Handling Missing Values

Missing values are gaps in your dataset where data was not recorded, and you need to handle them before training a model, since most algorithms cannot work with empty values.

Common ways to handle missing values

• Remove rows or columns with too many missing values.
• Fill missing values with the mean, median, or mode.
• Fill missing values using a fixed value or a prediction-based approach.

Checking for missing values

import pandas as pd

df = pd.read_csv("data.csv")

print(df.isnull().sum())

This shows how many missing values exist in each column, which helps you decide how to handle them.

Removing missing values

df = df.dropna()

dropna() removes any row that has at least one missing value, useful when missing data is very small in amount.

Filling missing values with mean

df["Age"] = df["Age"].fillna(df["Age"].mean())

This replaces missing values in the Age column with the average age, which keeps the dataset complete without losing rows.

Filling missing values with mode

df["City"] = df["City"].fillna(df["City"].mode()[0])

This fills missing categorical values with the most frequently occurring value in that column.

Handling missing values properly prevents training errors and keeps the dataset's overall pattern intact.