<jupyter_start><jupyter_text>Data Analysis<jupyter_code>import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt

sns.set_theme(style="dark")
sns.set_palette("colorblind")

sns.histplot(
    data=df,
    x="radius_mean", hue="diagnosis",
    multiple="stack",
    edgecolor=".3",
    linewidth=.5,
    log_scale=False
).set_title('mean radius over 17.5 usually means malignant cancer')
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt

sns.set_theme(style="dark")
sns.set_palette("colorblind")

sns.histplot(
    data=df,
    x="smoothness_mean", hue="diagnosis",
    multiple="stack",
    edgecolor=".3",
    linewidth=.5,
    log_scale=False,
)
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt

sns.set_theme(style="dark")
sns.set_palette("colorblind")

sns.scatterplot(
    data=df, 
    x="radius_mean", 
    y="smoothness_mean", 
    hue="diagnosis")<jupyter_output><empty_output><jupyter_text>* Este gráfico mostra algum padrão relevante?<jupyter_code>import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt

sns.set_theme(style="dark")
sns.set_palette("colorblind")

sns.scatterplot(
    data=df, 
    x="area_mean", 
    y="symmetry_mean", 
    hue="diagnosis")
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt

sns.set_theme(style="dark")
sns.set_palette("colorblind")

sns.displot(
    data=df, 
    kind="hist", 
    height=5,
    x="area_worst", 
    y="perimeter_worst", 
    hue="diagnosis")
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt

sns.set_theme(style="dark")
sns.set_palette("colorblind")

sns.histplot(
    data=df,
    x="concavity_mean", hue="diagnosis",
    multiple="stack",
    edgecolor=".3",
    linewidth=.5,
    log_scale=False,
)<jupyter_output><empty_output><jupyter_text>Separating dataset into training and validation datasetsObs.: Se você estiver executando este script no Colab então não é necessário executar esta parte de separação dos arquivos. Eles já estão disponíveis no GitHub do projeto e é possível ler o conteúdo deles a partir dos outros scripts (pipelines).<jupyter_code>from sklearn.model_selection import train_test_split

# no specific data processing will be done, that is repeated on every single pipeline for better maintenance
X_train, X_test, y_train, y_test = train_test_split(df.iloc[:,1:df.size-2], df['diagnosis'], test_size=0.1, random_state=4)

#training datasets
X_train.to_csv(path_or_buf="../../data/base_train.csv", header=True)
y_train.to_csv(path_or_buf="../../data/base_train_answer.csv", header=True)

#validating datasets
X_test.to_csv(path_or_buf="../../data/base_test.csv", header=True)
y_test.to_csv(path_or_buf="../../data/base_test_answer.csv", header=True)<jupyter_output><empty_output>
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{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '272528', 'license': 'MIT', 'n_tokens_mistral': 1063, 'n_tokens_neox': 1024, 'n_words': 226, 'path': 'scripts/2021_breast_cancer/exploratoryDataAnalysis.ipynb', 'repo_name': 'fbarth/ds-saint-paul', 'size': '2833'}