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import pandas as pd
from sklearn.model_selection import train_test_split
def load_data(file_path):
"""
Load dataset from a CSV file.
Args:
file_path (str): Path to the CSV file.
Returns:
pd.DataFrame: Loaded dataset.
"""
return pd.read_csv(file_path)
def preprocess_data(df):
"""
Preprocess the dataset by handling missing values and encoding categorical variables.
Args:
df (pd.DataFrame): Raw dataset.
Returns:
pd.DataFrame: Preprocessed dataset.
"""
# Handle missing values
df = df.dropna()
# Encode categorical variables
df = pd.get_dummies(df)
return df
def split_data(df, target_column, test_size=0.2):
"""
Split the dataset into training and testing sets.
Args:
df (pd.DataFrame): Preprocessed dataset.
target_column (str): Name of the target column.
test_size (float): Proportion of the dataset to include in the test split.
Returns:
X_train, X_test, y_train, y_test: Split datasets.
"""
X = df.drop(columns=[target_column])
y = df[target_column]
return train_test_split(X, y, test_size=test_size, random_state=42)
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