MACHINE LEARNING FOR CONCRETE COMPRESSIVE STRENGTH ANALYSIS AND PREDICTION WITH PYTHON
About this ebook
First, we will dive into the dataset, which includes various features related to concrete mix proportions, age, and other influential factors. We will explore the dataset's structure, dimensions, and feature types, ensuring that we have a solid understanding of the data we are working with. Then, we will focus on data exploration and visualization. We will utilize histograms, box plots, and scatter plots to gain insights into the distribution of features and their relationships with the target variable, enabling us to uncover valuable patterns and trends within the dataset. Before delving into machine learning algorithms, we must preprocess the data. We will handle missing values, encode categorical variables, and scale numerical features to ensure that our data is in the optimal format for training and testing our models.
Then, we will explore popular algorithms such as Linear Regression, Decision Trees, Random Forests, Support Vector, Naïve Bayes, K-Nearest Neighbors, Adaboost, Gradient Boosting, Extreme Gradient Boosting, Light Gradient Boosting, Catboost, and Multi-Layer Perceptron regression algorithms and use them to predict the concrete compressive strength accurately. We will evaluate and compare the performance of these models using regression metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R2) score.
Then, we will explore the exciting world of unsupervised learning by applying K-means clustering. This technique allows us to identify patterns within the data and group similar instances together, leading to valuable insights into the characteristics of different concrete samples. To determine the optimal number of clusters within the data, we will introduce evaluation methods such as the elbow method. We will then visualize the clusters using scatter plots or other appropriate techniques, allowing us to gain a deeper understanding of their distribution and distinct groups.
Next, we will we employed various machine learning models to predict the clusters in the dataset. These models included Logistic Regression, Decision Trees, Random Forests, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Adaboost, Gradient Boosting, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting (LGBM), Catboost, and Multi-Layer Perceptron (MLP). The metrics used are Accuracy: it measures the proportion of correctly classified instances out of the total number of instances. It provides an overall assessment of how well the model predicts the correct cluster memberships.; Recall: it, also known as sensitivity or true positive rate, measures the ability of the model to correctly identify instances belonging to a particular cluster. It is the ratio of true positives to the sum of true positives and false negatives.; Precision: it measures the ability of the model to correctly identify instances belonging to a specific cluster, without including any false positives. It is the ratio of true positives to the sum of true positives and false positives.; F1-score: it is the harmonic mean of precision and recall, providing a balanced measure of model performance. It is useful when the dataset is imbalanced, as it considers both false positives and false negatives.; Macro average (macro avg): it calculates the average performance of the model across all clusters by simply averaging the metric values for each cluster. It treats all clusters equally, regardless of their sizes.; and Weighted average (weighted avg): it calculates the average performance of the model across all clusters, taking into account the size of each cluster. It is calculated by weighting each cluster's metric value by its support, which is the number of instances in that cluster. These metrics help evaluate the model's ability to predict cluster memberships accurately. Accuracy measures the overall correctness of the predictions, while recall and precision focus on the model's performance in correctly assigning instances to specific clusters. Macro average and weighted average provide a summary of model performance across all clusters, considering both individual cluster performance and cluster sizes. By analyzing these metrics, we can assess the model's effectiveness in predicting clusters and compare the performance of different machine learning models.
By the end of this book, you will have gained valuable insights into how machine learning can be leveraged to analyze and predict the compressive strength of concrete. Get ready to embark on an exciting journey into the world of concrete analysis and prediction with machine learning!
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About the author
Vivian Siahaan is a fast-learner who likes to do new things. She was born, raised in Hinalang Bagasan, Balige, on the banks of Lake Toba, and completed high school education from SMAN 1 Balige. She started herself learning Java, Android, JavaScript, CSS, C ++, Python, R, Visual Basic, Visual C #, MATLAB, Mathematica, PHP, JSP, MySQL, SQL Server, Oracle, Access, and other programming languages. She studied programming from scratch, starting with the most basic syntax and logic, by building several simple and applicable GUI applications. Animation and games are fields of programming that are interests that she always wants to develop. Besides studying mathematical logic and programming, the author also has the pleasure of reading novels. Vivian Siahaan has written dozens of ebooks that have been published on Sparta Publisher: Data Structure with Java; Java Programming: Cookbook; C ++ Programming: Cookbook; C Programming For High Schools / Vocational Schools and Students; Java Programming for SMA / SMK; Java Tutorial: GUI, Graphics and Animation; Visual Basic Programming: From A to Z; Java Programming for Animation and Games; C # Programming for SMA / SMK and Students; MATLAB For Students and Researchers; Graphics in JavaScript: Quick Learning Series; JavaScript Image Processing Methods: From A to Z; Java GUI Case Study: AWT & Swing; Basic CSS and JavaScript; PHP / MySQL Programming: Cookbook; Visual Basic: Cookbook; C ++ Programming for High Schools / Vocational Schools and Students; Concepts and Practices of C ++; PHP / MySQL For Students; C # Programming: From A to Z; Visual Basic for SMA / SMK and Students; C # .NET and SQL Server for High School / Vocational School and Students. At the ANDI Yogyakarta publisher, Vivian Siahaan also wrote a number of books including: Python Programming Theory and Practice; Python GUI Programming; Python GUI and Database; Build From Zero School Database Management System In Python / MySQL; Database Management System in Python / MySQL; Python / MySQL For Management Systems of Criminal Track Record Database; Java / MySQL For Management Systems of Criminal Track Records Database; Database and Cryptography Using Java / MySQL; Build From Zero School Database Management System With Java / MySQL.
Rismon Hasiholan Sianipar was born in Pematang Siantar, in 1994. After graduating from SMAN 3 Pematang Siantar 3, the writer traveled to the city of Jogjakarta. In 1998 and 2001 the author completed his Bachelor of Engineering (S.T) and Master of Engineering (M.T) education in the Electrical Engineering of Gadjah Mada University, under the guidance of Prof. Dr. Adhi Soesanto and Prof. Dr. Thomas Sri Widodo, focusing on research on non-stationary signals by analyzing their energy using time-frequency maps. Because of its non-stationary nature, the distribution of signal energy becomes very dynamic on a time-frequency map. By mapping the distribution of energy in the time-frequency field using discrete wavelet transformations, one can design non-linear filters so that they can analyze the pattern of the data contained in it. In 2003, the author received a Monbukagakusho scholarship from the Japanese Government. In 2005 and 2008, he completed his Master of Engineering (M.Eng) and Doctor of Engineering (Dr.Eng) education at Yamaguchi University, under the guidance of Prof. Dr. Hidetoshi Miike. Both the master's thesis and his doctoral thesis, R.H. Sianipar combines SR-FHN (Stochastic Resonance Fitzhugh-Nagumo) filter strength with cryptosystem ECC (elliptic curve cryptography) 4096-bit both to suppress noise in digital images and digital video and maintain its authenticity. The results of this study have been documented in international scientific journals and officially patented in Japan. One of the patents was published in Japan with a registration number 2008-009549. He is active in collaborating with several universities and research institutions in Japan, particularly in the fields of cryptography, cryptanalysis and audio / image / video digital forensics. R.H. Sianipar also has experience in conducting code-breaking methods (cryptanalysis) on a number of intelligence data that are the object of research studies in Japan. R.H. Sianipar has a number of Japanese patents, and has written a number of national / international scientific articles, and dozens of national books. R.H. Sianipar has also participated in a number of workshops related to cryptography, cryptanalysis, digital watermarking, and digital forensics. In a number of workshops, R.H. Sianipar helps Prof. Hidetoshi Miike to create applications related to digital image / video processing, steganography, cryptography, watermarking, non-linear screening, intelligent descriptor-based computer vision, and others, which are used as training materials. Field of interest in the study of R.H. Sianipar is multimedia security, signal processing / digital image / video, cryptography, digital communication, digital forensics, and data compression / coding. Until now, R.H. Sianipar continues to develop applications related to analysis of signal, image, and digital video, both for research purposes and for commercial purposes based on the Python programming language, MATLAB, C ++, C, VB.NET, C # .NET, R, and Java.
