Professionals from Italian e-commerce players faced inflation's impact on their business. A survey from early 2023 showed that about **** in *** companies had decreased margins to keep similar prices, whereas ** percent of surveyed professionals stated their companies maintained similar prices but reduced discounts.

A new report summarizes the results of several tariff questions included in the February round of the Survey of Regional Conditions and Expectations.

About Dataset

All the following text is copied directly from the original dataset used: https://www.kaggle.com/datasets/fedesoriano/the-boston-houseprice-data
The only difference is that features 12 and 13 have been removed for simplicity. See original link for a version with those features in place.

Similar Datasets

Gender Pay Gap Dataset: https://www.kaggle.com/fedesoriano/gender-pay-gap-dataset
California Housing Prices Data (5 new features!): https://www.kaggle.com/fedesoriano/california-housing-prices-data-extra-features
Company Bankruptcy Prediction: https://www.kaggle.com/fedesoriano/company-bankruptcy-prediction
Spanish Wine Quality Dataset: https://www.kaggle.com/datasets/fedesoriano/spanish-wine-quality-dataset

Context

The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic prices and the demand for clean air', J. Environ. Economics & Management, vol.5, 81-102, 1978.

Attribute Information

Input features in order: 1) CRIM: per capita crime rate by town
2) ZN: proportion of residential land zoned for lots over 25,000 sq.ft.
3) INDUS: proportion of non-retail business acres per town
4) CHAS: Charles River dummy variable (1 if tract bounds river; 0 otherwise)
5) NOX: nitric oxides concentration (parts per 10 million) [parts/10M]
6) RM: average number of rooms per dwelling
7) AGE: proportion of owner-occupied units built prior to 1940
8) DIS: weighted distances to five Boston employment centres
9) RAD: index of accessibility to radial highways
10) TAX: full-value property-tax rate per $10,000 [$/10k]
11) PTRATIO: pupil-teacher ratio by town
[Original features 12 and 13 have been deliberately removed from this version of the dataset]

Output variable:
1) MEDV: Median value of owner-occupied homes in $1000's [k$]

Source

StatLib - Carnegie Mellon University

Relevant Papers

Harrison, David & Rubinfeld, Daniel. (1978). Hedonic housing prices and the demand for clean air. Journal of Environmental Economics and Management. 5. 81-102. 10.1016/0095-0696(78)90006-2. https://www.researchgate.net/profile/Daniel-Rubinfeld/publication/4974606_Hedonic_housing_prices_and_the_demand_for_clean_air/links/5c38ce85458515a4c71e3a64/Hedonic-housing-prices-and-the-demand-for-clean-air.pdf

Belsley, David A. & Kuh, Edwin. & Welsch, Roy E. (1980). Regression diagnostics: identifying influential data and sources of collinearity. New York: Wiley https://www.wiley.com/en-us/Regression+Diagnostics%3A+Identifying+Influential+Data+and+Sources+of+Collinearity-p-9780471691174

Various automotive companies recorded a decline in their share prince on Monday, February ***, 2025. Of the companies surveyed, French car parts supplier Valeo was the most impacted by this shift, with his share price dropping by *** percent. This market shock came after President Trump announced ** percent tariffs on goods from Mexico and Canada, and ** percent tariffs on imports from China.

United States Report On Business: PMI: Prices Index data was reported at 60.700 NA in Nov 2018. This records a decrease from the previous number of 71.600 NA for Oct 2018. United States Report On Business: PMI: Prices Index data is updated monthly, averaging 62.600 NA from Jan 1948 (Median) to Nov 2018, with 851 observations. The data reached an all-time high of 100.000 NA in Jun 1950 and a record low of 10.600 NA in Jun 1949. United States Report On Business: PMI: Prices Index data remains active status in CEIC and is reported by Institute for Supply Management. The data is categorized under Global Database’s United States – Table US.S001: Institute for Supply Management: Purchasing Manager Index.

The datasets here are useful for an online pet supply company for several reasons related to their top priorities of increasing sales and reducing expenses: Boosting Sales Through Upselling and Cross-selling: By analyzing which products are frequently bought together, the company can identify buying patterns. This allows them to recommend additional items (cross-selling) or higher-priced versions (upselling) at checkout, potentially increasing the average order value.

Reducing Shipping Costs: The data can reveal which products are frequently purchased together. The company can leverage this information to strategically bundle these items, reducing the number of packages shipped and lowering overall shipping costs.

Optimizing Warehouse Location: Understanding customer locations and their buying habits can help the company decide on the ideal location for a new warehouse. Placing the warehouse closer to high-demand areas could lead to faster deliveries and potentially reduce shipping costs.

In essence, the dataset allows the company to gain valuable insights into customer behavior and product trends. This information can be used to develop data-driven strategies that maximize sales and minimize expenses.

Datasets: It consists of four interconnected tables: Sales, Product, State Mapping, and Customer. The Sales table stores transaction details like date, customer ID, product description, quantity, and total price. The Product table houses information about each item, including its code, weight, cost, shipping data, and category. The State Mapping table creates a standardized format for state information by linking abbreviated codes with full descriptions and regional affiliations. Finally, the Customer table captures customer details like city, postal code, state code, and even location coordinates. By establishing these relationships between tables, the schema ensures organized and consistent data storage for the sales system.

The datasets contain fields that describe information about a sale, product, customer, and the state they live in. Here’s a breakdown of the fields in each table:

Sales Table: Transaction Date: Date of purchase Customer ID: Customer identifier Description: Product description Stock Code: Product code Invoice No: An invoice contains multiple products and represents a single checkout Quantity: Quantity of a product purchased Sales: Total amount of a product in a single checkout Unit Price: Unit price of a product

Product Table: Stock Code: Product code Weight: Weight of a single unit Landed Cost: Manufacturer cost + freight Shipping_Cost_1000_m: Average cost of shipping 1000 miles to customers Description: Most recent product description Category: Product category

State Mapping Table: Order State: State code, description and all its variations State: A standardized state code Region: Region name

Customer Table: Customer ID: Customer unique identifier Order City: City Order Postal: Postal code Order State: State Latitude: Latitude of customer location Longitude: Longitude of customer location

The greatest cost reduction overall from analytical AI use was in service operations, where AI is capable of assisting in a multitude of roles, reducing overall costs. The highest ration of most cost, that is with savings over ** percent, were in risk, legal, and compliance. This is likely because this is an expensive field, and any work hour saved in it reduces overall cost significantly.

Price Drop Refund Platform Market Outlook

According to our latest research, the global Price Drop Refund Platform market size reached USD 1.62 billion in 2024, driven by the increasing digitalization of commerce and the rising consumer demand for price protection solutions. The market is anticipated to expand at a CAGR of 16.9% during the forecast period, propelling the market value to approximately USD 5.03 billion by 2033. The surge in online transactions, coupled with the proliferation of e-commerce and retail platforms, is a key growth factor fueling the adoption of price drop refund solutions globally, as businesses strive to enhance customer satisfaction and loyalty in a highly competitive environment.

A primary driver for the rapid growth of the Price Drop Refund Platform market is the evolving landscape of consumer expectations in the digital era. Modern consumers are increasingly seeking transparency, value, and assurance in their purchasing experiences, especially in sectors like e-commerce and retail where price fluctuations are frequent. Price drop refund platforms empower customers by automatically tracking price changes post-purchase and enabling easy refund claims if a price drop occurs. This not only enhances customer trust but also encourages repeat business, as consumers feel more secure in making purchases without the fear of missing out on better prices. Retailers and online marketplaces are leveraging these platforms as a strategic tool to differentiate themselves, reduce cart abandonment rates, and foster long-term brand loyalty, all of which are critical for sustaining growth in a fiercely competitive digital marketplace.

Technological advancements and the integration of artificial intelligence (AI) and machine learning (ML) are further accelerating the expansion of the Price Drop Refund Platform market. AI-driven algorithms enable real-time price monitoring, dynamic price matching, and automated refund processing, significantly reducing manual intervention and operational costs for businesses. Furthermore, the rise of API-based integrations allows seamless connectivity between price drop refund platforms and various e-commerce, payment, and retail management systems, enhancing the overall efficiency and scalability of these solutions. The growing availability of cloud-based deployment options is making these platforms accessible even to small and medium enterprises (SMEs), democratizing the benefits of automated price protection across the business spectrum.

Another significant growth factor is the increasing regulatory emphasis on consumer protection and fair trade practices across major economies. Governments and industry bodies are encouraging transparent pricing mechanisms and refund policies, compelling businesses to adopt robust price drop refund solutions to remain compliant and competitive. Additionally, the global expansion of digital payment infrastructure and the surge in cross-border e-commerce transactions are creating new opportunities for price drop refund platforms, as businesses seek to streamline refund processes and minimize friction in international transactions. The convergence of these trends is expected to sustain the strong momentum of the price drop refund platform market throughout the forecast period.

From a regional perspective, North America currently leads the global price drop refund platform market, owing to the high penetration of e-commerce, advanced digital infrastructure, and strong consumer awareness about price protection services. However, Asia Pacific is anticipated to witness the fastest growth rate, driven by the rapid digital transformation of retail and e-commerce sectors in countries such as China, India, and Southeast Asian nations. Europe also represents a significant market, supported by stringent consumer rights regulations and the increasing adoption of digital commerce solutions. Meanwhile, Latin America and the Middle East & Africa are emerging as promising markets, fueled by rising internet penetration and the expansion of online retail channels. The regional dynamics are expected to shape the competitive landscape and innovation trajectories within the global price drop refund platform market.

Component Analysis

The Price Drop Refund Platform market can be segmented by component into Software and Services, each playing a distinct role in the overall ecosystem. Software

This dataset tracks annual reduced-price lunch eligibility from 2010 to 2023 for Business And Economics Academy Of Milwaukee Inc vs. Wisconsin and Business And Economics Academy Of Milwaukee Inc Agency School District

Graph and download economic data for 25) To the Extent That the Price or Nonprice Terms Applied to Insurance Companies Have Tightened or Eased over the Past Three Months (as Reflected in Your Responses to Questions 23 and 24), What Are the Most Important Reasons for the Change?| B. Possible Reasons for Easing | 4. Lower Internal Treasury Charges for Funding. | Answer Type: 2nd Most Important (ALLQ25B42MINR) from Q1 2012 to Q1 2025 about ease, change, funds, companies, 3-month, insurance, Treasury, price, and USA.

Consumer Edge is a leader in alternative consumer data for public and private investors and corporate clients. CE Vision USA includes consumer transaction data on 100M+ credit and debit cards, including 35M+ with activity in the past 12 months and 14M+ active monthly users. Capturing online, offline, and 3rd-party consumer spending on public and private companies, data covers 12K+ merchants, 800+ parent companies, 80+ same store sales metrics, and deep demographic and geographic breakouts. Review data by ticker in our Investor Relations module. Brick & mortar and ecommerce direct-to-consumer sales are recorded on transaction date and purchase data is available for most companies as early as 6 days post-swipe.

Consumer Edge’s consumer transaction datasets offer insights into industries across consumer and discretionary spend such as: • Apparel, Accessories, & Footwear • Automotive • Beauty • Commercial – Hardlines • Convenience / Drug / Diet • Department Stores • Discount / Club • Education • Electronics / Software • Financial Services • Full-Service Restaurants • Grocery • Ground Transportation • Health Products & Services • Home & Garden • Insurance • Leisure & Recreation • Limited-Service Restaurants • Luxury • Miscellaneous Services • Online Retail – Broadlines • Other Specialty Retail • Pet Products & Services • Sporting Goods, Hobby, Toy & Game • Telecom & Media • Travel

Private equity and venture capital firms can leverage insights from CE’s synthetic data to assess investment opportunities, while consumer insights teams and retailers can gain visibility into transaction data’s potential for competitive analysis, shopper behavior, and market intelligence.

CE Vision Benefits • Discover new competitors • Compare sales, average ticket & transactions across competition • Evaluate demographic and geographic drivers of growth • Assess customer loyalty • Explore granularity by geos • Benchmark market share vs. competition • Analyze business performance with advanced cross-cut queries

Corporate researchers and consumer insights teams use CE Vision for:

Corporate Strategy Use Cases • Ecommerce vs. brick & mortar trends • Real estate opportunities • Economic spending shifts

Marketing & Consumer Insights • Total addressable market view • Competitive threats & opportunities • Cross-shopping trends for new partnerships • Demo and geo growth drivers • Customer loyalty & retention

Investor Relations • Shareholder perspective on brand vs. competition • Real-time market intelligence • M&A opportunities

Most popular use cases for private equity and venture capital firms include: • Deal Sourcing • Live Diligences • Portfolio Monitoring

Use Case: Apparel Retailer, Enterprise-Wide Solution

Problem A $49B global apparel retailer was looking for a comprehensive enterprise-wide consumer data platform to manage and track consumer behavior across a variety of KPI's for use in weekly and monthly management reporting.

Solution The retailer leveraged Consumer Edge's Vision Pro platform to monitor and report weekly on: • market share, competitive analysis and new entrants • trends by geography and demographics • online and offline spending • cross-shopping trends

Impact Marketing and Consumer Insights were able to: • develop weekly reporting KPI's on market share for company-wide reporting • establish new partnerships based on cross shopping trends online and offline • reduce investment in slow channels in both online and offline channels • determine demo and geo drivers of growth for refined targeting • analyze customer retention and plan campaigns accordingly

Overnight trips and direct routes were some of the main benefits for business travelers cut by companies to save money, according to an April 2024 survey. The younger generations tended to think that business travel could be a driver for career advancement.

Price Optimisation Software Market size was valued at USD 1.67 Billion in 2023 and is projected to reach USD 4.24 Billion by 2030, growing at a CAGR of 17.48% during the forecasted period 2024 to 2030

Global Price Optimisation Software Market Drivers

Growing Competition: Organizations must constantly adapt their pricing strategies to stay competitive in today's cutthroat business climate. With the aid of price optimization software, firms may set prices that optimize profitability while maintaining their competitiveness in the market by analyzing market dynamics, rival pricing, and customer behavior.

Growing Need for Data-Driven Insights: As analytics technology progress and data sources proliferate, organizations are depending more and more on data-driven insights to guide their pricing decisions. Price optimization software helps organizations establish pricing based on consumer preferences, market demand, and competition activity by analyzing massive amounts of data and producing actionable insights through the use of advanced analytics, machine learning, and artificial intelligence algorithms.

Increasing Complexity of Pricing Models: Businesses are providing a wide range of goods and services along with a variety of pricing alternatives to cater to the varied needs of their clientele, which is making the pricing landscape more complex. By offering sophisticated pricing models, scenario analysis tools, and optimization algorithms that let them dynamically modify prices based on variables including product features, client segments, and market situations, price optimization software assists firms in navigating this complexity.

Need for Margin Improvement: Businesses in all sectors of the economy place a high premium on improving their margins, particularly in fiercely competitive markets with narrow profit margins. By optimizing pricing across product portfolios, modifying prices in real-time based on supply and demand dynamics, and discovering pricing strategies that maximize revenue and profitability, price optimization software assists firms in recognizing opportunities to enhance margins.

Transition to Usage- and Subscription-Based Pricing Models: As usage- and subscription-based pricing models proliferate across industries, companies are looking for pricing strategies that meet the models' demands for scalability and flexibility. Businesses can capture value based on consumer usage patterns and willingness to pay by using dynamic pricing strategies that are in line with subscription and usage-based pricing models with the help of price optimization software.

Consumers increasingly demand individualized pricing that takes into account their unique preferences, past purchases, and willingness to pay. Using consumer behavior, demographics, and purchasing patterns as a basis for customer segmentation, price optimization software helps firms offer tailored promotions and prices that increase customer happiness and loyalty.

Concentrate on Revenue Growth: For companies hoping to increase their market share and boost profitability, revenue growth is a critical goal. Price optimization software analyzes pricing elasticity, demand sensitivity, and cross-selling opportunities to assist firms find revenue opportunities and implement pricing strategies that optimize profits and promote long-term growth.

Need for Real-Time Pricing modifications: Companies must be able to react quickly to changing market conditions, rivalry, and consumer preferences in today's fast-paced business climate. This includes the capacity to make real-time price modifications. With the freedom to dynamically alter pricing in real-time based on market signals, price optimization software gives organizations the ability to take advantage of revenue opportunities and reduce risks.

United States SBP: IF: Change in Prices Prior to Mar 13: Large Decrease data was reported at 1.000 % in 20 Dec 2021. This records an increase from the previous number of 0.800 % for 13 Dec 2021. United States SBP: IF: Change in Prices Prior to Mar 13: Large Decrease data is updated weekly, averaging 1.400 % from Aug 2021 (Median) to 20 Dec 2021, with 13 observations. The data reached an all-time high of 2.100 % in 27 Sep 2021 and a record low of 0.500 % in 06 Dec 2021. United States SBP: IF: Change in Prices Prior to Mar 13: Large Decrease data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.S045: Small Business Pulse Survey: by Sector: Weekly. Beg Monday (Discontinued).

In 2024, the Belarusian frozen chicken cut market decreased by -18.9% to $26M, falling for the third year in a row after two years of growth. In general, consumption showed a slight contraction. Frozen chicken cut consumption peaked at $41M in 2021; however, from 2022 to 2024, consumption failed to regain momentum.

Description Ashtead (“Sunbelt”) is the second largest equipment rental company in the States, and cyclical fears plus a few minor operational missteps have created an attractive entry point into a secular winner. I also believe Sunbelt is under-earning to a larger degree than peers because of the organic nature of recent growth. Business Overview I'll keep this short because this and other equipment rental companies have been covered on VIC. Sunbelt buys and maintains a fleet of equipment including aerial work platforms (30% of fleet), forklifts (20%), earthmoving (14%), power and HVAC (11%) and more. Equipment is depreciated over 10 years (chosen to make equipment disposals breakeven at the low point of a cycle) and Sunbelt typically keeps it around for 7 years, getting more than 50% of original cost ("OEC" or original equipment cost) in rental revenue per year. After 7 years, equipment is disposed of at 40 cents on the dollar. Non-resi construction end markets are less than half of the business, and the rest is industrial, MRO and more. Renting equipment lets you get the exact right piece of equipment for a job. As an example, you used to find backhoes on jobsites much more, because a backhoe is the swiss army knife of earthmoving. That user might now prefer to rent either an excavator or a bucket loader, each of which peform half the function of the backhoe but in a more efficient manner. Rental also conserves capital, reduces the need for equipment yards/storage, solves logistics/ eliminates the need for vehicles that can move equipment, and solves the difficulty of maintaining owned equipment. Secular Trends The secular tailwinds come from both increased rental penetration as well as market share gains by the largest players. The use of rented equipment accounts for about 55% of the equipment market today and I expect it to hit at least 65% over time. Penetration is up from the low 40% range pre-GFC and single-digits in the 1990s. The top two players URI and Sunbelt have 15% and 11% share, respectively, and players smaller than the top 100 have 44% of the market. The top 10 players have grown market share from 20% in 2010 to about 45% today. The largest rental company businesses have improved over time. Scale gives purchasing economies with OEM suppliers, efficiencies in logistics and maintenance, and higher equipment utilization. URI and Sunbelt purchase equipment 15-20% cheaper than mom & pop operators. Moving heavy equipment to and from job sites requires a large fleet of dedicated vehicles. Equipment maintenance benefits from having expertise by equipment type, mechanic sharing and better utilization of parts and spares. In a typical branch, 6 out of 20 total employees might be mechanics. Utilization is measured both by time/physical utilization, which is just the amount of time the equipment is on rent, or by dollar utilization, which is measured by the rental revenue divided by the cost of the equipment (basically, asset turns). Dollar utilization is perhaps the most important metric, because it combines the time on rent and the rental rate. Dollar utilization is higher at the scale players for a large variety of reasons. More locations give larger players density and a higher likelihood that a given piece of equipment is needed by someone in that geography. It also lowers transportation costs and time and most importantly allows locations to share equipment. A better repair function means machines are on rent for longer and means that there is more equipment available to rent. A wider variety of equipment on rent also leads to higher rates. Sunbelt frequently mentions that they are not the lowest price, but they win business because of breadth, availability and service. The factors I’ve outlined above have led to stable dollar utilization, rising margins and thus rising returns on capital over time: Specialty rental equipment has become a larger part of Sunbelt’s mix over time. Specialty is a catch-all for equipment that can have more of a service component or more of a temporary, emergency, or one-off use case. When looking at historical results, note that specialty carries lower physical utilization but higher margins. Specialty equipment also depreciates more slowly and is generally less cyclical than general tool (i.e. non-specialty). Cyclical Factors Equipment rental is a cyclical business. Sunbelt will tell you that because equipment rental is now an essential part of customer’s businesses, rather than used as a top-up, future cycles will be more muted than the past. I mostly believe this for a few reasons. First, the large players are larger and more sophisticated. CEO Brendan Horgan likes to say that in the GFC they almost blindly lowered prices by 20% across the board without any pricing tools or great reason to do so. Second, the top 10 players are less leveraged. In the GFC, you not only had more leveraged companies, but some companies actually had covenants tied to time utilization. You can imagine what incentives that creates. Leverage at all the large players has decreased steadily over time. Finally, 70% of the industry contributes and subscribes to Rouse data (owned by RB Global), which provides detailed rate and utilization data by equipment type and geography. This was not the case in the GFC, and even a decade ago large players including Sunbelt did not contribute. Cycles will still happen, but the cash flow characteristics of the business blunt the impact. Rental equipment is typically sold and replaced after seven years, and you can see below that in past downturns capex can effectively be turned off for a time even as aged equipment is still sold. Sunbelt has a young fleet, partly because organic growth necessitates it, and so aging the equipment a year by turning off capex can easily be done in future downturns. Replacing a seventh of your capital every year is actually helpful in downturns because not replacing it means that equilibrium can be reached faster versus having something like a factory running at low levels of utilization. [a note on Sunbelt's fleet age: historically Sunbelt weighted age by net book value, versus gross book value at URI and other US based peers, and this flattered Sunbelt. Sunbelt's fleet is still younger, but this is because they've grown by adding brand new fleet versus M&A/acquiring fleet as URI has done] It’s worth noting that in the Oil & Gas Downturn and during Covid that rates did not fall despite used equipment values falling. Historically this was not the case. Current Conditions The pandemic was characterized by a quick and steep decline in (all) business activity, followed by a scramble to get new equipment. Lead times doubled and tripled and large companies like Sunbelt found themselves waiting almost a year for new equipment, while smaller companies had trouble getting it at all. In recent quarters, equipment availability normalized and Ashtead found itself with slightly more equipment than it would have liked. Inflation is a double edged sword here. Rates need to keep pace with inflation to maintain returns, but Sunbelt and other large players are relatively better off than others. Equipment prices are 20%+ higher than pre-pandemic levels, and Sunbelt has replaced a lot of the fleet at these higher levels, whereas mom & pop players were not able to because of availability. Rates will benefit as these small players replace aged fleet with new fleet at significantly higher prices. Megaprojects, roughly defined as those projects with more than $400mm of value, provide additional opportunities and challenges. The trio of the Jobs Act, the IRA, and the CHIPs act have created a large backlog of megaprojects that will (probably) offset any weakness in commercial construction. Megaprojects favor the larger players. Sunbelt claims 30% market share in these projects, i.e. almost triple their national share. Only the largest players can serve these projects. Sunbelt has examples where they have over $100mm of fleet on a single project. Pandemic related shortages and megaprojects have contributed to recent disappointments in the stock. Both of these factors make it difficult to perfectly plan equipment needs, and ordering equipment early because you’re worried about availability or because you’re staging it for a megaproject can hurt utilization. I view these challenges as easily surmountable. Construction, which is 40% of the customer mix, is rate sensitive, and recently Sunbelt has seen customers delay projects as they wait for clarity on rates. Most of the slack has been taken up by megaprojects ramping. We’re coming off good times, so I think of mid-cycle as normalizing utilization and margin while also accounting for maturing greenfield locations. I think the most likely scenario in the near term is that softness in construction continues to be mostly offset by megaprojects driven by the desire to re-shore and fix our crumbling infrastructure. Valuation As I mentioned earlier, I believe Sunbelt is under-earning. Sunbelt has grown organically to a much larger degree than URI, and they’ve done it by putting new equipment in greenfield locations. These locations take a while to scale from both a fleet and margin perspective. Locations 10+ years old have 56% EBITDA margins. Locations 0-2 years old have 46% margins and locations 2-5 years old have 53% margins. If you apply this to the current store base, mature margins would be three points higher. Margins will also be helped by what Sunbelt calls “cluster economics,” which is just increasing density in markets. Clustered markets carry a few more points of margin and return. I value the business by assuming continued rental penetration, further share gains, and higher returns/margins (note below that I have market share at 13%, but recently an industry publication changed their methodology to include more specialty lines in the market definition and thus share is now

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Description:

A simple yet challenging project, to predict the housing price based on certain factors like house area, bedrooms, furnished, nearness to mainroad, etc. The dataset is small yet, it's complexity arises due to the fact that it has strong multicollinearity. Can you overcome these obstacles & build a decent predictive model?

Acknowledgement:

Harrison, D. and Rubinfeld, D.L. (1978) Hedonic prices and the demand for clean air. J. Environ. Economics and Management 5, 81–102. Belsley D.A., Kuh, E. and Welsch, R.E. (1980) Regression Diagnostics. Identifying Influential Data and Sources of Collinearity. New York: Wiley.

Objective:

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

The Chinese metal cutting shear market fell slightly to $63M in 2024, declining by -2.1% against the previous year. The market value increased at an average annual rate of +2.4% from 2012 to 2024; the trend pattern indicated some noticeable fluctuations being recorded in certain years. As a result, consumption reached the peak level of $78M. From 2017 to 2024, the growth of the market remained at a somewhat lower figure.

The revenue of the plastic cutting machine market in CIS amounted to $X in 2017, jumping by X% against the previous year. The market value increased an average annual rate of +X% from 2007 to 2017; however, the trend pattern indicated some noticeable fluctuations throughout the analyzed period. The most prominent rate of growth was recorded in 2008, when market value increased by X% y-o-y.