#App Education app is coding related app in this app user can lean coding and get involve with his regular assessment and get improve his coding ability. #Design In this app we have created splash activity after opening splash activity we Have move to home page and their is a navigation page in that page their is list of courses and you can attempt these courses and their evaluation page and their user can evaluate him self by attending question. In a home activity their is a list of cardview in card view their is a leaning page you can learn different coding related courses. And In home page their is communication page from their you can communicate your course related query. #Engineering I have done B. Tech in Computer Science and Engineering from Lovely Professional University . I started my career as a Android Developer Intern where I working on Android , java, Firebase, Restful apis, UX/UI design ,Json. Another side I learnt flutter and dart from Udemy Want to build a Mobile app? Reach out to me. If you have an idea and want to convert it into Android app, do get in touch with us. apart from Android development we are working ios apps also Hire an android developer to get quick help for all your android app development needs. with the hands-on android assignment help and android project help by Codersarts android expert. You can contact the android programming help expert any time; we will help you overcome all the issues and find the right solution. Want to get help right now? Or Want to know price quote Please send your requirement files at contact@codersarts.com.and you'll get instant reply as soon as requirement receives

/*Prompt*/ Create a trigger to update the CUSTOMER_REPS table based on changes to service tickets.Every time a service ticket is updated to completed(i.e., IsCompletedchanges from 0 to 1), take care of the following requirements. a)For thecorresponding customer representative, increment the YTD_Completed_Tickets valueby 1.Treat null values in ytd_completed_tickets as 0. b)With the updated YTD_Completed_Tickets value, check the corredponding customer representative’s bonus eligibility. Every customer representative has the same annual quota they need to meet to receive an annual bonus. This quota is based onnumber of completed service tickets. For a customer representative, mark them as eligible for bonus if they’ve completed at least 5service tickets in the current year (otherwise ensure they are marked as not eligible). c)Update the corresponding columnsin the CUSTOMER_REPS table(i.e., YTD_Completed_Tickets, Bonus_Eligible). Notes: There might be other updates to the tuple that do not change the value of IsCompleted. (In that case, this trigger should not carry out the following tasks). You can also assume that the Emp_ID (customer representative) associated with a service ticket will not change. You can also assume that a completed ticket will have a value for complete_date and Ticket_Date will not change to a different year.For simplicity, don’t worry about the cases where a service ticket “isCompleted” attribute changes from 1 to 0. Instead focus on the cases when isCompleted changes from 0 to 1. Hint: review the use of the COALESCE() function to handle nulls. /*Created tables and insert statements*/ Creating table CUSTOMER_REPS: CREATE TABLE CUSTOMER_REPS ( Rep_ID char (4), Current_Assigned_Tickets number (2), YTD_Completed_Tickets number (4), YTD_Rating number (4,2), Manager_ID char (4), Bonus_Eligible number (1), -- recorded as 0 if not eligible, 1 if eligible Contact varchar2(6), -- extension number CONSTRAINT cust_rep_pk PRIMARY KEY (Rep_ID), FOREIGN KEY (Rep_ID) REFERENCES EMPLOYEES (Emp_ID), FOREIGN KEY (Manager_ID) REFERENCES Managers (Manager_ID), CONSTRAINT CR_Bonus_Eligible CHECK (Bonus_Eligible IN (1,0)) ); Creating table SERVICE_TICKETS: CREATE TABLE SERVICE_TICKETS ( Ticket_ID char (8), Ticket_Category char(10), Ticket_Date DATE, Player_ID char (7), Game_ID char (3), Emp_ID char(4), IsCompleted number (1), Complete_Date DATE, Rating number (4,2), CONSTRAINT Ticket_pk PRIMARY KEY (Ticket_ID), FOREIGN KEY (Emp_ID) REFERENCES CUSTOMER_REPS (Rep_ID), FOREIGN KEY (Game_ID) REFERENCES GAMES (Game_ID), FOREIGN KEY (Player_ID) REFERENCES PLAYERS (Player_ID), CONSTRAINT IsCompleted CHECK (IsCompleted IN (1,0)), CONSTRAINT Complete_Date CHECK (Complete_Date >= Ticket_Date), CONSTRAINT Rating_Date CHECK ((IsCompleted = 1 AND Rating IS NOT NULL AND Complete_Date IS NOT NULL) OR (IsCompleted = 0 AND Rating IS NULL AND Complete_Date IS NULL)) ); Insert record into the table: insert into CUSTOMER_REPS values ('E001',1,0,4,'E005',0,'1234'); insert into CUSTOMER_REPS values ('E002',0,4,5.0,'E008',0,'2345'); insert into CUSTOMER_REPS values ('E003',0,1,4,'E005',0,'3456'); insert into CUSTOMER_REPS values ('E004',1,4,5,'E006',0,'5678'); insert into CUSTOMER_REPS values ('E007',0,0,null,'E006',0,'1245'); insert into service_tickets values ('T0000001', 'Server','02-OCT-2020','P000002','G04', 'E001',0, null, null); insert into service_tickets values ('T0000002','Glitch','11-MAY-2020','P000004','G04','E002',1, '30-MAY-2020',5.0); insert into service_tickets values ('T0000003','Glitch','04-SEP-2020','P000001','G02','E003',1, '05-SEP-2020',4); insert into service_tickets values ('T0000004','Server','17-OCT-2020','P000002','G05','E004',0, null,null); insert into service_tickets values ('T0000005','Server','20-JUL-2020','P000003','G03','E004',1, '21-JUL-2020',5.0); insert into service_tickets values ('T0000006','Server','18-JUN-2020','P000001','G03','E004',1, '21-JUN-2020',5); insert into service_tickets values ('T0000007','Glitch','13-FEB-2020','P000002','G05','E004',1, '14-FEB-2020',5); insert into service_tickets values ('T0000008','Server','18-JAN-2020','P000003','G01','E004',1, '19-JAN-2020',5); insert into service_tickets values ('T0000009','Glitch','20-AUG-2020','P000002','G05','E002',1, '30-AUG-2020',5.0); insert into service_tickets values ('T0000010','Server','05-APR-2020','P000003','G05','E002',1, '06-APR-2020',5.0); insert into service_tickets values ('T0000011','Glitch','28-JUL-2020','P000004','G05','E002',1, '29-JUL-2020',5.0); /*Possible Outline (this is just what I created*/ CREATE SEQUENCE update_ticket START WITH 0 MAXVALUE 90 increment by 1; CREATE OR REPLACE TRIGGER ticket_check BEFORE INSERT OR UPDATE ON CUSTOMER_REPS FOR each ROW DECLARE BEGIN SELECT IF () THEN END IF; END; We are providing online database assignment help, database homework help, database project help, then you can send your project details at contact@codersarts.com

What is an Autoencoder? An autoencoder is a type of artificial neural network that applies backpropagation, setting the target values to be equal to the inputs in an unsupervised manner. Autoencoders compress the input into a lower-dimensional code and then reconstructs the output from this representation. The code is a compact “summary” or “compression” of the input, which is also known as the latent-space representation. The input in this kind of neural network is unlabelled, meaning the network is capable of learning without supervision. If one needs to retrieve the original data, they can reconstruct it from the compressed data. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for dimensionality reduction, by training the network to ignore signal “noise”. Along with the reduction side, a reconstructing side is learnt, where the autoencoder tries to generate from the reduced encoding a representation as close as possible to its original input, hence its name. Talking of dimensionality reduction, the first thing that may cross your mind is Principal Component Analysis (PCA), which is another machine learning algorithm that performs the same task. So then why do we need Autoencoders? Let’s discuss that in brief. Autoencoders are better than PCA because: An autoencoder can learn non-linear transformations with a non-linear activation function and multiple layers. It doesn’t have to learn dense layers. It can use convolutional layers to learn which is better for video, image and series data. It is more efficient to learn several layers with an autoencoder rather than learn one huge transformation with PCA. An autoencoder provides a representation of each layer as the output. It can make use of pre-trained layers from another model to apply transfer learning to enhance the encoder/decoder. Architecture of Autoencoders An autoencoder consists of 3 components: encoder, code and decoder. Encoder: This part of the network compresses the input into a latent space representation. The encoder layer encodes the input image as a compressed representation in a reduced dimension. The compressed image is the distorted version of the original image. Code: This part of the network represents the compressed input which is fed to the decoder. The code is also known as Bottleneck. This is a well-designed approach to decide which aspects of observed data is relevant information and what aspects can be discarded. It does this by balancing two criteria: Compactness of representation, measured as the compressibility. It retains some behaviourally relevant variables from the input. Decoder: This layer decodes the encoded image back to the original dimension. The decoded image is a lossy reconstruction of the original image and it is reconstructed from the latent space representation. The decoder architecture is the mirror image of an encoder. Properties and Hyperparameters Properties of Autoencoders: Data-specific: Autoencoders are only able to compress data similar to what they have been trained on. Therefore, we can’t expect an autoencoder trained on handwritten digits to compress landscape photos. Lossy: The decompressed outputs will be degraded compared to the original inputs. We won't be getting the exact inputs as the output, there will be some disortion added during the reconstruction phase. Learned automatically from examples: It is easy to train specialized instances of the algorithm that will perform well on a specific type of input. Hyperparameters of Autoencoders: There are 4 hyperparameters that we need to set before training an autoencoder: Code size: It represents the number of nodes in the middle layer. Smaller size results in more compression. Number of layers: The autoencoder can consist of as many layers as we want. Number of nodes per layer: The number of nodes per layer decreases with each subsequent layer of the encoder, and increases back in the decoder. The decoder is symmetric to the encoder in terms of the layer structure. Loss function: We either use mean squared error or binary cross-entropy. If the input values are in the range [0, 1] then we typically use cross-entropy, otherwise, we use the mean squared error. While building an autoencoder, the aim is to make sure that the autoencoder does not memorize all the information i.e. it should not simply copy and paste the input as the output. In order to do so, constraints should be added to the network to prioritize which information should be kept and which information should be discarded. This constraint is introduced in the following ways: 1. Reducing the number of units or nodes in the layers. 2. Adding some noise to the input images. 3. Adding some regularization. Summarising the working of an autoencoder: We will now describe the general working of an auto encoder. A typical autoencoder is defined with an input, an internal representation and an output (an approximation of the input). The learning occurs in the layers attached to the internal representation. In fact, there are two main blocks of layers which looks like a traditional neural network. The slight difference is the layer containing the output must be equal to the input. In the picture below, the original input goes into the first block called the encoder. This internal representation compresses (reduces) the size of the input. In the second block occurs the reconstruction of the input. This is the decoding phase. The model will update the weights by minimizing the loss function. The model is penalized if the reconstruction output is different from the input. Types of Autoencoders Convolution Autoencoders : Autoencoders in their traditional formulation does not take into account the fact that a signal can be seen as a sum of other signals. Convolutional Autoencoders use the convolution operator to exploit this observation. They learn to encode the input in a set of simple signals and then try to reconstruct the input from them, modify the geometry or the reflectance of the image. The convolutional autoencoder uses convolutional, relu and pooling layers in the encoder. In the decoder, the pooling layer is replaced by the upsampling layer for increasing the dimensions of the feature maps. Used in the following: Image Reconstruction Image Colorization latent space clustering generating higher resolution images Sparse Autoencoders (SAE): Sparse autoencoders offers an alternative method for introducing an information bottleneck without requiring a reduction in the number of nodes at our hidden layers. Instead, the loss function is created in such a way that we penalize activations within a layer. If you need implementation for any of the topics mentioned above or assignment help on any of its variants, feel free CONTACT US

The Breast cancer Wisconsin (diagnostic) dataset from scikit-learn contains information on two types of cancer: WDBC-Malignant and WDBC-Benign. The dataset contains 569 instances of data and each instance is described by 30 attributes. These attributes computed from medical images describe the following characteristics: radius (mean of distances from center to points on the perimeter) texture (standard deviation of gray-scale values) perimeter area smoothness (local variation in radius lengths) compactness (perimeter^2 / area - 1.0) concavity (severity of concave portions of the contour) concave points (number of concave portions of the contour) symmetry fractal dimension (“coastline approximation” - 1) More information on the dataset can be found in the following links: https://scikit-learn.org/stable/datasets/index.html https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic) Requirement of the project: • Built and implement a classifier of Breast cancer Wisconsin based on Multi-Layer Perceptron. Study the performance of the classifier in terms of accuracy with respect to the different parameters of the MLP (number of Layers, activation function in hidden layers, learning rate, batch length, iterations number, etc.). Study the generalization ability of the MLP classifier. To improve the Generalization ability of the MLP classifier, we suggest to implement two different techniques: 1. Early Stopping 2. Dropout Implement these two techniques and assess the impact of each technique on the improvement of the generalization performance. Compare the performance of the MLP classifier with the performance of the SVM classifier. Contact us to get machine learning project help with an affordable price at contact@codersarts.com

Submission Guidelines This assignment may be submitted for full credit until Friday, December 4th at 11:59pm. Because of the imminent end of the fall session, late submissions will not be accepted, and extensions will not be given. All assignments are to be submitted using the Blackboard upload facility. It is strongly recommended to submit your assignment early, in case problems arise with the submission process. Assignment For this assignment, you will write some simple HTML, CSS, and JavaScript code. You will build two HTML documents and one CSS document. The file names should be your NetID, the letters A/B/C, and the appropriate extension. If your NetID is jsmith6, your HTML pages should be jsmith6A.html, jsmith6B.html, and jsmith6C.css. These three documents (plus the three images) should be combined in a .zip file before being uploaded to Blackboard. The code should contain the following elements/features: 1. The CSS document will apply standard formatting to both HTML documents: a. At each page top, a banner image (banner.jpg) b. Set the header text to appear purple and in the Cambria font and aligned center. 2. HTML document A: a. Within an tag: “Parakeet Colors” b. Within a tag: The normal primary color for parakeets is green. This lets them hide better among tree leaves when they are pursued by predators. c. Within a second tag: The blue color is a recent mutation, caused by the absence of yellow coloration from the original green. Although a popular color for pet birds, a blue parakeet would probably not survive long in the wild. d. The text in the tags should be black and in the default style, except for the words yellow, green, and blue, which should appear in bold and in their color (e.g., the word blue should be blue). e. The size of the text in the tags should be 16pt. f. The tags in a), b), & c) should not be nested with each other 3. HTML document B: a. Within a tag, in 18pt black Times New Roman, "Parakeet Images"; b. Within an tag, initially the image “green_parakeet.jpg” should be shown. c. A button with the label “Show green!”. When the button is pressed, a script will execute causing the tag to display the “green_parakeet.jpg” image. d. Another button with the label "Show blue!". When the button is pressed, a script will execute causing the tag to display the “blue_parakeet.jpg” image. 4. The three images (from 1a, 3a, and 3b) should be zipped along with your three documents. All the files should be at the same folder depth, i.e., no subfolders. Evaluation Your assignment will be graded according to whether all the required elements are present and in the correct formats and all required functionalities are operable If you need any project assignment help related to HTML, CSS, JavaScript then you can send your requirement details at contact@codersarts.com

For this assignment, you will create a MongoDB cluster on Mongo Atlas, load a sample data set and perform some queries. First, create a Mongo Cluster in Atlas. Make sure you create the free tier as specified in the instructions provided. https://docs.atlas.mongodb.com/tutorial/create-atlas-account/ https://docs.atlas.mongodb.com/tutorial/deploy-free-tier-cluster/ https://docs.atlas.mongodb.com/security/add-ip-address-to-list/ https://docs.atlas.mongodb.com/tutorial/create-mongodb-user-for-cluster/ Once you have your clusters up and running, you should be able to connect to them: On the Collections tab, choose to load sample dataset Once the data set is loaded, you will have access to multiple databases. Work with the database “sample_mflix”. You can view this under the “collections” tab. You can use the following documentation to aid in your queries https://docs.mongodb.com/manual/reference/operator/query/ Once the data has been loaded, perform the following queries: Find the documents in the movie collection for with the title “The Perils of Pauline”. Find the documents in the movie collection which had movies in 1915 OR 1903. (Hint: use $or) Find the documents in the movie collection which have runtime of greater than 160 minutes and less than 162 minutes. (Hint: use $and, $gt, $lt) Find all the documents in the movie collection which do not have a rating of G. Submit a PDF document with 4 screenshots for each of the queries of the collections tab with your queries successfully executing on Atlas. PDF Document should have screenshots like this: Contact us to get MongoDB assignment help with an affordable price at contact@codersarts.com

List of Tables Table: IPL_User Table: IPL_Stadium Table: IPL_Team Table: IPL_Player Table: IPL_Team_players Table: IPL_Tournament Table: IPL_Match Table: IPL_Match_Schedule Table: IPL_Bidder_Details Table: IPL_Bidding_Details Table: IPL_Bidder_Points Table: IPL_Team_Standings Instructions: Create these tables in the database by running the database script provided The script also has statements to insert appropriate data into all these tables Test the successful execution of the script by selecting some rows from few tables Clearly understand the structure of each table and relationships among them Insert / update appropriate rows into relevant tables if you need to get more rows in the output to verify your answers Questions – Write SQL queries to get data for following requirements: Show the percentage of wins of each bidder in the order of highest to lowest percentage. Display the number of matches conducted at each stadium with stadium name, city from the database. In a given stadium, what is the percentage of wins by a team which has won the toss? Show the total bids along with bid team and team name. Show the team id who won the match as per the win details. Display total matches played, total matches won and total matches lost by team along with its team name. Display the bowlers for Mumbai Indians team. How many all-rounders are there in each team, Display the teams with more than 4 all-rounder in descending order. Creating and Inserting values into database tables -- -- Table structure for table `IPL_BIDDER_DETAILS` -- CREATE DATABASE IF NOT EXISTS ipl; USE ipl; DROP TABLE IF EXISTS IPL_BIDDER_DETAILS; /*!40101 SET @saved_cs_client = @@character_set_client */; SET character_set_client = utf8mb4 ; CREATE TABLE IPL_BIDDER_DETAILS ( BIDDER_ID int(6) NOT NULL, USER_ID varchar(20) DEFAULT NULL, BIDDER_NAME varchar(40) NOT NULL, CONTACT_NO bigint(12) DEFAULT NULL, EMAIL_ID varchar(100) DEFAULT NULL, REMARKS varchar(200) DEFAULT NULL, PRIMARY KEY (BIDDER_ID), KEY USER_ID (USER_ID), CONSTRAINT ipl_bidder_details_ibfk_1 FOREIGN KEY (USER_ID) REFERENCES ipl_user (user_id) ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_0900_ai_ci; /*!40101 SET character_set_client = @saved_cs_client */; -- -- Dumping data for table `IPL_BIDDER_DETAILS` -- INSERT INTO IPL_BIDDER_DETAILS VALUES (102,'USR002','Krishan Valimbe',7088118461,'valimbek@yahoo.com',NULL),(103,'USR003','Megaduta Dheer',9325049602,'dheerm@gmail.com',NULL),(104,'USR004','Chatur Mahalanabis',7135247807,'mahalanabisc@yahoo.co.in',NULL),(105,'USR005','Shackcham Bajpeyi',7836088705,'bajpeyis@yahoo.com',NULL),(106,'USR006','Vineet Hegadi',8824587622,'hegadiv@gmail.com',NULL),(107,'USR007','Vakpati Munshi',6547277826,'munshiv@gmail.com',NULL),(108,'USR008','Kusika Rajavade',8481700165,'rajavadek@yahoo.com',NULL),(109,'USR009','Gagan Panda',7121254186,'pandag@edge.com',NULL),(110,'USR010','Mishri Nayar',9824959314,'nayarm@edge.com',NULL),(111,'USR011','Amara Mudaliyar',9287282100,'mudaliyara@yahoo.com',NULL),(112,'USR012','Shinu Sanyal',6732250379,'sanyals@edge.com',NULL),(113,'USR013','Vijaya Mayadev',8844350689,'mayadevv@edge.com',NULL),(114,'USR014','Durgautti Misra',8169330574,'misrad@edge.com',NULL),(115,'USR015','Jayanti Chadda',9468271869,'chaddaj@edge.com',NULL),(116,'USR016','Ronald D\'Souza',6943983550,'kumarb@gmail.com',NULL),(117,'USR017','Nagini Sarkar',7458743576,'sarkarn@yahoo.com',NULL),(118,'USR018','Akshara Pandey',6475579718,'pandeya@ipl.co.in',NULL),(119,'USR019','Madri Valimbe',9807726348,'valimbem@gmail.com',NULL),(120,'USR020','Saurandhri Mahanta',8465838514,'mahantas@yahoo.co.in',NULL),(121,'USR021','Aryabhatta Parachure',6887518099,'parachurea@yahoo.com',NULL),(122,'USR022','Veer Tipanis',6709912746,'tipanisv@ipl.co.in',NULL),(123,'USR023','Ganesh Phadatare',6722490653,'phadatareg@ipl.co.in',NULL),(124,'USR024','Sackhcham Nayar',7563150546,'nayars@edge.com',NULL),(125,'USR025','Gagan Adwani',6665727109,'adwanig@yahoo.com',NULL),(126,'USR026','Vincy Fernandes',6815856957,'vinfern@yahoo.co.in',NULL),(127,'USR027','Panini Mallaya',7415107521,'mallayap@yahoo.co.in',NULL),(128,'USR028','Salil Choudhary',9187146065,'salilch@yahoo.co.in',NULL),(129,'USR029','Aryabhata Valimbe',8580175938,'valimbea@ipl.co.in',NULL),(130,'USR030','Nawazuddin Saif',9017365395,'nawazs@gmail.com',NULL),(131,'USR031','Maya Gharapure',7434834146,'gharapurem@gmail.com',NULL); -- -- Table structure for table `IPL_BIDDER_POINTS` -- DROP TABLE IF EXISTS IPL_BIDDER_POINTS; /*!40101 SET @saved_cs_client = @@character_set_client */; SET character_set_client = utf8mb4 ; CREATE TABLE IPL_BIDDER_POINTS ( BIDDER_ID int(6) DEFAULT NULL, TOURNMT_ID int(5) DEFAULT NULL, NO_OF_BIDS int(4) DEFAULT NULL, NO_OF_MATCHES int(4) DEFAULT NULL, TOTAL_POINTS int(5) DEFAULT NULL, REMARKS varchar(200) DEFAULT NULL ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_0900_ai_ci; /*!40101 SET character_set_client = @saved_cs_client */; -- -- Dumping data for table `IPL_BIDDER_POINTS` -- INSERT INTO IPL_BIDDER_POINTS VALUES (102,2018,4,4,0,NULL),(103,2018,5,5,19,'Runner up'),(104,2018,7,5,17,NULL),(105,2018,9,8,4,NULL),(106,2018,10,9,14,NULL),(107,2018,8,7,6,NULL),(108,2018,7,6,6,NULL),(109,2018,5,4,0,NULL),(110,2018,9,8,15,NULL),(111,2018,7,6,7,NULL),(112,2018,8,8,9,NULL),(113,2018,7,7,9,NULL),(114,2018,8,7,10,NULL),(115,2018,6,6,5,NULL),(116,2018,3,3,0,NULL),(117,2018,5,3,5,NULL),(118,2018,6,6,15,NULL),(119,2018,10,8,2,NULL),(120,2018,4,4,5,NULL),(121,2018,11,11,35,'Winner'),(122,2018,3,3,4,NULL),(123,2018,6,6,11,NULL),(124,2018,5,5,7,NULL),(125,2018,9,7,8,NULL),(126,2018,5,5,12,NULL),(127,2018,8,8,12,NULL),(128,2018,4,4,4,NULL),(129,2018,8,8,9,NULL),(130,2018,7,7,5,NULL),(131,2018,6,6,12,NULL); -- -- Table structure for table `IPL_BIDDING_DETAILS` -- DROP TABLE IF EXISTS IPL_BIDDING_DETAILS; /*!40101 SET @saved_cs_client = @@character_set_client */; SET character_set_client = utf8mb4 ; CREATE TABLE IPL_BIDDING_DETAILS ( BIDDER_ID int(6) NOT NULL, SCHEDULE_ID int(6) NOT NULL, BID_TEAM int(2) DEFAULT NULL, BID_DATE datetime NOT NULL, BID_STATUS varchar(20) NOT NULL, PRIMARY KEY (BIDDER_ID,SCHEDULE_ID,BID_DATE,BID_STATUS) ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_0900_ai_ci; /*!40101 SET character_set_client = @saved_cs_client */; -- -- Dumping data for table `IPL_BIDDING_DETAILS` -- INSERT INTO IPL_BIDDING_DETAILS VALUES (102,10002,1,'2017-04-05 00:00:00','Bid'),(102,10016,7,'2017-04-17 00:00:00','Lost'),(102,10080,7,'2018-04-23 00:00:00','Lost'),(102,10084,8,'2018-04-25 00:00:00','Lost'),(103,10017,1,'2017-04-17 00:00:00','Won'),(103,10041,8,'2017-05-05 00:00:00','Won'),(103,10042,7,'2017-05-05 00:00:00','Won'),(103,10086,8,'2018-04-26 00:00:00','Won'),(103,10098,6,'2018-05-04 00:00:00','Won'),(104,10002,1,'2017-04-05 00:00:00','Won'),(104,10006,7,'2017-04-09 00:00:00','Won'),(104,10053,6,'2017-05-13 00:00:00','Won'),(104,10053,6,'2017-05-14 00:00:00','Cancelled'),(104,10054,1,'2017-05-15 00:00:00','Won'),(104,10086,2,'2018-04-26 00:00:00','Cancelled'),(104,10119,8,'2018-05-23 00:00:00','Won'),(105,10031,3,'2017-04-30 00:00:00','Bid'),(105,10034,8,'2017-05-01 00:00:00','Lost'),(105,10054,1,'2017-05-15 00:00:00','Lost'),(105,10062,2,'2018-04-07 00:00:00','Lost'),(105,10074,3,'2018-04-16 00:00:00','Won'),(105,10076,3,'2018-04-20 00:00:00','Won'),(105,10086,8,'2018-04-26 00:00:00','Cancelled'),(105,10089,2,'2018-04-28 00:00:00','Bid'),(105,10096,8,'2018-05-02 00:00:00','Bid'),(106,10001,2,'2017-04-05 00:00:00','Won'),(106,10020,6,'2017-04-22 00:00:00','Bid'),(106,10047,5,'2017-05-09 00:00:00','Won'),(106,10058,7,'2017-05-17 00:00:00','Bid'),(106,10060,6,'2017-05-19 00:00:00','Cancelled'),(106,10073,1,'2018-04-15 00:00:00','Won'),(106,10077,5,'2018-04-20 00:00:00','Lost'),(106,10092,6,'2018-04-30 00:00:00','Won'),(106,10094,1,'2018-05-01 00:00:00','Won'),(106,10104,5,'2018-05-09 00:00:00','Lost'),(107,10029,5,'2017-04-29 00:00:00','Bid'),(107,10032,6,'2017-04-30 00:00:00','Won'),(107,10047,4,'2017-05-09 00:00:00','Lost'),(107,10057,8,'2017-05-17 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Table structure for table `IPL_MATCH` -- DROP TABLE IF EXISTS IPL_MATCH; /*!40101 SET @saved_cs_client = @@character_set_client */; SET character_set_client = utf8mb4 ; CREATE TABLE IPL_MATCH ( MATCH_ID int(6) NOT NULL, TEAM_ID1 int(5) NOT NULL, TEAM_ID2 int(5) NOT NULL, TOSS_WINNER int(2) DEFAULT NULL, MATCH_WINNER int(2) DEFAULT NULL, WIN_DETAILS varchar(200) DEFAULT NULL, REMARKS varchar(200) DEFAULT NULL, PRIMARY KEY (MATCH_ID) ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_0900_ai_ci; /*!40101 SET character_set_client = @saved_cs_client */; -- -- Dumping data for table `IPL_MATCH` -- INSERT INTO IPL_MATCH VALUES (1001,1,2,2,1,'Team CSK won by 7 Wkts',NULL),(1002,1,3,2,1,'Team CSK won by 7 Wkts',NULL),(1003,1,4,1,2,'Team KKR won by 35 Runs',NULL),(1004,1,5,2,1,'Team CSK won by 7 Wkts',NULL),(1005,1,6,1,2,'Team RR won by 35 Runs',NULL),(1006,1,7,2,2,'Team RCB won by 35 Runs',NULL),(1007,2,1,2,1,'Team DD won by 35 Runs',NULL),(1008,2,3,2,1,'Team DD won by 35 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Wkts',NULL),(1119,1,2,2,1,'Team CSK won by 7 Wkts',NULL),(1120,8,1,1,2,'Team CSK won by 35 Runs',NULL); -- -- Table structure for table `IPL_MATCH_SCHEDULE` -- DROP TABLE IF EXISTS IPL_MATCH_SCHEDULE; /*!40101 SET @saved_cs_client = @@character_set_client */; SET character_set_client = utf8mb4 ; CREATE TABLE IPL_MATCH_SCHEDULE ( SCHEDULE_ID int(6) NOT NULL, TOURNMT_ID int(4) NOT NULL, MATCH_ID int(6) NOT NULL, MATCH_TYPE varchar(20) DEFAULT NULL, MATCH_DATE datetime NOT NULL, START_TIME int(4) DEFAULT NULL, STADIUM_ID int(2) DEFAULT NULL, `STATUS` varchar(20) DEFAULT NULL, REMARKS varchar(200) DEFAULT NULL, PRIMARY KEY (SCHEDULE_ID) ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_0900_ai_ci; /*!40101 SET character_set_client = @saved_cs_client */; -- -- Dumping data for table `IPL_MATCH_SCHEDULE` -- INSERT INTO IPL_MATCH_SCHEDULE VALUES (10001,2017,1001,'League','2017-04-05 00:00:00',1600,6,'Completed',NULL),(10002,2017,1002,'League','2017-04-05 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00:00:00',1600,10,'Completed',NULL),(10120,2018,1074,'Semifinal','2018-05-25 00:00:00',2000,9,'Completed',NULL),(10121,2018,1078,'Semifinal','2018-05-25 00:00:00',1600,8,'Completed',NULL),(10122,2018,1120,'Final','2018-05-27 00:00:00',2000,10,'Completed',NULL); -- -- Table structure for table `IPL_PLAYER` -- DROP TABLE IF EXISTS IPL_PLAYER; /*!40101 SET @saved_cs_client = @@character_set_client */; SET character_set_client = utf8mb4 ; CREATE TABLE IPL_PLAYER ( PLAYER_ID int(6) NOT NULL, PLAYER_NAME varchar(50) NOT NULL, PERFORMANCE_DTLS varchar(500) DEFAULT NULL, REMARKS varchar(200) DEFAULT NULL, PRIMARY KEY (PLAYER_ID) ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_0900_ai_ci; /*!40101 SET character_set_client = @saved_cs_client */; -- -- Dumping data for table `IPL_PLAYER` -- INSERT INTO IPL_PLAYER VALUES (1,'Sunil Narine','Pts-379.5 Mat-16 Wkt-17 Dot-137 4s-40 6s-23 Cat-1 Stmp-0','Top performer'),(2,'Shane Watson','Pts-318 Mat-15 Wkt-6 Dot-57 4s-44 6s-35 Cat-3 Stmp-0','Second best'),(3,'Rishabh Pant','Pts-314.5 Mat-14 Wkt-0 Dot-0 4s-68 6s-37 Cat-4 Stmp-2','Third best'),(4,'Lokesh Rahul','Pts-304.5 Mat-14 Wkt-0 Dot-0 4s-66 6s-32 Cat-10 Stmp-1',NULL),(5,'Andre Russell','Pts-292.5 Mat-16 Wkt-13 Dot-76 4s-17 6s-31 Cat-8 Stmp-0',NULL),(6,'Rashid Khan','Pts-284 Mat-17 Wkt-21 Dot-167 4s-3 6s-6 Cat-6 Stmp-0',NULL),(7,'Kane Williamson','Pts-273 Mat-17 Wkt-0 Dot-0 4s-64 6s-28 Cat-6 Stmp-0',NULL),(8,'Hardik Pandya','Pts-269.5 Mat-13 Wkt-18 Dot-98 4s-20 6s-11 Cat-8 Stmp-0',NULL),(9,'Ambati Rayudu','Pts-256.5 Mat-16 Wkt-0 Dot-0 4s-53 6s-34 Cat-2 Stmp-0',NULL),(10,'Krunal Pandya','Pts-239.5 Mat-14 Wkt-12 Dot-85 4s-22 6s-10 Cat-9 Stmp-0',NULL),(11,'Shakib Al Hasan','Pts-237 Mat-17 Wkt-14 Dot-98 4s-26 6s-5 Cat-3 Stmp-0',NULL),(12,'Suryakumar Yadav','Pts-228.5 Mat-14 Wkt-0 Dot-0 4s-61 6s-16 Cat-8 Stmp-0',NULL),(13,'Jos Buttler','Pts-228.5 Mat-13 Wkt-0 Dot-0 4s-52 6s-21 Cat-9 Stmp-1',NULL),(14,'Shikhar Dhawan','Pts-226.5 Mat-16 Wkt-0 Dot-0 4s-59 6s-14 Cat-12 Stmp-0',NULL),(15,'Dinesh Karthik','Pts-223.5 Mat-16 Wkt-0 Dot-0 4s-49 6s-16 Cat-14 Stmp-4',NULL),(16,'Umesh Yadav','Pts-223 Mat-14 Wkt-20 Dot-148 4s-0 6s-0 Cat-2 Stmp-0',NULL),(17,'Andrew Tye','Pts-221 Mat-14 Wkt-24 Dot-116 4s-2 6s-1 Cat-5 Stmp-0',NULL),(18,'AB de Villiers','Pts-217.5 Mat-12 Wkt-0 Dot-0 4s-39 6s-30 Cat-6 Stmp-0',NULL),(19,'Dwayne Bravo','Pts-216.5 Mat-16 Wkt-14 Dot-90 4s-8 6s-10 Cat-9 Stmp-0',NULL),(20,'Virat Kohli','Pts-213 Mat-14 Wkt-0 Dot-0 4s-52 6s-18 Cat-8 Stmp-0',NULL),(21,'Chris Lynn','Pts-210.5 Mat-16 Wkt-0 Dot-0 4s-56 6s-18 Cat-3 Stmp-0',NULL),(22,'Siddarth Kaul','Pts-209.5 Mat-17 Wkt-21 Dot-131 4s-0 6s-0 Cat-2 Stmp-0',NULL),(23,'Krishnappa Gowtham','Pts-207.5 Mat-15 Wkt-11 Dot-90 4s-9 6s-9 Cat-10 Stmp-0',NULL),(24,'Jasprit Bumrah','Pts-205 Mat-14 Wkt-17 Dot-133 4s-1 6s-0 Cat-4 Stmp-0',NULL),(25,'Trent Boult','Pts-203.5 Mat-14 Wkt-18 Dot-118 4s-0 6s-0 Cat-9 Stmp-0',NULL),(26,'MS Dhoni','Pts-200 Mat-16 Wkt-0 Dot-0 4s-24 6s-30 Cat-11 Stmp-3',NULL),(27,'Shardul Thakur','Pts-186.5 Mat-13 Wkt-16 Dot-113 4s-3 6s-0 Cat-4 Stmp-0',NULL),(28,'Suresh Raina','Pts-179.5 Mat-15 Wkt-0 Dot-0 4s-46 6s-12 Cat-9 Stmp-0',NULL),(29,'Piyush Chawla','Pts-175 Mat-15 Wkt-14 Dot-110 4s-1 6s-1 Cat-4 Stmp-0',NULL),(30,'Yuzvendra Chahal','Pts-175 Mat-14 Wkt-12 Dot-128 4s-0 6s-0 Cat-2 Stmp-0',NULL),(31,'Chris Gayle','Pts-174.5 Mat-11 Wkt-0 Dot-0 4s-30 6s-27 Cat-2 Stmp-0',NULL),(32,'Ben Stokes','Pts-173.5 Mat-13 Wkt-8 Dot-77 4s-13 6s-6 Cat-6 Stmp-0',NULL),(33,'Evin Lewis','Pts-171.5 Mat-13 Wkt-0 Dot-0 4s-32 6s-24 Cat-3 Stmp-0',NULL),(34,'Deepak Chahar','Pts-171 Mat-12 Wkt-10 Dot-117 4s-1 6s-4 Cat-1 Stmp-0',NULL),(35,'Kuldeep Yadav','Pts-171 Mat-16 Wkt-17 Dot-94 4s-1 6s-0 Cat-6 Stmp-0',NULL),(36,'Ravindra Jadeja','Pts-169.5 Mat-16 Wkt-11 Dot-82 4s-3 6s-4 Cat-11 Stmp-0',NULL),(37,'Jaydev Unadkat','Pts-169 Mat-15 Wkt-11 Dot-97 4s-6 6s-1 Cat-6 Stmp-0',NULL),(38,'Bhuvneshwar Kumar','Pts-163 Mat-12 Wkt-9 Dot-124 4s-1 6s-0 Cat-2 Stmp-0',NULL),(39,'Sandeep Sharma','Pts-163 Mat-12 Wkt-12 Dot-116 4s-0 6s-0 Cat-2 Stmp-0',NULL),(40,'Robin Uthappa','Pts-161 Mat-16 Wkt-0 Dot-0 4s-30 6s-21 Cat-5 Stmp-0',NULL),(41,'Ravichandran Ashwin','Pts-159.5 Mat-14 Wkt-10 Dot-77 4s-7 6s-5 Cat-5 Stmp-0',NULL),(42,'Shreyas Iyer','Pts-158.5 Mat-14 Wkt-0 Dot-0 4s-29 6s-21 Cat-5 Stmp-0',NULL),(43,'Mitchell McClenaghan','Pts-158 Mat-11 Wkt-14 Dot-103 4s-1 6s-1 Cat-0 Stmp-0',NULL),(44,'Jofra Archer','Pts-158 Mat-10 Wkt-15 Dot-98 4s-2 6s-0 Cat-1 Stmp-0',NULL),(45,'Sanju Samson','Pts-154 Mat-15 Wkt-0 Dot-0 4s-30 6s-19 Cat-5 Stmp-0',NULL),(46,'Mayank Markande','Pts-152 Mat-14 Wkt-15 Dot-87 4s-2 6s-0 Cat-3 Stmp-0',NULL),(47,'Nitish Rana','Pts-150 Mat-15 Wkt-4 Dot-17 4s-26 6s-14 Cat-2 Stmp-0',NULL),(48,'Mujeeb Ur Rahman','Pts-149.5 Mat-11 Wkt-14 Dot-93 4s-2 6s-0 Cat-1 Stmp-0',NULL),(49,'Mohammed Siraj','Pts-149.5 Mat-11 Wkt-11 Dot-90 4s-2 6s-1 Cat-5 Stmp-0',NULL),(50,'Ishan Kishan','Pts-142 Mat-14 Wkt-0 Dot-0 4s-22 6s-17 Cat-9 Stmp-2',NULL),(51,'Shreyas Gopal','Pts-126.5 Mat-11 Wkt-11 Dot-68 4s-5 6s-0 Cat-3 Stmp-0',NULL),(52,'Glenn Maxwell','Pts-126 Mat-12 Wkt-5 Dot-32 4s-14 6s-9 Cat-4 Stmp-0',NULL),(53,'Ankit Rajpoot','Pts-126 Mat-8 Wkt-11 Dot-80 4s-1 6s-0 Cat-2 Stmp-0',NULL),(54,'Karun Nair','Pts-125.5 Mat-13 Wkt-0 Dot-0 4s-23 6s-13 Cat-9 Stmp-0',NULL),(55,'Ajinkya Rahane','Pts-125 Mat-15 Wkt-0 Dot-0 4s-39 6s-5 Cat-4 Stmp-0',NULL),(56,'Rohit Sharma','Pts-124.5 Mat-14 Wkt-0 Dot-0 4s-25 6s-12 Cat-8 Stmp-0',NULL),(57,'Lungi Ngidi','Pts-120 Mat-7 Wkt-11 Dot-79 4s-0 6s-0 Cat-1 Stmp-0',NULL),(58,'Amit Mishra','Pts-116 Mat-10 Wkt-12 Dot-74 4s-0 6s-0 Cat-0 Stmp-0',NULL),(59,'Harbhajan Singh','Pts-113.5 Mat-13 Wkt-7 Dot-68 4s-3 6s-1 Cat-4 Stmp-0',NULL),(60,'Prithvi Shaw','Pts-112.5 Mat-9 Wkt-0 Dot-0 4s-27 6s-10 Cat-4 Stmp-0',NULL),(61,'Yusuf Pathan','Pts-106.5 Mat-15 Wkt-1 Dot-2 4s-22 6s-11 Cat-3 Stmp-0',NULL),(62,'Quinton de Kock','Pts-103 Mat-8 Wkt-0 Dot-0 4s-20 6s-8 Cat-7 Stmp-3',NULL),(63,'Tim Southee','Pts-100 Mat-8 Wkt-5 Dot-59 4s-5 6s-1 Cat-3 Stmp-0',NULL),(64,'Prasidh Krishna','Pts-97.5 Mat-7 Wkt-10 Dot-60 4s-0 6s-0 Cat-1 Stmp-0',NULL),(65,'Manish Pandey','Pts-95 Mat-15 Wkt-0 Dot-0 4s-22 6s-5 Cat-9 Stmp-0',NULL),(66,'Vijay Shankar','Pts-92.5 Mat-13 Wkt-1 Dot-8 4s-11 6s-11 Cat-6 Stmp-0',NULL),(67,'Shivam Mavi','Pts-91.5 Mat-9 Wkt-5 Dot-64 4s-1 6s-0 Cat-3 Stmp-0',NULL),(68,'Ben Cutting','Pts-89 Mat-9 Wkt-2 Dot-24 4s-5 6s-8 Cat-7 Stmp-0',NULL),(69,'Mustafizur Rahman','Pts-88.5 Mat-7 Wkt-7 Dot-59 4s-0 6s-0 Cat-2 Stmp-0',NULL),(70,'Mandeep Singh','Pts-88.5 Mat-14 Wkt-0 Dot-0 4s-16 6s-11 Cat-4 Stmp-0',NULL),(71,'Colin de Grandhomme','Pts-85.5 Mat-9 Wkt-2 Dot-26 4s-4 6s-10 Cat-3 Stmp-0',NULL),(72,'Harshal Patel','Pts-84 Mat-5 Wkt-7 Dot-36 4s-1 6s-6 Cat-0 Stmp-0',NULL),(73,'Chris Woakes','Pts-83.5 Mat-5 Wkt-8 Dot-47 4s-1 6s-1 Cat-1 Stmp-0',NULL),(74,'Rahul Tewatia','Pts-83 Mat-8 Wkt-6 Dot-41 4s-5 6s-1 Cat-2 Stmp-0',NULL),(75,'Rahul Tripathi','Pts-83 Mat-12 Wkt-0 Dot-0 4s-18 6s-8 Cat-4 Stmp-0',NULL),(76,'Mohit Sharma','Pts-82 Mat-9 Wkt-7 Dot-50 4s-1 6s-0 Cat-2 Stmp-0',NULL),(77,'Shubman Gill','Pts-80 Mat-13 Wkt-0 Dot-0 4s-22 6s-5 Cat-3 Stmp-0',NULL),(78,'Axar Patel','Pts-78 Mat-9 Wkt-3 Dot-41 4s-3 6s-4 Cat-2 Stmp-0',NULL),(79,'Carlos Brathwaite','Pts-76 Mat-4 Wkt-5 Dot-23 4s-1 6s-8 Cat-2 Stmp-0',NULL),(80,'Moeen Ali','Pts-73.5 Mat-5 Wkt-3 Dot-27 4s-4 6s-6 Cat-2 Stmp-0',NULL),(81,'Parthiv Patel','Pts-71.5 Mat-6 Wkt-0 Dot-0 4s-20 6s-4 Cat-3 Stmp-0',NULL),(82,'Dhawal Kulkarni','Pts-70.5 Mat-8 Wkt-4 Dot-54 4s-0 6s-0 Cat-1 Stmp-0',NULL),(83,'Washington Sundar','Pts-69.5 Mat-7 Wkt-4 Dot-29 4s-5 6s-4 Cat-0 Stmp-0',NULL),(84,'Ish Sodhi','Pts-68.5 Mat-6 Wkt-5 Dot-51 4s-0 6s-0 Cat-0 Stmp-0',NULL),(85,'Ben Laughlin','Pts-68 Mat-7 Wkt-9 Dot-34 4s-0 6s-0 Cat-1 Stmp-0',NULL),(86,'Billy Stanlake','Pts-67 Mat-4 Wkt-5 Dot-47 4s-1 6s-0 Cat-0 Stmp-0',NULL),(87,'Faf du Plessis','Pts-66 Mat-6 Wkt-0 Dot-0 4s-17 6s-6 Cat-1 Stmp-0',NULL),(88,'Imran Tahir','Pts-66 Mat-6 Wkt-6 Dot-40 4s-0 6s-0 Cat-2 Stmp-0',NULL),(89,'Barinder Sran','Pts-66 Mat-6 Wkt-4 Dot-47 4s-0 6s-0 Cat-2 Stmp-0',NULL),(90,'Avesh Khan','Pts-65.5 Mat-6 Wkt-4 Dot-44 4s-0 6s-0 Cat-3 Stmp-0',NULL),(91,'Brendon McCullum','Pts-63.5 Mat-6 Wkt-0 Dot-0 4s-16 6s-6 Cat-1 Stmp-0',NULL),(92,'Mitchell Johnson','Pts-63 Mat-6 Wkt-2 Dot-46 4s-2 6s-0 Cat-2 Stmp-0',NULL),(93,'D\' Arcy Short','Pts-61.5 Mat-7 Wkt-1 Dot-8 4s-11 6s-5 Cat-2 Stmp-0',NULL),(94,'Chris Morris','Pts-61 Mat-4 Wkt-3 Dot-31 4s-3 6s-2 Cat-2 Stmp-0',NULL),(95,'Liam Plunkett','Pts-61 lMat-7 Wkt-4 Dot-42 4s-0 6s-0 Cat-2 Stmp-0',NULL),(96,'Wriddhiman Saha','Pts-61 Mat-11 Wkt-0 Dot-0 4s-17 6s-1 Cat-5 Stmp-1',NULL),(97,'Marcus Stoinis','Pts-59.5 Mat-7 Wkt-3 Dot-15 4s-6 6s-4 Cat-2 Stmp-0',NULL),(98,'Kieron Pollard','Pts-59.5 Mat-9 Wkt-0 Dot-0 4s-10 6s-7 Cat-4 Stmp-0',NULL),(99,'Alex Hales','Pts-58.5 Mat-6 Wkt-0 Dot-0 4s-13 6s-6 Cat-2 Stmp-0',NULL),(100,'Jason Roy','Pts-57 Mat-5 Wkt-0 Dot-0 4s-9 6s-7 Cat-4 Stmp-0',NULL),(101,'Aaron Finch','Pts-53 Mat-10 Wkt-0 Dot-0 4s-6 6s-8 Cat-4 Stmp-0',NULL),(102,'Sam Billings','Pts-50 Mat-10 Wkt-0 Dot-0 4s-8 6s-5 Cat-5 Stmp-0',NULL),(103,'Sandeep Lamichhane','Pts-47.5 Mat-3 Wkt-5 Dot-30 4s-0 6s-0 Cat-0 Stmp-0',NULL),(104,'Mohammed Shami','Pts-46 Mat-4 Wkt-3 Dot-28 4s-1 6s-0 Cat-2 Stmp-0',NULL),(105,'Tom Curran','Pts-46 Mat-5 Wkt-6 Dot-15 4s-3 6s-0 Cat-1 Stmp-0',NULL),(106,'Mayank Agarwal','Pts-45 Mat-11 Wkt-0 Dot-0 4s-9 6s-5 Cat-2 Stmp-0',NULL),(107,'Dan Christian','Pts-39.5 Mat-4 Wkt-4 Dot-22 4s-0 6s-1 Cat-0 Stmp-0',NULL),(108,'Basil Thampi','Pts-39.5 Mat-4 Wkt-5 Dot-17 4s-0 6s-0 Cat-2 Stmp-0',NULL),(109,'Karn Sharma','Pts-37.5 Mat-6 Wkt-4 Dot-16 4s-0 6s-0 Cat-3 Stmp-0',NULL),(110,'Colin Munro','Pts-36.5 Mat-5 Wkt-0 Dot-0 4s-7 6s-4 Cat-2 Stmp-0',NULL),(111,'Shahbaz Nadeem','Pts-34 Mat-6 Wkt-3 Dot-21 4s-0 6s-0 Cat-1 Stmp-0',NULL),(112,'Heinrich Klaasen','Pts-33.5 Mat-4 Wkt-0 Dot-0 4s-5 6s-1 Cat-3 Stmp-4',NULL),(113,'David Willey','Pts-32 Mat-3 Wkt-2 Dot-20 4s-0 6s-0 Cat-2 Stmp-0',NULL),(114,'Shreevats Goswami','Pts-31 Mat-6 Wkt-0 Dot-0 4s-6 6s-1 Cat-5 Stmp-0',NULL),(115,'Murugan Ashwin','Pts-30.5 Mat-2 Wkt-3 Dot-20 4s-0 6s-0 Cat-0 Stmp-0',NULL),(116,'Corey Anderson','Pts-30 Mat-3 Wkt-3 Dot-16 4s-0 6s-1 Cat-0 Stmp-0',NULL),(117,'Deepak Hooda','Pts-29 Mat-9 Wkt-0 Dot-6 4s-2 6s-3 Cat-3 Stmp-0',NULL),(118,'Abhishek Sharma','Pts-27.5 Mat-3 Wkt-0 Dot-0 4s-3 6s-5 Cat-1 Stmp-0',NULL),(119,'Javon Searles','Pts-27.5 Mat-4 Wkt-2 Dot-7 4s-0 6s-1 Cat-4 Stmp-0',NULL),(120,'KM Asif','Pts-26.5 Mat-2 Wkt-3 Dot-16 4s-0 6s-0 Cat-0 Stmp-0',NULL),(121,'Gautam Gambhir','Pts-26 Mat-6 Wkt-0 Dot-0 4s-8 6s-1 Cat-1 Stmp-0',NULL),(122,'Sarfaraz Khan','Pts-26 Mat-7 Wkt-0 Dot-0 4s-7 6s-1 Cat-2 Stmp-0',NULL),(123,'Kulwant Khejroliya','Pts-25 Mat-3 Wkt-2 Dot-18 4s-0 6s-0 Cat-0 Stmp-0',NULL),(124,'Yuvraj Singh','Pts-24.5 Mat-8 Wkt-0 Dot-0 4s-6 6s-2 Cat-1 Stmp-0',NULL),(125,'Manoj Tiwary','Pts-23.5 Mat-5 Wkt-0 Dot-0 4s-4 6s-1 Cat-4 Stmp-0',NULL),(126,'Mohammad Nabi','Pts-23 Mat-2 Wkt-1 Dot-12 4s-3 6s-0 Cat-0 Stmp-0',NULL),(127,'Vinay Kumar','Pts-22.5 Mat-2 Wkt-2 Dot-8 4s-1 6s-0 Cat-2 Stmp-0',NULL),(128,'JP Duminy','Pts-22 Mat-6 Wkt-0 Dot-6 4s-3 6s-1 Cat-2 Stmp-0',NULL),(129,'Rinku Singh','Pts-20 Mat-4 Wkt-0 Dot-0 4s-4 6s-0 Cat-4 Stmp-0',NULL),(130,'Manan Vohra','Pts-19 Mat-4 Wkt-0 Dot-0 4s-2 6s-4 Cat-0 Stmp-0',NULL),(131,'Stuart Binny','Pts-16.5 Mat-7 Wkt-0 Dot-2 4s-2 6s-2 Cat-1 Stmp-0',NULL),(132,'David Miller','Pts-14.5 Mat-3 Wkt-0 Dot-0 4s-3 6s-2 Cat-0 Stmp-0',NULL),(133,'Pawan Negi','Pts-12.5 Mat-2 Wkt-1 Dot-9 4s-0 6s-0 Cat-0 Stmp-0',NULL),(134,'Chris Jordan','Pts-11 Mat-1 Wkt-0 Dot-11 4s-0 6s-0 Cat-0 Stmp-0',NULL),(135,'Anureet Singh','Pts-10 Mat-3 Wkt-1 Dot-4 4s-0 6s-0 Cat-1 Stmp-0',NULL),(136,'Kedar Jadhav','Pts-9.5 Mat-1 Wkt-0 Dot-0 4s-1 6s-2 Cat-0 Stmp-0',NULL),(137,'Mark Wood','Pts-8.5 Mat-1 Wkt-0 Dot-6 4s-0 6s-0 Cat-1 Stmp-0',NULL),(138,'Junior Dala','Pts-7 Mat-1 Wkt-0 Dot-7 4s-0 6s-0 Cat-0 Stmp-0',NULL),(139,'Khaleel Ahmed','Pts-7 Mat-1 Wkt-0 Dot-7 4s-0 6s-0 Cat-0 Stmp-0',NULL),(140,'Akila Dananjaya','Pts-6 Mat-1 Wkt-0 Dot-6 4s-0 6s-0 Cat-0 Stmp-0',NULL),(141,'Akshdeep Nath','Pts-5 Mat-1 Wkt-0 Dot-0 4s-1 6s-0 Cat-1 Stmp-0',NULL),(142,'Pradeep Sangwan','Pts-5 Mat-1 Wkt-0 Dot-5 4s-0 6s-0 Cat-0 Stmp-0',NULL),(143,'Prashant Chopra','Pts-5 Mat-1 Wkt-0 Dot-0 4s-2 6s-0 Cat-0 Stmp-0',NULL),(144,'Mahipal Lomror','Pts-4.5 Mat-2 Wkt-0 Dot-2 4s-1 6s-0 Cat-0 Stmp-0',NULL),(145,'Dhruv Shorey','Pts-3.5 Mat-1 Wkt-0 Dot-0 4s-0 6s-1 Cat-0 Stmp-0',NULL),(146,'Murali Vijay','Pts-3.5 Mat-1 Wkt-0 Dot-0 4s-0 6s-1 Cat-0 Stmp-0',NULL),(147,'Ankit Sharma','Pts-1 Mat-1 Wkt-0 Dot-1 4s-0 6s-0 Cat-0 Stmp-0',NULL),(148,'Naman Ojha','Pts-0 Mat-1 Wkt-0 Dot-0 4s-0 6s-0 Cat-0 Stmp-0',NULL),(149,'Ricky Bhui','Pts-0 Mat-1 Wkt-0 Dot-0 4s-0 6s-0 Cat-0 Stmp-0',NULL); -- -- Table structure for table `IPL_STADIUM` -- DROP TABLE IF EXISTS IPL_STADIUM; /*!40101 SET @saved_cs_client = @@character_set_client */; SET character_set_client = utf8mb4 ; CREATE TABLE IPL_STADIUM ( STADIUM_ID int(5) NOT NULL, STADIUM_NAME varchar(100) NOT NULL, CITY varchar(50) NOT NULL, CAPACITY int(8) DEFAULT NULL, ADDRESS varchar(200) DEFAULT NULL, CONTACT_NO bigint(12) DEFAULT NULL, PRIMARY KEY (STADIUM_ID) ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_0900_ai_ci; /*!40101 SET character_set_client = @saved_cs_client */; -- -- Dumping data for table `IPL_STADIUM` -- INSERT INTO IPL_STADIUM VALUES (1,'Wankhede Stadium','Mumbai',45000,'Wankhede Stadium, Mumbai',6232108776),(2,'Feroz Shah Kotla','Delhi',48000,'Feroz Shah Kotla, Delhi',7471757880),(3,'Eden Gardens','Kolkata',90000,'Eden Gardens, Kolkata',6847069662),(4,'Rajiv Gandhi International Stadium','Hyderabad',55000,'Rajiv Gandhi International Stadium, Hyderabad',6555859360),(5,'MS Chidambaram Stadium','Chennai',50000,'MS Chidambaram Stadium, Chennai',9534880787),(6,'Sawai Mansingh Stadium','Jaipur',30000,'Sawai Mansingh Stadium, Jaipur',9255986370),(7,'M. Chinnaswamy Stadium','Bengaluru',40000,'M. Chinnaswamy Stadium, Bengaluru',6680348191),(8,'Is Bindra Stadium','Mohali',30000,'Is Bindra Stadium, Mohali',6370992699),(9,'Holkar Stadium','Indore',30000,'Holkar Stadium, Indore',9045919945),(10,'MCA Stadium','Pune',35000,'MCA Stadium, Pune',8541151117); -- -- Table structure for table `IPL_TEAM` -- DROP TABLE IF EXISTS IPL_TEAM; /*!40101 SET @saved_cs_client = @@character_set_client */; SET character_set_client = utf8mb4 ; CREATE TABLE IPL_TEAM ( TEAM_ID int(5) NOT NULL, TEAM_NAME varchar(50) NOT NULL, TEAM_CITY varchar(50) DEFAULT NULL, REMARKS varchar(200) DEFAULT NULL, PRIMARY KEY (TEAM_ID) ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_0900_ai_ci; /*!40101 SET character_set_client = @saved_cs_client */; -- -- Dumping data for table `IPL_TEAM` -- INSERT INTO IPL_TEAM VALUES (1,'Chennai Super Kings','Chennai, Tamil Nadu','CSK'),(2,'Delhi Daredevils','Delhi, NCR','DD'),(3,'Kings XI Punjab','Mohali (Chandigarh CR), Punjab','KXIP'),(4,'Kolkata Knight Riders','Kolkata, West Bengal','KKR'),(5,'Mumbai Indians','Mumbai, Maharashtra','MI'),(6,'Rajasthan Royals','Jaipur, Rajasthan','RR'),(7,'Royal Challengers Bangalore','Bangalore, Karnataka','RCB'),(8,'Sunrisers Hyderabad','Hyderabad, Telangana','SRH'); -- -- Table structure for table `IPL_TEAM_PLAYERS` -- DROP TABLE IF EXISTS IPL_TEAM_PLAYERS; /*!40101 SET @saved_cs_client = @@character_set_client */; SET character_set_client = utf8mb4 ; CREATE TABLE IPL_TEAM_PLAYERS ( TEAM_ID int(5) NOT NULL, PLAYER_ID int(6) NOT NULL, PLAYER_ROLE varchar(30) NOT NULL, REMARKS varchar(200) DEFAULT NULL, PRIMARY KEY (TEAM_ID,PLAYER_ID) ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_0900_ai_ci; /*!40101 SET character_set_client = @saved_cs_client */; -- -- Dumping data for table `IPL_TEAM_PLAYERS` -- INSERT INTO IPL_TEAM_PLAYERS VALUES (1,2,'Bowler','TEAM - RR'),(1,9,'All-Rounder','TEAM - MI'),(1,19,'Batsman','TEAM - KXIP'),(1,26,'Bowler','TEAM - MI'),(1,27,'All-Rounder','TEAM - MI'),(1,28,'Batsman','TEAM - SRH'),(1,34,'Bowler','TEAM - RCB'),(1,36,'Bowler','TEAM - DD'),(1,57,'Bowler','TEAM - SRH'),(1,59,'Batsman','TEAM - SRH'),(1,87,'Bowler','TEAM - CSK'),(1,88,'Batsman','TEAM - DD'),(1,102,'Batsman','TEAM - KXIP'),(1,109,'Bowler','TEAM - SRH'),(1,113,'Batsman','TEAM - MI'),(1,120,'Bowler','TEAM - CSK'),(1,136,'All-Rounder','TEAM - CSK'),(1,137,'Bowler','TEAM - CSK'),(1,145,'Batsman','TEAM - KXIP'),(1,146,'All-Rounder','TEAM - KXIP'),(2,3,'Batsman','TEAM - KXIP'),(2,25,'Batsman','TEAM - KXIP'),(2,42,'Batsman','TEAM - RCB'),(2,52,'All-Rounder','TEAM - DD'),(2,58,'Bowler','TEAM - SRH'),(2,60,'Batsman','TEAM - DD'),(2,66,'Bowler','TEAM - DD'),(2,72,'All-Rounder','TEAM - RCB'),(2,74,'Bowler','TEAM - CSK'),(2,90,'Bowler','TEAM - MI'),(2,94,'Batsman','TEAM - RCB'),(2,95,'Batsman','TEAM - DD'),(2,100,'All-Rounder','TEAM - KXIP'),(2,103,'Batsman','TEAM - MI'),(2,104,'Bowler','TEAM - RCB'),(2,107,'All-Rounder','TEAM - SRH'),(2,110,'Bowler','TEAM - RCB'),(2,111,'Bowler','TEAM - CSK'),(2,118,'All-Rounder','TEAM - MI'),(2,121,'All-Rounder','TEAM - DD'),(2,138,'All-Rounder','TEAM - CSK'),(2,148,'Batsman','TEAM - MI'),(3,4,'Batsman','TEAM - SRH'),(3,17,'All-Rounder','TEAM - CSK'),(3,31,'Wicket Keeper','TEAM - RR'),(3,41,'Bowler','TEAM - SRH'),(3,48,'All-Rounder','TEAM - KKR'),(3,54,'All-Rounder','TEAM - KKR'),(3,76,'Batsman','TEAM - KXIP'),(3,78,'All-Rounder','TEAM - SRH'),(3,89,'All-Rounder','TEAM - DD'),(3,97,'Batsman','TEAM - CSK'),(3,101,'Batsman','TEAM - KXIP'),(3,106,'Wicket Keeper','TEAM - SRH'),(3,124,'All-Rounder','TEAM - RR'),(3,125,'Bowler','TEAM - RR'),(3,132,'All-Rounder','TEAM - RR'),(3,141,'Bowler','TEAM - DD'),(4,1,'All-Rounder','TEAM - RR'),(4,5,'Batsman','TEAM - KKR'),(4,15,'Bowler','TEAM - KXIP'),(4,21,'Wicket Keeper','TEAM - MI'),(4,29,'Bowler','TEAM - CSK'),(4,35,'All-Rounder','TEAM - CSK'),(4,40,'Batsman','TEAM - MI'),(4,47,'All-Rounder','TEAM - DD'),(4,53,'Bowler','TEAM - KKR'),(4,64,'Wicket Keeper','TEAM - CSK'),(4,67,'Bowler','TEAM - KXIP'),(4,77,'Batsman','TEAM - KXIP'),(4,92,'Wicket Keeper','TEAM - DD'),(4,105,'All-Rounder','TEAM - RCB'),(4,119,'All-Rounder','TEAM - CSK'),(4,127,'Bowler','TEAM - CSK'),(4,129,'Batsman','TEAM - KKR'),(5,8,'Bowler','TEAM - SRH'),(5,10,'Wicket Keeper','TEAM - RR'),(5,12,'Bowler','TEAM - KXIP'),(5,24,'Bowler','TEAM - KKR'),(5,33,'Bowler','TEAM - KKR'),(5,43,'All-Rounder','TEAM - SRH'),(5,46,'Bowler','TEAM - DD'),(5,50,'All-Rounder','TEAM - RCB'),(5,56,'Bowler','TEAM - RCB'),(5,68,'Bowler','TEAM - MI'),(5,69,'All-Rounder','TEAM - MI'),(5,98,'Bowler','TEAM - CSK'),(5,128,'Bowler','TEAM - SRH'),(5,140,'Batsman','TEAM - DD'),(5,142,'All-Rounder','TEAM - DD'),(6,13,'Wicket Keeper','TEAM - KKR'),(6,23,'Bowler','TEAM - RCB'),(6,32,'Bowler','TEAM - DD'),(6,37,'Bowler','TEAM - KXIP'),(6,44,'All-Rounder','TEAM - KKR'),(6,45,'Batsman','TEAM - RR'),(6,51,'Bowler','TEAM - DD'),(6,55,'Batsman','TEAM - KXIP'),(6,75,'Bowler','TEAM - KKR'),(6,82,'All-Rounder','TEAM - SRH'),(6,84,'Wicket Keeper','TEAM - RCB'),(6,85,'All-Rounder','TEAM - RCB'),(6,93,'Bowler','TEAM - KXIP'),(6,112,'Batsman','TEAM - KKR'),(6,131,'Bowler','TEAM - RR'),(6,135,'All-Rounder','TEAM - CSK'),(6,143,'Batsman','TEAM - KKR'),(6,144,'Batsman','TEAM - KKR'),(6,147,'All-Rounder','TEAM - KXIP'),(7,16,'All-Rounder','TEAM - RR'),(7,18,'Wicket Keeper','TEAM - KKR'),(7,20,'All-Rounder','TEAM - KXIP'),(7,30,'Wicket Keeper','TEAM - SRH'),(7,49,'Batsman','TEAM - MI'),(7,62,'Bowler','TEAM - RCB'),(7,63,'Bowler','TEAM - KXIP'),(7,70,'Bowler','TEAM - RCB'),(7,71,'Bowler','TEAM - RCB'),(7,73,'Batsman','TEAM - CSK'),(7,80,'All-Rounder','TEAM - DD'),(7,81,'Bowler','TEAM - MI'),(7,83,'Batsman','TEAM - KKR'),(7,91,'Bowler','TEAM - MI'),(7,115,'Bowler','TEAM - RR'),(7,116,'Bowler','TEAM - DD'),(7,122,'Bowler','TEAM - MI'),(7,123,'Bowler','TEAM - RR'),(7,130,'Bowler','TEAM - RCB'),(7,133,'Wicket Keeper','TEAM - CSK'),(8,6,'Bowler','TEAM - KKR'),(8,7,'Batsman','TEAM - DD'),(8,11,'Batsman','TEAM - CSK'),(8,14,'All-Rounder','TEAM - RCB'),(8,22,'Bowler','TEAM - RR'),(8,38,'All-Rounder','TEAM - RR'),(8,39,'Batsman','TEAM - RR'),(8,61,'Wicket Keeper','TEAM - RCB'),(8,65,'Bowler','TEAM - CSK'),(8,79,'All-Rounder','TEAM - SRH'),(8,86,'Batsman','TEAM - CSK'),(8,96,'Batsman','TEAM - RCB'),(8,99,'Bowler','TEAM - KKR'),(8,108,'Bowler','TEAM - SRH'),(8,114,'All-Rounder','TEAM - DD'),(8,117,'All-Rounder','TEAM - DD'),(8,126,'Bowler','TEAM - SRH'),(8,134,'All-Rounder','TEAM - CSK'),(8,139,'Bowler','TEAM - DD'),(8,149,'Wicket Keeper','TEAM - MI'); -- -- Table structure for table `IPL_TEAM_STANDINGS` -- DROP TABLE IF EXISTS IPL_TEAM_STANDINGS; /*!40101 SET @saved_cs_client = @@character_set_client */; SET character_set_client = utf8mb4 ; CREATE TABLE IPL_TEAM_STANDINGS ( TEAM_ID int(4) NOT NULL, TOURNMT_ID int(4) NOT NULL, MATCHES_PLAYED int(2) DEFAULT NULL, MATCHES_WON int(2) DEFAULT NULL, MATCHES_LOST int(2) DEFAULT NULL, MATCHES_TIED int(2) DEFAULT NULL, NO_RESULT int(2) DEFAULT NULL, TOTAL_POINTS int(3) DEFAULT NULL, REMARKS varchar(200) DEFAULT NULL, PRIMARY KEY (TEAM_ID,TOURNMT_ID) ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_0900_ai_ci; /*!40101 SET character_set_client = @saved_cs_client */; -- -- Dumping data for table `IPL_TEAM_STANDINGS` -- INSERT INTO IPL_TEAM_STANDINGS VALUES (1,2017,14,9,5,0,0,18,NULL),(1,2018,14,9,5,0,0,18,'Champions'),(2,2017,14,6,8,0,0,12,NULL),(2,2018,14,5,9,0,0,10,NULL),(3,2017,14,7,7,0,0,14,NULL),(3,2018,14,6,8,0,0,12,NULL),(4,2017,14,8,6,0,0,16,NULL),(4,2018,14,8,6,0,0,16,NULL),(5,2017,14,10,4,0,0,20,'Champions'),(5,2018,14,6,8,0,0,12,NULL),(6,2017,14,9,5,0,0,8,NULL),(6,2018,14,7,7,0,0,14,NULL),(7,2017,14,3,10,0,1,7,NULL),(7,2018,14,6,8,0,0,12,NULL),(8,2017,14,8,5,0,1,17,NULL),(8,2018,14,9,5,0,0,18,NULL); -- -- Table structure for table `IPL_TOURNAMENT` -- DROP TABLE IF EXISTS IPL_TOURNAMENT; /*!40101 SET @saved_cs_client = @@character_set_client */; SET character_set_client = utf8mb4 ; CREATE TABLE IPL_TOURNAMENT ( TOURNMT_ID int(4) NOT NULL, TOURNMT_NAME varchar(100) NOT NULL, FROM_DATE datetime NOT NULL, TO_DATE datetime DEFAULT NULL, TEAM_COUNT int(2) DEFAULT NULL, TOTAL_MATCHES int(3) DEFAULT NULL, REMARKS varchar(200) DEFAULT NULL, PRIMARY KEY (TOURNMT_ID) ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_0900_ai_ci; /*!40101 SET character_set_client = @saved_cs_client */; -- -- Dumping data for table `IPL_TOURNAMENT` -- INSERT INTO IPL_TOURNAMENT VALUES (2008,'IPL SEASON - 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CSK'); -- -- Table structure for table `IPL_USER` -- DROP TABLE IF EXISTS IPL_USER; /*!40101 SET @saved_cs_client = @@character_set_client */; SET character_set_client = utf8mb4 ; CREATE TABLE IPL_USER ( USER_ID varchar(20) NOT NULL, `PASSWORD` varchar(40) NOT NULL, USER_TYPE varchar(20) DEFAULT 'Bidder', REMARKS varchar(200) DEFAULT NULL, PRIMARY KEY (USER_ID) ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_0900_ai_ci; /*!40101 SET character_set_client = @saved_cs_client */; -- -- Dumping data for table `IPL_USER` -- INSERT INTO IPL_USER VALUES ('admin','Qiheq667','Admin','System administrator'),('USR002','Uuauj599','Bidder',NULL),('USR003','Eouaj349','Bidder',NULL),('USR004','Xegah916','Bidder',NULL),('USR005','Fabai193','Bidder',NULL),('USR006','Debih891','Bidder',NULL),('USR007','Toias988','Bidder',NULL),('USR008','Oijiy996','Bidder',NULL),('USR009','Wixeb478','Bidder',NULL),('USR010','Dojoh725','Bidder',NULL),('USR011','Caxuc180','Bidder',NULL),('USR012','Zuvut888','Bidder',NULL),('USR013','Waiay336','Bidder',NULL),('USR014','Eunud150','Bidder',NULL),('USR015','Fodiy520','Bidder',NULL),('USR016','Taaon354','Bidder',NULL),('USR017','Qegat246','Bidder',NULL),('USR018','Latan774','Bidder',NULL),('USR019','Kenax859','Bidder',NULL),('USR020','Oehov784','Bidder',NULL),('USR021','Jidao220','Bidder',NULL),('USR022','Mesey643','Bidder',NULL),('USR023','Jotup765','Bidder',NULL),('USR024','Diooo769','Bidder',NULL),('USR025','Diyal596','Bidder',NULL),('USR026','Eotir416','Bidder',NULL),('USR027','Tojec918','Bidder',NULL),('USR028','Coiep158','Bidder',NULL),('USR029','Gazou951','Bidder',NULL),('USR030','Yuiip331','Bidder',NULL),('USR031','Kuoom311','Bidder',NULL); /*!40103 SET TIME_ZONE=@OLD_TIME_ZONE */; /*!40101 SET SQL_MODE=@OLD_SQL_MODE */; /*!40014 SET FOREIGN_KEY_CHECKS=@OLD_FOREIGN_KEY_CHECKS */; /*!40014 SET UNIQUE_CHECKS=@OLD_UNIQUE_CHECKS */; /*!40101 SET CHARACTER_SET_CLIENT=@OLD_CHARACTER_SET_CLIENT */; /*!40101 SET CHARACTER_SET_RESULTS=@OLD_CHARACTER_SET_RESULTS */; /*!40101 SET COLLATION_CONNECTION=@OLD_COLLATION_CONNECTION */; /*!40111 SET SQL_NOTES=@OLD_SQL_NOTES */; -- Dump completed Contact us to get any database assignment help with an affordable prices at contact@codersarts.com

Data The spreadsheet CLGoals.xlsx contains the number of goals scored in each UEFA Champions. League game to-date this season (three match weeks of sixteen games). The data are count data that take the values 0, 1, 2, .... Modeling Poisson Model The Poisson distribution is probably the most standard model for count data. The Poisson model, with parameter λ, assumes that Thus, P{X = 0} = exp(−λ), P{X = 1} = λ exp(−λ), P{X = 2} = λ^2 exp(−λ)/2, ... The expected value (mean) of the Poisson distribution is λ and the variance is also λ (thus, the standard deviation is √λ). Hurdle Model The Hurdle model, with parameters θ and λ, assumes that Thus, P{X = 0} = θ, P{X = 1} = (1 − θ)λ exp(−λ)/(1 − exp(−λ)), ... If θ = e^−λ then the Hurdle model is the same as the Poisson model. If θ < e^−λ , then zeros are less likely than under a Poisson model. If θ > e^−λ , then zeros are more likely than under a Poisson model. Likelihood The likelihood function is defined to be the probability of the observed data for a given param-eter value. If we have independent observations x1, x2, . . . , xn, then the likelihood is The log-likelihood is (natural) logarithm of the likelihood, thus it takes the form Task 1: Exploring 1. Read the data into R. Hint: The read.xlsx() function in the openxlsx R package is useful for doing this. 2. Produce a table that tabulates frequency of each number of goals. 3. Produce a plot of the frequency of each number of goals. 4. Calculate the mean and the standard deviation of the number of goals. Task 2a: Poisson Modelling 1. Write a function that calculates the log-likelihood function (for a specified value of λ) for the Poisson model for the UEFA Champions League data. 2. Plot the log-likelihood function for a range of values of λ. Hint: Make sure that λ = x is in the range. 3. Add a vertical line to the plot at the value x and visually verify that this maximizes the log-likelihood function. 4. Simulate 48 values from a Poisson model with λ = x and summarize the resulting values (contrasting them with the summaries produced in Task 1). 5. Simulate 48 values from a Poisson model for other values of λ and summarize Task 2b: Hurdle Modelling 1. Create a dHurdle() function that has arguments x, param that computes P{X = x} for the Hurdle model, where the first element of the vector param is θ and the second element of the vector param is λ. Ensure that the function can handle x being a vector of values. 2. Write a function that calculates the log-likelihood function (for a specified value of param) for the Hurdle model for the UEFA Champions League goal data. 3. Use the optim function to find the value of θ and λ that maximizes the log-likelihood. Hint: optim minimizes functions, by default, so you may want to write a function that computes minus the log-likelihood and minimize that. Alternatively, you can set control=list(fnscale=-1) as an argument in optim to make it maximize. 4. Comment on the value of θ found and compare the log-likelihood values found for the Poisson and Hurdle models. Contact us to get any machine learning project assignment help with an affordable prices at contact@codersarts.com

INTRODUCTION:- Over the last century, people’s eating habits have changed a lot. Technology has also contributed to the changes in consumer preference as their dependence on technology has moved them to do everything online including getting cooked meals delivered to their doorstep. Probably, this is the one of the reason why the mobile applications for ordering food online are so popular nowadays and became great source of ideas for UI/UX designers. Project Goals:- 1. To help people in various cities reach out to various restaurants of their choices. 2. To help restaurants reach out to a wider market. 3. To deliver orders to respective customers who place orders using the app. 4. To improve the cashless economy system. Challenge:- I found that factors contributing to the popularity of online food ordering were: lack of time to prepare food, availability of variety, rewards and cash-backs. It led me to think of convenient and more attractive app for users in order to meet their expectations more precisely while ordering food online. Task:- Create UI/UX design of Mob App for ordering food online. Problem Definition:- The mobile aggregator is an application that combines various thematic platforms in order to increase their level of sales and ensure the convenience of the choice of dishes and drinks by users. A distinctive feature of the application is a single design, user-friendly interface. I decided to create competing app where to make an order should be as simple as few clicks on mobile device. And it should be easy to understand and informative about the options and choices the users have. Process:- I’ve started the process with competitive research and I identified top three competitors. Analyzing and comparing the content of their apps helped me to determine the direction of development for this application. Empathy Mapping:- An Empathy map will help you understand your user’s needs while you develop a deeper understanding of the persons you are designing for. An Empathy Map is just one tool that can help you empathize and synthesize your observations from the research phase, and draw out unexpected insights about your user’s needs. Further, to build empathy with users, I started off with a set of casual interviews. This resulted in a preliminary set of requirements and creating User Personas. User persona:- A user persona is a representation of the goals and behavior of a hypothesized group of users. In most cases, personas are synthesized from data collected from interviews with users. Interviews helped me to discover list of main requests of the users: • Quality of the service • Good choice of listed restaurants • Delivery/Take away option • Price criterion • Reviews of other users Wireframes:- Wireframes are used early in the development process to establish the basic structure of an app before visual design and content is added. In the ideation phase I created wireframes presenting information architecture of the future layout. UI Design:- Choosing colors for designs in general is not easy. So why can’t we just choose colors for our projects based on how we feel about them or based on the trends? I’ve become more aware that knowing color theory, preferences and background of the client, culture of users and comprehension of your audience and competitors is the best way to choose right color for your project. Researching the competition is the best way to avoid unintentional plagiarism. It will give you an idea of common color threads that run through the other companies (whether they’re using correct colors or not) and it will show you what kind of direction to avoid to ensure that you don’t create something identical. Also I went through some interesting ideas on Pinterest and made a board with them for visualizing. Then I did analysis of colors, their perception and influence. The orange is a warm color. If you want to create an atmosphere of warmth and friendliness, you can safely choose orange. In addition, orange can add feelings of confidence and vitality, as well as stimulate the appetite. Orange should be used for products and services that are designed for a wide audience, as it creates the impression of low prices. This is how I came up with the mood board. Mood board:- Finally, after all researches and analysis were done I focused on designing all the screens through which a user will move, and created the visual elements — and their interactive properties — that facilitate this movement. I used things like patterns, spacing and color to guide the user and designed visual, interactive elements that respond in a way that feels natural to the user. Reflection:- Working on this project helped me learn that user research is crucial for the whole process of design application. Responsiveness is another important point in the UI/UX design. When the app shows the user immediate consequences of their action, there is a more personal connection. There are many people who support the idea that digital products of today should be minimalist and purely functional. However, my experience and practice shows that people want to both solve the problems and feel aesthetic satisfaction using apps. The balance of these things was my responsibility as UI/UX designer on this project. Conclusion:- Food delivery is not new anymore, and this market will only be more crowded and more competitive. The important thing to stand out from competitors may be not recruiting more users and restaurants registered. Instead, to identify the valuable restaurants where can bring loyal customers and increase customers and restaurants’ satisfaction when they interface with your platform can be more crucial in this market. In the past, only a few delivery platforms in this market and they tend to charge high commission and avoid direct contact among customers, drivers, and restaurants. However the delivery process in reality usually comes with a lot of uncertainty, so their tendency to cut off the communication actually cause users frustrated in their experience. This project was fun and awesome as I learnt new things regarding to user interface and experience design, the food industry, etc. Also, I tried to create a simple, functional and minimalistic app to improve the experience of users at the time of placing food orders. Hire Figma Experts for any kind of projects – urgent bug fixes, minor enhancement, full time and part time projects, If you need any type project hep, Our expert will help you start designing immediately. THANK YOU!!

The following database project will create an educational attainment "demand" forecast for the state of California for years greater than 2010. The demand forecast is the expected number of population who have obtained a certain level of education. The population is divided into age groups and education attainment is divided into different levels. The population of each group is estimated for each year up to year 2050. Implement the following steps to obtain and educational demand forecast for the state of California. The files can be downloaded below. 1. Create a ca_pop schema in your MySQL database. 2. Using your ca_pop schema, create an "educational_attainment" table which columns match the columns in the Excel spreadsheet "ca_pop_educational_attainment.csv". 3. Using your ca_pop schema, create a "pop_proj" table which columns match the columns in the Excel spreadsheet "pop_proj_1970_2050.csv". 4. Using the data loading technique for a csv file you learned in Module 1, load the data in "ca_pop_educational_attainment.csv" into the table "educational_attainment". 5. Using the data loading technique for a csv file you learned in Module 1, load the data in "pop_proj_1970_2050.csv" into the table "pop_proj". 6. Write a query to select the total population in each age group. 7. Use the query from Step 6 as a subquery to find each type of education attained by the population in that age group and the fraction of the population of that age group that has that educational attainment. Label the fraction column output as "coefficient". For instance, the fraction of the population in age group 00 - 17 who has an education attainment of Bachelor's degree or higher is 0.0015, which is the "coefficient". 8. Create a demographics table from the SQL query from Step 7. 9. Create a query on the "pop_proj" table which shows the population count by date_year and age. 10. Use that query from Step 9 as a subquery and join it to the demographics table using the following case statement: demographics.age = case when temp_pop.age 18 then '00 to 17' when temp_pop.age 64 then '65 to 80+' else '18 to 64' end "temp_pop" is an alias for the subquery. Use the following calculation for the demand output: round(sum(temp_pop.total_pop * demographics.coefficient)) as demand Output the demand grouped by year and education level. Write each query you used in Steps 1 - 8 in a text file. If a query produced a result set, then list the first ten rows of each row set after the query. Bundle your lessons learned report, your queries and your query results text file, and your MySQL query explanations from both before and after adding table indexes into a single zip file and submit that zip file as your final portfolio. If you need any help related to database project then you can contact at contact@codersarts.com and get instant help with an affordable prices.

Introduction:- Today, more and more smart devices are entering your home. These are regular household items, like a TV or fridge but now these items can sense information about you and pass it along. As you fill your home with these smart devices, how can they let you and your family knows more about your energy usage to help you save more money? Project Overview & the Challenge:- Utility bills are pretty light on information. They only tell us 1) How much electricity, water or gas we have used. 2) How much we have to pay. Unfortunately, that’s about it. If we want to cut our energy use to save money, we should do a guessing game. We either have to try to ax our unnecessary consumption as a whole or simply guess which devices are heavy users. Best solution comes to mind might be buying smart home devices which enable us to control home appliances remotely and reduce the power usage. But the problem is that the smart home devices only help in controlling and generating automation which does a big favor but is not enough. There are no data about monthly spending, estimated bills, and energy usage reduction tips in such devices. However; Home Energy Monitors connect to your circuit breaker. They allow you to track your energy consumption with much finer detail, put the ax away and cut your energy consumption with a scalpel instead. Goal:- So, the goal of this project is to design an intelligent energy monitoring system, which enables users to track energy usage of any device, control and set up routines for home appliances, get notifications for devices left turned on and view a timeline of daily home activity. With this app, users can get an insight of their electricity, water and gas bills, while they can pay them with one tap. Objectives: Energy Consumption: Homeowners see a visual report of energy usage to get readings of how their house operates and gain intuition about power consumption. Home Appliances Control: Users can easily control gadgets from anywhere. Real-time cost tracking: Tracking the cost of energy consumption in real-time will allow users to watch as their electricity consumption and costs increase or decrease. Goals: Users can define energy usage and spending targets to generate and manage the monthly household budgets and avoid unexpected expenses. Notifications To Reduce Energy Usage: Notifications for energy efficiency help to verify spurious utility billing and save more money on monthly bills. Setting Up Routines: Users can create automation with routines, to streamline their lives and also be sure that they’re conserving energy and saving money. Research:- User Reviews The first step was to analyze the competitors and get familiar with the existing Home Energy products, their pros and cons, and user’s reviews. User Research:- I decided to design a solution for millennial (25–36 years old) who are already accustomed to using technology on a daily basis, so I conducted a semi-structured interview with some students and young professionals to discover their pain points, needs, and requirements. Interview Questions:- How do you track your home energy usage? Do you own any smart devices? How was your experience with them? What problems could you see arising with the whole smart home ecosystem? What features or qualities you like about smart home energy monitor devices? Do you plan for monthly household budgets? Can you achieve those targets? How do you pay your bills? Do you pay on time? Major Findings:- In my market studies and initial user interviews, I discovered commonalities among users insights that helped me to find issues users ran into while working with such smart devices. Smart home devices take a long time to detect all the home appliances which result in incorrect data and wrong estimation of bills. Users prefer to be in charge and speed up the detection process by educating manually and providing human data. Users expect a smart home ecosystem to develop intelligent pattern recognition and operate according to user’s behavior and preferences. Moreover, they also expressed the preference for bill reminders and straightforward, online payment in such a system. Sketches:- Once I gained a clearer picture of users, I started sketching design solutions and preparing low-fi prototypes. By doing so, I was able to go through design ideas and quickly find the right ones. User Flow Wireframes:- I took a step forward and prepared wireframes to focus on usability and ensure that the app is built according to goals. Also, I had the chance to ask for feedback much sooner in the process. Visual Design:- Visual Design Iterations Before I start designing I knew how other applications in the market, look, so I tried new styles for showing energy consumption, with each iteration I made improvements. Colour Palette I love the bright side of the color palette and using gradients for grabbing more attention. I think it’s a great tool to enrich your design. Onboarding Flow:- The goal of onboarding is to say a lot with illustrations and very little text, so users get an insight of app’s functionality. At any moment, the user can opt to sign up by pressing Get Started, or he can Log in if he has got an account with already setup devices. Note that the Get Started button stands out more to make it easier for the app newbie have to sign up. Device Setup Flow:- If a user has an account with already setup devices, he can opt to log in. If he has just installed Sweet Home devices at home, first, he has to connect devices to the application. The process includes searching for nearby devices and entering Device ID Number to connect. If device setup is successful, the user can proceed with registration. Dashboard:- In the dashboard, the user can see an overview of energy usage, most energy consumption devices in each place at home and total spendings. The background color changes as user swipes for different places at home. Users can see energy usage in details, with statistics showing which and when a device has been using lots of energy, and how much each of them cost. Also, the user can see which places at home are high energy users. The goal of showing results for different places and energy types is to give correct data about total spending. Conclusion:- I think that the learnings from the app design process affect the way I will approach similar projects in the future. The way I have learned about the user-centered design process has given me a great amount of information needed to design apps with the user in mind by gaining a deeper understanding of human behavior and emotions. I liked trying different techniques to gather more information about smart home users, how they interact with these devices and what they wished to have in such apps.

To better understand this concept we will first discuss a few very common examples. The famous Wal-Mart’s beer and diaper parable: A sales person from Wal-Mart trying to increase the sales of the store analysed all sales records. He discovered a general trend that people who buy diapers often buy beers as well. Although the two products are unrelated this relation arises because parenting is a difficult task for many. By bundling the products together and giving discounts on them the sale person was able to escalate the sales. Likewise, you may have seen in supermarkets that bread and jams are placed together, which makes it easy for a customer to find them together and thereby increasing the chance of purchasing both the items. Also, you must have noticed that the local vegetable seller always bundles onions and potatoes together. He even offers a discount to people who buy these bundles. He does so because he realizes that people who buy potatoes also buy onions. Therefore, by bunching them together, he makes it convenient for the customers. At the same time, he also increases his sales performance. It also allows him to offer discounts. Moreover, apart from the field of sales, in medical diagnosis for instance, understanding which symptoms tend to co-morbid can help to improve patient care and medicine prescription. The above mentioned are examples of Association rules in data mining. It will help us get an idea about the Apriori algorithm. To generalise the above examples, if there is a pair of items, A and B (or more), that are frequently bought together: Both A and B can be placed together on a shelf, so that buyers of one item would most probably buy the other. Promotional discounts could be applied to just one out of the two items. Advertisements on A could be targeted at buyers of B. A and B could be combined into a new product, such as having B in flavours of A. To uncover such associations we make use of Association rules in data mining. Now we will define some concepts which will aid us in understanding the Apriori algorithm. Association rule mining: Association Rule Mining is used when we want to discover an association between different objects in a set, find frequent patterns in a transaction database, relational databases or any other information repository. It tells us what items do customers frequently buy together by generating a set of rules called Association Rules. To put it simply, it gives you output as rules in form if this then that. A formal definition of the problem of association rules given by Rakesh Agrawal, the President and Founder of the Data Insights Laboratories: Let I = {i1, i2, i3, …, in} be a set of n attributes called items and D={t1, t2, …, tn} be the set of transactions. It is called database. Every transaction, ti in D has a unique transaction ID, and it consists of a subset of itemsets in I. A rule can be defined as an implication, X⟶Y where X and Y are subsets of I(X, Y⊆ I), and they have no element in common, i.e., X∩Y. X and Y are the antecedent and the consequent of the rule, respectively. Let us take a look at an example. Consider the following database, where each row is a transaction and each cell is an individual item of the transaction: The association rules that can be determined from this database are the following: 100% of sets with alpha also contain beta 50% of sets with alpha, beta also have epsilon 50% of sets with alpha, beta also have theta There are three common ways to measure association: 1. Support: refers to items’ frequency of occurrence. It tells how popular an itemset is, as measured by the proportion of transactions in which an itemset appears. supp (X) = Number of transaction in which X appears / Total number of transactions 2. Confidence: It is a conditional probability. It tells us how likely item Y is purchased when item X is purchased, it is expressed as {X -> Y}. This is measured by the proportion of transactions with item X, in which item Y also appears. conf (X⟶Y) = supp (X ∪ Y) / supp (X) But there is a major drawback. It only takes into account the popularity of the itemset X and not the popularity of Y. If Y is as popular as X then there will be a higher probability that a transaction containing X will also contain Y thus increasing the confidence. To overcome this drawback there is another measure called lift. 3. Lift: It signifies the likelihood of the itemset Y being purchased when item X is purchased while taking into account the popularity of Y. lift ( X⟶Y) = supp (X ∪ Y) / supp (X) ∗ supp (Y) A lift value greater than 1 means that item Y is likely to be bought if item X is bought, while a value less than 1 means that item Y is unlikely to be bought if item X is bought. Frequent Itemset: Itemset: A set of items together is called an itemset. If any itemset has k-items it is called a k-itemset. An itemset consists of two or more items. An itemset that occurs frequently is called a frequent itemset. A set of items is called frequent if it satisfies a minimum threshold value for support and confidence. Support shows transactions with items purchased together in a single transaction. Confidence shows transactions where the items are purchased one after the other. We will now discuss Apriori algorithm. Apriori Algorithm Definition: It is an algorithm for frequent itemset mining and association rule learning over relational databases. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database. The frequent item sets determined by Apriori can be used to determine association rules which highlight general trends in the database. It was proposed by R. Agrawal and R. Srikant in 1994 for finding frequent itemsets in a dataset for boolean association rule. Name of the algorithm is Apriori because it uses prior knowledge of frequent itemset properties. A major concept in Apriori algorithm is the anti-monotonicity of the support measure. It assumes that All subsets of a frequent itemset must be frequent Similarly, for any infrequent itemset, all its supersets must be infrequent too Apriori uses a "bottom up" approach, where frequent subsets are extended one item at a time (a step known as candidate generation), and groups of candidates are tested against the data. The algorithm terminates when no further successful extensions are found. This algorithm uses two steps “join” and “prune” to reduce the search space. It is an iterative approach to discover the most frequent itemsets. The steps followed in the Apriori Algorithm of data mining are: Join Step: This step generates (K+1) itemset from K-itemsets by joining each item with itself. Prune Step: This step scans the count of each item in the database. If the candidate item does not meet minimum support, then it is regarded as infrequent and thus it is removed. This step is performed to reduce the size of the candidate itemsets. Steps In Apriori: This algorithm follows a sequence of steps to find the most frequent itemset in the given database. It follows the join and the prune steps iteratively until the most frequent itemset is achieved. A minimum support threshold is given in the problem or it is assumed by the user. Step 1: In the first iteration of the algorithm, each item is taken as a 1-itemset candidate. The algorithm will count the frequency of each item. Step 2: Let the minimum support be assigned ( e.g. 2). The set of 1 – itemset whose frequency is satisfying the minimum support are determined. Only those candidates which count more than or equal to minimum support, are taken ahead for the next iteration and the others are pruned. Step 3: Then, 2-itemset frequent items with minimum support are discovered. For this step, the 2-itemset is generated by forming a group of 2 by combining items with itself (join step). Step 4: The 2-itemset candidates are pruned using minimum support threshold value. Now the table will have 2 –itemsets with minimum support only. Step 5: The next iteration will form 3 –itemsets using join and prune step. This iteration will follow anti-monotone property where the subsets of 3-itemsets, that is the 2 –itemset subsets of each group fall in minimum support. If all 2-itemset subsets are frequent then the superset will be frequent otherwise it is pruned. Step 6: Next step will make 4-itemset by joining 3-itemset with itself and pruning if its subset does not meet the minimum support criteria. The algorithm is stopped when the most frequent itemset is achieved. Let us discuss an example to understand these steps more clearly. Consider the following table: We will generate the association rules for the above table using Apriori algorithm. Let the minimum support be 3. Step 1: Create the frequency distribution table of the items. Step 2: This is prune step, the item whose count does not meet minimum support are removed. Only I5 item does not meet minimum support of 3, thus it is removed. Items I1, I2, I3, I4 meet minimum support count and therefore they are frequent. Step 3: This is a join step, here we will generate a list of all pairs of the frequent items (2-itemset). Step 4: This is a prune step. The pairs I1,I4 and I3I4 do not meet the minimum support and are removed. Step 5: This is a join and prune step, here we will now look for frequent triples in the database, but we can already exclude all the triples that contain pairs which were not frequent in the previous step (following the anti-monotone property of support measure). For example, in the table below: We can see for itemset {I1, I2, I4}, the subsets are: {I1, I2}, {I1, I4}, {I2, I4}. The pair {I1, I4} is not frequent, as it do not occur in TABLE-5. Thus {I1, I2, I4} is not frequent, hence it is deleted. Similarly, all the triples are pruned in this way and we are left only the itemset {I1, I2, I3} which is frequent as all its subsets are frequent (again the anti-monotone property of support measure comes into play here). Step 6: Generate Association Rules: From the frequent itemset discovered above and setting the minimum confidence threshold to be 60%, the association could be: {I1, I2} => {I3} Confidence = support {I1, I2, I3} / support {I1, I2} = (3/ 4)* 100 = 75% {I1, I3} => {I2} Confidence = support {I1, I2, I3} / support {I1, I3} = (3/ 3)* 100 = 100% {I2, I3} => {I1} Confidence = support {I1, I2, I3} / support {I2, I3} = (3/ 4)* 100 = 75% {I1} => {I2, I3} Confidence = support {I1, I2, I3} / support {I1} = (3/ 4)* 100 = 75% {I2} => {I1, I3} Confidence = support {I1, I2, I3} / support {I2 = (3/ 5)* 100 = 60% {I3} => {I1, I2} Confidence = support {I1, I2, I3} / support {I3} = (3/ 4)* 100 = 75% This shows that all the above association rules are strong if minimum confidence threshold is 60%.