Machine Learning

We are living in the world of data, where data in getting generated every second throughout the world. Data Science has become a critical component of numerous industries and domains due to the increasing availability of data and the need to derive valuable insights from it. As data continues to grow in volume and complexity, data science’s importance will only increase in the coming years. Machine Learning and Deep Learning concepts are gaining wide range of attraction in today’s era. Scikit-learn, also known as Sklearn, is a famous open-source machine learning library for Python, Scikit learn being one of the most important library, is widely used to handle Machine learning algorithms for Data preprocessing, Feature Selection, Model Training, Model Evaluation and more. It offers a comprehensive set of functionalities that make it easier for developers and researchers to implement and experiment with machine learning techniques. We will discuss it more in this blog. What is Scikit Learn? It is an open-source library used for handling both supervised and unsupervised Machine Learning algorithms. It offers a powerful toolkit to streamline Machine Learning workflows and thus helping in solving real-world problems effectively. 5 Key Features of Scikit Learn 1. Comprehensive algorithms: It offers a wide collection of supervised and unsupervised learning algorithms, including linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering etc. 2. Preprocessing and feature engineering: Scikit-learn provides various data preprocessing techniques, such as MinMaxScalar for Normalization, StandardScalar for standardization, OneHotEncoder for encoding categorical variables, SimpleImputer for handling missing values and many more. It also offers feature selection and extraction methods to improve model performance. 3. Model Building and Evaluation: Several Machine Learning models are imported using Sklearn library, some of them are Tree based models, Ensemble models, Linear models, SVM based models etc. For Evaluation purposes, Scikit learn is providing metrics such as accuracy, precision, recall, F1 score, and area under the ROC curve (AUC-ROC). It also supports cross-validation and hyperparameter tuning for optimizing model parameters. 4. Integration with other libraries: Scikit-learn integrates with other Python libraries used in data science, such as pandas for data manipulation, matplotlib for visualization, and NumPy for basic numerical calculations and SciPy for advanced numerical calculations. 5. Easy-to-use API: Scikit-learn has a consistent and intuitive API, making it accessible for both beginners and experienced machine learning practitioners. Data Availability in Scikit Learning: Some of the data sets are available in this library for direct use. These data sets can be used for practising, exploring various data cleaning techniques, feature selection techniques, model evaluation techniques using scikit learn. Following Data Sets are available in this library: 1. Iris Dataset: It consists of measurements of sepals and petals of three different species of Iris flowers (Setosa, Versicolor, and Virginica). We need to classify these flowers based on measured values. 2. Boston Housing Dataset: The dataset contains information of housing prices in Boston. It contains 13 attributes and a numerical output variable. It can be used for regression problems. 3. Wine Dataset: The dataset contains chemical analysis of wine. It contains the details such as alcohol content, malic acid, and colour intensity. This dataset is used for classification purpose. 4. Breast Cancer Dataset: Digitized images of breast mass is present in the dataset. The attributes include various characteristics of the cell nuclei, and the task is to classify tumours as benign or malignant. 5. Digit Dataset: The Digit dataset consists of images of handwritten digits (0 to 9). The task is to classify the digit represented by each image. In addition to these, scikit-learn also provides utilities to load and work with other popular datasets, such as the California housing dataset, MNIST dataset, and more. How to load a particular dataset? import sklearn from sklearn import datasets #datasets imported from sklearn boston_data = datasets.load_boston()  ##boston dataset got loaded   ##data is getting saved in variable a and b.   a = boston_data.data print(a.shape); #(506, 13) b = boston_data.target print(b.shape); #(506,) #we have printed shape Sklearn and Preprocessing: Scikit-learn provides a wide range of preprocessing techniques to prepare and transform your data before feeding it into machine learning algorithms. These preprocessing methods can help with data cleaning, normalization, scaling, encoding categorical variables, handling missing values, and more. Here are some commonly used preprocessing techniques in scikit-learn: Data Cleaning: Handling Missing Values: Simple Imputer can be used to replace missing values into different strategy (mean,median,most frequent etc). import numpy as np import pandas as pd a=pd.DataFrame({‘A’:[2,3,4],’B’: [3,2,np.NaN]}) a.isna() from sklearn.impute import SimpleImputer mean_imputer = SimpleImputer(missing_values=np.nan, strategy=’mean’) a[“B”] = pd.DataFrame(mean_imputer.fit_transform(a[[“B”]])) BEFORE IMPUTATION                                 – A          B 0 False    False 1 False     False 2 False    True  AFTER IMPUTATION             – A      B 0  False  False 1  False  False 2  False  False Outlier Detection: Scikit-learn offers various methods, such as the EllipticEnvelope and IsolationForest, for detecting and handling outliers in your data. Feature Scaling: We are taking a sample dataset of WindTurbine Failure in the code. data=pd.read_csv(‘cleaned_data.csv’) from sklearn.preprocessing import MinMaxScaler data_x=data.loc[:,data.columns!=’Failure_status’] mms=MinMaxScaler() data_x_norm=mms.fit_transform(data_x) data_x_norm=pd.DataFrame(data_x_norm,columns=data_x.columns)   As MinMaxScalar is used, all the values are lying within the range of 0 to 1. Encoding Categorical Variables: One-Hot Encoding: The OneHotEncoder class encodes categorical features into binary vectors. Label Encoding: The Label Encoder class converts categorical labels into integer values. purpose purpose furniture/appliances 4 furniture/appliances 4 car0 0 car 1 car 1 car 1 car 1 car 1 business 3 business 3 car 1 car0 0 car    1 car 1 car0 0 car 1 business 3 business 3 car0 0 furniture/appliances 4 car 1 furniture/appliances 4 car0 0 As you can see before doing encoding, data were present in categorical format and after doing label encoding, data converted to numerical format. Feature Engineering using Scikit Learn: In simple words Feature Engineering is all about creating new features by working on existing features. This requires domain knowledge to get a new feature which could be increasing the accuracy of the model. Dimensionality Reduction: Principal Component Analysis (PCA): This decreases the number of columns by extracting

Welcome to the world of Automated Machine Learning – where dreams are data-driven, and the future is forever within our grasp. Imagine a world where anyone, regardless of technical expertise, can unlock the full potential of machine learning. This is the essence of Automated Machine Learning, or AutoML, a groundbreaking concept that promises to revolutionize the way we approach complex problem-solving. Embrace the art of automation and the magic of self-learning systems, as AutoML paves the way for organizations to embrace the future of AI-driven decisions. No longer limited by technical boundaries, the world of AutoML beckons us to unlock the full potential of data, guiding us towards a future where innovation knows no bounds. AutoML has several benefits, including reduced time and resources required for developing and deploying machine learning models. This can help to increase the adoption of machine learning in a wide range of applications. Let us embark on this remarkable journey, where the pursuit of knowledge and innovation propels us towards a brighter tomorrow. Here in this blog, we are going to discuss in detail about Light AutoML which is an open-source library aimed at automatic Machine Learning. Let us begin our journey. What is Light AutoML? Light AutoML is an advanced open-source AutoML library that basically automates the process of feature and model selection, it simplifies and accelerates the process of machine learning. Light AutoML leverages the synergy between different machine learning techniques, such as gradient boosting, linear models, and neural networks, to create an ensemble of models that complement each other’s strengths. This ensemble approach significantly improves prediction accuracy and generalization, making it a formidable tool for tackling various real-world challenges. Light AutoML excels at handling structured data, unstructured data, and even time series data, making it versatile and adaptable to a wide range of applications. Its flexibility allows data scientists and machine learning practitioners to focus on refining problem-specific aspects while the heavy lifting of model optimization is taken care of by the library. Some interesting facts about AutoML: India been 2nd most well established country after USA in the field of Data Science and AI, several companies are utilizing the concepts of AutoML throughout the day. It has been recorded that on an average more than 75 people have searched this keyword at a given time in India in the last 30 days. This is another interesting fact showing the states of India known for IT and its application, the term AutoML is widely used in these states in the last 30 days. Coming to worldwide facts, Auto ML keyword is searched for more than 80 times a day on an average and Slovakia in the top list in terms for web search of this word. Features of Light AutoML Light AutoML is an open-source AutoML library that offers a wide range of features to simplify and accelerate the machine learning process. Some of the important features of this library are: One of the main advantages of this library is to automate feature engineering. Automated feature engineering techniques like missing value imputation, scaling, and normalization, which can significantly improve the quality of the feature set and improve the accuracy of the final model. Custom feature engineering is an important feature of Light AutoML as it allows users to work on the feature set to their specific needs. The library provides a wide range of feature engineering techniques, such as categorical encoding, text processing, and feature selection, that can be combined in various ways to create a feature set that is enhanced for the given task. However, sometimes the built-in feature engineering techniques are not sufficient to capture the complexity of the data or to address domain-specific problems. Based on the domain knowledge and experience custom feature engineering allows users to set their own features. This can be done by using external data sources, such as weather or economic data, or by creating new features based on specific business rules or constraints. For example, if the task is to predict the demand for a particular product, a domain expert may built a feature that captures the impact of a marketing campaign on the demand for the product. Defining own features allows the user to make better model with better accuracy. However, it is important to remember that custom feature engineering can bring bias to the model if not done carefully. Therefore, it is important to validate the performance of the model. Model selection is an important step in the machine learning pipeline, and Light AutoML offers a variety of options to help users select the best model for their task. The library includes a wide range of models such as tree based models, linear models, and neural networks, which can be applied to various machine learning tasks. Ensemble learning is a popular technique in machine learning that involves combining the predictions of multiple models to improve the overall accuracy of the model. Light AutoML uses ensemble learning to create a final model that is meant for the given task. The library provides several ensemble learning techniques, such as stacking, bagging, boosting, which can be used to combine the predictions of multiple models. Light AutoML also supports custom models, that allows users to define their own models based on their domain knowledge and experience. This is basically used for complex problems where no pre-built models are available, or for problems that require specific model architectures. In addition to model selection techniques, Light AutoML also provides tools to select and finalize best hyperparameters. Hyperparameters are the parameters that are given by user to improve the accuracy, it is not constant and it varies based on Machine Learning models used. Examples of hyperparameters include the learning rate in neural networks, the number of trees in a random forest model, regularization parameter in a linear model, kernels in SVM or number of nearest neighbours in KNN. Optimizing hyperparameters can improve the performance of the model and reduce overfitting. The library gives several hyperparameters techniques, such as