predective-modeling

My Solutions to 120 commonly asked data science interview questions.

A ML Model That Predict The Percentage of Winning for Each Blue And Red Team in League of Legends

A project that uses ARIMA and LSTM models to predict future outcomes based on historical data.

Stockwise is a cutting-edge web application designed for efficient inventory management through advanced demand forecasting techniques. This project addresses the critical challenges organizations face in predicting demand, managing stock levels, and ensuring customer satisfaction.

Leveraging advanced machine learning algorithms, the app analyzes various factors such as distance, traffic conditions, time of day, and location to predict the cost of a taxi ride before it begins.

Machine learning model that predicts student dropout risk in online learning using the OULAD dataset to enable early academic intervention

Using past Sport (Cricket) data to predict next win for Team India, in any format of the cricket.

Forecasts smartphone battery degradation using sensor data from a Samsung device. After cleaning and analyzing time-series features like temperature, CPU usage, and voltage, an LSTM model predicts battery percentage and generates short-term forecasts, showing strong accuracy but limited real-world variability.

This is Final Capstone Project for ALY6040 Data Mining Fall 2021 CPS. Primarily to learn Data Analytics, Data Mining and Python. Residential and commercial properties were assessed in Boston. The Boston Globe reported in May 2021 that the competitive Boston housing market drives up costs. As the pandemic continues, people demand larger homes. Fi…

Project Foundation: Blazor Web App that uses AI to analyze sales trends and suggest top-performing dishes and menu adjustments.

AI-powered child safety image classifier that detects unsafe visual content using machine learning with real-time prediction, confidence scoring, and interactive Flask deployment.

AI-powered B2B invoice management system with payment delay prediction using machine learning (Random Forest), built with React, TypeScript, and Python.

To analyze state & city housing trends and affordability using data analytics.”

End-to-end ML platform predicting loan default risk — data pipeline, 4-model comparison (LogReg/RF/XGBoost/LightGBM), SHAP explainability, MLflow tracking, drift monitoring, FastAPI + Streamlit, Dockerized.

This repository explores the use of machine learning to predict heart attack risk factors. It analyzes a dataset of heart failure patients, examining various features like age, medical history, lab results, and the occurrence of death events.

MachineGuard-AI is an AI-powered predictive maintenance system that monitors industrial machines using real-time sensor data to predict failures before they occur. It combines machine learning models, feature engineering, and interactive dashboards to enable proactive maintenance and reduce downtime.

Study and analyze the ADS and ADAS Level 2 collision and summarize the trends.

Using Machine Learning Classification Algorithms to identify future Cryptocurrency investment

A research implementation of a context-conditioned, zero-shot video anomaly detection framework that integrates spatiotemporal features extracted via TimeSformer with contrastive predictive coding and semantic alignment through CLIP. The repository includes training and evaluation pipelines, configuration files, and the accompanying thesis/paper.

Prepare a prediction model for profit of 50_startups data. Do transformations for getting better predictions of profit and make a table containing R^2 value for each prepared model. Consider only the below columns and prepare a prediction model for predicting Price. Corolla<-Corolla[c("Price","Age_