A Comprehensive Guide to Data Preprocessing and Cleaning Techniques

Rishav dadwal
7 min readJul 17, 2023



Data forms the backbone of any successful machine learning project. However, real-world data is often messy, inconsistent, and incomplete. To ensure the accuracy and reliability of our machine learning models, it is crucial to apply data preprocessing and cleaning techniques. In this blog, we will explore the importance of data preprocessing and walk through essential techniques every machine learning enthusiast should know.

Understanding Data Preprocessing: Before diving into the techniques, let’s understand what data preprocessing is and why it is essential. Data preprocessing involves a series of steps to transform raw data into a clean, organized, and suitable format for analysis. The goal is to remove noise, handle missing values, and standardize the data to make it more amenable for machine learning algorithms.

Handling Missing Data

Missing data is a common problem in datasets and can significantly impact the performance of machine learning models. There are various strategies to handle missing data:

a. Deletion: Removing rows or columns with missing values can be a straightforward approach. However, this method should be used with caution, as it may lead to a loss of valuable information.

The output will show the original dataset with missing values and then the modified dataset after removing the rows containing missing values.

Deleting rows with missing values is just one way to handle missing data, and it might not always be the best approach depending on the specific situation and dataset characteristics. Other imputation methods like mean, median, or interpolation might be more appropriate in some cases to retain valuable information and maintain the dataset’s integrity. However, this code snippet demonstrates the process of handling missing values by deletion.

b. Imputation: Filling missing values using methods like mean, median, or interpolation is a more common approach. Imputation helps retain the data and can provide better insights for analysis.


In this code, we perform two different imputation techniques: mean imputation and interpolation.

Orignal Dataset

1. Mean Imputation:

  • We fill the missing values with the mean of the non-missing values in each respective column.
  • This technique is simple and can work well when the data is approximately normally distributed.
Mean Imputation

2. Interpolation:

  • We use interpolation to estimate the missing values based on the values of neighboring data points.
  • The method fills missing values based on the values before and after the missing data point, which can be useful for time-series or ordered data.

Data Transformation

Sometimes, the raw data might not be in the ideal format for analysis. This section will cover techniques to transform the data, such as:

a. Feature Scaling: Standardizing or normalizing features to bring them to a similar scale. Feature scaling is crucial when features have different units or scales. Scaling ensures that no single feature dominates the learning process, leading to more robust models.

Original dataset
After scaling

b. Log Transform: When data is heavily skewed, a log transform can make it more normally distributed. This transformation is useful when dealing with skewed data, as it reduces the impact of outliers and helps achieve a more symmetrical distribution.

Before Log Transformation
After log Transformation

The output will display the original dataset, the dataset after log transformation, and two histograms: one before the log transformation and another after the log transformation. You can observe how the log transformation affects the distribution of the data and reduces the skewness.

Keep in mind that log transformation is suitable for positively skewed data. If you have negatively skewed data or zeros or negative values, you might need to apply different transformations or use alternative methods to handle the skewness.

Handling Outliers

Outliers can adversely affect model training, making it essential to address them. We’ll discuss outlier detection techniques, such as:

a. Z-Score: Identifying outliers based on their deviation from the mean. Data points beyond a certain threshold (usually z-score > 3 or < -3) are considered outliers and can be dealt with accordingly.

b. IQR (Interquartile Range): Detecting outliers based on the data’s quartiles. Outliers are data points that fall below Q1–1.5 * IQR or above Q3 + 1.5 * IQR. The IQR method is robust and less sensitive to extreme values.

Encoding Categorical Variables

Most machine learning algorithms require numeric inputs, but real-world data often contains categorical variables. We’ll cover methods like:

a. One-Hot Encoding: Converting categorical variables into binary vectors. One-hot encoding creates binary columns for each category, allowing algorithms to process categorical data effectively.

In the one-hot encoded dataset, we have created three binary columns, one for each category (A, B, C) in the original ‘Category’ column. Each row in the ‘Category_A’, ‘Category_B’, and ‘Category_C’ columns represents whether the corresponding category is present or not for that particular row.

Using one-hot encoding, we transformed the original categorical data into binary vectors, which can be effectively processed by machine learning algorithms.

b. Label Encoding: Assigning numeric labels to categories. Label encoding is another way to represent categorical data numerically. However, it assigns ordinal values to categories, which may introduce unintended relationships between them.

Data Normalization and Standardization:

Data Normalization and Standardization are both techniques used to scale numerical data to a common range, but they operate differently and have distinct effects on the data. Let’s explore the differences between the two and understand how they can influence model training.

1.Data Normalization: Data normalization, also known as Min-Max scaling, rescales the data to a fixed range, typically between 0 and 1.

Effects of Data Normalization:

  • The range of all features is compressed to a fixed interval, making them comparable.
  • It preserves the original distribution’s shape, as only the scaling changes.
  • Normalization is sensitive to outliers; extreme values can disproportionately influence the scaling.

2. Data Standardization: Data standardization, also called z-score normalization, transforms data to have a mean of 0 and a standard deviation of 1.

Effects of Data Standardization:

  • Standardization centers the data around 0, making the mean of each feature 0.
  • It scales the data based on the standard deviation, allowing for easier comparison across different features.
  • Standardization is robust to outliers, as it takes into account the overall distribution’s characteristics.

When to Use Data Normalization vs. Data Standardization:

  • Use data normalization (Min-Max scaling) when the data has a bounded range and the actual numerical values’ magnitudes are important for the model. It works well for algorithms like Neural Networks and those that require input features to be within a specific range.
  • Use data standardization (z-score normalization) when the data has a significant amount of variability and the mean and standard deviation are meaningful. It is commonly used with algorithms like K-Nearest Neighbors (KNN) and Support Vector Machines (SVM).

In practice, the choice between normalization and standardization depends on the specific characteristics of the dataset and the requirements of the machine learning algorithm being used. As a best practice, it is often beneficial to experiment with both techniques and observe their impact on model performance before making a final decision. Additionally, some algorithms might be more sensitive to the choice of scaling method than others, so it is essential to consider the context of the problem and the algorithm’s assumptions during preprocessing.

Conclusion: Data preprocessing and cleaning are vital steps in any machine learning project. They can significantly impact the model’s performance and the reliability of its predictions. By applying the techniques discussed in this blog, you’ll be better equipped to handle real-world data challenges and build more accurate machine learning models.

Remember, the key to successful data preprocessing is understanding your data thoroughly and selecting the appropriate techniques based on the specific characteristics of your dataset. Happy preprocessing!



Rishav dadwal

Passionate machine learning student exploring AI's potential. Dreaming of positive impact and solving real-world problems. #ML #AI #Passion