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import pandas as pd
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import LabelEncoder
def load_data(file_path):
"""Load dataset from a CSV file."""
return pd.read_csv(file_path)
def handle_missing_values(df):
"""Handle missing values in the dataset."""
# Impute numerical columns with the median
numerical_cols = df.select_dtypes(include=['float64', 'int64']).columns
imputer = SimpleImputer(strategy='median')
df[numerical_cols] = imputer.fit_transform(df[numerical_cols])
# Impute categorical columns with the most frequent value
categorical_cols = df.select_dtypes(include=['object']).columns
imputer = SimpleImputer(strategy='most_frequent')
df[categorical_cols] = imputer.fit_transform(df[categorical_cols])
return df
def encode_categorical_variables(df):
"""Encode categorical variables using Label Encoding."""
categorical_cols = df.select_dtypes(include=['object']).columns
label_encoder = LabelEncoder()
for col in categorical_cols:
df[col] = label_encoder.fit_transform(df[col])
return df
def preprocess_data(file_path):
"""Load, preprocess, and return the dataset."""
df = load_data(file_path)
df = handle_missing_values(df)
df = encode_categorical_variables(df)
return df
if __name__ == "__main__":
file_path = 'path_to_your_data.csv' # Replace with your actual file path
processed_data = preprocess_data(file_path)
processed_data.to_csv('processed_data.csv', index=False)
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