The Sri Lankan IT industry has emerged as a significant player in the global market, specializing in business process outsourcing (BPO) and serving as an offshore development center for numerous Fortune 500 companies from various regions including North America, Europe, and Asia. The industry is known for its Skilled workforce: Sri Lanka boasts a large pool of highly skilled and qualified IT professionals, many of whom are proficient in various programming languages and technologies. Cost-effectiveness: Compared to other outsourcing destinations, Sri Lanka offers competitive rates for IT services, making it an attractive option for businesses seeking to reduce costs. Strong government support: The Sri Lankan government actively supports the development of the IT industry, providing various incentives and infrastructure improvements to attract foreign investment. Some of the well-known global IT companies with a presence in Sri Lanka include: HSBC IFS Intel Motorola WNS RR Donnelley Vi...
Using cansus data, let's use several traits of an individual to estimate what income class they belong to (>50k or <=50k). here with cansus data's drive link. https://drive.google.com/drive/folders/1NPPMs52EOkPUTwimmyCBx0b7qCeQ-9yB?usp=sharing Q1) what is the advantage of using TensorFlow? the main advantage is TensorFlow offers high-level APIs. In this case we will use estimator API to create Classifier (linearClassifier). in census_data .csv keep age, workclass, Final-weight, education,education-num,Marital-Status,occupation,relationship,race,sex,capital-Gain,Capital-loss,hours-per-week,native-country,class coloums.here class coloum is our target let's build a model read the census_data.csv using the pandas library. import pandas as pd census = pd.read_csv ( "/content/census_data.csv" ) cheak and view census_data.csv head/1st row ,using head() function. census.head () more... My_income-class-prediction-using-tenso...
TensorFlow Train/test /evaluation flow step1: Choose a model First, we'll need to choose which machine learning algorithm we want to use to build our model. we can pick any standard machine learning algorithm but in this section, we will be using neural networks. then we can start with the training phase. step2: we train the algorithm by showing its training data and the expected output for the data and it has to figure out how to come up with the expected result. in other words, the algorithm learns how to transform the input to produce the correct output. for example, we can train it to do multiplication by showing it two numbers and the expected result and it will eventually work out that we want to multiply the two numbers. After we train the model, we will load up the second set of data, which has never seen before called the testing data set. This is the test phase, we will feed this testing data through the model and make sure it is able to predict the correct result ...
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