A data-driven project forecasting intraday electricity prices in the GB power market using SARIMAX and XGBoost. Combines market data, demand/generation forecasts, and model explainability tools like SHAP.

Zephyr is a platform which provides users with the predicted AQI levels of air pollution for 39 cities of India with daily, monthly and yearly trends. It also provides some of the statistics observed for AQI over these cities and various latest articles and blogs related to air pollution.

Rice crop prediction using real time data recorded from 1961 to 2021. Time series models trained on ARIMA, SARIMA, LSTM, FbProphet algorithms. Achieved an r2_score above 90% for SARIMA, Fbprohpet models.

Three projects as a freelance Data Scientist for USISA, largest canning company in Andalucia. Forecasting prediction of sales, recommendation system and clustering.

Objective, to perform time series analysis and forecasting on container shipping prices for three major maritime routes: North America East Coast to China/East Asia, Europe to North America East Coast, and Europe to South America West Coast. The data used for this analysis was collected from The Baltic Exchange, a source of maritime market data.

Virtual Work Experience - Data analysis at STC x Misk

Analyse the mumbai temperature using the time series

This projects were made for the Machine Learning course at the AGH UST in 2024/2025. Obtained maximum grade 5.0.

A time series forecasting project using SARIMAX to predict future Instagram reach from historical data.

This model predicts co2 level in atmosphere on account of historical data.

Machine learning models for forecasting time-series data.

Forecasted SP-36 fertilizer allocation for 2026–2027 using ARIMAX and SARIMAX. ARIMAX captured annual trends, while SARIMAX modeled monthly seasonality. The results support strategic planning for post-subsidy distribution at both macro and micro levels.

Time Series Forecasting of Electricity Consumption | Spring 2025

Forecasting model : Forecasts returns of real estate using time series analysis.

Batch Name: MIP-ML-11 (Machine Learning Intern)

This Project Predicts/Forecast Next Day's Temperature By Using RNN.It compares ARIMA and RNN models on real-world time series data from Melbourne, Australia. The goal is to build accurate and generalizable temperature predictors using Python and TensorFlow.

Generates a trading strategy in python using the SARIMAX model for the forecast and yFinance for data.

This project uses time series forecasting to predict future milk production. The data used in this project is monthly milk production data from January 1962 to December 1975. The ARIMA (autoregressive integrated moving average) model is used to forecast the milk production. The model is evaluated using various metric.

This repository covers essential techniques for time series analysis and forecasting. It covers data manipulation and visualization using Numpy and Pandas, time series analysis with Statsmodels, ARIMA models, deep learning methods like RNNs, LSTM, GRU, etc. and Facebook's Prophet library.