How do you handle missing values in a dataset?

September 24, 2025

Quality Thought – The Best Data Science Training in Hyderabad

Looking for the best Data Science training in Hyderabad? Quality Thought offers industry-focused Data Science training designed to help professionals and freshers master machine learning, AI, big data analytics, and data visualization. Our expert-led course provides hands-on training with real-world projects, ensuring you gain in-depth knowledge of Python, R, SQL, statistics, and advanced analytics techniques.

Why Choose Quality Thought for Data Science Training?

✅ Expert Trainers with real-time industry experience
✅ Hands-on Training with live projects and case studies
✅ Comprehensive Curriculum covering Python, ML, Deep Learning, and AI
✅ 100% Placement Assistance with top IT companies
✅ Flexible Learning – Classroom & Online Training

Supervised and Unsupervised Learning are two primary types of machine learning, differing mainly in The primary goal of a data science project is to extract actionable insights from data to support better decision-making, predictions, or automation—ultimately solving a specific business or real-world problem.

Handling missing values is a crucial step in data preprocessing, as missing or incomplete data can affect model accuracy. There are several strategies depending on the type of data, the proportion of missing values, and the problem context.

1. Remove Missing Values

Drop rows with missing data (df.dropna() in pandas) if only a small portion of data is missing.
Drop columns with too many missing values if they are not essential.
Pros: Simple and avoids introducing bias.
Cons: May lose valuable information if missing data is substantial.

2. Impute Missing Values

Replace missing values with a reasonable estimate.

a) For Numerical Data

Mean or Median Imputation: Replace missing values with the mean/median of the column.
```
df['age'].fillna(df['age'].median(), inplace=True)
```
Mode Imputation: Useful for skewed distributions.

b) For Categorical Data

Mode Imputation: Replace missing values with the most frequent category.
"Unknown" Category: Create a separate category for missing values.

c) Advanced Techniques

K-Nearest Neighbors (KNN) Imputation: Use similar rows to estimate missing values.
Regression Imputation: Predict missing values using other features.
Multiple Imputation: Creates several possible values and averages them to handle uncertainty.

3. Use Algorithms That Handle Missing Values

Some algorithms (like XGBoost, LightGBM, or Random Forests) can handle missing values internally, reducing the need for imputation.

4. Flag Missing Values

Add a new column indicating whether a value was missing. This can sometimes help models detect patterns associated with missingness.

Best Practices

Understand why values are missing (Missing Completely at Random, Missing at Random, or Not Missing at Random).
Avoid blindly imputing data without understanding its context.
Keep track of imputed values for reproducibility and transparency.

✅ In short:
Options include dropping missing data, imputing with statistics or predictions, using algorithms that handle missing values, or flagging them. The choice depends on the dataset size, feature importance, and model requirements.

I can also provide a Python example showing multiple imputation techniques if you want a practical demonstration.

Visit QUALITY THOUGHT Training Institute in Hyderabad

Get Direction

Search This Blog

Data Science Training Course in Hyderabad