Zomato Data Analysis

Whenever we would like to visit a brand new place in delhi -NCR, we often search for the most effective restaurant or the most cost effective restaurant, but of decent quality. For looking of our greatest restaurants we frequently goes for various websites and apps to induce an overall idea of restaurants service. the foremost important criteria for all this is often rating and reviews of the those that have already got experience in these restaurants. People see for rating and compare these restaurants with one another and choose for his or her best. We restrict our data only to Delhi-NCR. This Zomato dataset provides us with enough information in order that one can decide which restaurants is suitable at which place and what kind of food they must serve so as get maximum profit. it's 9552 rows and 22 columns during this dataset. We'd wish to find the most affordable restaurant in Delhi-NCR.We can discuss various relationships between various columns of information sets like between rating and cuisine type , locality and cuisine etc. Since it's a true time data we might start first with data cleaning like cleaning spaces , garbage texts etc , then data exploratory like handling the None values, null values, dropping duplicates and other Transformations then randomization of dataset so analysis. Our target variable is that the "Aggregate Rating" column. We explore the link of the opposite features within the dataset with relevancy Rates. we'll the visualize the relation of all the opposite depend features with relevance our target variable, and hence find the foremost correlated features which effects our target variable. Keywords: Online food delivery, Marketing mix strategies, Competitive analysis, Pre-processing, Data Cleaning, Data Mining, Exploratory data analysis , Classification , Pandas , MatPlotLib.

Training Load in Different Age Category Soccer Players and Relationship to Different Pitch Size Small-Sided Games

This study sought to evaluate the training load in different age category soccer players associated with distinct pitch size small-sided games (SSGs). Twenty-four soccer players (eight in each age category: U-12, U-15, and U-23) performed three consecutive 4 vs. 4 ball possession SSGs (SSG1: 16 × 24 m; SSG2: 20 × 30 m; and SSG3: 24 × 36 m) all with 3 min duration and 3 min rest. Subjects carried ultra-wideband-based position-tracking system devices (WIMU PRO, RealTrack System). Total distance covered increased from SSG1 to SSG3 in all age categories and predominantly in running speeds below 12 km∙h−1. Moreover, distance covered in 12–18 km∙h−1 running speed was different in all performed SSGs and age categories. Residual or null values were observed at 18–21 km∙h−1 or above running speed, namely in U-12, the only age category where metabolic power and high metabolic load distance differences occurred throughout the performed SSGs. Edwards’ TRIMP differences between age categories was only observed in SSG2 (U-12 < U-15). The design of SSGs must consider that the training load of the players differs according to their age category and metabolic assessment should be considered in parallel to external load evaluation in SSGs. Wearable technology represents a fundamental support in soccer.

Prediction of House Price Using XGBoost Regression Algorithm

House price fluctuates each and every year due to changes in land value and change in infrastructure in and around the area. Centralised system should be available for prediction of house price in correlation with neighbourhood and infrastructure, will help customer to estimate the price of the house. Also, it assists the customer to come to a conclusion where to buy a house and when to purchase the house. Different factors are taken into consideration while predicting the worth of the house like location, neighbourhood and various amenities like garage space etc. Developing a model starts with Pre-processing data to remove all sort of discrepancies and fill null values or remove data outliers and make data ready to be processed. The categorical attribute can be converted into required attributes using one hot encoding methodology. Later the house price is predicted using XGBoost regression technique.

Resolving Interoperability Issues of Precision and Array with Null Value of Web Services Using WSIG-JADE Framework

Web services and agent technology play a significant role while resolving the issues related to platform interoperability. Web service interoperability organization (WS-I) provided the guidelines to remove the interoperability issues using basic profile 1.1/1.2 product. However, issues are still arising while transferring the precision values and an array with null values between different platforms like JAVA and .NET. As in a precision issue, JAVA supports data precision up to the 6th value and .NET up to the 5th value after the decimal and after increasing their limits, the whole number gets rounded off. In array with a null value issue, JAVA treats null as a value but .NET treats null as an empty string. To remove these issues, we use the WSIG-JADE framework that helps to build and demonstrate a multiagent system that does the mapping and conversions between agents and web services. It limits the number of digits to the 5th place after the decimal thereby increasing the precision in data sets, whereas it treats null as an empty string so that string length remains the same for both the platforms thereby helping in the correct count of data elements.

Data Mining Approach to Analyze Covid19 Dataset of Brazilian Patients

The pandemic originated by coronavirus(covid19), name coined by World Health Organization during the first month in 2020. Actually, almost all the countries presented covid19 positive cases and govern- ments are choosing different health policies to stop the infection and many research groups are working on patients data to understand the virus, at the same time scientists are looking for a vacuum to enhance imnulogy system to tack covid19 virus. One of top countries with more infections is Brazil, until August 11 had a total of 3,112,393 cases. Re- search Foundation of Sao Paulo State(Fapesp) released a dataset, it was an innovative in collaboration with hospitals(Einstein, Sirio-Libanes), laboratory(Fleury) and Sao Paulo University to foster reseach on this trend topic. The present paper presents an exploratory analysis of the datasets, using a Data Mining Approach, and some inconsistencies are found, i.e. NaN values, null references values for analytes, outliers on re- sults of analytes, encoding issues. The results were cleaned datasets for future studies, but at least a 20% of data were discarded because of non numerical, null values and numbers out of reference range.

Preprocessing Breast Cancer Data to Improve the Data Quality, Diagnosis Procedure, and Medical Care Services

In recent years, due to an increase in the incidence of different cancers, various data sources are available in this field. Consequently, many researchers have become interested in the discovery of useful knowledge from available data to assist faster decision-making by doctors and reduce the negative consequences of such diseases. Data mining includes a set of useful techniques in the discovery of knowledge from the data: detecting hidden patterns and finding unknown relations. However, these techniques face several challenges with real-world data. Particularly, dealing with inconsistencies, errors, noise, and missing values requires appropriate preprocessing and data preparation procedures. In this article, we investigate the impact of preprocessing to provide high-quality data for classification techniques. A wide range of preprocessing and data preparation methods are studied, and a set of preprocessing steps was leveraged to obtain appropriate classification results. The preprocessing is done on a real-world breast cancer dataset of the Reza Radiation Oncology Center in Mashhad with various features and a great percentage of null values, and the results are reported in this article. To evaluate the impact of the preprocessing steps on the results of classification algorithms, this case study was divided into the following 3 experiments: Breast cancer recurrence prediction without data preprocessing Breast cancer recurrence prediction by error removal Breast cancer recurrence prediction by error removal and filling null values Then, in each experiment, dimensionality reduction techniques are used to select a suitable subset of features for the problem at hand. Breast cancer recurrence prediction models are constructed using the 3 widely used classification algorithms, namely, naïve Bayes, k-nearest neighbor, and sequential minimal optimization. The evaluation of the experiments is done in terms of accuracy, sensitivity, F-measure, precision, and G-mean measures. Our results show that recurrence prediction is significantly improved after data preprocessing, especially in terms of sensitivity, F-measure, precision, and G-mean measures.

The Solution of a Spectral Task on Variable Section Compressed Beams Vibrations by Numerical Methods

Free flexural free vibrations of variable section are considered. The vibrations mathematical model represents the boundary value problem consisting of the hyperbolic type and boundary conditions main equation. By means of separation method of variables the task at the beginning comes to homogeneous differential equation of the fourth order for fundamental function with the corresponding boundary conditions. The grid area of an argument change and fundamental function in it are applied. That leads to an algebraic problem of eigenvalues. Multimodal non-negative function which null values match its eigenvalues is designed. The finite differences methods and coordinate descent in combination with the specified function sections graphic visualization at a small amount of descents with an adequate accuracy for eigenvalues practice are given. The known ways to define fundamental functions are applied.

Bayesian Approaches to Assessing the Parallel Lines Assumption in Cumulative Ordered Logit Models

We first review existing literature on cumulative logit models along with various ways to test the parallel lines assumption. Building on the traditional frequentist framework, we introduce a method of Bayesian assessment of null values to provide an alternative way to examine the parallel lines assumption using highest density intervals and regions of practical equivalence. Second, we propose a new hyperparameter cumulative logit model that can improve upon existing ones in addressing several challenges where traditional modeling techniques fail. We use two empirical examples from health research to showcase the Bayesian approaches.

Do We Need Many-valued Logics for Incomplete Information?

One of the most common scenarios of handling incomplete information occurs in relational databases. They describe incomplete knowledge with three truth values, using Kleene's logic for propositional formulae and a rather peculiar extension to predicate calculus. This design by a committee from several decades ago is now part of the standard adopted by vendors of database management systems. But is it really the right way to handle incompleteness in propositional and predicate logics? Our goal is to answer this question. Using an epistemic approach, we first characterize possible levels of partial knowledge about propositions, which leads to six truth values. We impose rationality conditions on the semantics of the connectives of the propositional logic, and prove that Kleene's logic is the maximal sublogic to which the standard optimization rules apply, thereby justifying this design choice. For extensions to predicate logic, however, we show that the additional truth values are not necessary: every many-valued extension of first-order logic over databases with incomplete information represented by null values is no more powerful than the usual two-valued logic with the standard Boolean interpretation of the connectives. We use this observation to analyze the logic underlying SQL query evaluation, and conclude that the many-valued extension for handling incompleteness does not add any expressiveness to it.