How do I preprocess data in Python for machine learning?

How do I preprocess data in Python for machine learning?

To preprocess data in Python, you can use libraries like Pandas and Scikit-learn. What specific preprocessing steps do you need help with?

I need to handle missing values and normalize the data.

You can handle missing values using fillna() or dropna() in Pandas. For normalization, you can use StandardScaler or MinMaxScaler from Scikit-learn. Would you like code examples?

Yes, please provide examples for both.

Sure! For missing values:

import pandas as pd

df.fillna(df.mean(), inplace=True)  # Fill with mean

For normalization:

from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler()
df_normalized = scaler.fit_transform(df)

That’s helpful! How can I split the data into training and testing sets?

You can use train_test_split from Scikit-learn:

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)