In the journey of building robust machine learning model, data preprocessing in machine learning is a fundamental step that ensures high-quality and reliable input for your algorithms.
Raw data, as collected from diverse sources, is often riddled with inconsistencies, missing values and noise. Through a systematic approach, data preprocessing transform this raw data into structured format that machine learning models can effectively analyze.
For those looking to implement data preprocessing in python, this process becomes even more accessible with libraries like Pandas, Numpy and Scikit-learn. In this blog we will explore importance of data preprocessing, its key steps and advantages of data preprocessing in machine learning along with how python can streamline this crucial task in the machine learning pipeline.
What is Data Preprocessing in Machine Learning ?
Data Preprocessing in machine learning is like preparing raw ingredients before cooking a meal. Just as you clean, chop and measure ingredients for a recipe, data preprocessing involves cleaning, organizing and formatting raw data so that it’s ready to be used by a machine learning model.
When we collect data from the real world (like sales records, survey results, or sensor readings), it’s often messy. There might be missing information, incorrect entries or even irrelevant details that confuse the model. Data preprocessing helps fix these issues, making the data clean and understandable for the machine learning algorithms.
Imagine try to build a cake with spoiled ingredients – no matter how good your recipe is, the result won’t be great. Similarly in machine learning model trained on a messy data will give poor results.
Preprocessing ensures that:
- The data is complete and correct.
- The machine learning model understands the data properly.
- This model performs well and gives accurate results.
Example : House Price Prediction
Let’s say we want to predict house prices using data like, size , number of bedrooms, and locations.
Here is the raw dataset:
| House Size (sq. ft.) | Bedrooms | Location | Price(s) |
| 3000 | 3 | Urban | 700,000 |
| NaN | 4 | Suburban | NaN |
| 2500 | NaN | Urban | 400,000 |
| 3500 | 5 | Rural | 900,000 |
Problem with this data :
- Missing values in “House Size” , “Bedrooms” and “Price”.
- Location is in text form, but machine learning model performs better with numbers.
- Price has a wide range of values, make it hard for model to learn.
Preprocessed Data :
| House Size (Sq. ft.) | Bedrooms | Location | Price(s) |
| 3000 | 3 | 0 | 700,000 |
| 3000 | 4 | 1 | 700,000 |
| 2500 | 3 | 0 | 400,000 |
| 3500 | 5 | 2 | 900,000 |
What Changed ?
- Missing values were filled (e.g, the average size for missing house sizes).
- Locations like “Urban”, “Suburban” and “Rural” converted to numbers 0, 1 and 2 and that makes easy for machine learning model to analyze and understands data.
- Everything gets changed in numerical format rather than using text format that becomes flexible for machine learning model to understand and gives accurate predictions.
Data Preprocessing Steps in Machine Learning
Data preprocessing is an essential step for machine learning. In this blog we are covering several steps that gives you insight, how data actually gets preprocessed and improves to train machine learning model performs better.
There are several steps for data preprocessing that are discussed below:
Data Collection
This is the very first and crucial step for data preprocessing. In this step we are collecting data from various sources including databases ( SQL , NoSQL), APIs ( Application Programming Interfaces ), web scrapping, IoT sensors, surveys or questionnaires, CSV / excel files and public domains (like, Kaggle, UCI repositories).
Data Cleaning
Once we have collected data the next step comes here for data cleaning. In this process, we are identifying, resolving errors and inaccuracies in the dataset to improve data quality. Once data gets cleaned, machine learning model will perform analysis without having inaccuracies or irrelevant information.
Data Integration
Third step is data integration, as the name suggest in this data preprocessing pipeline we are combining data from multiple sources into a dataset. This step basically ensures that we have unified the relevant data and made ready for analysis.
There are different tools that are required for data integration includes Talend, Apache Nifi, Snowflake, Amazon Redshift, Apache Hadoop and Apache Spark.
Data Transformation
Data transformation is the next step after data integration which refers to the process of converting data from its original structured format that is more appropriate for analysis or machine learning models. This is crucial step for maintaining the accuracy of the model.
Example :
If you have a dataset of house price prediction, you will normalize the price value to bring them within specific range. Impute missing values for square footage using the median value.
Feature Selection
Feature selection is another step for data preprocessing in which we are extracting useful information from the existing data. It basically works on feature engineering and when it comes to machine learning then it reduces the input variable so that it will improve model performance and reduce overfitting.
Splitting Data
It is very crucial step when it comes to a factor of overfitting. In this step we are basically divides dataset into training, validation and test sets. It is important in machine learning when we are working with complex and large datasets.
It is recommended to split the dataset into three parts viz, train, validate and test sets. As per information the data may split in 80 : 20 or 70 : 30 ratio respectively.
Data Balancing
Data balancing is another essential step in data preprocessing for machine learning, particularly when dealing with imbalanced datasets.
Imbalanced datasets can lead to biased models that consistently impact the performance.
Data Reduction
Data reduction is the next step after data balancing which reduces the volume of data while maintaining the completeness of data (integrity) and improving efficiency.
Outlier Detection and Removal
Outlier detection and removal are crucial steps in data preprocessing for improving the quality and accuracy of machine learning models.
Outliers will reduce and slow down the entire process that affects the model’s performance. So it’s key step to handle outliers so that entire model will perform well.
Data Augmentation
Data augmentation is a approach used in machine learning to increase the size and diversity of a dataset by applying various transformation to the existing data . This approach is especially useful in scenarios where acquiring new dataset is expensive or time-consuming.
Why do we need data preprocessing ?
Data preprocessing is a crucial step in machine learning because algorithms require simplified data to produce high-quality and reliable results.
It involves various steps to reduce data complexity and eliminate errors or irrelevant information. This ensures that when the training process is performed to train a machine learning model, it generates relevant and accurate results.
Additionally, data preprocessing not only improves the results but also reduces overfitting and complexity, enabling machine learning algorithms to perform effectively.
Advantages of Data Preprocessing
There are several advantages in machine learning when it comes to data preprocessing:
- Data Preprocessing ensures the model’s high performance as data is clean, well structured and relevant.
- It improves the data accuracy and because of that machine learning model predict consistent results ( highly rely on quality of information or data ).
- Overfitting problem can easily reduced with the help of data preprocessing.
Frequently Asked Questions (FAQs)
Why is data preprocessing is important in machine learning ?
Data preprocessing is essential in machine learning because raw data is often incomplete or inconsistence which can obstruct the performance of machine learning models.
It involves cleaning the data, handling missing values and removing irrelevant information. These steps ensures the data is well structured and ready for analysis, improving the model’s accuracy, efficiency and ability to generalize to new datasets. Proper preprocessing helps reduce errors and ensures the machine learning model produce meaningful output (results).
How do you handle missing data?
To handle missing data in machine learning, you can either remove it or fill in it. Removing rows or columns with missing values works if only a small part of the data is missing, but too much removal can loose important information.
For filling in, you can use simple methods like replacing missing numbers with the mean ,mode or most frequent value. Advanced methods like K-Nearest Neighbor or regression, predict missing values based on similar data.
Some algorithms like decision trees, handle missing data automatically. You can also flag missing data by adding a new column to show there where it’s missing or even use models to predict the missing parts. The best method depends on your dataset and the reason for the missing values.
What is One-Hot Encoding?
One-Hot encoding technique is a way to represent categorical data ( like colors or labels ) as numbers so that computer or machine learning algorithms can understand it.
Example :
Suppose we have two genders : Male and Female.
Male = [ 1 , 0 ]
Female = [ 0 , 1 ]
How do you handle outliers?
There are different ways to handle outliers in data:
- The basic step is to remove the outliers from the dataset itself.
- Limit extreme values by capping them to a maximum and minimum threshold.
- Apply mathematical transformations like square root to reduce the effects of outliers that provides irrelevant information.
- Using machine learning models like decision trees helps you to handle outliers in data due to less sensitivity.
What is cross-validation in preprocessing?
In preprocessing, cross-validation is used to ensure that the data preparation key steps like (scaling, encoding or missing values) are applied only on training data during the model evaluation process.
This process ensures prevention of data leakage, where the information from the testing data influences the process, leading to the positive results. By including cross-validation in preprocessing workflow, the data preparation steps are imposed separately for each layer of training process that splits testing and personate real world conditions.
What is the difference between training and testing data?
Training data and testing data are both very important for machine learning model. Training data is used to teach the model by showing it examples with the correct answers, helping it learn patterns. Testing data, however, is used to check how well the model performs on new unseen data. The key difference is that training data helps the model learn, while testing data helps us see if the model can make accurate predictions on data it hasn’t seen before.
