31 lines
1.2 KiB
Python
31 lines
1.2 KiB
Python
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import numpy as np
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import pandas as pd
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from sklearn.model_selection import train_test_split
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imbd_data = pd.read_csv('imdb_top_1000.csv')
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imbd_data.drop(columns=["Poster_Link"], inplace=True)
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imbd_data.drop(columns=["Overview"], inplace=True)
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imbd_data["Series_Title"] = imbd_data["Series_Title"].str.lower()
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imbd_data["Genre"] = imbd_data["Genre"].str.lower()
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imbd_data["Director"] = imbd_data["Director"].str.lower()
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imbd_data["Star1"] = imbd_data["Star1"].str.lower()
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imbd_data["Star2"] = imbd_data["Star2"].str.lower()
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imbd_data["Star3"] = imbd_data["Star3"].str.lower()
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imbd_data["Star4"] = imbd_data["Star4"].str.lower()
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imbd_data = imbd_data.replace(np.nan, '', regex=True)
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imbd_data["Gross"] = imbd_data["Gross"].str.replace(',', '')
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imbd_data["Gross"] = pd.to_numeric(imbd_data["Gross"], errors='coerce')
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imbd_data = imbd_data.dropna()
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data_train, data_test = train_test_split(imbd_data, test_size=230, random_state=1)
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data_test, data_dev = train_test_split(data_test, test_size=115, random_state=1)
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print("Dataset successfully divided into test/dev/train sets ")
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data_test.to_csv("data_test.csv", encoding="utf-8", index=False)
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data_dev.to_csv("data_dev.csv", encoding="utf-8", index=False)
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data_train.to_csv("data_train.csv", encoding="utf-8", index=False)
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