?24,000.00?Exc Tax
dsPIC Development board is proposed to smooth the progress of developing and debugging of various designs encompassing Microcontrollers from Microchip. It?s designed as to facilitate (dsPIC30F 40PIN DIP) On-board Programmer for PIC Microcontroller through ISP on Universal Serial port. It integrates on board USART,LEDs, keypads, 3 ADC inputs and LCD Display to create a stand-alone versatile test platform. User can easily engage in development in this platform, or use it as reference to application development.
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The main objective of this project is to analyse previous year?s student?s historical data and predict placement possibilities of current students and aids to increase the placement percentage of the institutions using Machine Learning Algorithms.
The objective of the project is to understand the concepts of natural language processing and create a tool for text summarization. The concern in automatic summarization is increasing broadly so the manual work is removed. The project concentrates on creating a tool that automatically summarizes the document.
In this project, we create a model to do the accurate prediction of heart disease problems in health care applications. Easier to analyse the scalable of health care big data. Less time consumption with the efficiency of data in heart disease. High performance in data maintained of heart disease prediction.
The main objective of this project is to predict the employee attrition rate using Machine Learning Algorithms such as SVM and Naive Bayes algorithms. After the results obtained, the performance of the model is evaluated by calculating the accuracy score and showing it in the form of a confusion matrix.
In this concept, we create Machine Learning Model for Smart Farming. Smart Farming Prediction and the recommendation can be made using Space Vector Modulation Classification and Neural Network Algorithm.
Churn Analysis is one of the worldwide used analyses on Subscription Oriented Industries to analyse customer behaviours to predict the customers which are about to leave the service agreement from a company. The proposed model ?rst classi?es churn customers data using classi?cation algorithms, in which the Random Forest (RF) and Decision tree (DT) algorithm performed well with 90.44% correctly classi?ed instances.
The idea of visualizing data by applying machine learning and pandas in python. Taking dataset from a medical background of different people (prime Indians dataset from UCI repository). This data set consists of information on the user’s age, sex type of symptoms related to diabetes. Design a testing and training set and predict are chances of patients having diabetes in the coming five years. Data is classified and shown in the form of different graphs.
The objective of the project to find the Network attacks using KDD Datas and Data Mining Approach.
To prevent malware attacks, researchers and developers have proposed different security solutions, applying static analysis, dynamic analysis, and artificial intelligence. Indeed, data science has become a promising area in cybersecurity, since analytical models based on data allow for the discovery of insights that can help to predict malicious activities.?We can analyse cyber threats using two techniques, static analysis, and dynamic analysis, the most important thing is that these are the approaches to get the features that we are going to use in data science.
The proposed framework focuses on merging the demographic and study related attributes with the educational psychology fields, by adding the student’s psychological characteristics. After surveying, we picked the most relevant attributes based on their rationale and correlation with the academic performance.
We apply the ML model on datasets like Twitter, Flickr, and YouTube. It will predict a similar type of hashtag with a detailed description. Unsupervised word embedding methods train with a reconstruction objective, in which the embedding is used to predict the original text.
The idea of visualizing data by applying machine learning and pandas in python. Taking dataset from a medical background of different people (prime Indians dataset from UCI repository). This data set consists of information on the user’s age, sex type of symptoms related to diabetes. Design a testing and training set and predict are chances of patients having diabetes in the coming five years. Data is classified and shown in the form of different graphs.
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