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Python
Showing all 11 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
Predictive Maintenance using Unsupervised Machine Learning
For any industrial machinery equipment, there is a need to increase operational flexibility and reduce operating costs. To achieve this objective, system engineers mainly focuses on 3 attributes of the machinery, namely reliability, maintainability and reliability. Maintenance strategy significantly improves the reliability and availability of assets and, as a result, decreases the number of unpredicted breakdowns. Recently unsupervised Machine Learning has received much attention in anomaly detection and predictive maintenance of equipments before they can fail. Unsupervised learning can help automate and improve feature engineering by extracting relevant and informative features from the data, without requiring labels or prior knowledge. In this project there are three different techniques that are applied: 1) PCA Model, 2) Auto-encoder Model and 3) LSTM Model with auto-encoder for detection of motor and compressor failures.
SKU: Predictive Maintenance using Unsupervised Machine Learning - Software Projects
Bank Statement Analysis and Transaction Category Prediction
Bank statement analysis involves summarizing cash inflows and outflows from statements and providing an overview of financial health of individuals. Businesses and NBFCs consider the financial history of borrowers during credit assessments and bank statement analysis tool is being used by various industries for faster processing times, efficiency, and document processing purposes. The objective of this project is to study the cashflows in terms of debits and credits for the retail customers and predict the transaction categories based on mode of transactions and counter parties etc. Various Machine Learning algorithms are used to classify the transaction categories using train data and predict the transaction categories using test data. It also recommends the algorithm that gives the best accuracy score.
SKU: Bank Statement Analysis and Transaction Category Prediction - Software Projects
Image Search using Deep Learning
Content based Image Retrieval (CBIR) is a popular technique used in computer vision for finding out a specific image out of a given set of images. Different category of images is stored in a folder of the repository. These are then preprocessed and visualized using image processing technique. Autoencoder architecture is used for image classification. Deep learning model is trained with the train set of data while model inferencing is done by using test component of dataset. When the user wants to search a particular image which will be matched with each of the images present in the repository and the image that is best matched with the given image will be displayed on the top followed by other similar images based the similarity score. Flask API is used to develop a web application running on local server. This project finds many useful applications such as fashion and retail, manufacturing and health care industries.
SKU: n/a - Software Projects
Recommendation System for eLearning contents
In this project eLearning contents are generated by scraping the websites and then keywords are extracted using Spacy library. Clustering techniques are used to cluster the contents as ‘Low’, ‘Medium’ and ‘High’ complexity. The contents are personalized by matching the contents with user’s profile. Cosine Similarity is used to recommend the websites and keywords based on search keywords as entered by the user. Top 10 search results along with the similarity scores will be displayed to the user when the user wants to search a specific eLearning material from the internet.
SKU: Recommendation System for eLearning contents - 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
Semantic Search Engine Using Generative AI
With the advent of Large Language Model (LLM) like ChatGPT, semantic search has received much attention in recent time. This project takes advantage of LLM to help build semantic search engine which finds wide spread applications across industries. It starts with chunking the documents and creating vector database using Amazon Titan text embedding model. When the user wants to search a specific information from the document(s), it fetches the information that are semantically closer to the user’s query and displays top 3 answers based on similarity score. An User Interface (UI) is developed using Gradio which helps user to interact with search query and receive response from the LLM.
SKU: n/a - Software Projects
Neural Machine Translation
The objective of this project is to convert Hindi sentence to English sentence for non-native speakers using Neural Machine Translation. The dataset is preprocessed by removing punctuations and all duplicate values as well as all quotes, numbers and white spaces that are present in text data. Then English and Hindi vocabulary is prepared by splitting sentences into words. Encoder Decoder architecture is used to build the model wherein decoder is an LSTM model with embedding layer, sequence to sequence layer, attention mechanism and dense layer. The model is appropriately trained with the train data and saved the model weights, which are loaded while inferencing the test data for making the prediction. The predicted English translation will be obtained from decoded sequence function.
SKU: n/a - Software Projects
AI Assistant for Tourists
This project is aimed to develop a virtual assistant chatbot that can help the tourists tell the weather for any city, places around a city and points of interests such as beaches, hotels, restaurants around a city etc. Rasa framework is employed to build the chatbot. The application can be run on local browser. The user can interact with the chatbot using a simple UI built on HTML and CSS.
SKU: n/a - 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
