Six Books In One Classification Prediction And Sentiment Analysis Using Machine Learning And Deep Learning With Python Gui

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SIX BOOKS IN ONE: Classification, Prediction, and Sentiment Analysis Using Machine Learning and Deep Learning with Python GUI

Author : Vivian Siahaan
Publisher : BALIGE PUBLISHING
ISBN 13 :
Total Pages : 1165 pages
Book Rating : 4./5 ( download)

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Book Synopsis SIX BOOKS IN ONE: Classification, Prediction, and Sentiment Analysis Using Machine Learning and Deep Learning with Python GUI by : Vivian Siahaan

Download or read book SIX BOOKS IN ONE: Classification, Prediction, and Sentiment Analysis Using Machine Learning and Deep Learning with Python GUI written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2022-04-11 with total page 1165 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

THREE PROJECTS: Sentiment Analysis and Prediction Using Machine Learning and Deep Learning with Python GUI

Author : Vivian Siahaan
Publisher : BALIGE PUBLISHING
ISBN 13 :
Total Pages : 620 pages
Book Rating : 4./5 ( download)

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Book Synopsis THREE PROJECTS: Sentiment Analysis and Prediction Using Machine Learning and Deep Learning with Python GUI by : Vivian Siahaan

Download or read book THREE PROJECTS: Sentiment Analysis and Prediction Using Machine Learning and Deep Learning with Python GUI written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2022-03-21 with total page 620 pages. Available in PDF, EPUB and Kindle. Book excerpt: PROJECT 1: TEXT PROCESSING AND SENTIMENT ANALYSIS USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON GUI Twitter data used in this project was scraped from February of 2015 and contributors were asked to first classify positive, negative, and neutral tweets, followed by categorizing negative reasons (such as "late flight" or "rude service"). This data was originally posted by Crowdflower last February and includes tweets about 6 major US airlines. Additionally, Crowdflower had their workers extract the sentiment from the tweet as well as what the passenger was dissapointed about if the tweet was negative. The information of main attributes for this project are as follows: airline_sentiment : Sentiment classification.(positivie, neutral, and negative); negativereason : Reason selected for the negative opinion; airline : Name of 6 US Airlines('Delta', 'United', 'Southwest', 'US Airways', 'Virgin America', 'American'); and text : Customer's opinion. 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, and LSTM. Three vectorizers used in machine learning are Hashing Vectorizer, Count Vectorizer, and TFID Vectorizer. Finally, you will develop a GUI using PyQt5 to plot cross validation score, predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 2: HOTEL REVIEW: SENTIMENT ANALYSIS USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON GUI The data used in this project is the data published by Anurag Sharma about hotel reviews that were given by costumers. The data is given in two files, a train and test. The train.csv is the training data, containing unique User_ID for each entry with the review entered by a costumer and the browser and device used. The target variable is Is_Response, a variable that states whether the costumers was happy or not happy while staying in the hotel. This type of variable makes the project to a classification problem. The test.csv is the testing data, contains similar headings as the train data, without the target variable. 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, and LSTM. Three vectorizers used in machine learning are Hashing Vectorizer, Count Vectorizer, and TFID Vectorizer. Finally, you will develop a GUI using PyQt5 to plot cross validation score, predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 3: STUDENT ACADEMIC PERFORMANCE ANALYSIS AND PREDICTION USING MACHINE LEARNING WITH PYTHON GUI The dataset used in this project consists of student achievement in secondary education of two Portuguese schools. The data attributes include student grades, demographic, social and school-related features) and it was collected by using school reports and questionnaires. Two datasets are provided regarding the performance in two distinct subjects: Mathematics (mat) and Portuguese language (por). In the two datasets were modeled under binary/five-level classification and regression tasks. Important note: the target attribute G3 has a strong correlation with attributes G2 and G1. This occurs because G3 is the final year grade (issued at the 3rd period), while G1 and G2 correspond to the 1st and 2nd period grades. It is more difficult to predict G3 without G2 and G1, but such prediction is much more useful. Attributes in the dataset are as follows: school - student's school (binary: 'GP' - Gabriel Pereira or 'MS' - Mousinho da Silveira); sex - student's sex (binary: 'F' - female or 'M' - male); age - student's age (numeric: from 15 to 22); address - student's home address type (binary: 'U' - urban or 'R' - rural); famsize - family size (binary: 'LE3' - less or equal to 3 or 'GT3' - greater than 3); Pstatus - parent's cohabitation status (binary: 'T' - living together or 'A' - apart); Medu - mother's education (numeric: 0 - none, 1 - primary education (4th grade), 2 - 5th to 9th grade, 3 - secondary education or 4 - higher education); Fedu - father's education (numeric: 0 - none, 1 - primary education (4th grade), 2 - 5th to 9th grade, 3 - secondary education or 4 - higher education); Mjob - mother's job (nominal: 'teacher', 'health' care related, civil 'services' (e.g. administrative or police), 'at_home' or 'other'); Fjob - father's job (nominal: 'teacher', 'health' care related, civil 'services' (e.g. administrative or police), 'at_home' or 'other'); reason - reason to choose this school (nominal: close to 'home', school 'reputation', 'course' preference or 'other'); guardian - student's guardian (nominal: 'mother', 'father' or 'other'); traveltime - home to school travel time (numeric: 1 - <15 min., 2 - 15 to 30 min., 3 - 30 min. to 1 hour, or 4 - >1 hour); studytime - weekly study time (numeric: 1 - <2 hours, 2 - 2 to 5 hours, 3 - 5 to 10 hours, or 4 - >10 hours); failures - number of past class failures (numeric: n if 1<=n<3, else 4); schoolsup - extra educational support (binary: yes or no); famsup - family educational support (binary: yes or no); paid - extra paid classes within the course subject (Math or Portuguese) (binary: yes or no); activities - extra-curricular activities (binary: yes or no); nursery - attended nursery school (binary: yes or no); higher - wants to take higher education (binary: yes or no); internet - Internet access at home (binary: yes or no); romantic - with a romantic relationship (binary: yes or no); famrel - quality of family relationships (numeric: from 1 - very bad to 5 - excellent); freetime - free time after school (numeric: from 1 - very low to 5 - very high); goout - going out with friends (numeric: from 1 - very low to 5 - very high); Dalc - workday alcohol consumption (numeric: from 1 - very low to 5 - very high); Walc - weekend alcohol consumption (numeric: from 1 - very low to 5 - very high); health - current health status (numeric: from 1 - very bad to 5 - very good); absences - number of school absences (numeric: from 0 to 93); G1 - first period grade (numeric: from 0 to 20); G2 - second period grade (numeric: from 0 to 20); and G3 - final grade (numeric: from 0 to 20, output target). 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.

PYTHON GUI PROJECTS WITH MACHINE LEARNING AND DEEP LEARNING

Author : Vivian Siahaan
Publisher : BALIGE PUBLISHING
ISBN 13 :
Total Pages : 917 pages
Book Rating : 4./5 ( download)

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Book Synopsis PYTHON GUI PROJECTS WITH MACHINE LEARNING AND DEEP LEARNING by : Vivian Siahaan

Download or read book PYTHON GUI PROJECTS WITH MACHINE LEARNING AND DEEP LEARNING written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2022-01-16 with total page 917 pages. Available in PDF, EPUB and Kindle. Book excerpt: PROJECT 1: THE APPLIED DATA SCIENCE WORKSHOP: Prostate Cancer Classification and Recognition Using Machine Learning and Deep Learning with Python GUI Prostate cancer is cancer that occurs in the prostate. The prostate is a small walnut-shaped gland in males that produces the seminal fluid that nourishes and transports sperm. Prostate cancer is one of the most common types of cancer. Many prostate cancers grow slowly and are confined to the prostate gland, where they may not cause serious harm. However, while some types of prostate cancer grow slowly and may need minimal or even no treatment, other types are aggressive and can spread quickly. The dataset used in this project consists of 100 patients which can be used to implement the machine learning and deep learning algorithms. The dataset consists of 100 observations and 10 variables (out of which 8 numeric variables and one categorical variable and is ID) which are as follows: Id, Radius, Texture, Perimeter, Area, Smoothness, Compactness, Diagnosis Result, Symmetry, and Fractal Dimension. 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, MLP classifier, and CNN 1D. Finally, you will develop a GUI using PyQt5 to plot boundary decision, ROC, 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. PROJECT 2: THE APPLIED DATA SCIENCE WORKSHOP: Urinary Biomarkers Based Pancreatic Cancer Classification and Prediction Using Machine Learning with Python GUI Pancreatic cancer is an extremely deadly type of cancer. Once diagnosed, the five-year survival rate is less than 10%. However, if pancreatic cancer is caught early, the odds of surviving are much better. Unfortunately, many cases of pancreatic cancer show no symptoms until the cancer has spread throughout the body. A diagnostic test to identify people with pancreatic cancer could be enormously helpful. In a paper by Silvana Debernardi and colleagues, published this year in the journal PLOS Medicine, a multi-national team of researchers sought to develop an accurate diagnostic test for the most common type of pancreatic cancer, called pancreatic ductal adenocarcinoma or PDAC. They gathered a series of biomarkers from the urine of three groups of patients: Healthy controls, Patients with non-cancerous pancreatic conditions, like chronic pancreatitis, and Patients with pancreatic ductal adenocarcinoma. When possible, these patients were age- and sex-matched. The goal was to develop an accurate way to identify patients with pancreatic cancer. The key features are four urinary biomarkers: creatinine, LYVE1, REG1B, and TFF1. Creatinine is a protein that is often used as an indicator of kidney function. YVLE1 is lymphatic vessel endothelial hyaluronan receptor 1, a protein that may play a role in tumor metastasis. REG1B is a protein that may be associated with pancreas regeneration. TFF1 is trefoil factor 1, which may be related to regeneration and repair of the urinary tract. 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. Finally, you will develop a GUI using PyQt5 to plot boundary decision, ROC, 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. PROJECT 3: DATA SCIENCE CRASH COURSE: Voice Based Gender Classification and Prediction Using Machine Learning and Deep Learning with Python GUI This dataset was created to identify a voice as male or female, based upon acoustic properties of the voice and speech. The dataset consists of 3,168 recorded voice samples, collected from male and female speakers. The voice samples are pre-processed by acoustic analysis in R using the seewave and tuneR packages, with an analyzed frequency range of 0hz-280hz (human vocal range). The following acoustic properties of each voice are measured and included within the CSV: meanfreq: mean frequency (in kHz); sd: standard deviation of frequency; median: median frequency (in kHz); Q25: first quantile (in kHz); Q75: third quantile (in kHz); IQR: interquantile range (in kHz); skew: skewness; kurt: kurtosis; sp.ent: spectral entropy; sfm: spectral flatness; mode: mode frequency; centroid: frequency centroid (see specprop); peakf: peak frequency (frequency with highest energy); meanfun: average of fundamental frequency measured across acoustic signal; minfun: minimum fundamental frequency measured across acoustic signal; maxfun: maximum fundamental frequency measured across acoustic signal; meandom: average of dominant frequency measured across acoustic signal; mindom: minimum of dominant frequency measured across acoustic signal; maxdom: maximum of dominant frequency measured across acoustic signal; dfrange: range of dominant frequency measured across acoustic signal; modindx: modulation index. Calculated as the accumulated absolute difference between adjacent measurements of fundamental frequencies divided by the frequency range; and label: male or female. 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, MLP classifier, and CNN 1D. Finally, you will develop a GUI using PyQt5 to plot boundary decision, ROC, 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. PROJECT 4: DATA SCIENCE CRASH COURSE: Thyroid Disease Classification and Prediction Using Machine Learning and Deep Learning with Python GUI Thyroid disease is a general term for a medical condition that keeps your thyroid from making the right amount of hormones. Thyroid typically makes hormones that keep body functioning normally. When the thyroid makes too much thyroid hormone, body uses energy too quickly. The two main types of thyroid disease are hypothyroidism and hyperthyroidism. Both conditions can be caused by other diseases that impact the way the thyroid gland works. Dataset used in this project was from Garavan Institute Documentation as given by Ross Quinlan 6 databases from the Garavan Institute in Sydney, Australia. Approximately the following for each database: 2800 training (data) instances and 972 test instances. This dataset contains plenty of missing data, while 29 or so attributes, either Boolean or continuously-valued. 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, MLP classifier, and CNN 1D. Finally, you will develop a GUI using PyQt5 to plot boundary decision, ROC, 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.

TEXT PROCESSING AND SENTIMENT ANALYSIS USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON GUI

Author : Vivian Siahaan
Publisher : BALIGE PUBLISHING
ISBN 13 :
Total Pages : 334 pages
Book Rating : 4./5 ( download)

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Book Synopsis TEXT PROCESSING AND SENTIMENT ANALYSIS USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON GUI by : Vivian Siahaan

Download or read book TEXT PROCESSING AND SENTIMENT ANALYSIS USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON GUI written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2023-06-26 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, we explored a code implementation for sentiment analysis using machine learning models, including XGBoost, LightGBM, and LSTM. The code aimed to build, train, and evaluate these models on Twitter data to classify sentiments. Throughout the project, we gained insights into the key steps involved and observed the findings and functionalities of the code. Sentiment analysis is a vital task in natural language processing, and the code was to give a comprehensive approach to tackle it. The implementation began by checking if pre-trained models for XGBoost and LightGBM existed. If available, the models were loaded; otherwise, new models were built and trained. This approach allowed for reusability of trained models, saving time and effort in subsequent runs. Similarly, the code checked if preprocessed data for LSTM existed. If not, it performed tokenization and padding on the text data, splitting it into train, test, and validation sets. The preprocessed data was saved for future use. The code also provided a function to build and train the LSTM model. It defined the model architecture using the Keras Sequential API, incorporating layers like embedding, convolutional, max pooling, bidirectional LSTM, dropout, and dense output. The model was compiled with appropriate loss and optimization functions. Training was carried out, with early stopping implemented to prevent overfitting. After training, the model summary was printed, and both the model and training history were saved for future reference. The train_lstm function ensured that the LSTM model was ready for prediction by checking the existence of preprocessed data and trained models. If necessary, it performed the required preprocessing and model building steps. The pred_lstm() function was responsible for loading the LSTM model and generating predictions for the test data. The function returned the predicted sentiment labels, allowing for further analysis and evaluation. To facilitate user interaction, the code included a functionality to choose the LSTM model for prediction. The choose_prediction_lstm() function was triggered when the user selected the LSTM option from a dropdown menu. It called the pred_lstm() function, performed evaluation tasks, and visualized the results. Confusion matrices and true vs. predicted value plots were generated to assess the model's performance. Additionally, the loss and accuracy history from training were plotted, providing insights into the model's learning process. In conclusion, this project provided a comprehensive overview of sentiment analysis using machine learning models. The code implementation showcased the steps involved in building, training, and evaluating models like XGBoost, LightGBM, and LSTM. It emphasized the importance of data preprocessing, model building, and evaluation in sentiment analysis tasks. The code also demonstrated functionalities for reusing pre-trained models and saving preprocessed data, enhancing efficiency and ease of use. Through visualization techniques, such as confusion matrices and accuracy/loss curves, the code enabled a better understanding of the model's performance and learning dynamics. Overall, this project highlighted the practical aspects of sentiment analysis and illustrated how different machine learning models can be employed to tackle this task effectively.

The Applied Data Science Workshop On Medical Datasets Using Machine Learning and Deep Learning with Python GUI

Author : Vivian Siahaan
Publisher : BALIGE PUBLISHING
ISBN 13 :
Total Pages : 1574 pages
Book Rating : 4./5 ( download)

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Book Synopsis The Applied Data Science Workshop On Medical Datasets Using Machine Learning and Deep Learning with Python GUI by : Vivian Siahaan

Download or read book The Applied Data Science Workshop On Medical Datasets Using Machine Learning and Deep Learning with Python GUI written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on with total page 1574 pages. Available in PDF, EPUB and Kindle. Book excerpt: Workshop 1: Heart Failure Analysis and Prediction Using Scikit-Learn, Keras, and TensorFlow with Python GUI Cardiovascular diseases (CVDs) are the number 1 cause of death globally taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worldwide. Heart failure is a common event caused by CVDs and this dataset contains 12 features that can be used to predict mortality by heart failure. People with cardiovascular disease or who are at high cardiovascular risk (due to the presence of one or more risk factors such as hypertension, diabetes, hyperlipidaemia or already established disease) need early detection and management wherein a machine learning models can be of great help. Dataset used in this project is from Davide Chicco, Giuseppe Jurman. Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC Medical Informatics and Decision Making 20, 16 (2020). Attribute information in the dataset are as follows: age: Age; anaemia: Decrease of red blood cells or hemoglobin (boolean); creatinine_phosphokinase: Level of the CPK enzyme in the blood (mcg/L); diabetes: If the patient has diabetes (boolean); ejection_fraction: Percentage of blood leaving the heart at each contraction (percentage); high_blood_pressure: If the patient has hypertension (boolean); platelets: Platelets in the blood (kiloplatelets/mL); serum_creatinine: Level of serum creatinine in the blood (mg/dL); serum_sodium: Level of serum sodium in the blood (mEq/L); sex: Woman or man (binary); smoking: If the patient smokes or not (boolean); time: Follow-up period (days); and DEATH_EVENT: If the patient deceased during the follow-up period (boolean). 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, MLP classifier, and CNN 1D. Finally, you will develop a GUI using PyQt5 to plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performace of the model, scalability of the model, training loss, and training accuracy. WORKSHOP 2: Cervical Cancer Classification and Prediction Using Machine Learning and Deep Learning with Python GUI About 11,000 new cases of invasive cervical cancer are diagnosed each year in the U.S. However, the number of new cervical cancer cases has been declining steadily over the past decades. Although it is the most preventable type of cancer, each year cervical cancer kills about 4,000 women in the U.S. and about 300,000 women worldwide. Numerous studies report that high poverty levels are linked with low screening rates. In addition, lack of health insurance, limited transportation, and language difficulties hinder a poor woman’s access to screening services. Human papilloma virus (HPV) is the main risk factor for cervical cancer. In adults, the most important risk factor for HPV is sexual activity with an infected person. Women most at risk for cervical cancer are those with a history of multiple sexual partners, sexual intercourse at age 17 years or younger, or both. A woman who has never been sexually active has a very low risk for developing cervical cancer. Sexual activity with multiple partners increases the likelihood of many other sexually transmitted infections (chlamydia, gonorrhea, syphilis). Studies have found an association between chlamydia and cervical cancer risk, including the possibility that chlamydia may prolong HPV infection. Therefore, early detection of cervical cancer using machine and deep learning models can be of great help. The dataset used in this project is obtained from UCI Repository and kindly acknowledged. This file contains a List of Risk Factors for Cervical Cancer leading to a Biopsy Examination. 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, MLP classifier, and CNN 1D. Finally, you will develop a GUI using PyQt5 to plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performace of the model, scalability of the model, training loss, and training accuracy. WORKSHOP 3: Chronic Kidney Disease Classification and Prediction Using Machine Learning and Deep Learning with Python GUI Chronic kidney disease is the longstanding disease of the kidneys leading to renal failure. The kidneys filter waste and excess fluid from the blood. As kidneys fail, waste builds up. Symptoms develop slowly and aren't specific to the disease. Some people have no symptoms at all and are diagnosed by a lab test. Medication helps manage symptoms. In later stages, filtering the blood with a machine (dialysis) or a transplant may be required The dataset used in this project was taken over a 2-month period in India with 25 features (eg, red blood cell count, white blood cell count, etc). The target is the 'classification', which is either 'ckd' or 'notckd' - ckd=chronic kidney disease. It contains measures of 24 features for 400 people. Quite a lot of features for just 400 samples. There are 14 categorical features, while 10 are numerical. The dataset needs cleaning: in that it has NaNs and the numeric features need to be forced to floats. Attribute Information: Age(numerical) age in years; Blood Pressure(numerical) bp in mm/Hg; Specific Gravity(categorical) sg - (1.005,1.010,1.015,1.020,1.025); Albumin(categorical) al - (0,1,2,3,4,5); Sugar(categorical) su - (0,1,2,3,4,5); Red Blood Cells(categorical) rbc - (normal,abnormal); Pus Cell (categorical) pc - (normal,abnormal); Pus Cell clumps(categorical) pcc - (present, notpresent); Bacteria(categorical) ba - (present,notpresent); Blood Glucose Random(numerical) bgr in mgs/dl; Blood Urea(numerical) bu in mgs/dl; Serum Creatinine(numerical) sc in mgs/dl; Sodium(numerical) sod in mEq/L; Potassium(numerical) pot in mEq/L; Hemoglobin(numerical) hemo in gms; Packed Cell Volume(numerical); White Blood Cell Count(numerical) wc in cells/cumm; Red Blood Cell Count(numerical) rc in millions/cmm; Hypertension(categorical) htn - (yes,no); Diabetes Mellitus(categorical) dm - (yes,no); Coronary Artery Disease(categorical) cad - (yes,no); Appetite(categorical) appet - (good,poor); Pedal Edema(categorical) pe - (yes,no); Anemia(categorical) ane - (yes,no); and Class (categorical) class - (ckd,notckd). 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, MLP classifier, and CNN 1D. Finally, you will develop a GUI using PyQt5 to plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performace of the model, scalability of the model, training loss, and training accuracy. WORKSHOP 4: Lung Cancer Classification and Prediction Using Machine Learning and Deep Learning with Python GUI The effectiveness of cancer prediction system helps the people to know their cancer risk with low cost and it also helps the people to take the appropriate decision based on their cancer risk status. The data is collected from the website online lung cancer prediction system. Total number of attributes in the dataset is 16, while number of instances is 309. Following are attribute information of dataset: Gender: M(male), F(female); Age: Age of the patient; Smoking: YES=2 , NO=1; Yellow fingers: YES=2 , NO=1; Anxiety: YES=2 , NO=1; Peer_pressure: YES=2 , NO=1; Chronic Disease: YES=2 , NO=1; Fatigue: YES=2 , NO=1; Allergy: YES=2 , NO=1; Wheezing: YES=2 , NO=1; Alcohol: YES=2 , NO=1; Coughing: YES=2 , NO=1; Shortness of Breath: YES=2 , NO=1; Swallowing Difficulty: YES=2 , NO=1; Chest pain: YES=2 , NO=1; and Lung Cancer: YES , NO. 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, MLP classifier, and CNN 1D. Finally, you will develop a GUI using PyQt5 to plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performace of the model, scalability of the model, training loss, and training accuracy. WORKSHOP 5: Alzheimer’s Disease Classification and Prediction Using Machine Learning and Deep Learning with Python GUI Alzheimer's is a type of dementia that causes problems with memory, thinking and behavior. Symptoms usually develop slowly and get worse over time, becoming severe enough to interfere with daily tasks. Alzheimer's is not a normal part of aging. The greatest known risk factor is increasing age, and the majority of people with Alzheimer's are 65 and older. But Alzheimer's is not just a disease of old age. Approximately 200,000 Americans under the age of 65 have younger-onset Alzheimer’s disease (also known as early-onset Alzheimer’s). The dataset consists of a longitudinal MRI data of 374 subjects aged 60 to 96. Each subject was scanned at least once. Everyone is right-handed. 206 of the subjects were grouped as 'Nondemented' throughout the study. 107 of the subjects were grouped as 'Demented' at the time of their initial visits and remained so throughout the study. 14 subjects were grouped as 'Nondemented' at the time of their initial visit and were subsequently characterized as 'Demented' at a later visit. These fall under the 'Converted' category. Following are some important features in the dataset: EDUC:Years of Education; SES: Socioeconomic Status; MMSE: Mini Mental State Examination; CDR: Clinical Dementia Rating; eTIV: Estimated Total Intracranial Volume; nWBV: Normalize Whole Brain Volume; and ASF: Atlas Scaling Factor. 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, MLP classifier, and CNN 1D. Finally, you will develop a GUI using PyQt5 to plot boundary decision, ROC, 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. WORKSHOP 6: Parkinson Classification and Prediction Using Machine Learning and Deep Learning with Python GUI The dataset was created by Max Little of the University of Oxford, in collaboration with the National Centre for Voice and Speech, Denver, Colorado, who recorded the speech signals. The original study published the feature extraction methods for general voice disorders. This dataset is composed of a range of biomedical voice measurements from 31 people, 23 with Parkinson's disease (PD). Each column in the table is a particular voice measure, and each row corresponds one of 195 voice recording from these individuals ("name" column). The main aim of the data is to discriminate healthy people from those with PD, according to "status" column which is set to 0 for healthy and 1 for PD. The data is in ASCII CSV format. The rows of the CSV file contain an instance corresponding to one voice recording. There are around six recordings per patient, the name of the patient is identified in the first column. Attribute information of this dataset are as follows: name - ASCII subject name and recording number; MDVP:Fo(Hz) - Average vocal fundamental frequency; MDVP:Fhi(Hz) - Maximum vocal fundamental frequency; MDVP:Flo(Hz) - Minimum vocal fundamental frequency; MDVP:Jitter(%); MDVP:Jitter(Abs); MDVP:RAP; MDVP:PPQ; Jitter:DDP – Several measures of variation in fundamental frequency; MDVP:Shimmer; MDVP:Shimmer(dB); Shimmer:APQ3; Shimmer:APQ5; MDVP:APQ; Shimmer:DDA - Several measures of variation in amplitude; NHR; HNR - Two measures of ratio of noise to tonal components in the voice; status - Health status of the subject (one) - Parkinson's, (zero) – healthy; RPDE,D2 - Two nonlinear dynamical complexity measures; DFA - Signal fractal scaling exponent; and spread1,spread2,PPE - Three nonlinear measures of fundamental frequency variation. 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, MLP classifier, and CNN 1D. Finally, you will develop a GUI using PyQt5 to plot boundary decision, ROC, 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. WORKSHOP 7: Liver Disease Classification and Prediction Using Machine Learning and Deep Learning with Python GUI Patients with Liver disease have been continuously increasing because of excessive consumption of alcohol, inhale of harmful gases, intake of contaminated food, pickles and drugs. This dataset was used to evaluate prediction algorithms in an effort to reduce burden on doctors. This dataset contains 416 liver patient records and 167 non liver patient records collected from North East of Andhra Pradesh, India. The "Dataset" column is a class label used to divide groups into liver patient (liver disease) or not (no disease). This data set contains 441 male patient records and 142 female patient records. Any patient whose age exceeded 89 is listed as being of age "90". Columns in the dataset: Age of the patient; Gender of the patient; Total Bilirubin; Direct Bilirubin; Alkaline Phosphotase; Alamine Aminotransferase; Aspartate Aminotransferase; Total Protiens; Albumin; Albumin and Globulin Ratio; and Dataset: field used to split the data into two sets (patient with liver disease, or no disease). 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, MLP classifier, and CNN 1D. Finally, you will develop a GUI using PyQt5 to plot boundary decision, ROC, 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.

OPINION MINING AND PREDICTION USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON GUI

Author : Vivian Siahaan
Publisher : BALIGE PUBLISHING
ISBN 13 :
Total Pages : 277 pages
Book Rating : 4./5 ( download)

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Book Synopsis OPINION MINING AND PREDICTION USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON GUI by : Vivian Siahaan

Download or read book OPINION MINING AND PREDICTION USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON GUI written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2023-06-27 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

DATA SCIENCE WORKSHOP: Parkinson Classification and Prediction Using Machine Learning and Deep Learning with Python GUI

Author : Vivian Siahaan
Publisher : BALIGE PUBLISHING
ISBN 13 :
Total Pages : 373 pages
Book Rating : 4./5 ( download)

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Book Synopsis DATA SCIENCE WORKSHOP: Parkinson Classification and Prediction Using Machine Learning and Deep Learning with Python GUI by : Vivian Siahaan

Download or read book DATA SCIENCE WORKSHOP: Parkinson Classification and Prediction Using Machine Learning and Deep Learning with Python GUI written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2023-07-26 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this data science workshop focused on Parkinson's disease classification and prediction, we begin by exploring the dataset containing features relevant to the disease. We perform data exploration to understand the structure of the dataset, check for missing values, and gain insights into the distribution of features. Visualizations are used to analyze the distribution of features and their relationship with the target variable, which is whether an individual has Parkinson's disease or not. After data exploration, we preprocess the dataset to prepare it for machine learning models. This involves handling missing values, scaling numerical features, and encoding categorical variables if necessary. We ensure that the dataset is split into training and testing sets to evaluate model performance effectively. With the preprocessed dataset, we move on to the classification task. Using various machine learning algorithms such as Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forests, Gradient Boosting, Naive Bayes, Adaboost, Extreme Gradient Boosting, Light Gradient Boosting, and Multi-Layer Perceptron (MLP), we train multiple models on the training data. To optimize the hyperparameters of these models, we utilize Grid Search, a technique to exhaustively search for the best combination of hyperparameters. For each machine learning model, we evaluate their performance on the test set using various metrics such as accuracy, precision, recall, and F1-score. These metrics help us understand the model's ability to correctly classify individuals with and without Parkinson's disease. Next, we delve into building an Artificial Neural Network (ANN) for Parkinson's disease prediction. The ANN architecture is designed with input, hidden, and output layers. We utilize the TensorFlow library to construct the neural network with appropriate activation functions, dropout layers, and optimizers. The ANN is trained on the preprocessed data for a fixed number of epochs, and we monitor its training and validation loss and accuracy to ensure proper training. After training the ANN, we evaluate its performance using the same metrics as the machine learning models, comparing its accuracy, precision, recall, and F1-score against the previous models. This comparison helps us understand the benefits and limitations of using deep learning for Parkinson's disease prediction. To provide a user-friendly interface for the classification and prediction process, we design a Python GUI using PyQt. The GUI allows users to load their own dataset, choose data preprocessing options, select machine learning classifiers, train models, and predict using the ANN. The GUI provides visualizations of the data distribution, model performance, and prediction results for better understanding and decision-making. In the GUI, users have the option to choose different data preprocessing techniques, such as raw data, normalization, and standardization, to observe how these techniques impact model performance. The choice of classifiers is also available, allowing users to compare different models and select the one that suits their needs best. Throughout the workshop, we emphasize the importance of proper evaluation metrics and the significance of choosing the right model for Parkinson's disease classification and prediction. We highlight the strengths and weaknesses of each model, enabling users to make informed decisions based on their specific requirements and data characteristics. Overall, this data science workshop provides participants with a comprehensive understanding of Parkinson's disease classification and prediction using machine learning and deep learning techniques. Participants gain hands-on experience in data preprocessing, model training, hyperparameter tuning, and designing a user-friendly GUI for efficient and effective data analysis and prediction.

In-Depth Tutorials: Deep Learning Using Scikit-Learn, Keras, and TensorFlow with Python GUI

Author : Vivian Siahaan
Publisher : BALIGE PUBLISHING
ISBN 13 :
Total Pages : 1459 pages
Book Rating : 4./5 ( download)

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Book Synopsis In-Depth Tutorials: Deep Learning Using Scikit-Learn, Keras, and TensorFlow with Python GUI by : Vivian Siahaan

Download or read book In-Depth Tutorials: Deep Learning Using Scikit-Learn, Keras, and TensorFlow with Python GUI written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2021-06-05 with total page 1459 pages. Available in PDF, EPUB and Kindle. Book excerpt: BOOK 1: LEARN FROM SCRATCH MACHINE LEARNING WITH PYTHON GUI In this book, you will learn how to use NumPy, Pandas, OpenCV, Scikit-Learn and other libraries to how to plot graph and to process digital image. Then, you will learn how to classify features using Perceptron, Adaline, Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN) models. You will also learn how to extract features using Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Kernel Principal Component Analysis (KPCA) algorithms and use them in machine learning. In Chapter 1, you will learn: Tutorial Steps To Create A Simple GUI Application, Tutorial Steps to Use Radio Button, Tutorial Steps to Group Radio Buttons, Tutorial Steps to Use CheckBox Widget, Tutorial Steps to Use Two CheckBox Groups, Tutorial Steps to Understand Signals and Slots, Tutorial Steps to Convert Data Types, Tutorial Steps to Use Spin Box Widget, Tutorial Steps to Use ScrollBar and Slider, Tutorial Steps to Use List Widget, Tutorial Steps to Select Multiple List Items in One List Widget and Display It in Another List Widget, Tutorial Steps to Insert Item into List Widget, Tutorial Steps to Use Operations on Widget List, Tutorial Steps to Use Combo Box, Tutorial Steps to Use Calendar Widget and Date Edit, and Tutorial Steps to Use Table Widget. In Chapter 2, you will learn: Tutorial Steps To Create A Simple Line Graph, Tutorial Steps To Create A Simple Line Graph in Python GUI, Tutorial Steps To Create A Simple Line Graph in Python GUI: Part 2, Tutorial Steps To Create Two or More Graphs in the Same Axis, Tutorial Steps To Create Two Axes in One Canvas, Tutorial Steps To Use Two Widgets, Tutorial Steps To Use Two Widgets, Each of Which Has Two Axes, Tutorial Steps To Use Axes With Certain Opacity Levels, Tutorial Steps To Choose Line Color From Combo Box, Tutorial Steps To Calculate Fast Fourier Transform, Tutorial Steps To Create GUI For FFT, Tutorial Steps To Create GUI For FFT With Some Other Input Signals, Tutorial Steps To Create GUI For Noisy Signal, Tutorial Steps To Create GUI For Noisy Signal Filtering, and Tutorial Steps To Create GUI For Wav Signal Filtering. In Chapter 3, you will learn: Tutorial Steps To Convert RGB Image Into Grayscale, Tutorial Steps To Convert RGB Image Into YUV Image, Tutorial Steps To Convert RGB Image Into HSV Image, Tutorial Steps To Filter Image, Tutorial Steps To Display Image Histogram, Tutorial Steps To Display Filtered Image Histogram, Tutorial Steps To Filter Image With CheckBoxes, Tutorial Steps To Implement Image Thresholding, and Tutorial Steps To Implement Adaptive Image Thresholding. You will also learn: Tutorial Steps To Generate And Display Noisy Image, Tutorial Steps To Implement Edge Detection On Image, Tutorial Steps To Implement Image Segmentation Using Multiple Thresholding and K-Means Algorithm, Tutorial Steps To Implement Image Denoising, Tutorial Steps To Detect Face, Eye, and Mouth Using Haar Cascades, Tutorial Steps To Detect Face Using Haar Cascades with PyQt, Tutorial Steps To Detect Eye, and Mouth Using Haar Cascades with PyQt, Tutorial Steps To Extract Detected Objects, Tutorial Steps To Detect Image Features Using Harris Corner Detection, Tutorial Steps To Detect Image Features Using Shi-Tomasi Corner Detection, Tutorial Steps To Detect Features Using Scale-Invariant Feature Transform (SIFT), and Tutorial Steps To Detect Features Using Features from Accelerated Segment Test (FAST). In Chapter 4, In this tutorial, you will learn how to use Pandas, NumPy and other libraries to perform simple classification using perceptron and Adaline (adaptive linear neuron). The dataset used is Iris dataset directly from the UCI Machine Learning Repository. You will learn: Tutorial Steps To Implement Perceptron, Tutorial Steps To Implement Perceptron with PyQt, Tutorial Steps To Implement Adaline (ADAptive LInear NEuron), and Tutorial Steps To Implement Adaline with PyQt. In Chapter 5, you will learn how to use the scikit-learn machine learning library, which provides a wide variety of machine learning algorithms via a user-friendly Python API and to perform classification using perceptron, Adaline (adaptive linear neuron), and other models. The dataset used is Iris dataset directly from the UCI Machine Learning Repository. You will learn: Tutorial Steps To Implement Perceptron Using Scikit-Learn, Tutorial Steps To Implement Perceptron Using Scikit-Learn with PyQt, Tutorial Steps To Implement Logistic Regression Model, Tutorial Steps To Implement Logistic Regression Model with PyQt, Tutorial Steps To Implement Logistic Regression Model Using Scikit-Learn with PyQt, Tutorial Steps To Implement Support Vector Machine (SVM) Using Scikit-Learn, Tutorial Steps To Implement Decision Tree (DT) Using Scikit-Learn, Tutorial Steps To Implement Random Forest (RF) Using Scikit-Learn, and Tutorial Steps To Implement K-Nearest Neighbor (KNN) Using Scikit-Learn. In Chapter 6, you will learn how to use Pandas, NumPy, Scikit-Learn, and other libraries to implement different approaches for reducing the dimensionality of a dataset using different feature selection techniques. You will learn about three fundamental techniques that will help us to summarize the information content of a dataset by transforming it onto a new feature subspace of lower dimensionality than the original one. Data compression is an important topic in machine learning, and it helps us to store and analyze the increasing amounts of data that are produced and collected in the modern age of technology. You will learn the following topics: Principal Component Analysis (PCA) for unsupervised data compression, Linear Discriminant Analysis (LDA) as a supervised dimensionality reduction technique for maximizing class separability, Nonlinear dimensionality reduction via Kernel Principal Component Analysis (KPCA). You will learn: Tutorial Steps To Implement Principal Component Analysis (PCA), Tutorial Steps To Implement Principal Component Analysis (PCA) Using Scikit-Learn, Tutorial Steps To Implement Principal Component Analysis (PCA) Using Scikit-Learn with PyQt, Tutorial Steps To Implement Linear Discriminant Analysis (LDA), Tutorial Steps To Implement Linear Discriminant Analysis (LDA) with Scikit-Learn, Tutorial Steps To Implement Linear Discriminant Analysis (LDA) Using Scikit-Learn with PyQt, Tutorial Steps To Implement Kernel Principal Component Analysis (KPCA) Using Scikit-Learn, and Tutorial Steps To Implement Kernel Principal Component Analysis (KPCA) Using Scikit-Learn with PyQt. In Chapter 7, you will learn how to use Keras, Scikit-Learn, Pandas, NumPy and other libraries to perform prediction on handwritten digits using MNIST dataset. You will learn: Tutorial Steps To Load MNIST Dataset, Tutorial Steps To Load MNIST Dataset with PyQt, Tutorial Steps To Implement Perceptron With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Perceptron With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Perceptron With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Logistic Regression (LR) Model With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Logistic Regression (LR) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Logistic Regression (LR) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement , Tutorial Steps To Implement Support Vector Machine (SVM) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Support Vector Machine (SVM) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Decision Tree (DT) Model With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Decision Tree (DT) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Decision Tree (DT) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Random Forest (RF) Model With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Random Forest (RF) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Random Forest (RF) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement K-Nearest Neighbor (KNN) Model With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement K-Nearest Neighbor (KNN) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, and Tutorial Steps To Implement K-Nearest Neighbor (KNN) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt. BOOK 2: THE PRACTICAL GUIDES ON DEEP LEARNING USING SCIKIT-LEARN, KERAS, AND TENSORFLOW WITH PYTHON GUI In this book, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to implement deep learning on recognizing traffic signs using GTSRB dataset, detecting brain tumor using Brain Image MRI dataset, classifying gender, and recognizing facial expression using FER2013 dataset In Chapter 1, you will learn to create GUI applications to display line graph using PyQt. You will also learn how to display image and its histogram. In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, Pandas, NumPy and other libraries to perform prediction on handwritten digits using MNIST dataset with PyQt. You will build a GUI application for this purpose. In Chapter 3, you will learn how to perform recognizing traffic signs using GTSRB dataset from Kaggle. There are several different types of traffic signs like speed limits, no entry, traffic signals, turn left or right, children crossing, no passing of heavy vehicles, etc. Traffic signs classification is the process of identifying which class a traffic sign belongs to. In this Python project, you will build a deep neural network model that can classify traffic signs in image into different categories. With this model, you will be able to read and understand traffic signs which are a very important task for all autonomous vehicles. You will build a GUI application for this purpose. In Chapter 4, you will learn how to perform detecting brain tumor using Brain Image MRI dataset provided by Kaggle (https://www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection) using CNN model. You will build a GUI application for this purpose. In Chapter 5, you will learn how to perform classifying gender using dataset provided by Kaggle (https://www.kaggle.com/cashutosh/gender-classification-dataset) using MobileNetV2 and CNN models. You will build a GUI application for this purpose. In Chapter 6, you will learn how to perform recognizing facial expression using FER2013 dataset provided by Kaggle (https://www.kaggle.com/nicolejyt/facialexpressionrecognition) using CNN model. You will also build a GUI application for this purpose. BOOK 3: STEP BY STEP TUTORIALS ON DEEP LEARNING USING SCIKIT-LEARN, KERAS, AND TENSORFLOW WITH PYTHON GUI In this book, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to implement deep learning on classifying fruits, classifying cats/dogs, detecting furnitures, and classifying fashion. In Chapter 1, you will learn to create GUI applications to display line graph using PyQt. You will also learn how to display image and its histogram. Then, you will learn how to use OpenCV, NumPy, and other libraries to perform feature extraction with Python GUI (PyQt). The feature detection techniques used in this chapter are Harris Corner Detection, Shi-Tomasi Corner Detector, and Scale-Invariant Feature Transform (SIFT). In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform classifying fruits using Fruits 360 dataset provided by Kaggle (https://www.kaggle.com/moltean/fruits/code) using Transfer Learning and CNN models. You will build a GUI application for this purpose. In Chapter 3, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform classifying cats/dogs using dataset provided by Kaggle (https://www.kaggle.com/chetankv/dogs-cats-images) using Using CNN with Data Generator. You will build a GUI application for this purpose. In Chapter 4, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform detecting furnitures using Furniture Detector dataset provided by Kaggle (https://www.kaggle.com/akkithetechie/furniture-detector) using VGG16 model. You will build a GUI application for this purpose. In Chapter 5, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform classifying fashion using Fashion MNIST dataset provided by Kaggle (https://www.kaggle.com/zalando-research/fashionmnist/code) using CNN model. You will build a GUI application for this purpose. BOOK 4: Project-Based Approach On DEEP LEARNING Using Scikit-Learn, Keras, And TensorFlow with Python GUI In this book, implement deep learning on detecting vehicle license plates, recognizing sign language, and detecting surface crack using TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries. In Chapter 1, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform detecting vehicle license plates using Car License Plate Detection dataset provided by Kaggle (https://www.kaggle.com/andrewmvd/car-plate-detection/download). In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform sign language recognition using Sign Language Digits Dataset provided by Kaggle (https://www.kaggle.com/ardamavi/sign-language-digits-dataset/download). In Chapter 3, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform detecting surface crack using Surface Crack Detection provided by Kaggle (https://www.kaggle.com/arunrk7/surface-crack-detection/download). BOOK 5: Hands-On Guide To IMAGE CLASSIFICATION Using Scikit-Learn, Keras, And TensorFlow with PYTHON GUI In this book, implement deep learning-based image classification on detecting face mask, classifying weather, and recognizing flower using TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries. In Chapter 1, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform detecting face mask using Face Mask Detection Dataset provided by Kaggle (https://www.kaggle.com/omkargurav/face-mask-dataset/download). In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform how to classify weather using Multi-class Weather Dataset provided by Kaggle (https://www.kaggle.com/pratik2901/multiclass-weather-dataset/download). In Chapter 3, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform how to recognize flower using Flowers Recognition dataset provided by Kaggle (https://www.kaggle.com/alxmamaev/flowers-recognition/download). BOOK 6: Step by Step Tutorial IMAGE CLASSIFICATION Using Scikit-Learn, Keras, And TensorFlow with PYTHON GUI In this book, implement deep learning-based image classification on classifying monkey species, recognizing rock, paper, and scissor, and classify airplane, car, and ship using TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries. In Chapter 1, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform how to classify monkey species using 10 Monkey Species dataset provided by Kaggle (https://www.kaggle.com/slothkong/10-monkey-species/download). In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform how to recognize rock, paper, and scissor using 10 Monkey Species dataset provided by Kaggle (https://www.kaggle.com/sanikamal/rock-paper-scissors-dataset/download). In Chapter 3, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform how to classify airplane, car, and ship using Multiclass-image-dataset-airplane-car-ship dataset provided by Kaggle (https://www.kaggle.com/abtabm/multiclassimagedatasetairplanecar).

The Practical Guides on Deep Learning Using SCIKIT-LEARN, KERAS, and TENSORFLOW with Python GUI

Author : Vivian Siahaan
Publisher : BALIGE PUBLISHING
ISBN 13 :
Total Pages : 386 pages
Book Rating : 4./5 ( download)

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Book Synopsis The Practical Guides on Deep Learning Using SCIKIT-LEARN, KERAS, and TENSORFLOW with Python GUI by : Vivian Siahaan

Download or read book The Practical Guides on Deep Learning Using SCIKIT-LEARN, KERAS, and TENSORFLOW with Python GUI written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2023-06-17 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to implement deep learning on recognizing traffic signs using GTSRB dataset, detecting brain tumor using Brain Image MRI dataset, classifying gender, and recognizing facial expression using FER2013 dataset In Chapter 1, you will learn to create GUI applications to display image histogram. It is a graphical representation that displays the distribution of pixel intensities in an image. It provides information about the frequency of occurrence of each intensity level in the image. The histogram allows us to understand the overall brightness or contrast of the image and can reveal important characteristics such as dynamic range, exposure, and the presence of certain image features. In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, Pandas, NumPy and other libraries to perform prediction on handwritten digits using MNIST dataset. The MNIST dataset is a widely used dataset in machine learning and computer vision, particularly for image classification tasks. It consists of a collection of handwritten digits from zero to nine, where each digit is represented as a 28x28 grayscale image. The dataset was created by collecting handwriting samples from various individuals and then preprocessing them to standardize the format. Each image in the dataset represents a single digit and is labeled with the corresponding digit it represents. The labels range from 0 to 9, indicating the true value of the handwritten digit. In Chapter 3, you will learn how to perform recognizing traffic signs using GTSRB dataset from Kaggle. There are several different types of traffic signs like speed limits, no entry, traffic signals, turn left or right, children crossing, no passing of heavy vehicles, etc. Traffic signs classification is the process of identifying which class a traffic sign belongs to. In this Python project, you will build a deep neural network model that can classify traffic signs in image into different categories. With this model, you will be able to read and understand traffic signs which are a very important task for all autonomous vehicles. You will build a GUI application for this purpose. In Chapter 4, you will learn how to perform detecting brain tumor using Brain Image MRI dataset. Following are the steps taken in this chapter: Dataset Exploration: Explore the Brain Image MRI dataset from Kaggle. Describe the structure of the dataset, the different classes (tumor vs. non-tumor), and any preprocessing steps required; Data Preprocessing: Preprocess the dataset to prepare it for model training. This may include tasks such as resizing images, normalizing pixel values, splitting data into training and testing sets, and creating labels; Model Building: Use TensorFlow and Keras to build a deep learning model for brain tumor detection. Choose an appropriate architecture, such as a convolutional neural network (CNN), and configure the model layers; Model Training: Train the brain tumor detection model using the preprocessed dataset. Specify the loss function, optimizer, and evaluation metrics. Monitor the training process and visualize the training/validation accuracy and loss over epochs; Model Evaluation: Evaluate the trained model on the testing dataset. Calculate metrics such as accuracy, precision, recall, and F1 score to assess the model's performance; Prediction and Visualization: Use the trained model to make predictions on new MRI images. Visualize the predicted results alongside the ground truth labels to demonstrate the effectiveness of the model. Finally, you will build a GUI application for this purpose. In Chapter 5, you will learn how to perform classifying gender using dataset provided by Kaggle using MobileNetV2 and CNN models. Following are the steps taken in this chapter: Data Exploration: Load the dataset using Pandas, perform exploratory data analysis (EDA) to gain insights into the data, and visualize the distribution of gender classes; Data Preprocessing: Preprocess the dataset by performing necessary transformations, such as resizing images, converting labels to numerical format, and splitting the data into training, validation, and test sets; Model Building: Use TensorFlow and Keras to build a gender classification model. Define the architecture of the model, compile it with appropriate loss and optimization functions, and summarize the model's structure; Model Training: Train the model on the training set, monitor its performance on the validation set, and tune hyperparameters if necessary. Visualize the training history to analyze the model's learning progress; Model Evaluation: Evaluate the trained model's performance on the test set using various metrics such as accuracy, precision, recall, and F1 score. Generate a classification report and a confusion matrix to assess the model's performance in detail; Prediction and Visualization: Use the trained model to make gender predictions on new, unseen data. Visualize a few sample predictions along with the corresponding images. Finally, you will build a GUI application for this purpose. In Chapter 6, you will learn how to perform recognizing facial expression using FER2013 dataset using CNN model. The FER2013 dataset contains facial images categorized into seven different emotions: anger, disgust, fear, happiness, sadness, surprise, and neutral. To perform facial expression recognition using this dataset, you would typically follow these steps; Data Preprocessing: Load and preprocess the dataset. This may involve resizing the images, converting them to grayscale, and normalizing the pixel values; Data Split: Split the dataset into training, validation, and testing sets. The training set is used to train the model, the validation set is used to tune hyperparameters and evaluate the model's performance during training, and the testing set is used to assess the final model's accuracy; Model Building: Build a deep learning model using TensorFlow and Keras. This typically involves defining the architecture of the model, selecting appropriate layers (such as convolutional layers, pooling layers, and fully connected layers), and specifying the activation functions and loss functions; Model Training: Train the model using the training set. This involves feeding the training images through the model, calculating the loss, and updating the model's parameters using optimization techniques like backpropagation and gradient descent; Model Evaluation: Evaluate the trained model's performance using the validation set. This can include calculating metrics such as accuracy, precision, recall, and F1 score to assess how well the model is performing; Model Testing: Assess the model's accuracy and performance on the testing set, which contains unseen data. This step helps determine how well the model generalizes to new, unseen facial expressions; Prediction: Use the trained model to make predictions on new images or live video streams. This involves detecting faces in the images using OpenCV, extracting facial features, and feeding the processed images into the model for prediction. Then, you will also build a GUI application for this purpose.

Step by Step Tutorials On Deep Learning Using Scikit-Learn, Keras, and Tensorflow with Python GUI

Author : Vivian Siahaan
Publisher : BALIGE PUBLISHING
ISBN 13 :
Total Pages : 324 pages
Book Rating : 4./5 ( download)

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Book Synopsis Step by Step Tutorials On Deep Learning Using Scikit-Learn, Keras, and Tensorflow with Python GUI by : Vivian Siahaan

Download or read book Step by Step Tutorials On Deep Learning Using Scikit-Learn, Keras, and Tensorflow with Python GUI written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2023-06-18 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to implement deep learning on classifying fruits, classifying cats/dogs, detecting furnitures, and classifying fashion. In Chapter 1, you will learn to create GUI applications to display line graph using PyQt. You will also learn how to display image and its histogram. In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform classifying fruits using Fruits 360 dataset provided by Kaggle (https://www.kaggle.com/moltean/fruits/code) using Transfer Learning and CNN models. You will build a GUI application for this purpose. Here's the outline of the steps, focusing on transfer learning: 1. Dataset Preparation: Download the Fruits 360 dataset from Kaggle. Extract the dataset files and organize them into appropriate folders for training and testing. Install the necessary libraries like TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, and NumPy; Data Preprocessing: Use OpenCV to read and load the fruit images from the dataset. Resize the images to a consistent size to feed them into the neural network. Convert the images to numerical arrays using NumPy. Normalize the image pixel values to a range between 0 and 1. Split the dataset into training and testing sets using Scikit-Learn. 3. Building the Model with Transfer Learning: Import the required modules from TensorFlow and Keras. Load a pre-trained model (e.g., VGG16, ResNet50, InceptionV3) without the top (fully connected) layers. Freeze the weights of the pre-trained layers to prevent them from being updated during training. Add your own fully connected layers on top of the pre-trained layers. Compile the model by specifying the loss function, optimizer, and evaluation metrics; 4. Model Training: Use the prepared training data to train the model. Specify the number of epochs and batch size for training. Monitor the training process for accuracy and loss using callbacks; 5. Model Evaluation: Evaluate the trained model on the test dataset using Scikit-Learn. Calculate accuracy, precision, recall, and F1-score for the classification results; 6. Predictions: Load and preprocess new fruit images for prediction using the same steps as in data preprocessing. Use the trained model to predict the class labels of the new images. In Chapter 3, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform classifying cats/dogs using dataset provided by Kaggle (https://www.kaggle.com/chetankv/dogs-cats-images) using Using CNN with Data Generator. You will build a GUI application for this purpose. The following steps are taken: Set up your development environment: Install the necessary libraries such as TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy, and any other dependencies required for the tutorial; Load and preprocess the dataset: Use libraries like OpenCV and NumPy to load and preprocess the dataset. Split the dataset into training and testing sets; Design and train the classification model: Use TensorFlow and Keras to design a convolutional neural network (CNN) model for image classification. Define the architecture of the model, compile it with an appropriate loss function and optimizer, and train it using the training dataset; Evaluate the model: Evaluate the trained model using the testing dataset. Calculate metrics such as accuracy, precision, recall, and F1 score to assess the model's performance; Make predictions: Use the trained model to make predictions on new unseen images. Preprocess the images, feed them into the model, and obtain the predicted class labels; Visualize the results: Use libraries like Matplotlib or OpenCV to visualize the results, such as displaying sample images with their predicted labels, plotting the training/validation loss and accuracy curves, and creating a confusion matrix. In Chapter 4, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform detecting furnitures using Furniture Detector dataset provided by Kaggle (https://www.kaggle.com/akkithetechie/furniture-detector) using VGG16 model. You will build a GUI application for this purpose. Here are the steps you can follow to perform furniture detection: Dataset Preparation: Extract the dataset files and organize them into appropriate directories for training and testing; Data Preprocessing: Load the dataset using Pandas to analyze and preprocess the data. Explore the dataset to understand its structure, features, and labels. Perform any necessary preprocessing steps like resizing images, normalizing pixel values, and splitting the data into training and testing sets; Feature Extraction and Representation: Use OpenCV or any image processing libraries to extract meaningful features from the images. This might include techniques like edge detection, color-based features, or texture analysis. Convert the images and extracted features into a suitable representation for machine learning models. This can be achieved using NumPy arrays or other formats compatible with the chosen libraries; Model Training: Define a deep learning model using TensorFlow and Keras for furniture detection. You can choose pre-trained models like VGG16, ResNet, or custom architectures. Compile the model with an appropriate loss function, optimizer, and evaluation metrics. Train the model on the preprocessed dataset using the training set. Adjust hyperparameters like batch size, learning rate, and number of epochs to improve performance; Model Evaluation: Evaluate the trained model using the testing set. Calculate metrics such as accuracy, precision, recall, and F1 score to assess the model's performance. Analyze the results and identify areas for improvement; Model Deployment and Inference: Once satisfied with the model's performance, save it to disk for future use. Deploy the model to make predictions on new, unseen images. Use the trained model to perform furniture detection on images by applying it to the test set or new data. In Chapter 5, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform classifying fashion using Fashion MNIST dataset provided by Kaggle (https://www.kaggle.com/zalando-research/fashionmnist/code) using CNN model. You will build a GUI application for this purpose. Here are the general steps to implement image classification using the Fashion MNIST dataset: Import the necessary libraries: Import the required libraries such as TensorFlow, Keras, NumPy, Pandas, and Matplotlib for handling the dataset, building the model, and visualizing the results; Load and preprocess the dataset: Load the Fashion MNIST dataset, which consists of images of clothing items. Split the dataset into training and testing sets. Preprocess the images by scaling the pixel values to a range of 0 to 1 and converting the labels to categorical format; Define the model architecture: Create a convolutional neural network (CNN) model using Keras. The CNN consists of convolutional layers, pooling layers, and fully connected layers. Choose the appropriate architecture based on the complexity of the dataset; Compile the model: Specify the loss function, optimizer, and evaluation metric for the model. Common choices include categorical cross-entropy for multi-class classification and Adam optimizer; Train the model: Fit the model to the training data using the fit() function. Specify the number of epochs (iterations) and batch size. Monitor the training progress by tracking the loss and accuracy; Evaluate the model: Evaluate the trained model using the test dataset. Calculate the accuracy and other performance metrics to assess the model's performance; Make predictions: Use the trained model to make predictions on new unseen images. Load the test images, preprocess them, and pass them through the model to obtain class probabilities or predictions; Visualize the results: Visualize the training progress by plotting the loss and accuracy curves. Additionally, you can visualize the predictions and compare them with the true labels to gain insights into the model's performance.

5 FIVE DATA SCIENCE PROJECTS FOR ANALYSIS, CLASSIFICATION, PREDICTION, AND SENTIMENT ANALYSIS WITH PYTHON GUI

Author : Vivian Siahaan
Publisher : BALIGE PUBLISHING
ISBN 13 :
Total Pages : 979 pages
Book Rating : 4./5 ( download)

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Book Synopsis 5 FIVE DATA SCIENCE PROJECTS FOR ANALYSIS, CLASSIFICATION, PREDICTION, AND SENTIMENT ANALYSIS WITH PYTHON GUI by : Vivian Siahaan

Download or read book 5 FIVE DATA SCIENCE PROJECTS FOR ANALYSIS, CLASSIFICATION, PREDICTION, AND SENTIMENT ANALYSIS WITH PYTHON GUI written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2022-04-29 with total page 979 pages. Available in PDF, EPUB and Kindle. Book excerpt: PROJECT 1: SUPERMARKET SALES ANALYSIS AND PREDICTION USING MACHINE LEARNING WITH PYTHON GUI The dataset used in this project consists of the growth of supermarkets with high market competitions in most populated cities. The dataset is one of the historical sales of supermarket company which has recorded in 3 different branches for 3 months data. Predictive data analytics methods are easy to apply with this dataset. Attribute information in the dataset are as follows: Invoice id: Computer generated sales slip invoice identification number; Branch: Branch of supercenter (3 branches are available identified by A, B and C); City: Location of supercenters; Customer type: Type of customers, recorded by Members for customers using member card and Normal for without member card; Gender: Gender type of customer; Product line: General item categorization groups - Electronic accessories, Fashion accessories, Food and beverages, Health and beauty, Home and lifestyle, Sports and travel; Unit price: Price of each product in $; Quantity: Number of products purchased by customer; Tax: 5% tax fee for customer buying; Total: Total price including tax; Date: Date of purchase (Record available from January 2019 to March 2019); Time: Purchase time (10am to 9pm); Payment: Payment used by customer for purchase (3 methods are available – Cash, Credit card and Ewallet); COGS: Cost of goods sold; Gross margin percentage: Gross margin percentage; Gross income: Gross income; and Rating: Customer stratification rating on their overall shopping experience (On a scale of 1 to 10). In this project, you will perform predicting rating using machine learning. 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. PROJECT 2: DETECTING CYBERBULLYING TWEETS USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON GUI As social media usage becomes increasingly prevalent in every age group, a vast majority of citizens rely on this essential medium for day-to-day communication. Social media’s ubiquity means that cyberbullying can effectively impact anyone at any time or anywhere, and the relative anonymity of the internet makes such personal attacks more difficult to stop than traditional bullying. On April 15th, 2020, UNICEF issued a warning in response to the increased risk of cyberbullying during the COVID-19 pandemic due to widespread school closures, increased screen time, and decreased face-to-face social interaction. The statistics of cyberbullying are outright alarming: 36.5% of middle and high school students have felt cyberbullied and 87% have observed cyberbullying, with effects ranging from decreased academic performance to depression to suicidal thoughts. In light of all of this, this dataset contains more than 47000 tweets labelled according to the class of cyberbullying: Age; Ethnicity; Gender; Religion; Other type of cyberbullying; and Not cyberbullying. The data has been balanced in order to contain ~8000 of each class. 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. PROJECT 3: HIGHER EDUCATION STUDENT ACADEMIC PERFORMANCE ANALYSIS AND PREDICTION USING MACHINE LEARNING WITH PYTHON GUI The dataset used in this project was collected from the Faculty of Engineering and Faculty of Educational Sciences students in 2019. The purpose is to predict students' end-of-term performances using ML techniques. Attribute information in the dataset are as follows: Student ID; Student Age (1: 18-21, 2: 22-25, 3: above 26); Sex (1: female, 2: male); Graduated high-school type: (1: private, 2: state, 3: other); Scholarship type: (1: None, 2: 25%, 3: 50%, 4: 75%, 5: Full); Additional work: (1: Yes, 2: No); Regular artistic or sports activity: (1: Yes, 2: No); Do you have a partner: (1: Yes, 2: No); Total salary if available (1: USD 135-200, 2: USD 201-270, 3: USD 271-340, 4: USD 341-410, 5: above 410); Transportation to the university: (1: Bus, 2: Private car/taxi, 3: bicycle, 4: Other); Accommodation type in Cyprus: (1: rental, 2: dormitory, 3: with family, 4: Other); Mother's education: (1: primary school, 2: secondary school, 3: high school, 4: university, 5: MSc., 6: Ph.D.); Father's education: (1: primary school, 2: secondary school, 3: high school, 4: university, 5: MSc., 6: Ph.D.); Number of sisters/brothers (if available): (1: 1, 2:, 2, 3: 3, 4: 4, 5: 5 or above); Parental status: (1: married, 2: divorced, 3: died - one of them or both); Mother's occupation: (1: retired, 2: housewife, 3: government officer, 4: private sector employee, 5: self-employment, 6: other); Father's occupation: (1: retired, 2: government officer, 3: private sector employee, 4: self-employment, 5: other); Weekly study hours: (1: None, 2: <5 hours, 3: 6-10 hours, 4: 11-20 hours, 5: more than 20 hours); Reading frequency (non-scientific books/journals): (1: None, 2: Sometimes, 3: Often); Reading frequency (scientific books/journals): (1: None, 2: Sometimes, 3: Often); Attendance to the seminars/conferences related to the department: (1: Yes, 2: No); Impact of your projects/activities on your success: (1: positive, 2: negative, 3: neutral); Attendance to classes (1: always, 2: sometimes, 3: never); Preparation to midterm exams 1: (1: alone, 2: with friends, 3: not applicable); Preparation to midterm exams 2: (1: closest date to the exam, 2: regularly during the semester, 3: never); Taking notes in classes: (1: never, 2: sometimes, 3: always); Listening in classes: (1: never, 2: sometimes, 3: always); Discussion improves my interest and success in the course: (1: never, 2: sometimes, 3: always); Flip-classroom: (1: not useful, 2: useful, 3: not applicable); Cumulative grade point average in the last semester (/4.00): (1: <2.00, 2: 2.00-2.49, 3: 2.50-2.99, 4: 3.00-3.49, 5: above 3.49); Expected Cumulative grade point average in the graduation (/4.00): (1: <2.00, 2: 2.00-2.49, 3: 2.50-2.99, 4: 3.00-3.49, 5: above 3.49); Course ID; and OUTPUT: Grade (0: Fail, 1: DD, 2: DC, 3: CC, 4: CB, 5: BB, 6: BA, 7: AA). 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. PROJECT 4: COMPANY BANKRUPTCY ANALYSIS AND PREDICTION USING MACHINE LEARNING WITH PYTHON GUI The dataset was collected from the Taiwan Economic Journal for the years 1999 to 2009. Company bankruptcy was defined based on the business regulations of the Taiwan Stock Exchange. Attribute information in the dataset are as follows: Y - Bankrupt?: Class label; X1 - ROA(C) before interest and depreciation before interest: Return On Total Assets(C); X2 - ROA(A) before interest and % after tax: Return On Total Assets(A); X3 - ROA(B) before interest and depreciation after tax: Return On Total Assets(B); X4 - Operating Gross Margin: Gross Profit/Net Sales; X5 - Realized Sales Gross Margin: Realized Gross Profit/Net Sales; X6 - Operating Profit Rate: Operating Income/Net Sales; X7 - Pre-tax net Interest Rate: Pre-Tax Income/Net Sales; X8 - After-tax net Interest Rate: Net Income/Net Sales; X9 - Non-industry income and expenditure/revenue: Net Non-operating Income Ratio; X10 - Continuous interest rate (after tax): Net Income-Exclude Disposal Gain or Loss/Net Sales; X11 - Operating Expense Rate: Operating Expenses/Net Sales; X12 - Research and development expense rate: (Research and Development Expenses)/Net Sales X13 - Cash flow rate: Cash Flow from Operating/Current Liabilities; X14 - Interest-bearing debt interest rate: Interest-bearing Debt/Equity; X15 - Tax rate (A): Effective Tax Rate; X16 - Net Value Per Share (B): Book Value Per Share(B); X17 - Net Value Per Share (A): Book Value Per Share(A); X18 - Net Value Per Share (C): Book Value Per Share(C); X19 - Persistent EPS in the Last Four Seasons: EPS-Net Income; X20 - Cash Flow Per Share; X21 - Revenue Per Share (Yuan ¥): Sales Per Share; X22 - Operating Profit Per Share (Yuan ¥): Operating Income Per Share; X23 - Per Share Net profit before tax (Yuan ¥): Pretax Income Per Share; X24 - Realized Sales Gross Profit Growth Rate; X25 - Operating Profit Growth Rate: Operating Income Growth; X26 - After-tax Net Profit Growth Rate: Net Income Growth; X27 - Regular Net Profit Growth Rate: Continuing Operating Income after Tax Growth; X28 - Continuous Net Profit Growth Rate: Net Income-Excluding Disposal Gain or Loss Growth; X29 - Total Asset Growth Rate: Total Asset Growth; X30 - Net Value Growth Rate: Total Equity Growth; X31 - Total Asset Return Growth Rate Ratio: Return on Total Asset Growth; X32 - Cash Reinvestment %: Cash Reinvestment Ratio X33 - Current Ratio; X34 - Quick Ratio: Acid Test; X35 - Interest Expense Ratio: Interest Expenses/Total Revenue; X36 - Total debt/Total net worth: Total Liability/Equity Ratio; X37 - Debt ratio %: Liability/Total Assets; X38 - Net worth/Assets: Equity/Total Assets; X39 - Long-term fund suitability ratio (A): (Long-term Liability+Equity)/Fixed Assets; X40 - Borrowing dependency: Cost of Interest-bearing Debt; X41 - Contingent liabilities/Net worth: Contingent Liability/Equity; X42 - Operating profit/Paid-in capital: Operating Income/Capital; X43 - Net profit before tax/Paid-in capital: Pretax Income/Capital; X44 - Inventory and accounts receivable/Net value: (Inventory+Accounts Receivables)/Equity; X45 - Total Asset Turnover; X46 - Accounts Receivable Turnover; X47 - Average Collection Days: Days Receivable Outstanding; X48 - Inventory Turnover Rate (times); X49 - Fixed Assets Turnover Frequency; X50 - Net Worth Turnover Rate (times): Equity Turnover; X51 - Revenue per person: Sales Per Employee; X52 - Operating profit per person: Operation Income Per Employee; X53 - Allocation rate per person: Fixed Assets Per Employee; X54 - Working Capital to Total Assets; X55 - Quick Assets/Total Assets; X56 - Current Assets/Total Assets; X57 - Cash/Total Assets; X58 - Quick Assets/Current Liability; X59 - Cash/Current Liability; X60 - Current Liability to Assets; X61 - Operating Funds to Liability; X62 - Inventory/Working Capital; X63 - Inventory/Current Liability X64 - Current Liabilities/Liability; X65 - Working Capital/Equity; X66 - Current Liabilities/Equity; X67 - Long-term Liability to Current Assets; X68 - Retained Earnings to Total Assets; X69 - Total income/Total expense; X70 - Total expense/Assets; X71 - Current Asset Turnover Rate: Current Assets to Sales; X72 - Quick Asset Turnover Rate: Quick Assets to Sales; X73 - Working capitcal Turnover Rate: Working Capital to Sales; X74 - Cash Turnover Rate: Cash to Sales; X75 - Cash Flow to Sales; X76 - Fixed Assets to Assets; X77 - Current Liability to Liability; X78 - Current Liability to Equity; X79 - Equity to Long-term Liability; X80 - Cash Flow to Total Assets; X81 - Cash Flow to Liability; X82 - CFO to Assets; X83 - Cash Flow to Equity; X84 - Current Liability to Current Assets; X85 - Liability-Assets Flag: 1 if Total Liability exceeds Total Assets, 0 otherwise; X86 - Net Income to Total Assets; X87 - Total assets to GNP price; X88 - No-credit Interval; X89 - Gross Profit to Sales; X90 - Net Income to Stockholder's Equity; X91 - Liability to Equity; X92 - Degree of Financial Leverage (DFL); X93 - Interest Coverage Ratio (Interest expense to EBIT); X94 - Net Income Flag: 1 if Net Income is Negative for the last two years, 0 otherwise; and X95 - Equity to Liabilitys. 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. PROJECT 5: DATA SCIENCE FOR RAIN CLASSIFICATION AND PREDICTION WITH PYTHON GUI This dataset contains about 10 years of daily weather observations from many locations across Australia. RainTomorrow is the target variable to predict. You will determine rain or not in the next day. This column is Yes if the rain for that day was 1mm or more. Observations were drawn from numerous weather stations. The daily observations are available from http://www.bom.gov.au/climate/data. The dataset contains 23 attributes. Some of them are as follows: About some of them are: DATE - The date of observation; LOCATION - The common name of the location of the weather station; MINTEMP - The minimum temperature in degrees celsius; MAXTEMP - The maximum temperature in degrees celsius; RAINFALL - The amount of rainfall recorded for the day in mm; EVAPORATION - The so-called Class A pan evaporation (mm) in the 24 hours to 9am; SUNSHINE - The number of hours of bright sunshine in the day; WINDGUESTDIR - The direction of the strongest wind gust in the 24 hours to midnight; WINDGUESTSPEED- The speed (km/h) of the strongest wind gust in the 24 hours to midnight; and WINDDIR9AM - Direction of the wind at 9am. 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.

Python Machine Learning

Author : Moubachir Madani Fadoul
Publisher :
ISBN 13 :
Total Pages : 52 pages
Book Rating : 4.6/5 (5 download)

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Book Synopsis Python Machine Learning by : Moubachir Madani Fadoul

Download or read book Python Machine Learning written by Moubachir Madani Fadoul and published by . This book was released on 2020-05-31 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt: Have you always wanted to learn deep learning but are afraid it'll be too difficult for you? This book is for you.Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.Book DescriptionPython Machine Learning, is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems.Packed with clear explanations, visualizations, and working examples, the book covers most of the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, this tutorial book teaches the principles behind machine learning, allowing you to build models and applications for yourself. Updated for TensorFlow, skit-learn, Keras, and theano, this edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores analysis by giving some examples, helping you learn how to use machine learning algorithms to classify or predict documents output.This book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.What you will learn-Master the frameworks, models, and techniques that enable machines to 'learn' from data-Use scikit-learn for machine learning and TensorFlow for deep learning-Apply machine learning to classification, predict predict customer churning, and more-Build and train neural networks, GANs, CNN, and other models-Discover best practices for evaluating and tuning models-Predict target outcomes using optimization algorithm such as Gradient Descent algorithm analysis-Overcome challenges in deep learning algorithms by using dropout, regulation-Who This Book Is ForIf you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for anyone who wants to teach computers how to learn from data.Table of Contents1.Giving Computers the Ability to Learn from Data2.Training Simple ML Algorithms for Classification3.ML Classifiers Using scikit-learn4.Building Good Training Datasets - Data Preprocessing5.Compressing Data via Dimensionality Reduction6.Best Practices for Model Evaluation and Hyperparameter Tuning7.Combining Different Models for Ensemble Learning8.Predicting Continuous Target Variables with supversized learning 9.Implementing Multilayer Artificial Neural Networks10.Modeling Sequential Data Using Recurrent Neural Networks11.GANs for Synthesizing New Data...and so much more....In every chapter, you can edit the examples online

Deep Learning-Based Approaches for Sentiment Analysis

Author : Basant Agarwal
Publisher : Springer Nature
ISBN 13 : 9811512167
Total Pages : 326 pages
Book Rating : 4.8/5 (115 download)

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Book Synopsis Deep Learning-Based Approaches for Sentiment Analysis by : Basant Agarwal

Download or read book Deep Learning-Based Approaches for Sentiment Analysis written by Basant Agarwal and published by Springer Nature. This book was released on 2020-01-24 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers deep-learning-based approaches for sentiment analysis, a relatively new, but fast-growing research area, which has significantly changed in the past few years. The book presents a collection of state-of-the-art approaches, focusing on the best-performing, cutting-edge solutions for the most common and difficult challenges faced in sentiment analysis research. Providing detailed explanations of the methodologies, the book is a valuable resource for researchers as well as newcomers to the field.

THREE BOOKS IN ONE: Deep Learning Using SCIKIT-LEARN, KERAS, and TENSORFLOW with Python GUI

Author : Vivian Siahaan
Publisher : BALIGE PUBLISHING
ISBN 13 :
Total Pages : 588 pages
Book Rating : 4./5 ( download)

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Book Synopsis THREE BOOKS IN ONE: Deep Learning Using SCIKIT-LEARN, KERAS, and TENSORFLOW with Python GUI by : Vivian Siahaan

Download or read book THREE BOOKS IN ONE: Deep Learning Using SCIKIT-LEARN, KERAS, and TENSORFLOW with Python GUI written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2021-05-20 with total page 588 pages. Available in PDF, EPUB and Kindle. Book excerpt: BOOK 1: THE PRACTICAL GUIDES ON DEEP LEARNING USING SCIKIT-LEARN, KERAS, AND TENSORFLOW WITH PYTHON GUI In this book, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to implement deep learning on recognizing traffic signs using GTSRB dataset, detecting brain tumor using Brain Image MRI dataset, classifying gender, and recognizing facial expression using FER2013 dataset In Chapter 1, you will learn to create GUI applications to display line graph using PyQt. You will also learn how to display image and its histogram. In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, Pandas, NumPy and other libraries to perform prediction on handwritten digits using MNIST dataset with PyQt. You will build a GUI application for this purpose. In Chapter 3, you will learn how to perform recognizing traffic signs using GTSRB dataset from Kaggle. There are several different types of traffic signs like speed limits, no entry, traffic signals, turn left or right, children crossing, no passing of heavy vehicles, etc. Traffic signs classification is the process of identifying which class a traffic sign belongs to. In this Python project, you will build a deep neural network model that can classify traffic signs in image into different categories. With this model, you will be able to read and understand traffic signs which are a very important task for all autonomous vehicles. You will build a GUI application for this purpose. In Chapter 4, you will learn how to perform detecting brain tumor using Brain Image MRI dataset provided by Kaggle (https://www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection) using CNN model. You will build a GUI application for this purpose. In Chapter 5, you will learn how to perform classifying gender using dataset provided by Kaggle (https://www.kaggle.com/cashutosh/gender-classification-dataset) using MobileNetV2 and CNN models. You will build a GUI application for this purpose. In Chapter 6, you will learn how to perform recognizing facial expression using FER2013 dataset provided by Kaggle (https://www.kaggle.com/nicolejyt/facialexpressionrecognition) using CNN model. You will also build a GUI application for this purpose. BOOK 2: STEP BY STEP TUTORIALS ON DEEP LEARNING USING SCIKIT-LEARN, KERAS, AND TENSORFLOW WITH PYTHON GUI In this book, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to implement deep learning on classifying fruits, classifying cats/dogs, detecting furnitures, and classifying fashion. In Chapter 1, you will learn to create GUI applications to display line graph using PyQt. You will also learn how to display image and its histogram. Then, you will learn how to use OpenCV, NumPy, and other libraries to perform feature extraction with Python GUI (PyQt). The feature detection techniques used in this chapter are Harris Corner Detection, Shi-Tomasi Corner Detector, and Scale-Invariant Feature Transform (SIFT). In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform classifying fruits using Fruits 360 dataset provided by Kaggle (https://www.kaggle.com/moltean/fruits/code) using Transfer Learning and CNN models. You will build a GUI application for this purpose. In Chapter 3, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform classifying cats/dogs using dataset provided by Kaggle (https://www.kaggle.com/chetankv/dogs-cats-images) using Using CNN with Data Generator. You will build a GUI application for this purpose. In Chapter 4, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform detecting furnitures using Furniture Detector dataset provided by Kaggle (https://www.kaggle.com/akkithetechie/furniture-detector) using VGG16 model. You will build a GUI application for this purpose. In Chapter 5, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform classifying fashion using Fashion MNIST dataset provided by Kaggle (https://www.kaggle.com/zalando-research/fashionmnist/code) using CNN model. You will build a GUI application for this purpose. BOOK 3: PROJECT-BASED APPROACH ON DEEP LEARNING USING SCIKIT-LEARN, KERAS, AND TENSORFLOW WITH PYTHON GUI In this book, implement deep learning on detecting vehicle license plates, recognizing sign language, and detecting surface crack using TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries. In Chapter 1, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform detecting vehicle license plates using Car License Plate Detection dataset provided by Kaggle (https://www.kaggle.com/andrewmvd/car-plate-detection/download). In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform sign language recognition using Sign Language Digits Dataset provided by Kaggle (https://www.kaggle.com/ardamavi/sign-language-digits-dataset/download). In Chapter 3, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform detecting surface crack using Surface Crack Detection provided by Kaggle (https://www.kaggle.com/arunrk7/surface-crack-detection/download).

Classification and Prediction Projects with Machine Learning and Deep Learning

Author : Vivian Siahaan
Publisher : BALIGE PUBLISHING
ISBN 13 :
Total Pages : 210 pages
Book Rating : 4./5 ( download)

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Book Synopsis Classification and Prediction Projects with Machine Learning and Deep Learning by : Vivian Siahaan

Download or read book Classification and Prediction Projects with Machine Learning and Deep Learning written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2022-02-06 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: PROJECT 1: DATA SCIENCE CRASH COURSE: Drinking Water Potability Classification and Prediction Using Machine Learning and Deep Learning with Python Access to safe drinking water is essential to health, a basic human right, and a component of effective policy for health protection. This is important as a health and development issue at a national, regional, and local level. In some regions, it has been shown that investments in water supply and sanitation can yield a net economic benefit, since the reductions in adverse health effects and health care costs outweigh the costs of undertaking the interventions. The drinkingwaterpotability.csv file contains water quality metrics for 3276 different water bodies. The columns in the file are as follows: ph, Hardness, Solids, Chloramines, Sulfate, Conductivity, Organic_carbon, Trihalomethanes, Turbidity, and Potability. Contaminated water and poor sanitation are linked to the transmission of diseases such as cholera, diarrhea, dysentery, hepatitis A, typhoid, and polio. Absent, inadequate, or inappropriately managed water and sanitation services expose individuals to preventable health risks. This is particularly the case in health care facilities where both patients and staff are placed at additional risk of infection and disease when water, sanitation, and hygiene services are lacking. The machine learning 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, MLP classifier, and CNN 1D. Finally, you will plot boundary decision, ROC, 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. PROJECT 2: DATA SCIENCE CRASH COURSE: Skin Cancer Classification and Prediction Using Machine Learning and Deep Learning Skin cancer develops primarily on areas of sun-exposed skin, including the scalp, face, lips, ears, neck, chest, arms and hands, and on the legs in women. But it can also form on areas that rarely see the light of day — your palms, beneath your fingernails or toenails, and your genital area. Skin cancer affects people of all skin tones, including those with darker complexions. When melanoma occurs in people with dark skin tones, it's more likely to occur in areas not normally exposed to the sun, such as the palms of the hands and soles of the feet. Dataset used in this project contains a balanced dataset of images of benign skin moles and malignant skin moles. The data consists of two folders with each 1800 pictures (224x244) of the two types of moles. The machine learning 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, MLP classifier, and CNN 1D. The deep learning models used are CNN and MobileNet.

Python Machine Learning

Author : Sebastian Raschka
Publisher :
ISBN 13 : 9781783555130
Total Pages : 454 pages
Book Rating : 4.5/5 (551 download)

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Book Synopsis Python Machine Learning by : Sebastian Raschka

Download or read book Python Machine Learning written by Sebastian Raschka and published by . This book was released on 2015-08-24 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analyticsAbout This Book- Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization- Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms- Ask - and answer - tough questions of your data with robust statistical models, built for a range of datasetsWho This Book Is ForIf you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning - whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource.What You Will Learn- Explore how to use different machine learning models to ask different questions of your data- Learn how to build neural networks using Keras and Theano- Find out how to write clean and elegant Python code that will optimize the strength of your algorithms- Discover how to embed your machine learning model in a web application for increased accessibility- Predict continuous target outcomes using regression analysis- Uncover hidden patterns and structures in data with clustering- Organize data using effective pre-processing techniques- Get to grips with sentiment analysis to delve deeper into textual and social media dataIn DetailMachine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data - its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success.Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization.Style and approachPython Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.

Applied Deep Learning with Python

Author : Alex Galea
Publisher : Packt Publishing Ltd
ISBN 13 : 1789806992
Total Pages : 317 pages
Book Rating : 4.7/5 (898 download)

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Book Synopsis Applied Deep Learning with Python by : Alex Galea

Download or read book Applied Deep Learning with Python written by Alex Galea and published by Packt Publishing Ltd. This book was released on 2018-08-31 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: A hands-on guide to deep learning that’s filled with intuitive explanations and engaging practical examples Key Features Designed to iteratively develop the skills of Python users who don’t have a data science background Covers the key foundational concepts you’ll need to know when building deep learning systems Full of step-by-step exercises and activities to help build the skills that you need for the real-world Book Description Taking an approach that uses the latest developments in the Python ecosystem, you’ll first be guided through the Jupyter ecosystem, key visualization libraries and powerful data sanitization techniques before we train our first predictive model. We’ll explore a variety of approaches to classification like support vector networks, random decision forests and k-nearest neighbours to build out your understanding before we move into more complex territory. It’s okay if these terms seem overwhelming; we’ll show you how to put them to work. We’ll build upon our classification coverage by taking a quick look at ethical web scraping and interactive visualizations to help you professionally gather and present your analysis. It’s after this that we start building out our keystone deep learning application, one that aims to predict the future price of Bitcoin based on historical public data. By guiding you through a trained neural network, we’ll explore common deep learning network architectures (convolutional, recurrent, generative adversarial) and branch out into deep reinforcement learning before we dive into model optimization and evaluation. We’ll do all of this whilst working on a production-ready web application that combines Tensorflow and Keras to produce a meaningful user-friendly result, leaving you with all the skills you need to tackle and develop your own real-world deep learning projects confidently and effectively. What you will learn Discover how you can assemble and clean your very own datasets Develop a tailored machine learning classification strategy Build, train and enhance your own models to solve unique problems Work with production-ready frameworks like Tensorflow and Keras Explain how neural networks operate in clear and simple terms Understand how to deploy your predictions to the web Who this book is for If you're a Python programmer stepping into the world of data science, this is the ideal way to get started.