The main component of this repository is the Arima_Sales.ipynb notebook. This notebook provides a step-by-step implementation of the ARIMA model for sales forecasting. It includes the following sections:
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Data Preprocessing: This section involves loading and preparing the sales data for analysis. It includes steps such as data cleaning, handling missing values, and ensuring the data is in a suitable format for the ARIMA model.
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Exploratory Data Analysis: Here, the notebook explores the sales data by visualizing it through various plots and statistical summaries. This helps in understanding the underlying patterns and trends present in the data.
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ARIMA Model: This section covers the implementation of the ARIMA model for sales forecasting. It includes the identification of model parameters (order of differencing, autoregressive and moving average terms), model fitting, and model evaluation.
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Forecasting: Using the trained ARIMA model, this section demonstrates how to make future sales predictions. It includes generating forecasts for a specified time period and visualizing the predicted sales values.