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Description:

One of the leading retail stores in the US, Walmart, would like to predict the sales and demand accurately. There are certain events and holidays which impact sales on each day. There are sales data available for 45 stores of Walmart. The business is facing a challenge due to unforeseen demands and runs out of stock some times, due to the inappropriate machine learning algorithm. An ideal ML algorithm will predict demand accurately and ingest factors like economic conditions including CPI, Unemployment Index, etc.

Walmart runs several promotional markdown events throughout the year. These markdowns precede prominent holidays, the four largest of all, which are the Super Bowl, Labour Day, Thanksgiving, and Christmas. The weeks including these holidays are weighted five times higher in the evaluation than non-holiday weeks. Part of the challenge presented by this competition is modeling the effects of markdowns on these holiday weeks in the absence of complete/ideal historical data. Historical sales data for 45 Walmart stores located in different regions are available.

Acknowledgements

The dataset is taken from Kaggle.

Objective:

• Understand the Dataset & cleanup (if required).
• Build Regression models to predict the sales w.r.t single & multiple features.
• Also evaluate the models & compare their respective scores like R2, RMSE, etc.

Walmart Sales Forecast

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Problem:

There are many seasons that sales are significantly higher or lower than averages. If the company does not know about these seasons, it can lose too much money. Predicting future sales is one of the most crucial plans for a company. Sales forecasting gives an idea to the company for arranging stocks, calculating revenue, and deciding to make a new investment. Another advantage of knowing future sales is that achieving predetermined targets from the beginning of the seasons can have a positive effect on stock prices and investors' perceptions. Also, not reaching the projected target could significantly damage stock prices, conversely. And, it will be a big problem especially for Walmart as a big company.

Aim:

My aim in this project is to build a model which predicts sales of the stores. With this model, Walmart authorities can decide their future plans which is very important for arranging stocks, calculating revenue and deciding to make new investment or not.

Solution:

With the accurate prediction company can;

• Determine seasonal demands and take action for this
• Protect from money loss because achieving sales targets can have a positive effect on stock prices and investors' perceptions
• Forecast revenue easily and accurately
• Manage inventories
• Do more effective campaigns

Plan:

1. Understanding, Cleaning and Exploring Data

2. Preparing Data to Modeling

3. Random Forest Regressor

4. ARIMA/ExponentialSmooting/ARCH Models

Metric:

The metric of the competition is weighted mean absolute error (WMAE). Weight of the error changes when it is holiday.

Understanding, Cleaning and Exploring Data: The first challange of this data is that there are too much seasonal effects on sales. Some departments have higher sales in some seasons but on average the best departments are different. To analyze these effects, data divided weeks of the year and also holiday dates categorized.

Preparing Data to Modeling: Boolean and string features encoded and whole columns encoded.

Random Forest Regressor: Feature selection was done according to feature importance and as a best result 1801 error found.

ARIMA/ExponentialSmooting/ARCH Models: Second challange in this data is that it is not stationary. To make data more stationary taking difference,log and shift techniques applied. The least error was found with ExponentialSmooting as 821.

Findings:

• Although some departments has higher sales, on average others can be best. It shows us, some departments has effect on sales on some seasons like Thanksgiving.
• It is same for stores, means that some areas has higher seasonal sales.
• Stores has 3 types as A, B and C according to their sizes. Almost half of the stores are bigger than 150000 and categorized as A. According to type, sales of the stores are changing.
• As expected, holiday average sales are higher than normal dates.
• Top 4 sales belongs to Christmas, Thankgiving and Black Friday times. Interestingly, 22th week of the year is the 5th best sales. It is end of May and the time when schools are closed.
• Christmas holiday introduces as the last days of the year. But people generally shop at 51th week. So, when we look at the total sales of holidays, Thankgiving has higher sales between them which was assigned by Walmart. But, when we look at the data we can understand it is not a good idea to assign Christmas sales in data to last days of the year. It must assign 51th week.
• January sales are significantly less than other months. This is the result of November and December high sales. After two high sales month, people prefer to pay less on January.
• CPI, temperature, unemployment rate and fuel price have no pattern on weekly sales.

More detailed finding can be found in notebooks with explorations.

Future Improvements:

• Data will be made more stationary with different techniques.

• More detailed feature engineering and feature selection will be done.

• More data can be found to observe holiday effects on sales and different holidays will be added like Easter, Halloween and Come Back to School times.

• Markdown effects on model will be improved according to department sales.

• Different models can be build for special stores or departments.

• Market basket analysis can be done to find higher demand items of departments.

Please feel free look at EDA on Tableau.

And you can find CRISP-DM on my Github.

About Dataset: Walmart Sales Forecast

This dataset focuses on predicting weekly store sales at Walmart by examining holiday effects, temporal patterns, and other influential factors. The goal is to enable efficient stock planning, revenue calculations, and strategic decision-making by understanding patterns related to seasonal sales fluctuations. This machine learning model is developed based on resources from : https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting/overview/evaluation .

Dataset Overview

1. Test Data Contains 115,064 rows with information: Store, Department, Date, IsHoliday. "IsHoliday" indicates whether the week includes a special holiday. Holidays tend to show higher average sales than non-holiday periods.

2. Train Data Also contains 115,064 rows with Store, Department, Date, IsHoliday, Weekly Sales. Weekly sales are the recorded weekly sales for specific departments at certain stores.

3. Features Data Consists of 8,190 rows with variables such as Temperature, Fuel Price, CPI, Unemployment, Markdown 1-5, IsHoliday * Temperature: Average temperature (Fahrenheit) in a region. * Fuel Price: Can impact consumer spending and sales. * Markdowns 1-5: Promotional markdowns (missing values marked as NA). * CPI: Consumer Price Index (reflects inflation/deflation). * Unemployment: Unemployment rate in a region that affects consumer spending.

4.Store Data Includes details about Walmart stores such as store numbers, store types, and store sizes. Walmart has 45 stores categorized into 3 types: * Type A: Sizes from 39.690 to 219.622 * Type B: Sizes from 34.875 to 140.167 * Type C: Sizes from 39.690 to 42.988 The target variables for prediction are weekly sales, is holiday, and date. The other features are explored to identify patterns and generate insights to build accurate prediction models.

Modeling Objective

The goal is to predict the impact of holidays on weekly store sales. To achieve this, a Time Series modeling approach was applied using variables such as date, weekly sales, is holiday, lag features, rolling averages, and XGBoost. The evaluation metric used was Weighted Mean Absolute Error (WMAE), which emphasizes periods of higher significance, such as holidays.

Final Model Metrics: * Weighted Mean Absolute Error = 211 * Error rate relative to average weekly sales = ~1.32%.

The low error percentage highlights the model's accuracy in forecasting weekly sales and assessing seasonal fluctuations.

Insights

• The analysis provides actionable insights by identifying holiday effects on sales trends.
• This supports better stock planning, strategic financial planning, and risk management.

📢 Published on : My LinkedIn

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We will study the sales data of one of the largest retailers in the world. Let's figure out what factors influence its revenue. Can factors such as air temperature and fuel cost influence the success of a huge company along with the purchasing power index and seasonal discounts? And how does machine learning minimize costs and increase economic impact?

The data contains the following columns:

• Store: Store number
• Date: Sales week start date
• Weekly_Sales: Sales
• Holiday_Flag: Mark on the presence or absence of a holiday
• Temperature: Air temperature in the region
• Fuel_Price: Fuel cost in the region
• CPI: Consumer price index
• Unemployment: Unemployment rate _

The retail chain – Walmart, has multiple outlets across the country which are facing issues in managing their inventory – to match supply with respect to demand. Using available data, we must: • Extract useful insights. • Make prediction models to forecast the sales for "X" number of months/years.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Walmart Stock (WMT) Outlook Mixed as Retail Giant Navigates Shifting Consumer Trends

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

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Snapshot img

Spreads Market Size 2024-2028

The spreads market size is forecast to increase by USD 7.52 billion at a CAGR of 4.2% between 2023 and 2028.

The market is experiencing significant growth, driven primarily by the increasing trend towards on-the-go consumption and the growing popularity of e-commerce channels. The consumers' busy lifestyles have led to a surge in demand for convenient and portable food options, including spreads and sandwiches. Moreover, the rise of e-commerce platforms has made it easier for consumers to access a wide range of spreads from various brands, further fueling market growth. However, the market also faces challenges,One major obstacle is the health concerns associated with spreads, particularly those high in sugar and saturated fats.
As consumers become more health-conscious, there is a growing demand for healthier spread options. Another challenge is the intense competition in the market, with numerous players vying for market share. Companies must differentiate themselves by offering unique and innovative products to meet the evolving needs and preferences of consumers. To capitalize on opportunities and navigate challenges effectively, market participants must stay abreast of consumer trends and respond with agility and innovation.

What will be the Size of the Spreads Market during the forecast period?

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Predictive modeling and collaboration tools facilitate real-time data analysis and scenario planning for investment firms. Business intelligence software and data visualization tools provide valuable insights for business users, while risk management and operations optimization rely on prescriptive analytics and data analytics software. Portfolio management and investment analysis benefit from interactive reports and data integration, enabling advanced analytics and mobile accessibility. Data storytelling and user interfaces enhance the value of data, while data security remains a critical concern. Subscription models and project management tools enable data mining and workflow automation for power users. The continuous dynamism of the market underscores the importance of staying informed and adaptable to evolving trends and patterns.

How is this Spreads Industry segmented?

The spreads industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Distribution Channel

  Offline
  Online


End-User

  Households
  Food Service
  Industrial


Product Type

  Jams & Jellies
  Nut Butters
  Cheese Spreads
  Savory Spreads


Packaging

  Jars
  Tubes
  Packets


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    Italy
    Spain
    UK


  Middle East and Africa

    UAE


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Distribution Channel Insights

The offline segment is estimated to witness significant growth during the forecast period.

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Machine learning and predictive analytics enable sales forecasting and customer segmentation. Data visualization tools help in data storytelling and risk management. Cloud-based platforms facilitate business planning and col

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