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Showing all 4 resultsSorted by popularity
- Software Projects
Spam Filtering Using Supervised Machine Learning Algorithms
The objective of this project is to detect Spam and ham messages using various supervised machine learning algorithms like Naive Bayes, Support Vector Machines algorithm, Bidirectional LSTM, and Transfer Learning with USE Encoder and compare their performance in filtering the Ham and Spam messages. As people indulge more in Web-based activities, and with rising sharing of private-data by companies, SMS spam is very common. Scammers create fraudulent text messages to deceive you into giving them your personal information, such as your password, account number, or Social Security number. If they have such information, they may be able to gain access to your email, bank, or other accounts. SMS spam filter inherits much functionality from E-mail Spam Filtering. Comparative study is performed based on the performance of various supervised learning algorithms and the algorithm that gives us the most accurate result is recommended. A simple UI is developed to demonstrate the working of spam filtering in practice.
SKU: Spam Filtering Using Supervised Machine Learning Algorithms - Software Projects
Forecasting Corona Virus Outbreak
The outbreak of COVID-19 made a significant health impact across the globe. This project is designed to analyze how coronavirus affect different nations and to predict potential COVID-19 cases across all the globe on an everyday basis. The objective was to gauge COVID-19 on three metrics- confirmed cases, recovered cases and death events for the next day using historical data as on a given date. Various forecasting models such as Linear Regressor, Random Forest Regressor, ARIMA, Prophet, Holt Winter etc. are used for time series analysis and forecasting COVID cases.
SKU: Forecasting Corona Virus Outbreak - Software Projects
Malware Prediction in Software
Malwares are software viruses. Once a computer can be infected by malware, criminals can hurt consumers and enterprises in many ways. The purpose of this project is to explore and analyse the data to find out the varieties of software issues. All potential features are extracted and feature selection technique is applied to define the target variable and input feature matrix. Various classification algorithms are used to classify the potential malwares and best classifier is recommended. Model explainability is used explain the top few candidate features based on feature importance.
SKU: n/a - Software Projects
Ground Water Quality Prediction
This project leverages Machine Learning techniques to determine the quality of ground water being considered as drikable. It starts with exploratory data analysis followed by visualizations and classification algorithms such as Logistic Regression, kNN, DecisionTree, Random Forest, XGBoost and so on. The performance of the models are evaluated using confusion metrics and compared to understand their suitability of their usage to solve the business problem.
SKU: n/a
