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movie.py
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movie.py
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import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer as cv
from sklearn.metrics.pairwise import cosine_similarity as cs
cv = cv()
# a = np.array(4000)
import random
def find_title(index):
try:
return df[df.index == index]["title"].values[0]
except:
pass
def find_ref(title):
try:
return df[df.title == title]["index"].values[0]
except:
return 1932
df = pd.read_csv("movie.csv")
columns = df.columns
for column in columns:
df[column] = df[column].fillna('')
df[column] = df[column].dropna()
def recommend_by_feature(row):
try:
return row['keywords'] +" "+row['cast']+" "+row["genres"]+" "+row["director"]
except:
print("Error:", row)
df["combined_features"] = df.apply(recommend_by_feature,axis=1)
#https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html
count_matrix = cv.fit_transform(df["combined_features"])
#https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.cosine_similarity.html
cosine_sim = cs(count_matrix)
def show(movie):
print(movie)
try:
a =[]
i = 0
movie_to_bot = movie
ref = find_ref(movie_to_bot)
top_recommends = sorted(list(enumerate(cosine_sim[ref])),key=lambda x:x[1])
for element in top_recommends:
a.append(find_title(element[0]))
i=i+1
if i>5:
break
return a
except:
return ['did not find any recommendations']