Opinion Mining And Prediction Using Machine Learning And Deep Learning With Python Gui
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Opinion Mining And Prediction Using Machine Learning And Deep Learning With Python Gui
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-06-27
Opinion Mining And Prediction Using Machine Learning And Deep Learning With Python Gui written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-27 with Computers categories.
In the context of sentiment analysis and opinion mining, this project began with dataset exploration. The dataset, comprising user reviews or social media posts, was examined to understand the sentiment labels' distribution. This analysis provided insights into the prevalence of positive or negative opinions, laying the foundation for sentiment classification. To tackle sentiment classification, we employed a range of machine learning algorithms, including Support Vector, Logistic Regression, K-Nearest Neighbours Classiier, Decision Tree, Random Forest Classifier, Gradient Boosting, Extreme Gradient Boosting, Light Gradient Boosting, and Adaboost Classifiers. These algorithms were combined with different vectorization techniques such as Hashing Vectorizer, Count Vectorizer, and TF-IDF Vectorizer. By converting text data into numerical representations, these models were trained and evaluated to identify the most effective combination for sentiment classification. In addition to traditional machine learning algorithms, we explored the power of recurrent neural networks (RNNs) and their variant, Long Short-Term Memory (LSTM). LSTM is particularly adept at capturing contextual dependencies and handling sequential data. The text data was tokenized and padded to ensure consistent input length, allowing the LSTM model to learn from the sequential nature of the text. Performance metrics, including accuracy, were used to evaluate the model's ability to classify sentiments accurately. Furthermore, we delved into Convolutional Neural Networks (CNNs), another deep learning model known for its ability to extract meaningful features. The text data was preprocessed and transformed into numerical representations suitable for CNN input. The architecture of the CNN model, consisting of embedding, convolutional, pooling, and dense layers, facilitated the extraction of relevant features and the classification of sentiments. Analyzing the results of our machine learning models, we gained insights into their effectiveness in sentiment classification. We observed the accuracy and performance of various algorithms and vectorization techniques, enabling us to identify the models that achieved the highest accuracy and overall performance. LSTM and CNN, being more advanced models, aimed to capture complex patterns and dependencies in the text data, potentially resulting in improved sentiment classification. Monitoring the training history and metrics of the LSTM and CNN models provided valuable insights. We examined the learning progress, convergence behavior, and generalization capabilities of the models. Through the evaluation of performance metrics and convergence trends, we gained an understanding of the models' ability to learn from the data and make accurate predictions. Confusion matrices played a crucial role in assessing the models' predictions. They provided a detailed analysis of the models' classification performance, highlighting the distribution of correct and incorrect classifications for each sentiment category. This analysis allowed us to identify potential areas of improvement and fine-tune the models accordingly. In addition to confusion matrices, visualizations comparing the true values with the predicted values were employed to evaluate the models' performance. These visualizations provided a comprehensive overview of the models' classification accuracy and potential areas for improvement. They allowed us to assess the alignment between the models' predictions and the actual sentiment labels, enabling a deeper understanding of the models' strengths and weaknesses. Overall, the exploration of machine learning, LSTM, and CNN models for sentiment analysis and opinion mining aimed to develop effective tools for understanding public opinions. The results obtained from this project showcased the models' performance, convergence behavior, and their ability to accurately classify sentiments. These insights can be leveraged by businesses and organizations to gain a deeper understanding of the sentiments expressed towards their products or services, enabling them to make informed decisions and adapt their strategies accordingly.
Hate Speech Detection And Sentiment Analysis Using Machine Learning And Deep Learning With Python Gui
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-08-04
Hate Speech Detection And Sentiment Analysis Using Machine Learning And Deep Learning With Python Gui written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-04 with Computers categories.
The purpose of this project is to develop a comprehensive Hate Speech Detection and Sentiment Analysis system using both Machine Learning and Deep Learning techniques. The project aims to create a robust and accurate system that can automatically identify hate speech in text data and perform sentiment analysis to determine the emotions and opinions expressed in the text. The project is designed to address the growing concern over the spread of hate speech and offensive content online. By implementing an automated detection system, it can help social media platforms, content moderators, and online communities to proactively identify and remove harmful content, fostering a safer and more inclusive online environment. Additionally, sentiment analysis plays a crucial role in understanding public opinions, customer feedback, and social media trends. By accurately predicting sentiment, businesses can make data-driven decisions, improve customer satisfaction, and gain valuable insights into consumer preferences. This project focuses on Hate Speech Detection and Sentiment Analysis using both Machine Learning and Deep Learning techniques. It begins with exploring the dataset, analyzing feature distributions, and predicting sentiment using Machine Learning models like Logistic Regression, Support Vector Machines, K-Nearest Neighbors, Decision Trees, Random Forests, Gradient Boosting, Extreme Gradient Boosting, Light Gradient Boosting, and AdaBoost, while optimizing their performance through Grid Search for hyperparameter tuning. Subsequently, Deep Learning LSTM and 1D CNN models are implemented for sentiment analysis to capture long-term dependencies and local patterns in the text data. The project starts with exploring the dataset, understanding its structure, and analyzing the distribution of classes for hate speech and sentiment labels. This initial step allows us to gain insights into the dataset and potential challenges. After exploring the data, the distribution of text features, such as word frequency and sentiment scores, is analyzed to identify any patterns or biases that could impact the model's performance. The dataset is then divided into training, validation, and testing sets to evaluate the models' generalization capabilities. Early stopping techniques are utilized during training to prevent overfitting and enhance model generalization. Performance evaluation involves calculating metrics like accuracy, precision, recall, and F1-score to gauge the models' effectiveness. Confusion matrices and visualizations provide further insights into model predictions and potential areas for improvement. A graphical user interface (GUI) is developed using PyQt to facilitate user interaction with the Hate Speech Detection and Sentiment Analysis system. Before training the Deep Learning models, the text data is tokenized and padded for uniform input sequences. The dataset is split into training and validation sets for model evaluation, and early stopping is used to prevent overfitting during training. The final system combines predictions from both Machine Learning and Deep Learning models to provide robust sentiment analysis results. The PyQt GUI allows users to input text and receive real-time sentiment analysis predictions. The LSTM and 1D CNN models, along with their optimized hyperparameters, are saved and deployed for future sentiment analysis tasks. Users can interact with the GUI, analyze sentiment in different texts, and provide feedback for continuous improvement of the Hate Speech Detection and Sentiment Analysis system.
Six Books In One Classification Prediction And Sentiment Analysis Using Machine Learning And Deep Learning With Python Gui
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2022-04-11
Six Books In One Classification Prediction And Sentiment Analysis Using Machine Learning And Deep Learning With Python Gui written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-11 with Computers categories.
Book 1: BANK LOAN STATUS CLASSIFICATION AND PREDICTION USING MACHINE LEARNING WITH PYTHON GUI The dataset used in this project consists of more than 100,000 customers mentioning their loan status, current loan amount, monthly debt, etc. There are 19 features in the dataset. The dataset attributes are as follows: Loan ID, Customer ID, Loan Status, Current Loan Amount, Term, Credit Score, Annual Income, Years in current job, Home Ownership, Purpose, Monthly Debt, Years of Credit History, Months since last delinquent, Number of Open Accounts, Number of Credit Problems, Current Credit Balance, Maximum Open Credit, Bankruptcies, and Tax Liens. The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, and XGB classifier. Three feature scaling used in machine learning are raw, minmax scaler, and standard scaler. Finally, you will develop a GUI using PyQt5 to plot cross validation score, predicted values versus true values, confusion matrix, learning curve, decision boundaries, performance of the model, scalability of the model, training loss, and training accuracy. Book 2: OPINION MINING AND PREDICTION USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON GUI Opinion mining (sometimes known as sentiment analysis or emotion AI) refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. This dataset was created for the Paper 'From Group to Individual Labels using Deep Features', Kotzias et. al,. KDD 2015. It contains sentences labelled with a positive or negative sentiment. Score is either 1 (for positive) or 0 (for negative). The sentences come from three different websites/fields: imdb.com, amazon.com, and yelp.com. For each website, there exist 500 positive and 500 negative sentences. Those were selected randomly for larger datasets of reviews. Amazon: contains reviews and scores for products sold on amazon.com in the cell phones and accessories category, and is part of the dataset collected by McAuley and Leskovec. Scores are on an integer scale from 1 to 5. Reviews considered with a score of 4 and 5 to be positive, and scores of 1 and 2 to be negative. The data is randomly partitioned into two halves of 50%, one for training and one for testing, with 35,000 documents in each set. IMDb: refers to the IMDb movie review sentiment dataset originally introduced by Maas et al. as a benchmark for sentiment analysis. This dataset contains a total of 100,000 movie reviews posted on imdb.com. There are 50,000 unlabeled reviews and the remaining 50,000 are divided into a set of 25,000 reviews for training and 25,000 reviews for testing. Each of the labeled reviews has a binary sentiment label, either positive or negative. Yelp: refers to the dataset from the Yelp dataset challenge from which we extracted the restaurant reviews. Scores are on an integer scale from 1 to 5. Reviews considered with scores 4 and 5 to be positive, and 1 and 2 to be negative. The data is randomly generated a 50-50 training and testing split, which led to approximately 300,000 documents for each set. Sentences: for each of the datasets above, labels are extracted and manually 1000 sentences are manually labeled from the test set, with 50% positive sentiment and 50% negative sentiment. These sentences are only used to evaluate our instance-level classifier for each dataset3. They are not used for model training, to maintain consistency with our overall goal of learning at a group level and predicting at the instance level. The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, and XGB classifier. Three feature scaling used in machine learning are raw, minmax scaler, and standard scaler. Finally, you will develop a GUI using PyQt5 to plot cross validation score, predicted values versus true values, confusion matrix, learning curve, decision boundaries, performance of the model, scalability of the model, training loss, and training accuracy. Book 3: EMOTION PREDICTION FROM TEXT USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON GUI In the dataset used in this project, there are two columns, Text and Emotion. Quite self-explanatory. The Emotion column has various categories ranging from happiness to sadness to love and fear. You will build and implement machine learning and deep learning models which can identify what words denote what emotion. The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, and XGB classifier. Three feature scaling used in machine learning are raw, minmax scaler, and standard scaler. Finally, you will develop a GUI using PyQt5 to plot cross validation score, predicted values versus true values, confusion matrix, learning curve, decision boundaries, performance of the model, scalability of the model, training loss, and training accuracy. Book 4: HATE SPEECH DETECTION AND SENTIMENT ANALYSIS USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON GUI The objective of this task is to detect hate speech in tweets. For the sake of simplicity, a tweet contains hate speech if it has a racist or sexist sentiment associated with it. So, the task is to classify racist or sexist tweets from other tweets. Formally, given a training sample of tweets and labels, where label '1' denotes the tweet is racist/sexist and label '0' denotes the tweet is not racist/sexist, the objective is to predict the labels on the test dataset. The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, XGB classifier, LSTM, and CNN. Three feature scaling used in machine learning are raw, minmax scaler, and standard scaler. Finally, you will develop a GUI using PyQt5 to plot cross validation score, predicted values versus true values, confusion matrix, learning curve, decision boundaries, performance of the model, scalability of the model, training loss, and training accuracy. Book 5: TRAVEL REVIEW RATING CLASSIFICATION AND PREDICTION USING MACHINE LEARNING WITH PYTHON GUI The dataset used in this project has been sourced from the Machine Learning Repository of University of California, Irvine (UC Irvine): Travel Review Ratings Data Set. This dataset is populated by capturing user ratings from Google reviews. Reviews on attractions from 24 categories across Europe are considered. Google user rating ranges from 1 to 5 and average user rating per category is calculated. The attributes in the dataset are as follows: Attribute 1 : Unique user id; Attribute 2 : Average ratings on churches; Attribute 3 : Average ratings on resorts; Attribute 4 : Average ratings on beaches; Attribute 5 : Average ratings on parks; Attribute 6 : Average ratings on theatres; Attribute 7 : Average ratings on museums; Attribute 8 : Average ratings on malls; Attribute 9 : Average ratings on zoo; Attribute 10 : Average ratings on restaurants; Attribute 11 : Average ratings on pubs/bars; Attribute 12 : Average ratings on local services; Attribute 13 : Average ratings on burger/pizza shops; Attribute 14 : Average ratings on hotels/other lodgings; Attribute 15 : Average ratings on juice bars; Attribute 16 : Average ratings on art galleries; Attribute 17 : Average ratings on dance clubs; Attribute 18 : Average ratings on swimming pools; Attribute 19 : Average ratings on gyms; Attribute 20 : Average ratings on bakeries; Attribute 21 : Average ratings on beauty & spas; Attribute 22 : Average ratings on cafes; Attribute 23 : Average ratings on view points; Attribute 24 : Average ratings on monuments; and Attribute 25 : Average ratings on gardens. The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, XGB classifier, and MLP classifier. Three feature scaling used in machine learning are raw, minmax scaler, and standard scaler. Finally, you will develop a GUI using PyQt5 to plot cross validation score, predicted values versus true values, confusion matrix, learning curve, decision boundaries, performance of the model, scalability of the model, training loss, and training accuracy. Book 6: ONLINE RETAIL CLUSTERING AND PREDICTION USING MACHINE LEARNING WITH PYTHON GUI The dataset used in this project is a transnational dataset which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail. The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers. You will be using the online retail transnational dataset to build a RFM clustering and choose the best set of customers which the company should target. In this project, you will perform Cohort analysis and RFM analysis. You will also perform clustering using K-Means to get 5 clusters. The machine learning models used in this project to predict clusters as target variable are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, LGBM, Gradient Boosting, XGB, and MLP. Finally, you will plot boundary decision, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy.
Python Machine Learning By Example
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Author : Yuxi (Hayden) Liu
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-02-28
Python Machine Learning By Example written by Yuxi (Hayden) Liu and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-28 with Mathematics categories.
Grasp machine learning concepts, techniques, and algorithms with the help of real-world examples using Python libraries such as TensorFlow and scikit-learn Key FeaturesExploit the power of Python to explore the world of data mining and data analyticsDiscover machine learning algorithms to solve complex challenges faced by data scientists todayUse Python libraries such as TensorFlow and Keras to create smart cognitive actions for your projectsBook Description The surge in interest in machine learning (ML) is due to the fact that it revolutionizes automation by learning patterns in data and using them to make predictions and decisions. If you’re interested in ML, this book will serve as your entry point to ML. Python Machine Learning By Example begins with an introduction to important ML concepts and implementations using Python libraries. Each chapter of the book walks you through an industry adopted application. You’ll implement ML techniques in areas such as exploratory data analysis, feature engineering, and natural language processing (NLP) in a clear and easy-to-follow way. With the help of this extended and updated edition, you’ll understand how to tackle data-driven problems and implement your solutions with the powerful yet simple Python language and popular Python packages and tools such as TensorFlow, scikit-learn, gensim, and Keras. To aid your understanding of popular ML algorithms, the book covers interesting and easy-to-follow examples such as news topic modeling and classification, spam email detection, stock price forecasting, and more. By the end of the book, you’ll have put together a broad picture of the ML ecosystem and will be well-versed with the best practices of applying ML techniques to make the most out of new opportunities. What you will learnUnderstand the important concepts in machine learning and data scienceUse Python to explore the world of data mining and analyticsScale up model training using varied data complexities with Apache SparkDelve deep into text and NLP using Python libraries such NLTK and gensimSelect and build an ML model and evaluate and optimize its performanceImplement ML algorithms from scratch in Python, TensorFlow, and scikit-learnWho this book is for If you’re a machine learning aspirant, data analyst, or data engineer highly passionate about machine learning and want to begin working on ML assignments, this book is for you. Prior knowledge of Python coding is assumed and basic familiarity with statistical concepts will be beneficial although not necessary.
Extracting Knowledge From Opinion Mining
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Author : Agrawal, Rashmi
language : en
Publisher: IGI Global
Release Date : 2018-09-07
Extracting Knowledge From Opinion Mining written by Agrawal, Rashmi and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-07 with Computers categories.
Data mining techniques are commonly used to extract meaningful information from the web, such as data from web documents, website usage logs, and hyperlinks. Building on this, modern organizations are focusing on running and improving their business methods and returns by using opinion mining. Extracting Knowledge From Opinion Mining is an essential resource that presents detailed information on web mining, business intelligence through opinion mining, and how to effectively use knowledge retrieved through mining operations. While highlighting relevant topics, including the differences between ontology-based opinion mining and feature-based opinion mining, this book is an ideal reference source for information technology professionals within research or business settings, graduate and post-graduate students, as well as scholars.
Covid 19 Analysis Classification And Detection Using Scikit Learn Keras And Tensorflow With Python Gui
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-08-11
Covid 19 Analysis Classification And Detection Using Scikit Learn Keras And Tensorflow With Python Gui written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-11 with Computers categories.
In this comprehensive project, "COVID-19: Analysis, Classification, and Detection Using Scikit-Learn, Keras, and TensorFlow with Python GUI," the primary objective is to leverage various machine learning and deep learning techniques to analyze and classify COVID-19 cases based on numerical data and medical image data. The project begins by exploring the dataset, gaining insights into its structure and content. This initial data exploration aids in understanding the distribution of categorized features, providing valuable context for subsequent analysis. With insights gained from data exploration, the project delves into predictive modeling using machine learning. It employs Scikit-Learn to build and fine-tune predictive models, harnessing grid search for hyperparameter optimization. This meticulous process ensures that the machine learning models, such as Naïve Bayes, K-Nearest Neighbors, Decision Trees, Random Forests, Gradient Boosting, Extreme Gradient Boosting, Multi-Layer Perceptron, AdaBoost, and Logistic Regression, are optimized to accurately predict the risk of COVID-19 based on the input features. Transitioning to the realm of deep learning, the project employs Convolutional Neural Networks (CNNs) to perform intricate image classification tasks. Leveraging Keras and TensorFlow, the CNN architecture is meticulously crafted, comprising convolutional and pooling layers, dropout regularization, and dense layers. The project also extends its deep learning capabilities by utilizing the VGG16 pre-trained model, harnessing its powerful feature extraction capabilities for COVID-19 image classification. To gauge the effectiveness of the trained models, an array of performance metrics is utilized. In this project, a range of metrics are used to evaluate the performance of machine learning and deep learning models employed for COVID-19 classification. These metrics include Accuracy, which measures the overall correctness of predictions; Precision, emphasizing the accuracy of positive predictions; Recall (Sensitivity), assessing the model's ability to identify positive instances; and F1-Score, a balanced measure of accuracy. The Mean Squared Error (MSE) quantifies the magnitude of errors in regression tasks, while the Confusion Matrix summarizes classification results by showing counts of true positives, true negatives, false positives, and false negatives. These metrics together provide a comprehensive understanding of model performance. They help gauge the model's accuracy, the balance between precision and recall, and its proficiency in classifying both positive and negative instances. In the medical context of COVID-19 classification, these metrics play a vital role in evaluating the models' reliability and effectiveness in real-world applications. The project further enriches its analytical capabilities by developing an interactive Python GUI. This graphical user interface streamlines the user experience, facilitating data input, model training, and prediction. Users are empowered to input medical images for classification, leveraging the trained machine learning and deep learning models to assess COVID-19 risk. The culmination of the project lies in the accurate prediction of COVID-19 risk through a combined approach of machine learning and deep learning techniques. The Python GUI using PyQt5 provides a user-friendly platform for clinicians and researchers to interact with the models, fostering informed decision-making based on reliable and data-driven predictions. In conclusion, this project represents a comprehensive endeavor to harness the power of machine learning and deep learning for the vital task of COVID-19 classification. Through rigorous data exploration, model training, and performance evaluation, the project yields a robust framework for risk prediction, contributing to the broader efforts to combat the ongoing pandemic.
Tkinter Data Science And Machine Learning
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-09-02
Tkinter Data Science And Machine Learning written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-02 with Computers categories.
In this project, we embarked on a comprehensive journey through the world of machine learning and model evaluation. Our primary goal was to develop a Tkinter GUI and assess various machine learning models on a given dataset to identify the best-performing one. This process is essential in solving real-world problems, as it helps us select the most suitable algorithm for a specific task. By crafting this Tkinter-powered GUI, we provided an accessible and user-friendly interface for users engaging with machine learning models. It simplified intricate processes, allowing users to load data, select models, initiate training, and visualize results without necessitating code expertise or command-line operations. This GUI introduced a higher degree of usability and accessibility to the machine learning workflow, accommodating users with diverse levels of technical proficiency. We began by loading and preprocessing the dataset, a fundamental step in any machine learning project. Proper data preprocessing involves tasks such as handling missing values, encoding categorical features, and scaling numerical attributes. These operations ensure that the data is in a format suitable for training and testing machine learning models. Once our data was ready, we moved on to the model selection phase. We evaluated multiple machine learning algorithms, each with its strengths and weaknesses. The models we explored included Logistic Regression, Random Forest, K-Nearest Neighbors (KNN), Decision Trees, Gradient Boosting, Extreme Gradient Boosting (XGBoost), Multi-Layer Perceptron (MLP), and Support Vector Classifier (SVC). For each model, we employed a systematic approach to find the best hyperparameters using grid search with cross-validation. This technique allowed us to explore different combinations of hyperparameters and select the configuration that yielded the highest accuracy on the training data. These hyperparameters included settings like the number of estimators, learning rate, and kernel function, depending on the specific model. After obtaining the best hyperparameters for each model, we trained them on our preprocessed dataset. This training process involved using the training data to teach the model to make predictions on new, unseen examples. Once trained, the models were ready for evaluation. We assessed the performance of each model using a set of well-established evaluation metrics. These metrics included accuracy, precision, recall, and F1-score. Accuracy measured the overall correctness of predictions, while precision quantified the proportion of true positive predictions out of all positive predictions. Recall, on the other hand, represented the proportion of true positive predictions out of all actual positives, highlighting a model's ability to identify positive cases. The F1-score combined precision and recall into a single metric, helping us gauge the overall balance between these two aspects. To visualize the model's performance, we created key graphical representations. These included confusion matrices, which showed the number of true positive, true negative, false positive, and false negative predictions, aiding in understanding the model's classification results. Additionally, we generated Receiver Operating Characteristic (ROC) curves and area under the curve (AUC) scores, which depicted a model's ability to distinguish between classes. High AUC values indicated excellent model performance. Furthermore, we constructed true values versus predicted values diagrams to provide insights into how well our models aligned with the actual data distribution. Learning curves were also generated to observe a model's performance as a function of training data size, helping us assess whether the model was overfitting or underfitting. Lastly, we presented the results in a clear and organized manner, saving them to Excel files for easy reference. This allowed us to compare the performance of different models and make an informed choice about which one to select for our specific task. In summary, this project was a comprehensive exploration of the machine learning model development and evaluation process. We prepared the data, selected and fine-tuned various models, assessed their performance using multiple metrics and visualizations, and ultimately arrived at a well-informed decision about the most suitable model for our dataset. This approach serves as a valuable blueprint for tackling real-world machine learning challenges effectively.
Learning Data Mining With Python
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Author : Robert Layton
language : en
Publisher: Packt Publishing Ltd
Release Date : 2015-07-29
Learning Data Mining With Python written by Robert Layton and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-07-29 with Computers categories.
The next step in the information age is to gain insights from the deluge of data coming our way. Data mining provides a way of finding this insight, and Python is one of the most popular languages for data mining, providing both power and flexibility in analysis. This book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. Next, we move on to more complex data types including text, images, and graphs. In every chapter, we create models that solve real-world problems. There is a rich and varied set of libraries available in Python for data mining. This book covers a large number, including the IPython Notebook, pandas, scikit-learn and NLTK. Each chapter of this book introduces you to new algorithms and techniques. By the end of the book, you will gain a large insight into using Python for data mining, with a good knowledge and understanding of the algorithms and implementations.
Stock Price Analysis Prediction And Forecasting Using Machine Learning And Deep Learning With Python
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2022-05-27
Stock Price Analysis Prediction And Forecasting Using Machine Learning And Deep Learning With Python written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-27 with Computers categories.
This dataset is a playground for fundamental and technical analysis. It is said that 30% of traffic on stocks is already generated by machines, can trading be fully automated? If not, there is still a lot to learn from historical data. The dataset consists of data spans from 2010 to the end 2016, for companies new on stock market date range is shorter. To perform forecasting based on regression adjusted closing price of gold, you will use: Linear Regression, Random Forest regression, Decision Tree regression, Support Vector Machine regression, Naïve Bayes regression, K-Nearest Neighbor regression, Adaboost regression, Gradient Boosting regression, Extreme Gradient Boosting regression, Light Gradient Boosting regression, Catboost regression, MLP regression, and LSTM (Long-Short Term Memory) regression. The machine learning models used predict gold daily returns as target variable are K-Nearest Neighbor classifier, Random Forest classifier, Naive Bayes classifier, Logistic Regression classifier, Decision Tree classifier, Support Vector Machine classifier, LGBM classifier, Gradient Boosting classifier, XGB classifier, MLP classifier, Gaussian Mixture Model classifier, and Extra Trees classifier. Finally, you will plot boundary decision, distribution of features, feature importance, predicted values versus true values, confusion matrix, learning curve, performance of the model, and scalability of the model.
Detecting Cyberbullying Tweets Using Machine Learning And Deep Learning With Python Gui
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-08-05
Detecting Cyberbullying Tweets Using Machine Learning And Deep Learning With Python Gui written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-05 with Computers categories.
This project focuses on detecting cyberbullying tweets using both Machine Learning and Deep Learning techniques with a Python GUI implemented using PyQt. The first step involves data exploration, where the dataset is loaded and analyzed to gain insights into its structure and contents. Visualizations are created to understand the distribution of cyberbullying types and other features in the data. After data exploration, preprocessing is performed to clean and prepare the tweets for analysis. Text cleaning techniques, such as removing emojis, punctuation, links, and stop words, are applied to the tweet text. The data is then categorized based on the length of the tweets to facilitate further analysis. Next, the data is split into input and output variables, where the tweet text becomes the input feature (X) and the cyberbullying type becomes the output (y). The cyberbullying types are converted into numerical labels for ML models' compatibility. Machine Learning models are trained and evaluated using TF-IDF, Count Vectorizer, and Hashing Vectorizer as feature extraction techniques. SMOTE is applied to handle class imbalance, and the data is split into training and testing sets. Grid Search is utilized to find the best hyperparameters for ML models, optimizing their performance. Machine Learning models used are Logistic Regression, Support Vector Machines, K-Nearest Neighbors, Decision Trees, Random Forests, Gradient Boosting, Extreme Gradient Boosting, Light Gradient Boosting. Moving to Deep Learning, LSTM (Long Short-Term Memory) and 1D CNN (Convolutional Neural Network) models are constructed to detect cyberbullying types. The tweet text is embedded, and various layers are added to the models to extract meaningful features. The models are then compiled with appropriate loss functions and optimizers. The evaluation process is carried out using the test set for both Machine Learning and Deep Learning models. Metrics like accuracy, precision, recall, and F1-score are used to assess the models' performance in detecting cyberbullying types. To enable easy access to the functionalities, a Graphical User Interface (GUI) is developed using PyQt. The GUI allows users to interact with the models and dataset easily. Users can select the feature extraction technique, choose the classifier, and initiate the model training process using the GUI's buttons and dropdown menus. For better data visualization, the GUI includes various plots, such as bar plots and pie charts, to show the distribution of cyberbullying types and other features. These visualizations help users understand the data and model performance intuitively. The final stage involves testing the GUI with different inputs and exploring model predictions on user-provided text. The GUI provides predictions for the given tweet text, indicating the likelihood of each cyberbullying type based on the trained models. Overall, this project combines data exploration, preprocessing, feature extraction using Machine Learning and Deep Learning models, GUI development, and data visualization to detect cyberbullying tweets effectively and provide an accessible interface for users to interact with the models. The end product allows users to analyze tweets for potential cyberbullying and contributes to promoting a safer and more respectful online environment.