چالش‎ها و ملاحظات در پاکسازی داده یا Data Cleansing

# Denoising با Autoencoder
denoised = autoencoder.predict(noisy_mri)
# اصلاح برچسب با Active Learning
uncertain = uncertainty_model.predict(X_mri)
corrected_labels = human_in_loop(uncertain)

# تشخیص Spoofing
df['vp_ratio'] = df['volume'] / (df['high'] - df['low'] + 1e-5)
df_clean = df[df['vp_ratio'] > df['vp_ratio'].quantile(0.01)]

df['email'] = df['email'].str.lower().str.strip()
df = df.drop_duplicates(subset=['email'], keep='last')
df['valid_address'] = df['address'].apply(validate_with_api)

# حذف برچسب‌های نادرست با Confidence Learning
from cleanlab import Datalab
lab = Datalab(data, label_name="label")
lab.find_issues(features=X, issue_types={"label"})
df_clean = lab.get_clean_data()

from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("BigDataCleansing").config(
    "spark.executor.memory", "4g").getOrCreate()
df = spark.read.parquet("path/to/big_data")
df_clean = df.dropDuplicates().na.fill({"column": 0})
df_clean.write.parquet("path/to/cleaned_big_data")
spark.stop()

pythonfrom anonymizer import Anonymizer
df = pd.read_csv("path/to/data.csv")
anonymizer = Anonymizer()
df["ssn"] = anonymizer.mask(df["ssn"], pattern="###-##-####")

from prefect import task, Flow

@task
def clean_data():
    df = pd.read_csv("path/to/data.csv")
    df_clean = df.drop_duplicates().fillna(df.mean())
    df_clean.to_csv("path/to/cleaned_data.csv")

with Flow("DataCleansing") as flow:
    clean_data()
flow.run()