sklearn, tensorflow, random-forest, adaboost, decision-tress, polynomial-regression, g-boost, knn, extratrees, svr, ridge, bayesian-ridge

A python based project to predict the future prices of the top 10 trending cryptocurrencies using ML Algorithms like SVR, Decision Tree and LSTM with an interactive frontend using streamlit. Analysis using PowerBi and has DBMS connectivity.

A Machine Learning Model built in scikit-learn using Support Vector Regressors, Ensemble modeling with Gradient Boost Regressor and Grid Search Cross Validation.

The dataset used for this project is taken from the official UCI Machine Learning Repository.

Utilized machine learning algorithms to analyze expenses and perform forecasting

Predicting house prices can help determine the selling price of a house in a particular region and can help people find the right time to buy a home.

Finding Needles in Emb(a)dding Haystacks: Legal Document Retrieval via Bagging and SVR Ensembles

Stock Price Forecast App is based on Machine Learning. By providing number of days , we can predict trend in Stock Price. The frontend of App is based on Dash-plotly framework. Model is predicting stock price using Support Vector Regression algorithm. App can predict next 5-10 days trend using past 60 days data.

The Zomato Delivery Time Prediction Application is a machine learning-driven Flask web application designed to predict the estimated delivery time for food orders placed on the Zomato platform.

Regression Machine Learning Project

Developed a predicting model for automatic bike sharing system using different machine learning and deep learning techniques like XGBoost, SVM, Decision Tree, Random Forest, and CNN and compared the accuracy of different algorithms. And applied grid search and random search to improve the accuracy, score, and reduced the random mean square error.

This repository presents a time series forecasting model for the stock market using SVR and LSTM to build a model that can predict the appropriate time for trading.

Models for Practice

Streamlit website to analyze Website Traffic

Deciphering how customer's purchasing habits are influenced by wholesale pricing and examining its impact on final retail cost.

Aqueous solubility prediction

Machine Learning practice, Linear Regression, Multi-Linear Regression, Polynomial, Support Vector, Decission Tree, Random Forest.

This project addresses problem of early detection of Parkinson disease using Machine learning techniques

deploy with streamlit community

Dự án này sử dụng các thuật toán machine learning bao gồm học có giám sát (KNN, hồi quy Logistic, SVM,...), học không giám sát (PCA, K-means) và các kỹ thuật giảm chiều để phân loại ung thư vú dựa trên bộ dữ liệu Wisconsin. Phù hợp để học tập về khoa học dữ liệu và ứng dụng thực tế trong phân tích dữ liệu y tế..