Data Cleansing for Machine Learning
Databases may generally contain incorrect, incomplete, duplicate or improperly-formatted data. It is essential to employ data cleaning to remove these inconsistencies and prepare data for further analysis.
Forecasting Product Demand
Demand forecasting provides an estimate of the amount of goods and services that will be purchased in the foreseeable future. Demand forecasting facilitates decision-making and strategic planning.
Time-Series Forecasting using ARIMA model
ARIMA is a time-series forecasting tool that accommodates seasonality, varying trends and multiple parameters.
Time-Series Forecasting using TBATS model
Time-series data with multiple seasonal effects are difficult to model and require the use of specialised algorithms. TBATS is a time-series forecasting method that accounts for multiple seasonalities.
Extracting Meaningful Insights from Data using Exploratory Data Analysis
Raw data is normally ambiguous and difficult to interpret. Cleaning it is essential in order to understand the relationships between the variables present in the data.
Predicting Customer Churn using Machine Learning
Predicting customer churn can help your business improve upon those areas where customer service is lacking and improve revenue.
