BACKGROUND: INCOME CLASSIFIER Census data is one of the largest sources of a variety of statistical information related to population. It typically includes information related to Age, Gender, Household composition, Employment Details, Accommodation Details, and so on. Till recent years, collecting census data has been a manual process involving field visits and registrations. With advances in technology, the methods of collecting this data have improved to a great extent. And so is the population! With a population of more than 7 billion, one can imagine the volume of the census data associated with it. This data is collected from a variety of sources such as manual entries, online surveys, data from social media and search engines and is in various formats. Traditional database systems are inefficient at handling such data. This is where Big Data Technologies come into picture. As per a study by U.S. Census Bureau, analytics on census data could have been helpful during the Great Recession in various ways such as avoiding job loss in Supply-Chain businesses, reducing housing foreclosure rates, and so on. Big Data Analytics refers to a set of tools and methods used to obtain knowledge from information. Application of Big Data Analytics on census data can facilitate better decision making in various Government and Industrial sectors such as Healthcare, Education, Finance, Retail, and Housing. One such application is an Income Classifier. In this project, let us take a sample of world census data and build an Income Classifier using various Big Data Techniques described in subsequent sections. LEARNING OBJECTIVES 1. HDFS and Hive for Data Storage and Management 2. Data Ingestion using Sqoop 3. Machine Learning using PySpark This Project is divided into three parts to cover the above learning objectives. DATASET The dataset named censusdata.csv is provided in your LMS. We will be using the same dataset for all the three parts. Input: The dataset contains 15 columns Targeted Column: Income; the income is provided in the form of two values: < 50k or >50k Number of other columns:14; these are demographics and other features used for describing a person List of Attributes: age: continuous workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked fnlwgt: continuous. education: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool education-num: continuous marital-status: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse occupation: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof- specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces relationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black sex: Female, Male capital-gain: continuous capital-loss: continuous hours-per-week: continuous native-country: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands income: >50K, <=50K TASKS 1. HDFS AND HIVE Problem Statement 1 Census Analytics is a project where you need to collect the data of people along with their incomes. As the census data is usually in large volume, the analysis of the data will be a cumbersome task. To overcome this, we will be using the Hadoop Ecosystem. As a first step, you need to load the data into HDFS and create a table in Hive that can be used for querying the data. You have to create different types of tables and execute queries, as mentioned below and compare the time required for execution for different types of tables. Steps to be performed: 1. Download the dataset named censusdata.csv that is provided in your LMS 2. Load the downloaded data into HDFS 3. Create an internal table in Hive to store the data a. Create the table structure b. Load the data from HDFS into the Hive table 4. Create an internal table in Hive with partitions a. Create a Partition Table in Hive using “workclass” as the Partition Key b. Load data from the staging table (Table created in Step 3) into this table 5. Create an external table in Hive to hold the same data stored in HDFS 6. Create an external table in Hive with partitions using “workclass” as Partition Key 7. For each of the four tables created above, perform the following operations Find out the number of adults based on income and gender. Note the time taken for getting the result Find out the number of adults based on income and workclass. Note the time taken for getting the result Write your observations by comparing the time taken for executing the commands between: a. Internal & External Tables b. Partitioned & Non-partitioned Tables 8. Delete the internal as well as external tables. Comment on the effect on data and metadata after the deletion is performed for both internal and external tables. 2. DATA INGESTION Problem Statement 2 In a similar scenario as above, the data is available in a MySQL database. Due to the inefficiency of RDBMS systems to store and analyze Big Data, it is recommended that we move the data to the Hadoop Ecosystem. Ingest the data from MySQL database into Hive using Sqoop. Data pipeline needs to be created to ingest data from an RDBMS into Hadoop Cluster and then load data into Hive. To make the analysis faster, use Spark on top of Hive after getting data into the Hadoop cluster. Using Spark, query different tables from Hive to analyze the dataset. Steps to be performed: 1. Create the necessary structure in a MySQL database using the steps mentioned below: a. Create a new database in MySQL with the name midproject b. Create a table in this database with the name census_adult to store the input dataset c. Load the dataset into the table d. Verify whether data is loaded properly e. Verify the table for unwanted data such as ‘?’,’Nan’ and ‘Null’ f. Get the counts for the columns which contain unwanted data g. Clean the data by replacing the unwanted data with others 2. Import the above data from MySQL into a Hive table using Sqoop 3. Connect to PySpark using web console to access the created Hive table. Perform the following queries and note the time taken for execution in each of the queries. a. Query the table to get the number of adults based on income and gender b. Query the table to get the number of adults based on income and workclass Hint: To access Hive tables using Spark console, use the following commands: >>pyspark2 >>from pyspark.context import SparkContext >>from pyspark.sql import HiveContext >>sqlContext = HiveContext(sc) 4. Access the following two tables created as part of Problem 1 (HDFS and Hive) and perform the steps as mentioned below: a. Access Hive External Table with partition i. Query the table to get the number of adults based on income and gender ii. Query the table to get the number of adults based on income and workclass b. Access Hive Internal Table with Partition i. Query the table to get the number of adults based on income and gender ii. Query the table to get the number of adults based on income and workclass Make a note of the time taken for getting the result in comparison with the time taken to get results with Hive. 5. Comment on the time taken for executing these commands using Spark as compared to the time taken for execution in Hive (Problem Statement 1). 3. INCOME CLASSIFIER Problem Statement 3 Income Classifier is an application that will be used to classify individuals based on the annual income. An individual’s annual income may be influenced by various factors such as age, gender, occupation, education level, and so on. Write a program to build classification models using PySpark. Explore the possibility of classifying income based on an individual’s personal information. Perform the following steps to build and compare different classifiers. Use Jupyter Notebook to write the program. Steps to be performed: 1. Load data using PySpark 2. Perform Exploratory Data Analysis (EDA) and Data Cleaning based on the following points: a. Find the shape and schema of the dataset b. Obtain insights (statistics) of different columns c. Obtain the Unique values of Categorical Columns d. Check if any unwanted values are present in the data such as Null, ? or NaN e. Remove unwanted values if present in any of the columns (numerical as well as categorical columns) f. Obtain the relationship between different columns using covariance which shows the degree of interdependence of the two columns g. Obtain distinct values and their counts in categorical columns. h. Create a crosstab on two different columns (example, age & workclass) i. Perform an “Integer Type Check” on the columns of the Spark DataFrame and display the columns satisfying the same j. Obtain correlation between the above columns using pandas scatter plot 3. Data Preprocessing Since we are going to use classification algorithms like Logistic Regression, we will have to convert all the categorical columns in the dataset to numerical values. We can achieve this using 1) Category Indexing In this, we assign a numerical value to each category (eg: Male: 0, Female: 1) 2) One-Hot Encoding a. Conversion of categorical columns into Numerical Columns i. Category Indexing using string indexing for all categorical columns ii. Label Indexing for income column as income_class iii. One Hot Encoding which generates binary columns for features iv. Use Vector assembler to get a single vector column for features v. Make it as an array of stages so that it can be passed to a pipeline This converts categories into binary vectors with at most one nonzero value (eg: (Blue: [1, 0]), (Green: [0, 1]), (Red: [0, 0])) In this step, we will be using a combination of Category Indexing and One-Hot Encoding (Note: Make sure that the output column name for Income should be income_class) 4. Build the Pipeline to perform multiple tasks a. Pass the stages of Data Preprocessing (created in Step 3) to the pipeline to create an instance with the stages b. Estimator that can fit on a DataFrame to produce a model c. Transform the DataFrame with features to DataFrame with predictions d. Generate a DataFrame which can hold a variety of datatypes including feature vectors 5. Split the dataset into two parts (80%-20%) as Train and Test Datasets a. Check the shape of the datasets b. Check the distribution of income class (0,1) in train and test dataset 6. Build the following Classifiers a. Logistic Regression b. Decision Tree c. Random Forest d. Gradient Boosted Tree e. Naïve Bayes Common Tasks for all the Classifiers: Train and Evaluate the Model Print ROC metrics & model accuracy Tune the Hyperparameters and print the improved accuracy Compare the accuracy of the 5 models and comment on the models which performed better as compared to other in the list. If you need solution for this assignment or have project a similar assignment, you can leave us a mail at contact@codersarts.com directly.

This App Help parents and Teachers to track progress report of student by using this App In this app we have added features like student information teacher information and student parent details into this App. Teacher can assign the student Work and his/her progress and update student progress day by day, then teacher can read the information about the student. when parent or teacher open the app we go to splash activity of an app n go to the home page. 2. When student open navigation page there is a List Of Terms, Courses Term Wise, List of Courses and List Of Assessments. 3. In the assessment page there is Add New Courses Give the Information of Tittle Course Start date , Course End Date, Status , Mentor Information Mentor Mobile Number ,Email at the end on Note page Give the Student Progress details. In In the Term wise parent and teacher can track student progress Report Know where student needs improvement, 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

Project Details Your tasks in this project are as follows: Data wrangling, which consists of: Gathering data (downloadable file in the Resources tab in the left most panel of your classroom and linked in step 1 below). Assessing data Cleaning data Storing, analyzing, and visualizing your wrangled data Reporting on 1) your data wrangling efforts and 2) your data analyses and visualizations. Gathering Data for this Project Gather each of the three pieces of data as described below in a Jupyter Notebook titled wrangle_act.ipynb: 1. The WeRateDogs Twitter archive. I am giving this file to you, so imagine it as a file on hand. Download this file manually by clicking the following link: twitter_archive_enhanced.csv 2. The tweet image predictions, i.e., what breed of dog (or other object, animal, etc.) is present in each tweet according to a neural network. This file (image_predictions.tsv) is hosted on Udacity's servers and should be downloaded programmatically using the Requests library and the following URL: https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv 3. Each tweet's retweet count and favorite ("like") count at minimum, and any additional data you find interesting. Using the tweet IDs in the WeRateDogs Twitter archive, query the Twitter API for each tweet's JSON data using Python's Tweepy library and store each tweet's entire set of JSON data in a file called tweet_json.txt file. Each tweet's JSON data should be written to its own line. Then read this .txt file line by line into a pandas DataFrame with (at minimum) tweet ID, retweet count, and favorite count. Note: do not include your Twitter API keys, secrets, and tokens in your project submission. If you decide to complete your project in the Project Workspace, note that you can upload files to the Jupyter Notebook Workspace by clicking the "Upload" button in the top righthand corner of the dashboard. Assessing Data for this Project After gathering each of the above pieces of data, assess them visually and programmatically for quality and tidiness issues. Detect and document at least eight (8) quality issues and two (2) tidiness issues in your wrangle_act.ipynb Jupyter Notebook. To meet specifications, the issues that satisfy the Project Motivation (see the Key Points header on the previous page) must be assessed. Cleaning Data for this Project Clean each of the issues you documented while assessing. Perform this cleaning in wrangle_act.ipynb as well. The result should be a high quality and tidy master pandas DataFrame (or DataFrames, if appropriate). Again, the issues that satisfy the Project Motivation must be cleaned. Storing, Analyzing, and Visualizing Data for this Project Store the clean DataFrame(s) in a CSV file with the main one named twitter_archive_master.csv. If additional files exist because multiple tables are required for tidiness, name these files appropriately. Additionally, you may store the cleaned data in a SQLite database (which is to be submitted as well if you do). Analyze and visualize your wrangled data in your wrangle_act.ipynb Jupyter Notebook. At least three (3) insights and one (1) visualization must be produced. Reporting for this Project Create a 300-600 word written report called wrangle_report.pdf or wrangle_report.html that briefly describes your wrangling efforts. This is to be framed as an internal document. Create a 250-word-minimum written report called act_report.pdf or act_report.html that communicates the insights and displays the visualization(s) produced from your wrangled data. This is to be framed as an external document, like a blog post or magazine article, for example. Both of these documents can be created in separate Jupyter Notebooks using the Markdown functionality of Jupyter Notebooks, then downloading those notebooks as PDF files or HTML files (see image below). You might prefer to use a word processor like Google Docs or Microsoft Word, however. If you are need solution of these type of problems then you can contact us at conact@codersarts.com and get instant help.

BACKGROUND: INCOME CLASSIFIER Census data is one of the largest sources of a variety of statistical information related to population. It typically includes information related to Age, Gender, Household composition, Employment Details, Accommodation Details, and so on. Till recent years, collecting census data has been a manual process involving field visits and registrations. With advances in technology, the methods of collecting this data have improved to a great extent. And so is the population! With a population of more than 7 billion,one can imagine the volumeof the census data associated with it. This data is collected from a variety of sources such as manual entries, online surveys, data from social media and search engines and is in various formats. Traditional database systems are inefficient at handling such data. This is where Big Data Technologies come into picture. As per a study by U.S. Census Bureau, analytics on census data could have been helpful during the Great Recession in various ways such as avoiding job loss in Supply-Chain businesses, reducing housing foreclosure rates, and so on. Big Data Analytics refers to a set of tools and methods used to obtain knowledge from information. Application of Big Data Analytics on census data can facilitate better decision making in various Government and Industrial sectors such as Healthcare, Education, Finance, Retail, and Housing. One such application is an Income Classifier. In this project, let us take a sample of world census data and build an Income Classifier using various Big Data Techniques described in subsequent sections. LEARNING OBJECTIVES 1. HDFS and Hive for Data Storage and Management 2. Data Ingestion using Sqoop 3. Machine Learning using PySpark This Project is divided into three parts to cover the above learning objectives. DATASET The dataset named censusdata.csv is provided in your LMS. We will be using the same dataset for all the three parts. Input: The dataset contains 15 columns Targeted Column: Income; the income is provided in the form of two values: <=50k or >50k Number of other columns: 14; these are demographics and other features used for describing a person List of Attributes: age: continuous workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked fnlwgt: continuous. education: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool education-num: continuous marital-status: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse occupation: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof- specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces relationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other,Black sex: Female, Male capital-gain: continuous capital-loss: continuous hours-per-week: continuous native-country: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands income: >50K, <=50K TASKS 1. HDFS AND HIVE Problem Statement 1 Census Analytics is a project where you need to collect the data of people along with their incomes. As the census data is usually in large volume, the analysis of the data will be a cumbersome task. To overcome this, we will be using the Hadoop Ecosystem. As a first step, you need to load the data into HDFS and create a table in Hive that can be used for querying the data. You have to create different types of tables and execute queries, as mentioned below and compare the time required for execution for different types of tables. Steps to be performed: 1. Download the dataset named censusdata.csv that is provided in your LMS 2. Load the downloaded data into HDFS 3. Create an internal table in Hive to store the data a. Create the table structure b. Load the data from HDFS into the Hive table 4. Create an internal table in Hive with partitions a. Create a Partition Table in Hiveusing “workclass” as the Partition Key b. Load data from the staging table (Table created in Step 3) into this table 5. Create an external table in Hive to hold the same data stored in HDFS 6. Create an externaltable in Hivewith partitions using“workclass” as Partition Key 7. For each of the four tables created above, perform the following operations Find out the number of adults based on income and gender. Note the time taken for getting the result Find out the number of adults based on income and workclass. Note the time taken for getting the result Write your observations by comparing the time taken for executing the commands between: a. Internal & External Tables b. Partitioned & Non-partitioned Tables 8. Delete the internal as well as external tables. Comment on the effect on dataand metadata after the deletion is performed for both internal and external tables. 2. DATA INGESTION Problem Statement 2 In a similar scenario as above, the data is available in a MySQL database. Due to the inefficiency of RDBMS systems to store and analyze Big Data, it is recommended that we move the data to the Hadoop Ecosystem. Ingest the data from MySQL database into Hive using Sqoop. Data pipeline needs to be created to ingest data from an RDBMS into Hadoop Cluster and then load data into Hive To make the analysis faster, use Spark on top of Hive after getting data into the Hadoop cluster. Using Spark, query different tables from Hive to analyze the dataset. Steps to be performed: 1. Create the necessary structure in a MySQL database using the steps mentioned below: a. Create a new database in MySQL with the name midproject b. Create a table in this database with the name census_adult to store the input dataset c. Load the dataset into the table d. Verify whether data is loaded properly e. Verify the table for unwanted data such as ‘?’,’Nan’ and ‘Null’ f. Get the counts for the columns which contain unwanted data g. Clean the data by replacing the unwanted data with others 2. Import the above data from MySQL into a Hive table using Sqoop 3. Connect to PySpark using web console to access the created Hive table. Perform the following queries and note the time taken for execution in each of the queries. Contact us to get any assignment help related to hadoop big data then you can contact us at contact@codersarts.com

1. Consider a dataset with attributes x, y, and z, where the decision attribute is z. Suppose that we have determined that there are two support vectors: the 2D point (-7, 10) which corresponds to an instance in the dataset that has x = -7, y = 10, and z = -1, and the 2D point (-6, 9) which corresponds to an instance in the dataset that has x = -6, y = 9, and z = 1. The equations for the support vector machine are shown below where s1 = (-7 10 1) is the augmented support vector for (-7, 10), s2 = (-6 9 1) is the augmented support vector for (-6, 9), and α1 and α2 are the respective parameters for the support vectors that will be used to define the 2D hyperplane. α1φ(s1) • φ(s1) + α2φ(s2) • φ(s1) = -1 α1φ(s1) • φ(s2) + α2φ(s2) • φ(s2) = 1 For φ, use φ(x y) = ( x+y 10-y ) a. Solve for each αi showing ALL of your work! (2 pts.) b. Using your results from part a., define the discriminating 2D hyperplane for this dataset; that is, give an equation for the 2D hyperplane. Show your work! (2 pts.) c. Using the support vector machine you have defined, predict the value for the decision attribute (z) for an instance that has x = 2 and y = 5. Show your work! (2 pts.) 2. Write a Python function which, given a dataframe, constructs (and returns) a Naïve Bayesian network. You can assume that all of the attributes have nominal values and that the decision attribute is the last attribute in the dataframe. Apply Laplace smoothing to the conditional probabilities of the attributes (as explained in class) using a value of λ = 1. Output the conditional probability table for each node in the Bayesian network so that your work can be checked! Test your function by running it on contact-lenses.csv AND hypothyroid.csv (both of which are posted on Canvas with this assignment). Note that you can check your work by running Classify -> weka -> Classifiers -> bayes -> NaiveBayesSimple in Weka. ALSO demonstrate that you have successfully created these particular Bayesian networks by executing code that predict the following: For contact-lenses: contact-lenses = soft, age = presbyopic, other attributes = None For hypothyroid: class = negative, sex = U, other attributes = None Note: You will NOT get full credit for your solution if you hard-code your code to work just for the specified test datasets! Get help with an affordable prices if you face any problem in machine learning and send your request at contact@codersarts.com

Use the tables below to create queries to answer these questions: 1. What is the average initial margin and average actual margin (if applicable) by make and model? 2. For each month by region, on average how long does a customer take topurchase? 3. For each month by region, what percentage of test drive requests result insales? 4. By region, what is the average number of test drives completed by a vehicle within the first week of being listed? 5. How often does a customer complete the first test drive appointment she schedules? If you need any help then please contact us at contact@codersarts.com and get instant help within affordable price.

Multiple Choice Questions For each question below, please select a single correct answer. 1 point for each question. (1) You have a reading of a patient’s temperature at 99.3 0 F. Which category of data this reading belongs to? A. Unstructured data B. Nominal data C. Ordinal data D. Quantitative data (2) CHEM-7, a basic metabolic panel, is a group of blood test that provides information about a patient’s metabolism. It has 7 components: blood urea nitrogen (BUN), carbon dioxide (CO 2 ), creatinine, glucose, serum chloride (Cl - ), serum potassium (K + ), and serum sodium (Na + ). Considering the following result of a CHEM-7 test: CHEM-7 BUN: 15 mg/dl CO 2 : 23 mmol/l Creatinine: 1.1 mg/dl Glucose: 92 mg/dl Cl - : 108 mmol/l K + : 4.1 mEq/l Na + : 138 mEq/l If you select to use entity, attribute and value model to represent the CHEM-7 data, which following statement is incorrect? A. 108 mmol/l is the value of the data attribute Cl - B. CHEM-7 is the data entity C. Creatinine is the value of the data entity CHEM-7 D. Glucose is a data attribute for the CHEM-7 data entity (3) Which of the following statement is not a benefit of using a DBMS? A. Addressing information needs B. Concurrent data access C. Data integrity D. Efficient data access (4) Consider the following design of a table with the records populated. Please identify the problem in the data fields of this table: A. Calculated field B. Multipart field C. Multivalued field D. Unspecified field (5) Considering the following two tables and relationship in a database: Patient Table: Diagnosis Table: The relationship between the two tables? If you need any tutorial or assignment related help then you can contact us at conact@codersarts.com

Description: Load the Heart Disease Dataset from UCI Repository Encrypt the Dataset using Blowfish and ECC Save the encrypted dataset as csv file. Decrypt the Encrypted dataset using Blowfish and ECC Save the decrypted dataset as csv file. Note: All the process is going to be done as per the description given above. Not a real time project. No GUI is provided. Language: Python Front End: Anaconda Navigator - Spyder Contact us to get solution of this cryptography algorithms then you can contact at contact@codersarts.com

Question 1: Explain what overfitting is and three different techniques to help avoid it when optimizing deep neural networks. Question 2: You have trained five different models on the same set of data and they each get 90% precision. Can you combine these models, without retraining, to get better results? If so explain how. If not, explain why not. Question 3: If you are using learners with regularization and AdaBoost underfits the training data, how should you adjust the parameters of AdaBoost or its learners? Question 4: An office building with 10 floors has 3 elevators, each of which can hold up to 4 people. Every floor has a pair of call buttons to request up or down service, except the top and bottom floors which have only one button each. When the elevator arrives, a person enters and presses the number of the floor they want. Each elevator can store the floor numbers entered and stops at each floor that is requested. Describe the state and action spaces and calculate their size. Describe a reinforcement learner (reward function and learning method) that can learn to control the elevators, delivering passengers as expected while not wasting energy. Be sure to indicate whether delayed rewards should be used. Question 5: Given the three-unit neural network with weights as indicated in which units form products of their weighted inputs rather than sums, write a function for the output value y of node C based on the one input, x, and other network parameters. For example, the input to C would be the product of all incoming weights and associated activations. There are no biases. Unit C is a linear unit, whereas units A and B are sigmoids, φ(z)=(1−e^−z)^−1 If you need solution of above machine learning project then you can contact us at contact@codersarts.com

Rest acronym for Representation state transfer. It is architectural style for distributed hyper media systems and was first represented by fielding in 2000 in his famous, Like any other architectural style REST it also have its own 6 guiding constrains which must be satisfied if an interface need to be referred as Restful, Principles of rest : Client - Server stateless cacheable Uniform Interface Layered System code on demand Rest and HTTP not same A lot of people prefer to compare HTTP with REST. rest and http are not same. REST != HTTP Though, because REST also intends to make the web (internet) more streamline and standard, he advocates using REST principles more strictly. And that’s from where people try to start comparing REST with web (HTTP). Roy fielding, in his dissertation, nowhere mentioned any implementation directive – including any protocol preference and HTTP. Till the time, you are honoring the 6 guiding principles of REST, you can call your interface RESTful. In simplest words, in the REST architectural style, data and functionality are considered resources and are accessed using Uniform Resource Identifiers (URIs). The resources are acted upon by using a set of simple, well-defined operations. The clients and servers exchange representations of resources by using a standardized interface and protocol – typically HTTP. Resources are decoupled from their representation so that their content can be accessed in a variety of formats, such as HTML, XML, plain text, PDF, JPEG, JSON, and others. Metadata about the resource is available and used, for example, to control caching, detect transmission errors, negotiate the appropriate representation format, and perform authentication or access control. And most importantly, every interaction with a resource is stateless Android assignment Related Help : Contact us : Email :contact@codersarts.com #codersart

Predict articles that will generate negative impact(reactions on Facebook) For the current assignment you are to read the dataset understand it and predict which articles will generate negative impact which could be based on the reactions that the user gives on the provided Facebook dataset. You need to analyze the dataset and provide some good insight of it like the number of reactions used, which topic is the most talked about topic and which topics generate negative impact through different visualization techniques. Your code must have all data mining stages and any data mining task https://en.wikipedia.org/wiki/Data_mining#Process. The link provided can be used to see what data mining stages are to be included and select any data mining task. You must also show the accuracy of your model by printing out the scores. You need to add comment box as to why you have selected the method. The code that is submitted must be thoroughly documented. Any submission that does not have enough documentation or documentation that is unclear will not get any points. Suggestions for the Project: 1. You can remove any rows in the dataset that does not have any reactions and just keep the rows that have some numeric value. 2. Yu can also divide the reaction as positive and negative where negative contains too sad and to angry all the other reactions can be grouped under the positive side. 3. You can use various methods for the modelling to show how the accuracy improves from one model to other and in the end, you could plot a graph for the accuracy for all the models. 4. You could classify if the article will have negative impact based on the fact that which articles have large amount of to sad and to angry reactions. You are free to use any other method also. 5. You can also display a word cloud which represents the topics that have received negative reactions. For reference you could also go through different papers some of them are given here: 1. https://arxiv.org/pdf/1905.10975.pdf 2.https://towardsdatascience.com/project-topher-facebook-page-reaction-prediction-program-95bcf4916892 The final submission should have all the code documented with a video recording. If you are looking any project assignment help with an affordable price then you can contact us at contact@codersarts.com

Topic : Courses and Tutors The following data model is designed to hold information relating to Students, Student Courses and Instructors who tutor students. For this scenario, we need to define the following entities: Student Information Courses Student Courses (enrollment) Employees (instructors) Student Contacts (Contact between the Student and the Instructor) Contact Types (Tutor, Test support,etc..) The entities are based on the ER diagram below and use the following rules to determine the table relationships. A Student can enroll in one or many Courses A Course can have one or many Students enrolled in it. A Student can have zero, one, or many forms of contact with the Course Tutor An Employee (Tutor) can have many contacts A contact Type (Tutor, Test support,etc..) can have zero, one, or many contacts The design allows ~ a Student to enroll in one or multiple Courses, a Course allowing one or more Students enrolled in it. a student may be in contact with the Course Tutor many times using many different forms of contact. an instructor can connect with many contacts involving many Students Setting up the project Make a copy of the project.sql template file (also linked in Canvas) to help guide you through the project. a. Download it as a text file and work on it locally (can still have the .sql extension) b. In Google Drive -> File->Download As -> Text File Read through the A-I part (sections) below, and add your responses to the problems in your local copy of the project.sql file. Ensure the ENTIRE project.sql runs without errors (use sql commenting if there are any problems you are unable to finish) Upload your project.sql text file through Canvas in the Final Project Assignment. Notes on project.sql : Each part has some documentation(below and in the project.sql template) to describe the specific statements needed to answer each part. While the execution order of the script should remain sequential (e.g. Part C executes after B, which executes after A), the order in which you work on the script can happen in any order you want (e.g. if you want to start with part G, and it doesn’t depend on something earlier in the script, go for it). Also, the HINTS with test data are merely “examples”, and are NOT REQUIRED in your response. They are there to help guide you. I’ll be looking at how you constructed your logic for each of the Parts below instead of resulting data from each Part’s query execution. Rubric: Part A & Part F are supplied 0 points No errors when executing the entire script - 25 points Part B - 40 points Part C - 60 points Part D - 25 points Part E - 40 points Part G - 40 points Part H - 25 points Part I - 25 points Part Task Descriptions Part A - Creating the database Use the provided template, no action required. Part B -Define and execute usp_dropTables Create a Stored Procedure : usp_dropTables, which executes a series of DROP TABLE statements to remove all the tables created (from the ERD). To prevent errors on trying to drop a table that may not exist, use DROP TABLE IF EXISTS version of your statements. HINT: Looking at the ERD, CONSTRAINTS are implied.( trying to drop a table that is a FK to another table will fail). The order in which you drop the tables is important. When running the stored procedure and the script multiple times, it should run without errors. HINT: test with EXEC usp_dropTables; Part C - Define and create the tables from the ERD Write the CREATE TABLE statements for each of the tables in the ERD. Integrate the PRIMARY KEY and FOREIGN KEY CONSTRAINTS in the CREATE TABLE statement itself. ■ Note: We didn't cover this, but here's a reference to the statement format ■ https://docs.microsoft.com/en-us/sql/t-sql/statements/create-table-transact -sql or try Google for examples on specifying the PRIMARY and FOREIGN KEY CONSTRAINTS when the table is created. General notes about Table and ERD ■ Many of the fields accept NULL, review the INSERT statements in PART F to determine the “NOT NULL” fields, as well as the implied field types. ■ For alpha-numeric data, use char() datatype. ● Refer to the test examples and the INSERT statements (in Part F) to determine the length of each field (e.g. script should execute without the need to truncate data) ■ Use the following int IDENTITIES for the relevant PRIMARY KEYS in each of the Tables. Again, refer to PART F to help guide how the columns are declared. ● StudentInformation. StudentID - starts at 100, increments 1 ● CourseList.CourseID - starts at 10, increments 1 ● Employees.EmployeeID - starts at 1000, increments 1 ● StudentContacts.ContactID -starts at 10000, increments 1 ● StudentCourseID, EmpJobPositionID, ContactTypeID all start at 1, increments 1 Part D - Adding columns, constraints and indexes Modify the table structures and constraints for the following scenarios: ■ Prevent duplicate records in Student_Courses. ● Specifically, consider a duplicate as a matching of both StudentIDs and CourseIDs (e.g. Composite Key needs to be unique) ■ Add a new column to the StudentInformation table called CreatedDateTime. It should default to the current timestamp when a record is added to the table. ■ Remove the AltTelephone column from the StudentInformation table ■ Add an Index called IX_LastName on the StudentInformation table. Part E - Create and apply a Trigger called trg_assignEmail Create a trigger on the StudentInfomation table : trg_assignEmail ■ When a new row is inserted into StudentInformation without the Email field specified, the trigger will fire and will automatically update the Email field of the record. The Email field will be automatically constructed using the following pattern firstName.lastName@disney.com (e.g. Erik Kellener would be Erik.Kellener@disney.com) ■ If the insert statement already contains an email address, the trigger does not update the Email field (e.g. ignores the trigger’s action) HINT: Use the following test cases ■ Case #1 Test when the email is specified. ●INSERT INTO StudentInformation (FirstName,LastName,Email) VALUES ('Porky', 'Pig','porky.pig@warnerbros.com'); ■ Case #2 Test when the email address is not specified. ● INSERT INTO StudentInformation (FirstName,LastName) VALUES ('Snow', 'White'); Part F - Populating sample data ○ Use the template, no action required Part G - Create and execute usp_addQuickContacts ○ Create a stored procedure that allows for quick adds of Student and Instructor “contact activities”. In other words, recording an activity log for meetings between the student and the instructor:: usp_addQuickContacts ○ The procedure will accept 4 parameters: ■ Student Email ■ EmployeeName ■ contactDetails ■ contactType ○ And performs an INSERT into the StudentsContacts table. ○ When inserting into StudentsContacts, the ContactDate field will automatically default to the current Date. ○ Additionally, upon calling the usp_addQuickContacts procedure, ■ If the contactType parameter value doesn’t already exist in the ContactType table, it's first inserted as an additional contactType (e.g. append a new record) AND then used with an INSERT statement to the StudentContacts. ■ If the contactType parameter value does already exist in ContactType, it's corresponding ID is added as part of the StudentsContacts INSERT statement. ○ Note: Assume parameters passed to the procedure are valid (e.g. all Student email addresses, and Employee names are correctly entered passed to the procedure) ○ HINT: You'll want to initially establish the contactTypeID before moving onto the INSERT statement. ○ HINT:Use these test cases to verify the desired output (Note: Make sure the trg_assignEmail is created and applied before running these test cases) ■ EXEC usp_addQuickContacts 'minnie.mouse@disney.com','John Lasseter','Minnie getting Homework Support from John','Homework Support' ■ EXEC usp_addQuickContacts 'porky.pig@warnerbros.com','John Lasseter','Porky studying with John for Test prep','Test Prep' Part H - Create and execute usp_getCourseRosterByName ○ Create a stored procedure: usp_getCourseRosterByName. ○ It takes 1 parameter, CourseDescription and returns the list of the student’s FirstName, and LastName along with the CourseDescription they are enrolled in. (E.g. Student_Courses and CourseList tables are used) ○ Note: Use JOINS. Do not use multiple query statements AND subqueries in the procedure to form your answer.. ○ HINT : Use this as a test example: ■ EXEC usp_getCourseRosterByName 'Intermediate Math'; ■ Expected results ● Intermediate Math Mickey Mouse ● Intermediate Math Minnie Mouse ● Intermediate Math Donald Duck Part I Create and Select from vtutorContacts View ○ Create a view : vtutorContacts, which returns the results from StudentContacts displaying fields EmployeeName, StudentName, ContactDetails, and ContactDate where the contactType is ‘Tutor’. ■ EmployeeName doesn’t exist in StudentContacts, and may require a JOIN from another table. ■ StudentName doesn't exist, but should be in the form FirstName+' '+LastName. Ensuring both First and Last name are properly trimmed. 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