A ride-sharing company wants to implement a dynamic pricing strategy to optimize fares based on real-time market conditions. The company only uses ride duration to decide ride fares currently. The company aims to leverage data-driven techniques to analyze historical data and develop a predictive model that can dynamically adjust prices in response to changing factors.

The dataset containing historical ride data has been provided. It includes features such as the number of riders, number of drivers, location category, customer loyalty status, number of past rides, average ratings, time of booking, vehicle type, expected ride duration, and historical cost of the rides.

Your goal is to build a dynamic pricing model that incorporates the provided features to predict optimal fares for rides in real-time. The model must consider factors such as demand patterns and supply availability.

https://i0.wp.com/vitalflux.com/wp-content/uploads/2023/07/dynamic-pricing-machine-learning-strategies-examples.png?resize=1536%2C698&ssl=1" alt="ridimage">

Features:

'Number_of_Riders', 'Number_of_Drivers', 'Location_Category', 'Customer_Loyalty_Status', 'Number_of_Past_Rides', 'Average_Ratings', 'Time_of_Booking', 'Vehicle_Type', 'Expected_Ride_Duration', 'Historical_Cost_of_Ride'

Some References:

- Dynamic Pricing Explained: Machine Learning in Revenue Management and Pricing Optimization

- Dynamic Pricing using Reinforcement Learning

- Dynamic Pricing on E-commerce Platform with Deep Reinforcement Learning: A Field Experiment

- Engineering Extreme Event Forecasting at Uber with Recurrent Neural Networks

https://raw.githubusercontent.com/Masterx-AI/Project_Retail_Analysis_with_Walmart/main/Wallmart1.jpg" alt="">

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.

This data was downloaded from the Australian Energy Market Operator (AEMO), and contains 30 minute increments of electricity demand and price in NSW, Australia, from 2018 to mid 2023. This data was made publicly available by AEMO, as per their Copyright Permission.

Grocery Sales Prediction

This dataset provides a rich resource for researchers and practitioners interested in retail sales prediction and analysis. It contains information about various grocery products, the outlets where they are sold, and their historical sales data.

Product Characteristics:

Item_Identifier: Unique identifier for each product. Item_Weight: Weight of the product item. Item_Fat_Content: Categorical variable indicating the fat content of the product (e.g., low fat, regular). Item_Visibility: Numerical attribute reflecting the visibility of the product in the store (likely a promotional measure). Item_Type: Category of the product (e.g., Snacks, Beverages, Bakery). Item_MRP: Maximum Retail Price of the product. Outlet Information:

Outlet_Identifier: Unique identifier for each outlet (store). Outlet_Establishment_Year: Year the outlet was established. Outlet_Size: Categorical variable indicating the size of the outlet (e.g., Small, Medium, Large). (Note: This data may have missing values) Outlet_Location_Type: Categorical variable indicating the type of location the outlet is in (e.g., Tier 1 City, Tier 2 City, Upstate). Outlet_Type: Categorical variable indicating the type of outlet (e.g., Supermarket, Grocery Store, Convenience Store). Sales Data:

Item_Outlet_Sales: The historical sales data for each product-outlet combination. Profit: The profit margin earned on each product sold. Potential Uses

This dataset can be used for various retail sales analysis and prediction tasks, including:

Demand forecasting: Build models to predict future sales of individual products or product categories at specific outlets. Promotion optimization: Analyze the effectiveness of different promotional strategies (reflected by Item_Visibility) on sales. Assortment planning: Optimize product selection and placement within stores based on sales history and outlet characteristics. Outlet performance analysis: Compare the performance of different outlets based on sales figures and profit margins. Customer segmentation: Identify customer segments with distinct purchasing behavior based on product types and outlet locations. By analyzing these rich data points, retailers can gain valuable insights to improve their sales strategies, optimize inventory management, and maximize profits.

Retail Fashion Boutique Data Sales Analytics 2025

Overview

This comprehensive fashion retail synthetic dataset contains 2,176 real-world style records spanning seasonal collections, customer purchasing behavior, pricing strategies, and return analytics. Perfect for data science projects, machine learning models, and business intelligence dashboards focused on retail analytics and e-commerce insights.

Dataset Highlights

• 📊 Complete Sales Cycle: Purchase patterns, pricing strategies, and customer feedback
• 🔄 Return Analytics: Detailed return tracking with specific reasons and patterns
• 🛍️ Multi-Brand Coverage: 8 major fashion brands across diverse product categories
• 📈 Seasonal Intelligence: Four-season data with realistic markdown strategies
• ⭐ Customer Insights: Rating systems and purchasing behavior analysis
• 💰 Pricing Analytics: Original pricing, markdown percentages, and final pricing data

Key Applications

• Retail Analytics: Sales performance analysis and trend identification
• Customer Segmentation: Behavior analysis and purchasing pattern recognition
• Inventory Management: Stock optimization and seasonal demand forecasting
• Return Prediction: Machine learning models for return likelihood prediction
• Pricing Strategy: Dynamic pricing and markdown optimization analysis
• Business Intelligence: Comprehensive retail KPI dashboards and reporting

Column Details

Data Quality Features

• ✅ Realistic Business Logic: 15% return rate matching industry standards
• ✅ Seasonal Pricing: Authentic markdown patterns aligned with retail cycles
• ✅ Missing Data Handling: Strategic nulls for data cleaning practice (15% in ratings, size nulls for accessories)
• ✅ Balanced Distribution: Even representation across brands, categories, and seasons
• ✅ Price Consistency: Mathematically accurate pricing with discount calculations

Perfect For

• Data Analytics Projects: Retail KPI analysis, sales forecasting, customer behavior studies
• Machine Learning Models: Return prediction, demand forecasting, recommendation systems
• Business Intelligence: Executive dashboards, performance tracking, trend analysis
• Academic Research: Retail analytics case studies, pricing strategy research
• Portfolio Development: Comprehensive data science project demonstrations

File Formats Available

• CSV: Universal compatibility for data analysis tools
• Excel: Business reporting and stakeholder presentations
• JSON: API integration and web applications
• SQL: Database integration and advanced querying

Sample Use Cases

1. Return Prediction Model: Build ML models to predict return likelihood based on product attributes
2. Seasonal Demand Forecasting: Analyze purchasing patterns across different seasons and categories
3. Pricing Optimization: Study markdown effectiveness and optimal pricing strategies
4. Customer Satisfaction Analysis: Correlate ratings with return patterns and product characteristi...

Analysis of ‘Spanish Electricity Market: Demand, Gen. & Price’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/manualrg/spanish-electricity-market-demand-gen-price on 30 September 2021.

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

Context

This dataset is a daily time series of electricity demand, generation and prices in Spain from 2014 to 2018. It is gathered from ESIOS, a website managed by REE (Red Electrica Española) which is the Spanish TSO (Transmission System Operator)

A TSO main function is to operate the electrical system and to invest in new transmission (high voltage) infrastructure. (https://www.ree.es/en/about-us/business-activities/electricity-business-in-Spain)

As a system operator, REE forecast electricity demand and offer and runs daily actions . As a result of daily actions, a PBF Plan Básico de Funcionamiento) is yielded. This is a basic schedulling of energy production (upon it several mechanisms are triggerd to ensure supply)

Enery and prices data can be downloaded from : https://www.esios.ree.es/en

OMIE (Operador del Mercado Iberico de Electricidad) is responsible for running those daily actions and also offers interesting data. http://www.omie.es/en/inicio

Content

Original values are kept, so some names in Spanish are shown. Column name is describes each time series, so I provide a description of each name:

• Demanda programada PBF total (MWh): Schedulled Total Demand
• Demanda real (MW): Actual demanded power
• Energía asignada en Mercado SPOT Diario España (MWh): Energy traded in daily spot Spanish market (OMIE)
• Energía asignada en Mercado SPOT Diario Francia (MWh): Energy traded in daily spot French market
• Generación programada PBF Carbón (MWh): Schedulled Coal electricity generation
• Generación programada PBF Ciclo combinado (MWh): Schedulled Combined Cycle electricity generation
• Generación programada PBF Eólica: (MWh): Schedulled Wind electricity generation
• Generación programada PBF Gas Natural Cogeneración (MWh): Schedulled Natural Gas electricity Co-generation
• Generación programada PBF Nuclear (MWh): Schedulled Nuclear electricity generation
• Generación programada PBF Solar fotovoltaica (MWh): Schedulled Photovoltaic electricity generation
• Generación programada PBF Turbinación bombeo (MWh): Schedulled Reversible-Hydro electricity generation
• Generación programada PBF UGH + no UGH (MWh): Schedulled Total Hydro electricity generation
• Generación programada PBF total (MWh): Schedulled Total electricity generation
• Precio mercado SPOT Diario ESP (€/MWh): Daily spot Spain market price
• Precio mercado SPOT Diario FRA (€/MWh): Daily spot France market price Precio mercado SPOT Diario POR (€/MWh): Daily spot Portugal market price
• Rentas de congestión mecanismos implícitos diario Francia exportación (€/MWh): Daily spot export from France price
• Rentas de congestión mecanismos implícitos diario Francia importación (€/MWh): Daily spot import to France price
• Rentas de congestión mecanismos implícitos diario Portugal exportación (€/MWh): Daily spot export from Portugal price
• Rentas de congestión mecanismos implícitos diario Portugal importación (€/MWh): Daily spot import to Portugal price

Original data format is maintained, just in case it is necessary to append new data downloaded from Esios, as a result, geo columns are null.

Inspiration

I love energy and data and I couldn't find many datasets to practice daily forecasts. I hope that many data and energy enthusiasts find it interesting and enjoy it.

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

This dataset was synthetically generated to simulate ride-sharing pricing dynamics. It includes features such as Distance, Time of Day, Demand, Weather, Base Price, Weather Multiplier, and Final Price. The dataset aims to model real-world scenarios for ride-sharing services, providing a rich resource for machine learning, data analysis, and predictive modeling tasks.

• Distance: Distance of the ride in kilometers.
• Time_of_Day: Time of the ride, categorized into Morning, Afternoon, Evening, and Night.
• Demand: A demand score indicating ride demand during the time period.
• Weather: Categorical variable representing weather conditions (e.g., Clear, Rainy, Snowy).
• Base_Price: Base price for the ride in local currency.
• Weather_Multiplier: A multiplier applied to the base price based on weather conditions.
• Price: Final price of the ride after applying the weather multiplier.

Note : "This dataset is static and will not be updated regularly."

The USA Hotels Dataset from Booking.com is a rich collection of data related to hotels across the United States, extracted from Booking.com. This dataset includes essential information about hotel listings, such as hotel names, locations, prices, star ratings, customer reviews, and amenities offered. It's an ideal resource for researchers, data analysts, and businesses looking to explore the hospitality industry, analyze customer preferences, and understand pricing patterns in the U.S. hotel market.

Access 3 million+ US hotel reviews — submit your request today.

Key Features:

• Hotel Information: Includes hotel names, addresses, star ratings, and descriptions.
• Pricing Data: Nightly rates, discounts, and price variations by room type and season.
• Customer Reviews: Aggregated ratings and detailed user feedback from verified guests.
• Amenities: Detailed list of amenities provided by each hotel (e.g., Wi-Fi, parking, spa, swimming pool).
• Geographical Information: Hotel locations including city, state, and proximity to major landmarks.

Use Cases:

• Sentiment Analysis: Analyze customer reviews to gauge hotel service quality and guest satisfaction.
• Price Analysis: Compare pricing across different hotels, locations, and time periods to identify trends.
• Recommendation Systems: Build recommendation engines based on customer ratings, reviews, and preferences.
• Tourism and Hospitality Research: Understand patterns in hotel demand and services across various U.S. cities.

Dataset Description Title: Electricity Market Dataset for Long-Term Forecasting (2018–2024)

Overview: This dataset provides a comprehensive collection of electricity market data, focusing on long-term forecasting and strategic planning in the energy sector. The data is derived from real-world electricity market records and policy reports from Germany, specifically the Frankfurt region, a major European energy hub. It includes hourly observations spanning from January 1, 2018, to December 31, 2024, covering key economic, environmental, and operational factors that influence electricity market dynamics. This dataset is ideal for predictive modeling tasks such as electricity price forecasting, renewable energy integration planning, and market risk assessment.

Features Description Feature Name Description Type Timestamp The timestamp for each hourly observation. Datetime Historical_Electricity_Prices Hourly historical electricity prices in the Frankfurt market. Continuous (Float) Projected_Electricity_Prices Forecasted electricity prices (short, medium, long term). Continuous (Float) Inflation_Rates Hourly inflation rate trends impacting energy markets. Continuous (Float) GDP_Growth_Rate Hourly GDP growth rate trends for Germany. Continuous (Float) Energy_Market_Demand Hourly electricity demand across all sectors. Continuous (Float) Renewable_Investment_Costs Investment costs (capital and operational) for renewable energy projects. Continuous (Float) Fossil_Fuel_Costs Costs for fossil fuels like coal, oil, and natural gas. Continuous (Float) Electricity_Export_Prices Prices for electricity exports from Germany to neighboring regions. Continuous (Float) Market_Elasticity Sensitivity of electricity demand to price changes. Continuous (Float) Energy_Production_By_Solar Hourly solar energy production. Continuous (Float) Energy_Production_By_Wind Hourly wind energy production. Continuous (Float) Energy_Production_By_Coal Hourly coal-based energy production. Continuous (Float) Energy_Storage_Capacity Available storage capacity (e.g., batteries, pumped hydro). Continuous (Float) GHG_Emissions Hourly greenhouse gas emissions from energy production. Continuous (Float) Renewable_Penetration_Rate Percentage of renewable energy in total energy production. Continuous (Float) Regulatory_Policies Categorical representation of regulatory impact on electricity markets (e.g., Low, Medium, High). Categorical Energy_Access_Data Categorization of energy accessibility (Urban or Rural). Categorical LCOE Levelized Cost of Energy by source. Continuous (Float) ROI Return on investment for energy projects. Continuous (Float) Net_Present_Value Net present value of proposed energy projects. Continuous (Float) Population_Growth Population growth rate trends impacting energy demand. Continuous (Float) Optimal_Energy_Mix Suggested optimal mix of renewable, non-renewable, and nuclear energy. Continuous (Float) Electricity_Price_Forecast Predicted electricity prices based on various factors. Continuous (Float) Project_Risk_Analysis Categorical analysis of project risks (Low, Medium, High). Categorical Investment_Feasibility Indicator of the feasibility of energy investments. Continuous (Float) Use Cases Electricity Price Forecasting: Utilize historical and projected price trends to predict future electricity prices. Project Risk Classification: Categorize projects into risk levels for better decision-making. Optimal Energy Mix Analysis: Analyze the balance between renewable, non-renewable, and nuclear energy sources. Policy Impact Assessment: Study the effect of regulatory and market policies on energy planning. Long-Term Strategic Planning: Provide insights into investment feasibility, GHG emission reduction, and energy market dynamics. Acknowledgment This dataset is based on publicly available records and market data specific to the Frankfurt region, Germany. The dataset is designed for research and educational purposes in energy informatics, computational intelligence, and long-term forecasting.

Dataset Overview: Contains sales data from Blinkit, including product details, order quantities, revenue, and timestamps. Useful for demand forecasting, price optimization, trend analysis, and business insights. Helps in understanding customer behavior and seasonal variations in online grocery shopping. Potential Use Cases: - Time Series Analysis: Analyze sales trends over different periods. - Demand Forecasting: Predict future product demand based on historical data. - Price Optimization: Identify the impact of pricing on sales and revenue. - Customer Behavior Analysis: Understand buying patterns and preferences. - Market Trends: Explore how different factors affect grocery sales performance. This dataset can be beneficial for data scientists, business analysts, and researchers looking to explore e-commerce and retail trends. Feel free to use it for analysis, machine learning models, and business intelligence projects. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16299142%2Fa633fb36dc370263696b5d2ec940c74f%2FScreenshot%202025-06-16%20082824.png?generation=1750086765806732&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16299142%2F8843129c88c2f57d66006a3ac9d37dc7%2FScreenshot%202025-06-16%20084001.png?generation=1750086777975125&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16299142%2Ffa4f29a8f4cc763a1cc66c7913c077e8%2FScreenshot%202025-06-16%20084007.png?generation=1750086787100561&alt=media" alt="">

📄 Description: This synthetic dataset is designed for practising regression tasks, particularly for predicting Sales Revenue based on product, market, and economic factors. It contains both categorical (nominal) and numerical features, simulating real-world sales data across various product categories and regions.

📌 Dataset Summary: Rows: 2000

Columns: 12 features + 1 target (SalesRevenue)

🏷️ Columns Description: Column Name Type Description ProductCategory Categorical Type of product: Electronics, Clothing, Furniture, Toys Region Categorical Sales region: North, South, East, West CustomerSegment Categorical Customer income group: Low, Middle, High IsPromotionApplied Categorical Whether promotion was applied: Yes/No ProductionCost Numerical Cost to produce the product MarketingSpend Numerical Money spent on marketing SeasonalDemandIndex Numerical Factor representing seasonal demand CompetitorPrice Numerical Average price of competing products CustomerRating Numerical Average customer rating (out of 5) EconomicIndex Numerical Indicator of overall economic conditions StoreCount Numerical Number of stores selling the product OnlinePresence Numerical Online presence score of the product SalesRevenue Numerical Target Variable: Revenue from product sales

Description: This dataset, "Restaurant Pricing and Menu Analysis," contains 5000 records detailing menu items from various restaurants, likely based in a Southeast Asian market like Malaysia or Singapore, given the cuisine types and dish names (e.g., Rendang, Laksa, Teh Tarik). The data provides a rich blend of financial, categorical, and descriptive information for each menu item, including its ingredient cost, market price, cuisine type, and an estimated demand indicator.

Columns:

1.**menu_item_id:** A unique identifier for each menu item, combined with a suffix indicating the day type (_WD for Weekday, _WE for Weekend).

2.**menu_item_name**: The name of the dish or beverage.

3.**typical_ingredient_cost:** The average cost of the ingredients required to prepare the menu item.

4.**category:** The culinary category the item belongs to (e.g., Soup, Noodle Dish, Local Drink).

5.**cuisine_type:** The style of cuisine (e.g., Malay, Chinese, Indian, Nyonya, Western).

6.**key_ingredients_tags:** A comma-separated list of tags describing the key ingredients or characteristics of the item.

7.**day_type:**Indicates whether the data point is for a "Weekday" or "Weekend".

8.**observed_market_price:** The selling price of the menu item on the given day type.

9.**estimated_market_demand_indicator:** A numerical indicator (from 1 to 5) representing the estimated market demand for the item, where a higher number suggests greater demand.

10.**restaurant_type:**The type of establishment where the item is sold (e.g., Fine Dining, Casual Diner, Food Stall).

11.**meal_type:**The meal period during which the item is typically available (e.g., Lunch Special, Dinner Special, All Day).

12.**has_promotion:** A binary flag indicating if the item was under a promotion at the time of observation (1 for Yes, 0 for No).

As in any competitive market, wholesale electricity prices are a function of supply and demand. Prices are set by auction where generators submit bids roughly equivalent to their operating cost (for nuclear plants this may be the cost of maintenance, while for gas and coal plants this is the cost of fuel. Wind is free and is therefore always dispatched). Gas and coal plants are often the marginal generators (the last to clear the auction and set the price) and therefore their fuel costs are highly correlated with electricity prices. Wind generation, which is growing rapidly, reduces electricity prices by pushing out the supply curve.

For these reasons we chose the following features to use in our predictive model:

peak hours natural gas prices coal prices wind generation

Luxury Cosmetics Pop‑Up Events Dataset

A comprehensive, real-world–anchored synthetic dataset capturing 2,133 luxury beauty pop-up events across global retail hotspots. It focuses on limited-edition product drops, experiential formats, and performance KPIs—especially footfall and sell‑through. The data is designed for analytics use cases such as demand forecasting, footfall modeling, merchandising optimization, pricing analysis, and market expansion studies across regions and venue types.

What this dataset contains

• 2,133 events from global hubs across North America, Europe, Middle East, Asia‑Pacific, and Latin America
• Luxury/premium cosmetics brands and their limited‑release SKUs
• Event formats and retail venue archetypes typical of pop‑up retail
• Time windows and lease lengths aligned with short‑term pop‑up activations
• Core commercial KPIs: price, units sold, sell‑through percentage
• Footfall KPI: average daily footfall modeled by location/format/marketing intensity

Ideal use cases

• Pop‑up ROI and performance benchmarking by brand, city, venue type, and format
• Footfall prediction and location strategy (high‑street vs mall vs airport vs districts)
• Limited-edition launch analytics: pricing vs sell‑through dynamics
• Event planning: lease length and timing windows vs outcomes
• Territory planning: region and city segmentation performance
• Portfolio dashboards: cross‑brand comparisons and trend reporting

File formats

• CSV, JSON, XLSX, and SQLite (table: popups)

Target users

• Retail strategy and analytics teams
• Growth, trade marketing, and brand managers
• Data scientists building forecasting and optimization models
• BI developers building dashboards for pop‑up performance

Column Dictionary

Data quality notes

• City and end_date contain a small proportion of nulls to reflect real‑world reporting gaps (e.g., ongoing events).
• avg_daily_footfall varies by locati...

Participants will use the data provided for 10 markets comprising of all flights operated between these origin and destination cities by a typical carrier to estimate the demand and price by various point in time. To begin with a model of choice can be built and use the 12 months data to train the respective model and then predict the demand & fare for the flights in 13th to 15th months. It is mandatory to estimate the demand for each flight, different price estimation for available for sale at a given time for the respective flights at different days to departure. The model must determine time varying demand, pricing the product at different time points. As an extension of basic modeling, participants are encouraged to show how the respective model can be extended for continuous pricing or classless revenue management as innovation concept. The use of additional concepts like cancellation/no-show rate, overbooking etc can also be included in the model as added features.

Data Description In the airline industry, one of the objectives of the airlines is to correctly predict future bookings on the Flights based on the historical booking behavior. Depending on the expected demand, airlines can choose to sell seats at different fares. 1. Booking Data: The table or data sheet contains historical bookings by Flight ID, departure date, Origin & Destination ID, Cabin, Booking class of service, Dep Time & Arr Time, Booking count, avg booking fare. 2. Final count of flown passenger for the first 12 months of flight is provided along with avg fare (only at cabin level is provided) 3. Use the data for first 12 months to create a forecast for the last 3 months and predict forecasted bookings and corresponding fare. 4. Schedule & Market Data: The table includes flight ID details and corresponding competition flight details for the same Origin & Destination ID, airline market share. 5. Measure forecast accuracy, explain features and errors for different flight at different days to departure.

Gold Historically gold coinage was widely used as currency; when paper money was introduced, it typically was a receipt redeemable for gold coin or bullion. In a monetary system known as the gold standard, a certain weight of gold was given the name of a unit of currency. For a long period, the United States government set the value of the US dollar so that one troy ounce was equal to $20.67 ($0.665 per gram), but in 1934 the dollar was devalued to $35.00 per troy ounce ($0.889/g). By 1961, it was becoming hard to maintain this price, and a pool of US and European banks agreed to manipulate the market to prevent further currency devaluation against increased gold demand.

This dataset is a part of this https://www.kaggle.com/datasets/psycon/daily-gold-price-historical-data dataset by BATUCAN SENKAL

Food prices refer to the average price of particular food commodities.

Food prices can vary a lot, and changes over time can often give us insights into the underlying markets and agricultural production within countries.

The price of foods gives an important indicator of the balance between agricultural production and market demand. These prices matter to consumers and producers. They have obvious impacts on consumer affordability. But they also affect the income of farmers and producers.

In low-to-middle-income countries, a large share of the population is employed in agriculture. Producers typically benefit from higher food prices; consumers from lower prices. Food markets can therefore have a strong impact on food affordability, hunger and undernourishment, and dietary quality.

By Hannah Ritchie, Pablo Rosado and Max Roser (2023) - "Food Prices".

Description for each of the variables:

1. Brands: The brands of smartphones included in the dataset.
2. Colors: The colors available for the smartphones.
3. Memory: The storage capacity of the smartphones, typically measured in gigabytes (GB) or megabytes (MB).
4. Storage: The internal storage capacity of the smartphones, often measured in gigabytes (GB) or megabytes (MB).
5. Rating: The user ratings or scores assigned to the smartphones, reflecting user satisfaction or performance.
6. Selling Price: The price at which the smartphones are sold to consumers.
7. Original Price: The original or list price of the smartphones before any discounts or promotions.
8. Mobile: Indicates whether the device is a mobile phone.
9. Discount: The discount applied to the original price to calculate the selling price.
10. Discount percentage: The percentage discount applied to the original price to calculate the selling price.

This dataset provides detailed information on airline flight routes, fares, and passenger volumes within the United States from 1993 to 2024. The data includes metrics such as the origin and destination cities, distances between airports, the number of passengers, and fare information segmented by different airline carriers. It serves as a comprehensive resource for analyzing trends in air travel, pricing, and carrier competition over a span of three decades.

Data Features:

• tbl: Table identifier
• Year: Year of the data record
• quarter: Quarter of the year (1-4)
• citymarketid_1: Origin city market ID
• citymarketid_2: Destination city market ID
• city1: Origin city name
• city2: Destination city name
• airportid_1: Origin airport ID
• airportid_2: Destination airport ID
• airport_1: Origin airport code
• airport_2: Destination airport code
• nsmiles: Distance between airports in miles
• passengers: Number of passengers
• fare: Average fare
• carrier_lg: Code for the largest carrier by passengers
• large_ms: Market share of the largest carrier
• fare_lg: Average fare of the largest carrier
• carrier_low: Code for the lowest fare carrier
• lf_ms: Market share of the lowest fare carrier
• fare_low: Lowest fare
• Geocoded_City1: Geocoded coordinates for the origin city
• Geocoded_City2: Geocoded coordinates for the destination city
• tbl1apk: Unique identifier for the route

Potential Uses:

• Market Analysis: Assess trends in air travel demand, fare changes, and market share of airlines over time.
• Price Optimization: Develop models to predict optimal pricing strategies for airlines.
• Route Planning: Identify profitable routes and underserved markets for new route planning.
• Economic Studies: Analyze the economic impact of air travel on different cities and regions.
• Travel Behavior Research: Study changes in passenger preferences and travel behavior over the years.
• Competitor Analysis: Evaluate the performance of different airlines on various routes.

This dataset is (surplus or shortage) imbalance price in the Japanese electricity market from March 2022. The data source is here.

• After the full liberalization of retail electricity in 2016, a new system of simultaneous balancing of planned values ​​was introduced, and retailers and power generators create demand and power generation plans for each 30-minute period, dividing a day into 48 periods, and operate to match supply and demand by one hour before actual supply and demand (gate close).
• If an imbalance occurs in actual supply and demand, the general power transmission and distribution company issues a command to the power source (adjustment power) to adjust to eliminate the imbalance.
• Power generators and retailers each formulate a power generation plan and a demand plan for the next 48 periods by 12:00 the day before. Power generation plans and demand plans can be changed up to one hour before each period.
• The difference between the plan and the actual result (imbalance) is compensated for and absorbed by the power transmission and distribution company using its adjustment power.
• The person who caused the imbalance must subsequently settle the unbalanced amount of electricity with the electricity transmission and distribution company as an imbalance price.

This dataset includes the following columns:

• date_time: Date & Time (30 minute granularity)
• Imbalance price: Prices for each of the 9 areas (Hokkaido, Tohoku, Tokyo, Chubu, Hokuriku, Kansai, Chugoku, Shikoku, Kyushu) [Yen/kWh]

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F14688007%2Fca38bb90a75a741d06d79611a5111563%2F640px-Electric_power_distribution_sector_in_Japan.svg.png?generation=1717371642658737&alt=media" alt="">