416c750aca4b", "department": "Production", "gender": 1, "id": 6347, "job": "Casting", "name": "Margery Simkin"}, {"credit_id": "570b6f419251417da70032fe", "department": "Art", "gender": 2, "id": 6878, "job": "Supervising Art Director", "name": "Kevin Ishioka"}, {"credit_id": "5495a0fac3a3686ae9004468", "department": "Sound", "gender": 0, "id": 6883, "job": "Music Editor", "name": "Dick Bernstein"}, {"credit_id": "54959706c3a3686af3003e81", "department": "Sound", "gender": 0, "id": 8159, "job": "Sound Effects Editor", "name": "Shannon Mills"}, {"credit_id": "54491d58c3a3680fb1001ccb", "department": "Sound", "gender": 0, "id": 8160, "job": "Foley", "name": "Dennie Thorpe"}, {"credit_id": "54491d6cc3a3680fa5001b2c", "department": "Sound", "gender": 0, "id": 8163, "job": "Foley", "name": "Jana Vance"}, {"credit_id": "52fe48009251416c750aca57", "department": "Costume & Make-Up", "gender": 1, "id": 8527, "job": "Costume Design", "name": "Deborah Lynn Scott"}, {"credit_id": "52fe48009251416c750aca2f", "department": "Production", "gender": 2, "id": 8529, "job": "Producer", "name": "Jon Landau"}, {"credit_id": "539c4937c3a36810ba002194", "department": "Art", "gender": 0, "id": 9618, "job": "Art Direction", "name": "Sean Haworth"}, {"credit_id": "539c49b6c3a36810c10020e6", "department": "Art", "gender": 1, "id": 12653, "job": "Set Decoration", "name": "Kim Sinclair"}, {"credit_id": "570b6f2f9251413a0e00020d", "department": "Art", "gender": 1, "id": 12653, "job": "Supervising Art Director", "name": "Kim Sinclair"}, {"credit_id": "54491a6c0e0a26748c001b19", "department": "Art", "gender": 2, "id": 14350, "job": "Set Designer", "name": "Richard F. Mays"}, {"credit_id": "56928cf4c3a3684cff0025c4", "department": "Production", "gender": 1, "id": 20294, "job": "Executive Producer", "name": "Laeta Kalogridis"}, {"credit_id": "52fe48009251416c750aca51", "department": "Costume & Make-Up", "gender": 0, "id": 17675, "job": "Costume Design", "name": "Mayes C. Rubeo"}, {"credit_id": "52fe48009251416c750aca11", "department": "Camera", "gender": 2, "id": 18265, "job": "Director of Photography", "name": "Mauro Fiore"}, {"credit_id": "5449194d0e0a26748f001b39", "department": "Art", "gender": 0, "id": 42281, "job": "Set Designer", "name": "Scott Herbertson"}, {"credit_id": "52fe48009251416c750aca05", "department": "Crew", "gender": 0, "id": 42288, "job": "Stunts", "name": "Woody Schultz"}, {"credit_id": "5592aefb92514152de0010f5", "department": "Costume & Make-Up", "gender": 0, "id": 29067, "job": "Makeup Artist", "name": "Linda DeVetta"}, {"credit_id": "5592afa492514152de00112c", "department": "Costume & Make-Up", "gender": 0, "id": 29067, "job": "Hairstylist", "name": "Linda DeVetta"}, {"credit_id": "54959ed592514130fc002e5d", "department": "Camera", "gender": 2, "id": 33302, "job": "Camera Operator", "name": "Richard Bluck"}, {"credit_id": "539c4891c3a36810ba002147", "department": "Art", "gender": 2, "id": 33303, "job": "Art Direction", "name": "Simon Bright"}, {"credit_id": "54959c069251417a81001f3a", "department": "Visual Effects", "gender": 0, "id": 113145, "job": "Visual Effects Supervisor", "name": "Richard Martin"}, {"credit_id": "54959a0dc3a3680ff5002c8d", "department": "Crew", "gender": 2, "id": 58188, "job": "Visual Effects Editor", "name": "Steve R. 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Fetching the Director from crew field

def fetch_dir(obj):
    L=[]
    for i in ast.literal_eval(obj):
        if i['job']=='Director':
            L.append(i['name'])
            break
    return L

movies['crew']=movies['crew'].apply(fetch_dir)

movies['overview'][0]

'In the 22nd century, a paraplegic Marine is dispatched to the moon Pandora on a unique mission, but becomes torn between following orders and protecting an alien civilization.'

Converting paragraph into the list of word

movies['overview']=movies['overview'].apply(lambda x:x.split())
movies.head()

def collapse(L):
    L1 = []
    for i in L:
        L1.append(i.replace(" ",""))
    return L1

movies['cast'] = movies['cast'].apply(collapse)
movies['crew'] = movies['crew'].apply(collapse)
movies['genres'] = movies['genres'].apply(collapse)
movies['keywords'] = movies['keywords'].apply(collapse)

movies.head()

Merging the 5 different fields into Tag field

movies['tags'] = movies['overview'] + movies['genres'] + movies['keywords'] + movies['cast'] + movies['crew']

new = movies.drop(columns=['overview','genres','keywords','cast','crew'])

new['tags'] = new['tags'].apply(lambda x: " ".join(x))

new['tags']=new['tags'].apply(lambda x:x.lower())

new.head()

nltk library is used to convert different kind of same word the one word

import nltk

from nltk.stem.porter import PorterStemmer
ps=PorterStemmer()

def stem(text):
    y=[]
    for i in text.split():
        y.append(ps.stem(i))
    return " ".join(y)

new['tags']=new['tags'].apply(stem)

Converting the word into vector form Using CountVectorizer

from sklearn.feature_extraction.text import CountVectorizer
cv=CountVectorizer(max_features=500,stop_words='english')
# we can use TfidfVectorizer() also

vectors=cv.fit_transform(new['tags']).toarray()

vectors

array([[1, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       ...,
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 1, 0, 0],
       [0, 0, 0, ..., 0, 0, 0]])

cv.get_feature_names()

['3d',
 'accident',
 'act',
 'action',
 'adventur',
 'affair',
 'aftercreditssting',
 'age',
 'agent',
 'alcohol',
 'alien',
 'alway',
 'america',
 'american',
 'angel',
 'ani',
 'anim',
 'anoth',
 'apart',
 'armi',
 'arriv',
 'art',
 'artist',
 'assassin',
 'assign',
 'attack',
 'attempt',
 'author',
 'away',
 'babi',
 'bad',
 'band',
 'bank',
 'bar',
 'base',
 'basedonnovel',
 'battl',
 'beauti',
 'becom',
 'befor',
 'begin',
 'believ',
 'best',
 'big',
 'biographi',
 'black',
 'blood',
 'bond',
 'book',
 'boss',
 'boy',
 'boyfriend',
 'break',
 'bring',
 'british',
 'brother',
 'brutal',
 'busi',
 'california',
 'captain',
 'captur',
 'car',
 'career',
 'case',
 'caught',
 'caus',
 'celebr',
 'center',
 'chanc',
 'chang',
 'charact',
 'chase',
 'chicago',
 'child',
 'childhood',
 'children',
 'christian',
 'christma',
 'cia',
 'citi',
 'city',
 'class',
 'close',
 'coach',
 'colleg',
 'come',
 'comedi',
 'commun',
 'compani',
 'competit',
 'confront',
 'conspiraci',
 'continu',
 'control',
 'convinc',
 'cop',
 'corrupt',
 'countri',
 'coupl',
 'crash',
 'creat',
 'crew',
 'crime',
 'crimin',
 'cross',
 'danc',
 'danger',
 'dark',
 'date',
 'daughter',
 'day',
 'dead',
 'deadli',
 'deal',
 'death',
 'decid',
 'demon',
 'desert',
 'desper',
 'despit',
 'destroy',
 'detect',
 'determin',
 'develop',
 'die',
 'differ',
 'director',
 'disappear',
 'discov',
 'divorc',
 'doctor',
 'documentari',
 'doesn',
 'dog',
 'dr',
 'drama',
 'dream',
 'drug',
 'dure',
 'duringcreditssting',
 'dyinganddeath',
 'dystopia',
 'earth',
 'embark',
 'encount',
 'end',
 'england',
 'enter',
 'entir',
 'epic',
 'escap',
 'event',
 'everi',
 'everyon',
 'everyth',
 'evil',
 'ex',
 'experi',
 'explos',
 'eye',
 'face',
 'faith',
 'fall',
 'famili',
 'family',
 'fantasi',
 'far',
 'fate',
 'father',
 'fathersonrelationship',
 'fbi',
 'fear',
 'fight',
 'film',
 'final',
 'follow',
 'forc',
 'form',
 'frank',
 'free',
 'french',
 'friend',
 'friends',
 'friendship',
 'futur',
 'game',
 'gang',
 'gangster',
 'gay',
 'gener',
 'ghost',
 'girl',
 'girlfriend',
 'god',
 'goe',
 'good',
 'govern',
 'great',
 'group',
 'grow',
 'gun',
 'guy',
 'ha',
 'hand',
 'happen',
 'hard',
 'head',
 'heart',
 'heist',
 'help',
 'hero',
 'hi',
 'high',
 'highschool',
 'hire',
 'histori',
 'hit',
 'hold',
 'holiday',
 'hollywood',
 'home',
 'hope',
 'horror',
 'hospit',
 'hotel',
 'hous',
 'human',
 'hunt',
 'husband',
 'includ',
 'independentfilm',
 'inspir',
 'intern',
 'investig',
 'involv',
 'island',
 'jack',
 'jackson',
 'jame',
 'jean',
 'job',
 'john',
 'join',
 'journalist',
 'journey',
 'just',
 'kid',
 'kidnap',
 'kill',
 'killer',
 'king',
 'know',
 'known',
 'la',
 'land',
 'late',
 'later',
 'law',
 'lawyer',
 'lead',
 'leader',
 'learn',
 'leav',
 'led',
 'left',
 'legendari',
 'lie',
 'life',
 'like',
 'littl',
 'live',
 'lives',
 'lo',
 'local',
 'london',
 'londonengland',
 'long',
 'look',
 'losangel',
 'lose',
 'lost',
 'love',
 'lover',
 'mafia',
 'magic',
 'make',
 'man',
 'manag',
 'mani',
 'marri',
 'marriag',
 'mean',
 'meet',
 'member',
 'men',
 'michael',
 'militari',
 'million',
 'miss',
 'mission',
 'money',
 'monster',
 'mother',
 'movi',
 'murder',
 'music',
 'mysteri',
 'nation',
 'need',
 'neighbor',
 'new',
 'newyork',
 'night',
 'noth',
 'nuditi',
 'obsess',
 'offic',
 'old',
 'onc',
 'onli',
 'open',
 'oper',
 'order',
 'owner',
 'pair',
 'parent',
 'pari',
 'parti',
 'partner',
 'past',
 'path',
 'peopl',
 'perfect',
 'person',
 'place',
 'plan',
 'planet',
 'play',
 'plot',
 'polic',
 'polit',
 'popular',
 'post',
 'power',
 'presid',
 'princess',
 'prison',
 'problem',
 'professor',
 'prostitut',
 'protect',
 'prove',
 'queen',
 'quest',
 'question',
 'race',
 'rais',
 'real',
 'realiz',
 'realli',
 'relationship',
 'remak',
 'report',
 'rescu',
 'retir',
 'return',
 'reveal',
 'reveng',
 'right',
 'rise',
 'road',
 'robot',
 'rock',
 'romanc',
 'romant',
 'rule',
 'run',
 'russian',
 'save',
 'school',
 'sciencefict',
 'scientist',
 'search',
 'secret',
 'secur',
 'seek',
 'self',
 'send',
 'sent',
 'sequel',
 'seri',
 'set',
 'sever',
 'sex',
 'sexual',
 'share',
 'sheriff',
 'ship',
 'singer',
 'singl',
 'sister',
 'small',
 'soldier',
 'someth',
 'son',
 'soon',
 'south',
 'space',
 'spend',
 'spi',
 'spirit',
 'sport',
 'stand',
 'star',
 'start',
 'state',
 'stay',
 'stop',
 'stori',
 'strang',
 'stranger',
 'street',
 'struggl',
 'student',
 'success',
 'suddenli',
 'suicid',
 'summer',
 'superhero',
 'supernatur',
 'surviv',
 'suspect',
 'suspens',
 'taken',
 'tale',
 'talent',
 'target',
 'teacher',
 'team',
 'teen',
 'teenag',
 'tell',
 'terror',
 'terrorist',
 'test',
 'themselv',
 'thi',
 'thing',
 'think',
 'threaten',
 'thriller',
 'time',
 'togeth',
 'town',
 'track',
 'train',
 'transform',
 'trap',
 'travel',
 'tri',
 'trip',
 'troubl',
 'true',
 'truth',
 'turn',
 'ultim',
 'uncov',
 'undercov',
 'unit',
 'unlik',
 'use',
 'vampir',
 'veri',
 'victim',
 'villag',
 'violenc',
 'visit',
 'wa',
 'want',
 'war',
 'way',
 'wed',
 'western',
 'white',
 'widow',
 'wife',
 'win',
 'wit',
 'witch',
 'woman',
 'womandirector',
 'women',
 'work',
 'world',
 'writer',
 'year',
 'years',
 'york',
 'young',
 'zombi']

Measuring the cosine distance between the words

from sklearn.metrics.pairwise import cosine_similarity

similarity=cosine_similarity(vectors)

def recommend(movie):
    index = new[new['title'] == movie].index[0]
    distances = sorted(list(enumerate(similarity[index])),reverse=True,key = lambda x: x[1])
    for i in distances[1:11]:
        print(new.iloc[i[0]].title)

recommend('Avatar')

Independence Day
Beowulf
Aliens vs Predator: Requiem
Predators
The Fifth Element
Small Soldiers
Titan A.E.
Jupiter Ascending
Treasure Planet
Attack the Block

Importing the pickle library to export the train file

import pickle

pickle.dump(new,open('movie.pkl','wb'))

pickle.dump(similarity,open('similarity.pkl','wb'))