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@ -9,20 +9,15 @@ RUN pip3 install pandas
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RUN pip3 install matplotlib
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RUN pip3 install matplotlib
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RUN pip3 install sklearn
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RUN pip3 install sklearn
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RUN apt-get install unzip
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RUN apt-get install unzip
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#RUN mkdir root/.kaggle
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#RUN touch root/.kaggle/kaggle.json
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#RUN echo "{\"username\":\"maciejczajka\",\"key\":\"d8f044ad2b96d32372e22f9054c02bb1\"}" >> root/.kaggle/kaggle.json
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#RUN chmod 666 root/.kaggle
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#RUN chmod 666 root/.kaggle/kaggle.json
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#ENV KAGGLE_USERNAME=maciejczajka
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#ENV KAGGLE_KEY=d8f044ad2b96d32372e22f9054c02bb1
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ENV CUTOFF=1000
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ENV CUTOFF=1000
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ARG KAGGLE_USERNAME
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ARG KAGGLE_USERNAME
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ARG KAGGLE_KEY
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ARG KAGGLE_KEY
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ENV KAGGLE_USERNAME=${KAGGLE_USERNAME}
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ENV KAGGLE_USERNAME=${KAGGLE_USERNAME}
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ENV KAGGLE_KEY=${KAGGLE_KEY}
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ENV KAGGLE_KEY=${KAGGLE_KEY}
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RUN mkdir /dane
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WORKDIR /app
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WORKDIR /app
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COPY download.sh .
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COPY download.sh .
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@ -152,7 +152,7 @@ def dataset_information(games):
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# os.system("unzip top-video-games-19952021-metacritic.zip")
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# os.system("unzip top-video-games-19952021-metacritic.zip")
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# wczytanie danych
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# wczytanie danych
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games=pd.read_csv('/app/all_games1.csv')
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games=pd.read_csv('/dane/all_games1.csv')
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# usunięcie 5 wyników "Stadia"
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# usunięcie 5 wyników "Stadia"
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delete_stadia(games)
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delete_stadia(games)
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@ -8,7 +8,7 @@ dataset_operation() {
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head -n $len2 all_games.csv.shuf > all_games.csv.test
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head -n $len2 all_games.csv.shuf > all_games.csv.test
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head -n $len3 all_games.csv.shuf | tail -n $len2 > all_games.csv.dev
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head -n $len3 all_games.csv.shuf | tail -n $len2 > all_games.csv.dev
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tail -n +$len4 all_games.csv.shuf > all_games.csv.train
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tail -n +$len4 all_games.csv.shuf > all_games.csv.train
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cp all_games.csv.shuf /app/all_games1.csv
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cp all_games.csv /dane/all_games.csv
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wc -l all_games.csv.*
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wc -l all_games.csv.*
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}
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}
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