The objective is to create a ML Model by providing a critical analysis and review of latest data mining techniques, used for rainfall prediction. This latest work on rainfall prediction with the focus on data mining techniques and also will provide a baseline for future directions and comparisons.
Platform? ? ? ? ?: Python
Delivery? ? ? ? ? :? One Day
Support? ? ? ? ? : Online Live Session
Deliverables? : Project Files, Report and Presentation
In this Project, we are building a Machine Learning Model to detect the Credit Card Fraud using Random Forest Algorithm. Random Forest is an algorithm for classification and regression. Summarily, it is a collection of decision tree classifiers. The random forest has an advantage over the decision tree as it corrects the habit of overfitting to their training set. A subset of the training set is sampled randomly so that to train each individual tree and then a decision tree is built, each node then splits on a feature selected from a random subset of the full feature set.
Platform? ? ? ? ?: Python
Delivery? ? ? ? ? :? One Day
Support? ? ? ? ? : Online Live Session
Deliverables? : Project Files, Report and Presentation
The main objective is to detect fake news, which is a classic text classification problem with a straightforward proposition. It is needed to build a model that can differentiate between Real news and Fake news using Machine Learning Algorithm.
Platform? ? ? ? ?: Python
Delivery? ? ? ? ? :? One Day
Support? ? ? ? ? : Online Live Session
Deliverables? : Project Files, Report and Presentation
Fake Profile Identification using Machine learning
This project is about to create a framework, by this we can detect a fake profiles using ML algorithms, makes people social life more secure. The model presented in this project demonstrates that Support Vector Machine (SVM) is an elegant and robust method for binary classification in a large dataset. Regardless of the non-linearity of the decision boundary, SVM is able to classify between fake and genuine profiles with a reasonable degree of accuracy (>90%)
Platform? ? ? ? ?: Python
Delivery? ? ? ? ? :? One Day
Support? ? ? ? ? : Online Demo with Explanation
Deliverables? : Project Files, Report and Presentation
The objective of this project is to review totally different techniques to predict stock worth movement victimization the sentiment analysis from social media, data processing. During this process, we are going to realize economic technique which may predict stock movement additional accurately. Social media offers a robust outlet for people’s thoughts and feelings it’s a fast-ever-growing supply of texts starting from everyday observations to concerning discussions
Platform? ? ? ? ?: Python
Delivery? ? ? ? ? :? One Day
Support? ? ? ? ? : Online Demo with Explanation
Deliverables? : Project Files, Report and Presentation
Student Performance Analysis using Machine Learning
Performance analysis of outcomes based on learning is a system that will strive for excellence at different levels and diverse dimensions in the field of students’ interests.? The proposed framework analyse the students demographic data, study-related and psychological characteristics to extract all possible knowledge from students, teachers, and parents. Seeking the highest possible accuracy in academic performance prediction using a set of powerful data mining techniques.
Platform? ? ? ? ?: Python
Delivery? ? ? ? ? :? One Day
Support? ? ? ? ? : Online Demo with Explanation
Deliverables? : Project Files, Report and Presentation
Student feedback classification using Random Forest with ML
This project is about to create a framework, by this we can detect a fake profiles using ML algorithms, makes people social life more secure. The model presented in this project demonstrates that Support Vector Machine (SVM) is an elegant and robust method for binary classification in a large dataset. Regardless of the non-linearity of the decision boundary, SVM is able to classify between fake and genuine profiles with a reasonable degree of accuracy (>90%)
Platform? ? ? ? ?: Python
Delivery? ? ? ? ? :? One Day
Support? ? ? ? ? : Online Demo with Explanation
Deliverables? : Project Files, Report and Presentation
The objective of this project to create a ML Model to predict the liver disease from the huge liver disease datasets using the algorithms. Time complexity and accuracy can measured by various machine learning models ,so that we can measures different. Risky factors can be predicted early by machine learning models.
Platform? ? ? ? ?: Python
Delivery? ? ? ? ? :? One Day
Support? ? ? ? ? : Online Demo with Explanation
Deliverables? : Project Files, Report and Presentation
The primary goal of this project is to extract patterns from a common loan-approved dataset, and then build a model based on these extracted patterns, in order to predict the likely loan defaulters by using classification data mining algorithms. The historical data of the customers like their age, income, loan amount, employment length etc. will be used in order to do the analysis.
Platform? ? ? ? ?: Python
Delivery? ? ? ? ? :? One Day
Support? ? ? ? ? : Online Demo with Explanation
Deliverables? : Project Files, Report and Presentation
This aims to classify textual content into non-hate or hate speech, in which case the method may also identify the targeting characteristics (i.e., types of hate, such as race, and religion) in the hate speech. To Analysis of the language in the typical datasets to get hate speech by features in the ?long tail? in a dataset using Machine Learning.
Platform? ? ? ? ?: Python
Delivery? ? ? ? ? :? One Day
Support? ? ? ? ? : Online Demo with Explanation
Deliverables? : Project Files, Report and Presentation
Ground Water level Prediction using Machine Learning
Models for the prediction of water table depth were developed based on Artificial Neural Networks (ANN) with different combinations of hydrological parameters. The best combination was confirmed with factor analysis. The input parameters for groundwater level forecasting were derived using Time Series Analysis (TSA).
Platform? ? ? ? ?: Python
Delivery? ? ? ? ? :? One Day
Support? ? ? ? ? : Online Demo with Explanation
Deliverables? : Project Files, Report and Presentation
Road accident Analysis and classification using Machine Learning
Models are created using accident data records which can help to understand the characteristics of many features like driver’s behaviour, over speed, mobile usage, sleeping conditions. This can help the users to compute the safety measures which is useful to avoid accidents. It can be illustrated how statistical method based on directed graphs, by comparing two scenarios based on out-of-sample forecasts. The model is performed to identify statistically significant factors which can be able to predict the probabilities of crashes and injury that can be used to perform a risk factor and reduce it.
Platform? ? ? ? ?: Python
Delivery? ? ? ? ? :? One Day
Support? ? ? ? ? : Online Demo with Explanation
Deliverables? : Project Files, Report and Presentation
Building a ML Model to recognize the Human Activity using Machine Learning Algorithms. A Bayesian network has been applied for activity prediction based on individual and multiple appliance usage.
Platform? ? ? ? ?: Python
Delivery? ? ? ? ? :? One Day
Support? ? ? ? ? : Online Demo with Explanation
Deliverables? : Project Files, Report and Presentation
The objective of this project is build a ML model to analyse the Crime using K Means. Analysing and examining crimes happening in the world will give us a Broadview in understanding the crime regions.
Platform? ? ? ? ?: Python
Delivery? ? ? ? ? :? One Day
Support? ? ? ? ? : Online Demo with Explanation
Deliverables? : Project Files, Report and Presentation
The objective of the project is to build a Intrusion Detection Model using Machine Learning. An intrusion detection system (IDS) is a system that monitors and analyses data to detect any intrusion in the system or network.
Platform? ? ? ? ?: Python
Delivery? ? ? ? ? :? One Day
Support? ? ? ? ? : Online Demo with Explanation
Deliverables? : Project Files, Report and Presentation
