ales forecasting is the process of predicting future sales for a business or organization. Accurate sales forecasting can help businesses make informed decisions about inventory management, staffing, and overall strategy. Sparkflows provides a range of tools and frameworks for building machine learning models that can be used for sales forecasting. To build a sales forecasting model using Sparkflows, you would typically follow these steps: Data preparation: Gather and preprocess your sales data, which typically includes historical sales data, seasonality data, and other relevant data such as pricing and promotions. Feature selection: Select the most relevant features that are likely to impact sales, such as time of year, marketing campaigns, and changes in pricing. Model selection: Choose a machine learning algorithm to use for predicting sales, such as linear regression, time series forecasting, or neural networks. Model training: Use historical sales data to train your model on examples of past sales trends. Model evaluation: Evaluate the performance of your model using a set of metrics such as mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE). Deployment: Once you have a model that performs well, you can deploy it to your production environment to make predictions on new sales data. Sparkflows provides a variety of tools and frameworks that can help with each of these steps, including data preprocessing and transformation tools, feature selection algorithms, and machine learning libraries such as Apache Spark MLlib. Additionally, Sparkflows allows you to automate the entire workflow using pipelines, which can be useful for large-scale, complex projects. One of the key challenges in sales forecasting is accounting for seasonality, or the regular patterns of sales that occur over time. Sparkflows provides several tools that can help with this, such as time series forecasting models and seasonal decomposition algorithms that can identify and account for seasonal trends in your sales data. Another important aspect of sales forecasting is feature selection, or identifying which variables are likely to have the most impact on sales. Sparkflows provides a range of feature selection algorithms that can help you identify the most relevant variables, such as correlation-based feature selection and recursive feature elimination. Overall, Sparkflows provides a comprehensive set of tools and frameworks for building accurate sales forecasting models, and can be a powerful tool for businesses looking to make informed decisions based on their sales data. For more details visit - https://www.sparkflows.io/

1. Accelerate Business Growth of Industries with
Best-In-Class Self-Service Vertical Use case Builder.
Business Solution for Sales Forecasting

2. Business Objective of the Case Study
Challenges
Solution Approach
Implementation Process
Dataset Definition
Data Profiling
Exploratory Data Analysis
EDA Workflow View
Key EDA Insights
Feature Engineering
Machine Learning Workflow
Classification ML Model
Model Registry
Model Inference
AutoML
Business Apps
Business Impact
References

3. Business Objective of the Case Study
● Forecasting the quantity of items for each Product
Category in a Market Segment helps in smooth management
of the inventory, optimizing shelf life and maximizing the sales
of the items.
Challenges
● In this unprecedented time of uncertainty, businesses
need to rely on forecasts, now more than ever, to drive
their entire growth strategy.
● Smooth Resource allocation and planned management of cash flow
is a major challenge
● Its extremely important to manage the budget under inflation and
downward economic situation
● Investors need to visualize the sales performance and goals.

4. Solution Approach
● Quantitative Sales Forecasting involves statistical
analysis of past sales data to forecast.
It is based on objective calculations and yields more
accurate results.
● Past sales performance is a good leading indicator of future sales
performance. Historical conversion rates tell you how many
prospects, teams, or individuals were able to convert over a given
period of time.
● Automatically capturing sales activity and customer engagement
data.
● Using Machine learning and AI to analyze that deal data to provide
you with meaningful and actionable insights for better forecast
accuracy and efficiency. ARIMA is one of the best-performing
algorithms used for time series forecasting. In the workflow below, we
use ARIMA to forecast the number of items that would be sold for a
Category in a Market Segment.
● Real-time forecasting that gives you better accuracy with up-to-the
minute opportunity data and saves you time by avoiding telephone
calls.
Implementation Process
● Dataset Definition
● Exploratory Data Analysis
● Data Enrichment
● Feature Engineering

5. ● Customer Clustering
● Cluster Evaluation
Dataset Definition
We have used the dataset available at Kaggle which was made
available by Walmart. This can be downloaded from Walmart
Store Sales Forecasting | Kaggle
Stores: It provides the type and size
Sales: It contains information like for a store, its weekly sales,
whether the week is a special holiday week.
Features: This file holds all the main features which need to be
analyzed for sales. This contains data for a store on a particular
day, the area temperature, whether that day was a holiday.

6. Exploratory Data Analysis
Powerful Exploratory Data Analysis Workflow can be designed
using the Fire Insight Studio.The workflow provides crucial
insights in graphical form.
Key EDA Insights
Weekly Sales by Store
Workflow performs following tasks:
1. Reads Sales dataset
2. Processes Store wise Weekly Sales data
3. Processes comparison between Holiday and Non-Holiday sales
4. Saves data in CSV format
5. Displays processed data in Graphical format

7. EDA Workflow Design
Sales By Type and Size
Workflow performs following tasks:
1. Reads Sales and Stores dataset
2. Processes Sales By Type data
3. Processes Sales By Size
4. Saves data in CSV format
5. Displays processed data in Graphical format

8. EDA Workflow Design
Report displays following KPIs:
1. Store Weekly Sales
2. Store Sales By Type Analysis
3. Store Sales By Size Analysis
4. Store Sales during Holiday Vs Non-Holiday Analysis

9. EDA Insights
Data Preparation
Data Pre-processing: Combining Datasets

10. Workflow View
Sample Dataset
Data Validation and Indexing

11. Feature Engineering
Feature Selection and Correlation

12. From The Correlation Matrix, we can see that :
1. There exists a strong relationship between the size of the
store and the holiday period.
2. There exists a relationship between temperature and the
Month which can be analyzed further to have store specific
sales/markdowns.

13. Machine Learning Workflow
Model Building and Forecasting
Random Forest Classifier to predict Churn
● This

14. Feature Importance

15. Model Inference

16. Model Inference
Predicted Result
Business Impact
● Accurate sales forecast allows companies to efficiently allocate
resources for future growth and manage their cash flow.
● Sales forecasts help set benchmarks for future trends and allow
leaders to course correct early. Revenue leaders can align sales
quotas and revenue expectations and optimize for more wins.
● Conveys confidence to the board, and the management team that
your business is supported by a reliable forecasting machine that will
scale well in the future.
● Sales projections facilitate strategic planning and tell you how soon
you will be ready for executing and implementing your plans.

17. References
https://www.sparkflows.io/ https://docs.sparkflows.io https://www.sparkflows.io/videos https://www.sparkflows.io/data-sheets