Data Preprocessing in Python: Improve Your Machine Learning Model with Scikit-Learn Techniques
Supervised learning with scikit-learn (Part 5)
📚Chapter:3-Data Preprocessing and Pipelines
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Introduction :
In the ever-evolving realm of machine learning, the journey from raw data to meaningful insights is a labyrinthine process. At the heart of this journey lies a critical phase that acts as a gateway to model accuracy and performance — preprocessing. While often overlooked, the significance of preprocessing in machine learning cannot be overstated. It is the unsung hero that transforms raw data into a form that can be harnessed effectively by learning algorithms. However, all the data that you have used so far has been relatively nice and in a format that allows you to plug and play into sci-kit-learn from the get-go. With real-world data, this will rarely be the case, and instead, you will have to preprocess your data before you can build models. Data preprocessing is an integral step in Machine Learning as the quality of data and the useful information that can be derived from it directly affects the ability of our model to learn; therefore, it is extremely important that we preprocess our data before feeding it into our model.
Sections
Understanding Preprocessing
Key steps of Preprocessing in sklearn
The Impact on Model Performance
Advantages of Preprocessing
Conclusion
Section1- Understanding Preprocessing:
What is Preprocessing?
Def: Preprocessing involves the cleaning, transforming, and organizing of raw data to make it suitable for machine learning algorithms. Raw data is rarely in a pristine state, and preprocessing is the crucial step that bridges the gap between raw information and actionable insights.
Def: Predictive modeling projects involve learning from data. Data refer to examples or cases from the domain that characterize the problem you want to solve. On a predictive modeling project, such as classification or regression, raw data typically cannot be used directly [1].In data science, data Preprocessing is the process of identifying incorrect data and fixing the errors so the final dataset is ready to be used. Errors could include duplicate fields, incorrect formatting, incomplete fields, irrelevant or inaccurate data, and corrupted data.
The Raw Data Challenge:
Data Collection is the first step in the project of the data science life cycle. It is one of the most important things in the life cycle. The data can be taken from various places likes the internet, company data, database, and many more. Raw data, straight from the source, can be noisy, incomplete, or inconsistent.
It may contain missing values, outliers, or irrelevant information. The quality of the input data profoundly influences the performance of machine learning models. Imagine trying to analyze a complex puzzle with missing pieces — preprocessing is the art of filling in the gaps.If you feed subpar or low-quality data into your model, it will not produce satisfactory outcomes. This holds true regardless of the model’s complexity, the expertise of the data scientist, or the financial investment in the project [2].
Section2- Key steps of Preprocessing in sklearn
There are common or standard tasks that you may use or explore during the data preparation step in a machine learning project using sklearn.
These tasks include:
1- Importing the required libraries: These Two are essential libraries that we will import every time. NumPy is a Library which contains Mathematical functions. Pandas is the library used to import and manage the data sets.
2- Importing the Data Set: Data sets are generally available in .csv format. A CSV file stores tabular data in plain text. Each line of the file is a data record. We use the read_csv method of the pandas library to read a local CSV file as a dataframe. Then we make separate Matrix and Vector of independent and dependent variables from the data frame.
3- Handling Missing Data: Dealing with missing data is a common challenge in real-world datasets. Preprocessing techniques such as imputation or removal of incomplete records ensure that the model is fed with complete and reliable information. The data we get is rarely homogeneous. Data can be missing due to various reasons and needs to be handled so that it does not reduce the performance of our machine learning model. We can replace the missing data by the Mean or Median of the entire column. We use the Imputer class of sklearn. preprocessing for this task.
4-Data imbalanced problem: An imbalance occurs when one or more classes have very low proportions/probability in the training data as compared to the other classes.
5- Data Cleaning: Identifying and correcting mistakes or errors in the data.
6- Dealing with Outliers: Outliers, anomalies that deviate significantly from the norm, can distort the learning process. Preprocessing methods like scaling or transformation help mitigate the impact of outliers on model performance
6- Encoding Categorical Data: Machine learning algorithms typically work with numerical data, and categorical variables need to be encoded appropriately. Techniques such as one-hot encoding convert categorical variables into a format that models can understand. Data Categorical data are variables that contain label values rather than numeric values. The number of possible values is often limited to a fixed set. Example values such as “Yes” and “No” cannot be used in mathematical equations of the model so we need to encode these variables into numbers. To achieve this we import LabelEncoder class from sklearn.preprocessing library.
7- Feature Engineering: Creating new features or modifying existing ones can enhance the model’s ability to extract meaningful patterns from the data. Feature engineering is a preprocessing step that involves selecting, combining, or transforming features to improve model performance. Identifying those input variables that are most relevant to the task.
8- Dimensionality Reduction: Creating compact projections of the data.
9- Data Splitting: Dividing the dataset into training and testing sets is essential to evaluate model performance. Preprocessing ensures that this split is representative, preventing data leakage and providing a reliable assessment of the model’s generalization capabilities.We make two partitions of dataset one for training the model called the training set and the other for testing the performance of the trained model called the test set. The split is generally 80/20. We import train_test_split() method of sklearn.cross-validation library.
10- Normalization and Standardization: Ensuring that all features are on a consistent scale is crucial. Normalization and standardization techniques transform data into a common scale, preventing certain features from dominating the learning process due to their magnitude.Most of the machine learning algorithms use the Euclidean distance between two data points in their computations, features highly varying in magnitudes. units and range pose problems. High magnitudes features will weigh more in the distance calculations than features with low magnitudes. Done by Feature standardization or Z-score normalization. StandardScalar of sklearn. preprocessing is imported.
Section 3- The Impact on Model Performance:
The quality of preprocessing directly influences the accuracy and efficiency of machine learning models. A well-preprocessed dataset enables models to learn more efficiently, extracting relevant patterns and making better predictions on new, unseen data.For example, handling missing values, removing outliers, and scaling the data can help to prevent overfitting, which can lead to models that generalize better to new data [3]. This step helps to ensure that the data is in a format that the algorithm can understand and that it is free of errors or outliers that can negatively impact the model’s performance. [3]
Section4- Advantages of Preprocessing Data
Model performance: One of the main advantages of preprocessing data is that it helps to improve the accuracy of the model [3].
Training times: it can help to reduce the time and resources required to train the model [3].
Biased results: Preprocessing data can also help to prevent overfitting. Overfitting occurs when a model is trained on a dataset that is too specific, and as a result, it performs well on the training data but poorly on new, unseen data [3].If the data is not preprocessed, it may contain errors or biases that can lead to unfair or inaccurate results. For example, if the data contains missing values, the algorithm may be working with a biased sample of the data, which can lead to incorrect conclusions.
Understanding the model: Preprocessing data can also improve the interpretability of the model. By cleaning and formatting the data, we can make it easier to understand the relationships between different variables and how they are influencing the model’s predictions [3]. If the data is not preprocessed, it can be difficult to understand the relationships between different variables and how they are influencing the model’s predictions. This can make it harder to identify errors or areas for improvement in the model [3]
In general, not preprocessing data can lead to models that are less accurate, less interpretable, and more difficult to work with [3].
Conclusion:
In the complex tapestry of machine learning, preprocessing is the invisible hand that shapes raw data into a coherent and usable form. As data scientists and machine learning enthusiasts, understanding the nuances of preprocessing is crucial for unlocking the full potential of models. In subsequent articles, we will delve deeper into specific preprocessing techniques, exploring their applications and implications on different types of datasets. Stay tuned as we unravel the layers of preprocessing in the fascinating world of machine learning.
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Source
1- Data Preparation (Day 1)
2-Yuliia Kniazieva, What is Data Collectio in Machine Learning (2022)