>Dataset Overview: Grocery Store Sales Prediction

This dataset contains historical sales data from a large grocery store located in Islamabad, Pakistan. With an average daily footfall of around 1,500 customers, the store serves a broad consumer base, making it ideal for analyzing and predicting sales trends.

In this project, we focus specifically on predicting the sale of rice by leveraging historical data from January 22, 2024, to October 14, 2024. Using this dataset, we trained a Random Forest Regressor model to forecast rice sales based on past patterns.

Columns Details

The dataset includes the following columns:

1. Date: The date on which the sale occurred.
2. Store: A unique store code to identify the location.
3. Item: The code representing the specific item (rice) sold.
4. Sale: The quantity of rice sold on each date.

The goal of this project is to predict future sales of rice at this store using historical data. By accurately forecasting sales, the store can optimize inventory and improve stock management for this essential product.

The Global Sales Forecasting Software Market is projected to grow from USD 7.4 billion in 2021 to USD XX billion by 2028, at a CAGR of 10.1% during the forecast period (2021-2028). Growing demand for accurate and precise sales forecasts as well as rising adoption of cloud-based solutions are key factors driving the growth of this market over the coming years.

Sales forecasting software is a tool that helps businesses predict future sales. The software uses historical data and current market conditions to create a forecast. This information can help businesses make decisions about inventory, staffing, and other areas of the business.

On the basis of Type, Global Sales Forecasting Software Market is segmented into Cloud-based, On-premises.

Cloud-based:

Cloud-based Forecasting Software is software that resides on a remote server and can be accessed by authorized users through the internet. It eliminates the need for installing and maintaining software on local computers, which makes it ideal for businesses with multiple locations or those that want to share data among employees. Cloud-based solutions are typically subscription services that charge monthly or annual fees based on usage.

On-premises:

On-premises Forecasting Software is software that resides on the customer's computer network and can be accessed only by authorized users. It offers more customization options and control over data than cloud-based solutions but requires more maintenance and management. On-premises solutions are typically licensed software that charges a one-time fee for use.

On the basis of Application, Global Sales Forecasting Software Market is segmented into Small Business, Midsize Enterprise, Large Enterprise, Other.

Small Business:

Sales forecasting is a critical part of managing any business, but it can be especially challenging for small businesses. It's difficult to predict how much revenue the business will generate and what type of inventory should be kept on hand because there are fewer historical sales records to work with. However, forecasts enable companies to plan by determining staffing levels and ordering supplies based on expected demand. Sales forecasting software helps these small businesses overcome those challenges as well as increase both productivity and profitability.

Midsize Enterprise:

Sales forecasting offers these businesses a way to predict future sales, better manage inventory levels, maintain higher staffing levels during peak months and reduce the risk of over-ordering supplies. These benefits help midsize enterprises make more accurate decisions about how much product should be produced or purchased as well as where those products should go in the warehouse. Midsize enterprises typically have annual revenues between USD 100 million and USD 500 million, making them an attractive target market for software vendors looking at this segment of the industry.

On the basis of Region, Global Sales Forecasting Software Market is segmented into North America, Latin America, Europe, Asia Pacific, and the Middle East & Africa.

North America: Sales forecasting can help businesses make more informed decisions about how much product should be produced or purchased as well as where those products should go in the warehouse. These benefits are especially helpful for organizations that sell their goods and services across North America, which is why it's expected to account for a significant share of this market over the next few years.

Europe: The European market for sales forecasting software is expected to grow at a healthy pace over the next several years as more businesses embrace automation and advanced technology. Sales forecasting can help companies make better decisions about staffing, inventory levels, and other areas of the business that positively impact their bottom line.

Asia Pacific: The Asia-Pacific region offers opportunities for growth in terms of both volume and value thanks to its rapidly growing economy and thriving small business sector. This will be an important area to watch as it develops since those trends bode well not only for this market but also for many others throughout the Asia Pacific during the forecast period.

Latin America: Latin American countries such as Brazil offer major potential due to their burgeoning middle-class population and increasing demand for goods and services. This region is expected to experience healthy growth in the sales forecasting software market over the next decade as businesses become more sophistic

Retail Sales in the United States decreased 0.90 percent in May of 2025 over the previous month. This dataset provides - U.S. December Retail Sales Increased More Than Forecast - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Analysis of ‘Retail Sales Forecasting’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/tevecsystems/retail-sales-forecasting on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

This dataset contains lot of historical sales data. It was extracted from a Brazilian top retailer and has many SKUs and many stores. The data was transformed to protect the identity of the retailer.

Content

[TBD]

Acknowledgements

This data would not be available without the full collaboration from our customers who understand that sharing their core and strategical information has more advantages than possible hazards. They also support our continuos development of innovative ML systems across their value chain.

Inspiration

Every retail business in the world faces a fundamental question: how much inventory should I carry? In one hand to mush inventory means working capital costs, operational costs and a complex operation. On the other hand lack of inventory leads to lost sales, unhappy customers and a damaged brand.

Current inventory management models have many solutions to place the correct order, but they are all based in a single unknown factor: the demand for the next periods.

This is why short-term forecasting is so important in retail and consumer goods industry.

We encourage you to seek for the best demand forecasting model for the next 2-3 weeks. This valuable insight can help many supply chain practitioners to correctly manage their inventory levels.

--- Original source retains full ownership of the source dataset ---

Overview:

This dataset contains 1000 rows of synthetic online retail sales data, mimicking transactions from an e-commerce platform. It includes information about customer demographics, product details, purchase history, and (optional) reviews. This dataset is suitable for a variety of data analysis, data visualization and machine learning tasks, including but not limited to: customer segmentation, product recommendation, sales forecasting, market basket analysis, and exploring general e-commerce trends. The data was generated using the Python Faker library, ensuring realistic values and distributions, while maintaining no privacy concerns as it contains no real customer information.

Data Source:

This dataset is entirely synthetic. It was generated using the Python Faker library and does not represent any real individuals or transactions.

Data Content:

Potential Use Cases (Inspiration):

Customer Segmentation: Group customers based on demographics, purchasing behavior, and preferences.

Product Recommendation: Build a recommendation system to suggest products to customers based on their past purchases and browsing history.

Sales Forecasting: Predict future sales based on historical trends.

Market Basket Analysis: Identify products that are frequently purchased together.

Price Optimization: Analyze the relationship between price and demand.

Geographic Analysis: Explore sales patterns across different cities.

Time Series Analysis: Investigate sales trends over time.

Educational Purposes: Great for practicing data cleaning, EDA, feature engineering, and modeling.

Sales Forecasting Software Market size was valued at USD 68 Billion in 2024 and is projected to reach USD 158.98 Billion by 2031, growing at a CAGR of 11.20% from 2024 to 2031.

Sales Forecasting Software Market Drivers

Increased Demand for Accurate Sales Forecasting: Businesses are increasingly recognizing the importance of accurate sales forecasting to optimize inventory management, reduce costs, and improve decision-making. Accurate forecasts help companies align their production and supply chain activities with market demand. Adoption of Advanced Analytics and Big Data: The integration of advanced analytics and big data technologies enables more precise and insightful sales forecasts. These technologies allow businesses to analyze vast amounts of historical and real-time data to identify trends and patterns that inform forecasting models. Advancements in AI and Machine Learning: AI and machine learning algorithms have significantly enhanced the capabilities of sales forecasting software. These technologies can process complex datasets, learn from historical data, and generate highly accurate predictive models, thereby improving the reliability of sales forecasts.

Context

This dataset contains lot of historical sales data. It was extracted from a Brazilian top retailer and has many SKUs and many stores. The data was transformed to protect the identity of the retailer.

Content

[TBD]

Acknowledgements

This data would not be available without the full collaboration from our customers who understand that sharing their core and strategical information has more advantages than possible hazards. They also support our continuos development of innovative ML systems across their value chain.

Inspiration

Every retail business in the world faces a fundamental question: how much inventory should I carry? In one hand to mush inventory means working capital costs, operational costs and a complex operation. On the other hand lack of inventory leads to lost sales, unhappy customers and a damaged brand.

Current inventory management models have many solutions to place the correct order, but they are all based in a single unknown factor: the demand for the next periods.

This is why short-term forecasting is so important in retail and consumer goods industry.

We encourage you to seek for the best demand forecasting model for the next 2-3 weeks. This valuable insight can help many supply chain practitioners to correctly manage their inventory levels.

Context

Demand Forecasting is the process in which historical sales data is used to develop an estimate of an expected forecast of customer demand. To businesses, Demand Forecasting provides an estimate of the amount of goods and services that its customers will purchase in the foreseeable future. Critical business assumptions like turnover, profit margins, cash flow, capital expenditure, risk assessment and mitigation plans, capacity planning, etc. are dependent on Demand Forecasting.

Demand Forecasting is the pivotal business process around which strategic and operational plans of a company are devised. Based on the Demand Forecast, strategic and long-range plans of a business like budgeting, financial planning, sales and marketing plans, capacity planning, risk assessment and mitigation plans are formulated.

Short to medium term tactical plans like pre-building, make-to-stock, make-to-order, contract manufacturing, supply planning, network balancing, etc. are execution based. Demand Forecasting also facilitates important management activities like decision making, performance evaluation, judicious allocation of resources in a constrained environment and business expansion planning.

This time we bring to you another Weekend Hackathon to apply your machine learning and time series forecasting skills to build a successful demand forecasting model

Problem Statement

One of the largest retail chains in the world wants to use their vast data source to build an efficient forecasting model to predict the sales for each SKU in its portfolio at its 76 different stores using historical sales data for the past 3 years on a week-on-week basis. Sales and promotional information is also available for each week - product and store wise.

However, no other information regarding stores and products are available. Can you still forecast accurately the sales values for every such product/SKU-store combination for the next 12 weeks accurately? If yes, then dive right in!

Data Description

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1279142%2F4f09fa27a17b01fa40700e7b80d87add%2Fdataset_description.jpg?generation=1594430740572308&alt=media" alt="">

Evaluation Metric

The evaluation metric for this competition is 100*RMSLE (Root Mean Squared Log Error).

Sales Automation Software Market Outlook

The global sales automation software market size was valued at approximately USD 2.1 billion in 2023 and is projected to reach around USD 8.3 billion by 2032, growing at a remarkable CAGR of 16.5% during the forecast period. The growth of the sales automation software market can be attributed to the increasing need for enhanced customer relationship management (CRM) and the rising adoption of advanced technologies such as artificial intelligence (AI) and machine learning (ML) in sales processes.

One of the significant growth factors driving the sales automation software market is the increasing demand for efficient and streamlined sales processes. As businesses strive to improve their sales efficiency and effectiveness, the adoption of sales automation software has risen exponentially. These tools empower sales teams to automate repetitive tasks, manage customer data more effectively, and focus on closing deals, thus significantly enhancing productivity. Furthermore, the push towards digital transformation across various sectors is fostering the integration of such applications into business operations, ensuring a seamless experience for both employees and customers.

Another crucial driver is the growing importance of customer relationship management (CRM). In an era where customer experience is a significant differentiator, companies are investing in sales automation software to gain deeper insights into customer behavior, preferences, and needs. These tools facilitate better customer engagement by providing sales teams with real-time data and analytics, enabling them to tailor their approaches according to individual customer requirements. This not only helps in building stronger customer relationships but also aids in customer retention and loyalty, contributing to sustained business growth.

The rise of artificial intelligence (AI) and machine learning (ML) technologies is also a major catalyst for market expansion. AI-powered sales automation software can predict customer buying patterns, provide sales forecasts, and personalize customer interactions, thereby enhancing the overall sales strategy. These advanced capabilities are crucial for businesses looking to stay ahead in a competitive market. Machine learning algorithms can analyze vast amounts of data, offering actionable insights that help sales teams make informed decisions quickly. The integration of AI and ML is thus transforming the sales landscape, making it more data-driven and efficient.

In the context of the sales automation software market, understanding the dynamics of Stimate Sales can offer valuable insights into how businesses are leveraging technology to enhance their sales strategies. Stimate Sales refers to the estimated sales that a company expects to achieve within a specific period, based on historical data, market trends, and predictive analytics. By utilizing sales automation tools, companies can more accurately forecast their Stimate Sales, allowing them to make informed decisions about inventory management, resource allocation, and marketing strategies. This capability is particularly crucial in today's fast-paced business environment, where agility and responsiveness can significantly impact a company's competitive edge.

Regionally, North America holds the largest share of the sales automation software market, primarily due to the early adoption of advanced technologies and the presence of major market players. Europe follows closely, driven by the increasing digitalization efforts and strong economic conditions in the region. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, propelled by the rapidly growing economies of China, India, and Southeast Asian countries. The surge in small and medium enterprises (SMEs) adopting sales automation tools in these regions further accelerates market growth. Latin America and the Middle East & Africa also show potential for significant growth due to increasing investments in technology and infrastructure development.

Component Analysis

The sales automation software market can be segmented into software and services under the component category. The software segment primarily includes various types of sales automation tools such as CRM platforms, sales forecasting software, and lead management systems. This segment holds the largest share and is anticipated to maintain its dominance throughout the forecast period. The widesp

Retail Sales in the United States increased 3.30 percent in May of 2025 over the same month in the previous year. This dataset provides - United States Retail Sales YoY - actual values, historical data, forecast, chart, statistics, economic calendar and news.

New Home Sales in the United States decreased to 623 Thousand units in May from 722 Thousand units in April of 2025. This dataset provides the latest reported value for - United States New Home Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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.

Can we predict stock market using machine learning? (META Stock Forecast)

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

This operations dashboard shows historic and current data related to this performance measure. The performance measure page is available at 5.10 Revenue Forecast Variance.Data Dictionary

The dataset contains historical data for inventory-active products from the previous 8 weeks of the week we would like to predict, captured as a photo of all inventory at the beginning of the week.

Attributes SKU: Unique material identifier; INV: Current inventory level of material; TIM: Registered transit time; FOR-: Forecast sales for the next 3, 6, and 9 months; SAL-: Sales quantity for the prior 1, 3, 5, and 9 months; MIN: Minimum recommended amount in stock (MIN); OVRP: Parts overdue from source; SUP-: Supplier performance in last 1 and 2 semesters; OVRA: Amount of stock orders overdue (OVRA); RSK-: General risk flags associated to the material; BO: Product went on backorder.

Evaluation Metrics We applied Area Under Receiver Operator Curve (AUROC) for primary evaluation, and Precision-Recall curves for post-analysis.

File descriptions

train.csv - the training set test.csv - the test set

The dataset is historical data, which contains information of an anonymous company’s products for the 8 weeks prior to the week that is going to be predicted.

Data fields

• sku - Stock keeping unit, a unique product identifier
• national_inv - Current inventory level for the product
• lead_time - Transit time for the product
• in_transit_qty - Amount of the product in transit from source
• forecast_3_month - Forecast sales for the next 3 months
• forecast_6_month - Forecast sales for the next 6 months
• forecast_9_month - Forecast sales for the next 9 months
• sales_1_month - Sales quantity for the prior 1 month
• sales_3_month - Sales quantity for the prior 3 months
• sales_6_month - Sales quantity for the prior 6 months
• sales_9_month - Sales quantity for the prior 9 months
• min_bank - Minimum recommend amount to stock
• potential_issue - Source issue for part identified; 1 indicates that some issues are identified, and 0 indicates no issue
• pieces_past_due - Amount overdue from source
• perf_6_month_avg - Source performance for the prior 6 months
• perf_12_month_avg - Source performance for the prior 12 months
• local_bo_qty - Amount of stock orders overdue
• deck_risk - Part risk flag; 1 indicates risk identified, and 0 indicates no risk
• oe_constraint - Part risk flag; 1 indicates risk identified, and 0 indicates no risk
• ppap_risk - Part risk flag; 1 indicates risk identified, and 0 indicates no risk
• stop_auto_buy - Part risk flag; 1 indicates risk identified, and 0 indicates no risk
• rev_stop - Part risk flag; 1 indicates risk identified, and 0 indicates no risk
• went_on_back_order - Product actually went on backorder. This is the target value.

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. We are provided with historical sales data for 1,115 Rossmann stores. The task is to forecast the "Sales" column for the test set.

The dataset is about a retail sales dataset containing information about store sales for various products over time.

The specific variables include: Store: Unique identifier for the store location Date: Calendar date of the sales data Product: Name of the product being sold Weekly Sales: Total number of units sold for the product in a week Inventory Level: Number of units of the product currently in stock at the store Temperature: Average temperature for the week at the store location Past Promotion of Product (in lac): Total value (in lakhs) of any past promotions for the product during the week (1 lac = 100,000) Demand Forecast: Predicted number of units to be sold for the product in the next week (provided for baseline model comparison)

This dataset can be used for various analytical purposes related to retail sales and inventory management, including:

Demand forecasting: By analyzing historical sales data, temperature, past promotions, and other relevant factors, you can build models to predict future demand for products. This information can be used to optimize inventory levels and prevent stock outs or overstocking. Promotion analysis: You can compare sales data during promotional periods with non-promotional periods to assess the effectiveness of different promotions and identify products that respond well to promotions. Product analysis: By analyzing sales data across different stores and time periods, you can identify which products are most popular and in which locations. This information can be used to inform product placement, marketing strategies, and assortment planning. Store performance analysis: You can compare sales performance across different stores to identify top-performing stores and understand factors contributing to their success. This information can be used to identify areas for improvement in underperforming stores.

By utilizing this dataset for these analytical purposes, retail organizations can gain valuable insights into their sales patterns, customer behavior, and inventory management practices. This information can be used to make data-driven decisions that improve sales performance, profitability, and customer satisfaction.

Precise demand forecasting has become crucial for merchants due to the growing complexity of client behavior and market dynamics. This allows them to enhance inventory management, minimize instances of stock outs, and enhance overall operational efficiency. In Bangladesh, there is a significant lack of emphasis on demand forecasting to enhance corporate performance. In recognition of these difficulties, the study seeks to produce predictions by employing two statistical models and three machine learning models. The historical sales data was obtained from a restaurant in Bangladesh, and five specific products were chosen for the purpose of predicting sales. The models have been rated according to their average score of deviation from the optimal root mean squared error. The Multilayer Perceptron and Random Forest algorithms have attained the top two positions. Statistical models such as simple exponential smoothing and Croston’s method have exhibited superior performance compared to XGBOOST model. This study advances demand forecasting techniques in Bangladesh’s restaurant industry by providing valuable insights, comparing different approaches, and suggesting ways to improve forecast accuracy and operational efficiency, thereby demonstrating the practical relevance and applicability of the research to the reader.