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from sklearn import preprocessing

import pandas as pd

import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

df = pd.read_csv('../input/movies_metadata.csv')

df_numeric = df[['budget', 'popularity', 'revenue', 'runtime', 'vote_average', 'vote_count', 'title']]

df_numeric.isnull().sum()

df_numeric = df_numeric.dropna()

df_numeric = df_numeric[df_numeric['vote_count'] > 30]

df_numeric.shape

from sklearn import preprocessing
minmax_processed = preprocessing.MinMaxScaler().fit_transform(df_numeric.drop('title', axis=1))
df_numeric_scaled = pd.DataFrame(minmax_processed, index=df_numeric.index, columns=df_numeric.columns[:-1])

df_numeric.head()