Data Science For Rain Classification And Prediction With Python Gui

Download Data Science For Rain Classification And Prediction With Python Gui PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Data Science For Rain Classification And Prediction With Python Gui book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page

Data Science For Rain Classification And Prediction With Python Gui

Data Science For Rain Classification And Prediction With Python Gui written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-29 with Computers categories.

The dataset used in this book consists of daily weather observations from various locations in Australia spanning a 10-year period. The target variable is "RainTomorrow," which predicts whether it will rain the following day. The dataset comprises 23 attributes, including: DATE: The date of observation.; LOCATION: The name of the weather station's location.; 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: Class A pan evaporation in mm for the 24 hours until 9 am.; SUNSHINE: The number of hours of bright sunshine in a day.; WINDGUSTDIR: The direction of the strongest wind gust in the 24 hours until midnight.; WINDGUSTSPEED: The speed of the strongest wind gust in km/h in the 24 hours until midnight.; WINDDIR9AM: The direction of the wind at 9 am. The project utilizes several machine learning models, including 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 techniques, namely raw scaling, MinMax scaling, and standard scaling, are employed. These machine learning models are utilized to analyze the weather attributes and make predictions about the occurrence of rainfall. Each model has its strengths and may perform differently based on the characteristics of the dataset. Additionally, a GUI is developed using PyQt5 to visualize cross-validation scores, predicted values versus true values, confusion matrix, learning curves, decision boundaries, model performance, scalability, training loss, and training accuracy. These visualizations within the GUI provide a comprehensive understanding of the model's performance, learning behavior, decision-making boundaries, and the quality of its predictions. Users can leverage these insights to fine-tune the model and improve its accuracy and generalization capabilities. In addition, the GUI developed using PyQt5 also includes the capability to visualize features on a year-wise and month-wise basis. This functionality allows users to explore the variations and trends in different weather attributes across different years and months. With the year-wise and month-wise visualizations, users can gain insights into the temporal patterns and trends present in the weather data. It enables them to observe how specific attributes change over time and across different seasons, providing a deeper understanding of the weather patterns and their potential influence on rainfall occurrences.

5 Five Data Science Projects For Analysis Classification Prediction And Sentiment Analysis With Python Gui

5 Five Data Science Projects For Analysis Classification Prediction And Sentiment Analysis With Python Gui written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-29 with Computers categories.

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.

Time Series Weather Forecasting And Prediction With Python

Time Series Weather Forecasting And Prediction With Python written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-12 with Computers categories.

In this project, we embarked on a journey of exploring time-series weather data and performing forecasting and prediction using Python. The objective was to gain insights into the dataset, visualize feature distributions, analyze year-wise and month-wise patterns, apply ARIMA regression to forecast temperature, and utilize machine learning models to predict weather conditions. Let's delve into each step of the process. To begin, we started by exploring the dataset, which contained historical weather data. We examined the structure and content of the dataset to understand its variables, such as temperature, humidity, wind speed, and weather conditions. Understanding the dataset is crucial for effective analysis and modeling. Next, we visualized the distributions of different features. By creating histograms, box plots, and density plots, we gained insights into the range, central tendency, and variability of the variables. These visualizations allowed us to identify any outliers, skewed distributions, or patterns within the data. Moving on, we explored the dataset's temporal aspects by analyzing year-wise and month-wise distributions. This involved aggregating the data based on years and months and visualizing the trends over time. By examining these patterns, we could observe any long-term or seasonal variations in the weather variables. After gaining a comprehensive understanding of the dataset, we proceeded to apply ARIMA regression for temperature forecasting. ARIMA (Autoregressive Integrated Moving Average) is a powerful technique for time-series analysis. By fitting an ARIMA model to the temperature data, we were able to make predictions and assess the model's accuracy in capturing the underlying patterns. In addition to temperature forecasting, we aimed to predict weather conditions using machine learning models. We employed various classification algorithms such as Logistic Regression, Decision Trees, Random Forests, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Adaboost, Gradient Boosting, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting (LGBM), and Multi-Layer Perceptron (MLP). These models were trained on the historical weather data, with weather conditions as the target variable. To evaluate the performance of the machine learning models, we utilized several metrics: accuracy, precision, recall, and F1 score. Accuracy measures the overall correctness of the predictions, while precision quantifies the proportion of true positive predictions out of all positive predictions. Recall, also known as sensitivity, measures the ability to identify true positives, and F1 score combines precision and recall into a single metric. Throughout the process, we emphasized the importance of data preprocessing, including handling missing values, scaling features, and splitting the dataset into training and testing sets. Preprocessing ensures the data is in a suitable format for analysis and modeling, and it helps prevent biases or inconsistencies in the results. By following this step-by-step approach, we were able to gain insights into the dataset, visualize feature distributions, analyze temporal patterns, forecast temperature using ARIMA regression, and predict weather conditions using machine learning models. The evaluation metrics provided a comprehensive assessment of the models' performance in capturing the weather conditions accurately. In conclusion, this project demonstrated the power of Python in time-series weather forecasting and prediction. Through data exploration, visualization, regression analysis, and machine learning modeling, we obtained valuable insights and accurate predictions regarding temperature and weather conditions. This knowledge can be applied in various domains such as agriculture, transportation, and urban planning, enabling better decision-making based on weather forecasts.

Rainfall Estimation Based On Artificial Neural Network Ann Models For Monsoon Season

Rainfall Estimation Based On Artificial Neural Network Ann Models For Monsoon Season written by Bhaskar Pratap Singh and has been published by GRIN Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-24 with Science categories.

Master's Thesis from the year 2014 in the subject Geography / Earth Science - Meteorology, Aeronomy, Climatology, grade: 6.84, , course: Soil and Water Conservation Engineering, language: English, abstract: In this master thesis the author will estimate the rainfall based on artificial neural network (ANN) models for monsoon season. The accurate rainfall prediction is one of the greatest challenges in hydrology. Forecast of any natural and usual event call for information regarding its phase of occurrence as well as nature. In the present study, artificial neural network (ANN) with different activation functions has been employed, to estimate daily monsoon rainfall of Pusa, Samastipur, in Bihar, India. The daily mean temperature, relative humidity, vapour pressure and rainfall data of period (1st June to 30th September) for years 1981-1989, 1992-1994, 1996-2002 and 2004-2008 were used for training and data for years 2009-2013 were used to test the models. The sensitivity analysis was carried out to identify the most significant parameter for daily rainfall prediction. The Neuro solution 5.0 software was used for designing of ANN models based on sigmoid axon and hyperbolic tangent axon activation functions. All the ANN networks were trained and tested with feed forward back propagation algorithm. The performance of the models were evaluated qualitatively by visual observation and quantitatively using different statistical and hydrological indices viz. mean square error, correlation coefficient, akaike’s information criterion, coefficient of efficiency and pooled average relative error. It was found that the performance of the ANN single hidden layer model based on sigmoid axon activation function is better than the ANN model based on hyperbolic tangent axon activation function. The best ANN models revealed that two days lag time was found to be satisfactory for set of inputs to the model. The sensitivity analysis indicated that the most significant input parameter besides rainfall itself is the vapour pressure in daily rainfall prediction for study area.