Case Studies and Projects in Machine Learning/EDA/DL

Keeping Inventory of spare in various service centre to the market demand is always a challenge as most service centres spends significant amount in spare parts inventory costs. In spite of this, availability of spare parts is been one of the problem areas.

This repository is a guide to using various time-series analysis models like ARIMA, SARIMA, and SARIMAX in forecasting the exchange rates of 5 currencies as compared to US Dollars

Improved the accuracy of Bitcoin stock price predictions on ARIMA model by reducing the seasonality factor. Achieved RMSE value of 68.99 after implementation of SARIMAX model to reduce seasonality.

Sales forecasting system prototype w/ dashboard using SARIMAX model

This repository contains a collection of time series analysis and forecasting projects, featuring both classical statistical models and deep learning approaches.

In this section, we will estimate airline passengers using time series methods.

In this section, we will examine the Statistical Methods in time series analysis.

This project uses Random Forest and ARIMA models to predict daily gold prices with 97% accuracy. By cleaning and analyzing historical data (2016–2021), we created a model that provides actionable insights. Deployed with Streamlit, it offers real-time forecasting for investors and traders to stay ahead of the market.

This project creates Sales Volume predictions based on historical analysis of product, sales, and purchase data using SARIMAX time series model.

Predicting orders for Glovo

Software Implementation of a Tourist Flow Model

Using time series modeling to forecast the top 3 zipcodes to invest in Washington state.

Development of An Automated Conflict Prediction System by State Space ARIMA Methods

Predict the apple stock market price for next 30 days. There are Open, High, Low and Close price has been given for each day starting from 2012 to 2019 for Apple stock.

This project explores multiple time series forecasting techniques, including Facebook Prophet, ARIMA, and SARIMAX, to predict airline passenger trends. It compares the effectiveness of each model in capturing seasonality and long-term patterns

This analysis seeks to forecast the daily number of car accidents in the county of Denver, Colorado using both traditional time series methods from the statsmodels library, as well as the Prophet forecasting framework as implemented in the prophet library.

Sarimax - Seasonal Auto Regressive Integrated Moving Average with eXogenous Regressors – Time Series Forcasting Model for forecasting amrodd future financial values

Top-Down Investment Strategy Optimization with Time Series Forecasting

AI-Driven Crime Forecasting Across Indian States — A pioneering machine learning project that harnesses time series modeling (SARIMAX, Ridge Regression) to uncover patterns and forecast crime trends using real-world multi-state temporal and socio-economic data.