Introduction to time series preprocessing and forecasting in Python using AR, MA, ARMA, ARIMA, SARIMA and Prophet model with forecast evaluation.

COVID-19 Growth Forecast

Detection and Prediction of Air quality Index

Exploratory Data Analysis on the Coronavirus data set available in South Korea and Time Series Forecasting using Facebook's Prophet

Used prophet to forecast enrollments for courses published on Coursera for Michigan

Web scrapped the stocks fundamentals for 502 stocks and carried out data analysis using SQL and POWER BI. Built a very useful visualization for Value Investing. Selected 5 stocks and predicted their price.

Predicting Dengue Fever outbreaks in San Juan and Iquitos

Predicting disease spread, a DrivenData competition. I'am currently participating in this competition. I used it as submission for the second capstone project in the course 'Professional Certificate in Data Science' provided by Harvard University (HarvardX) on EDX.

COVID-19 forecasting dashboard using data from John Hopkins University, PHE and www.gov.uk. An NHS Bed forecasting model for England is also added to the UK table and plotted. There is also a notebook for forecasting the Italy COVID-19 cases which implements a logistic model, and exponential model, and tries the fb prophet model. Important note: Please note that the case forecasting and bed forecasting model is experimental and not yet validated (as cases have not yet arrived since the model is being developed as the pandemic unfolds). The model is only as good as its assumptions and starting data both of which are likely to have limitations, especially this early in the pandemic and therefore it should not be used for clinical decision making.

Contains notebooks of Time series preprocessing and forecasting in Python using AR, MA, ARMA, ARIMA, SARIMA, Prophet model and LSTM model with forecast evaluation.

Hybrid anomaly-based intrusion detection system

This repository is B.Tech. major project on COVID-19 Global and India Forecast

Bootcamp-Python Study

Predict coffee prices in Kenya

Rossmann operates over 3,000 drug stores in 7 European countries. Currently, Rossmann store managers are tasked with predicting their daily sales for up to six weeks in advance. Store sales are influenced by many factors, including promotions, competition, school and state holidays, seasonality, and locality. With thousands of individual managers predicting sales based on their unique circumstances, the accuracy of results can be quite varied.

To forecast the number of cases/applications a company will receive in next 3 months for 2 different segments

Sales forecast using facebooks library "prophet"

Basic Idea - Predict the price of avocado sold in the US using a historical dataset using Facebook Prophet and visualizations.

S&P 500 Stocks - Time Series Analysis using FB Prophet Model

Kaggle competition on Walmart data