Category Archives: Data Mining
Supply Chain Management: Lessons to Drive Growth and Profits Using Data Mining and Analytics | Quantzig – Business Wire
LONDON--(BUSINESS WIRE)--Quantzig, a leader in delivering scalable analytics solutions and data science services, today announced the completion of its recent article that sheds light on the growing importance of data mining in supply chain management.
Data mining techniques, including regression analysis, clustering analysis, outlier detection, and classification analysis, along with analytical tools, help analyze data from different perspectives and deliver real-time, meaningful insights that are immensely helpful in responding to many situations that impact supply chain operations. One of our retail clients, for instance, deployed an analytics stack that leveraged data mining soon after the outbreak of COVID-19 to analyze data and ensure business continuity with faster data-driven decisions.
Request a FREE proposal to learn how we can help digitize your supply chain management activities to enhance supply chain efficiency and drive margins.
Key highlights of this report by Quantzigs supply chain experts include-
Speak to our experts to learn more about our supply chain solutions that leverage data mining techniques to enable faster data analysis.
According to the supply chain analytics experts at Quantzig, Today, data mining has emerged as a vital tool that aids supply chain management as it enables seamless integration of complex networks like production, inventory, and warehousing.
How Data Mining Can Enhance Supply Chain Management
The adoption of data mining is crucial to improve the decision-making process and build competitive supply chains, given the pace at which global supply chains are growing today. However, the challenge lies in interpreting the technological and logistical implications of the vast reserve of information. And as if this was not challenging enough, the integration of various business verticals in the supply chain is another major hurdle that supply chain managers have to overcome. Read the complete article (https://bit.ly/3kAlKjB) to gain detailed insights.
Today, quick turn-around time is crucial to gaining higher market share across industries. Hence, it is essential to make the right decisions at the right time. Data mining helps avoid the bullwhip effect and facilitates the integration of various verticals in the supply chain.
Leveraging data mining can help businesses-
Book a FREE trial to gain limited-period access to our proprietary supply analytics platforms and learn how our supply chain management solutions can help you drive supply chain efficiency and tackle crises like the COVID-19.
1. How Quantzig Can Help Transform The IT Supply Chain Using Analytics
2. How Demand Forecasting Helps Businesses Adapt To Capricious Customers
Keep abreast of the latest supply chain analytics trends by following the latest insights from our analytics experts. Follow us on LinkedIn and Twitter.
Quantzig is a global analytics and advisory firm with offices in the US, UK, Canada, China, and India. For more than 15 years, we have assisted our clients worldwide with end-to-end data modeling capabilities to leverage analytics for prudent decision making. Today, our firm consists of 120+ clients, including 45 Fortune 500 companies. For more information on our engagement policies and pricing plans, visit: https://www.quantzig.com/request-for-proposal
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Some of the tech stocks that have been bid up to the stratosphere lately with the help of retail investors are coming back to earth a little today.
Palantir Technologies (PLTR) - Get Reportis among the hardest-hit names: The data-mining/analytics software firm is down 12.6% after Morgan Stanley downgraded shares to Underweight.
With PLTR up 155% since [its] listing with very little change in the fundamental story, the risk/reward paradigm shifts decidedly negative for the shares, wrote analyst Keith Weiss. As a quick Twitter search of its stock symbol demonstrates, Palantir has become a retail favorite since going public in late September, thanks in part to hopes that it will win more large government contracts under a Biden Administration.
Meanwhile, electric car and clean energy plays have bounced from their morning lows, but select names are still seeing large declines. Workhorse Group (WKHS) - Get Reportis down 21.9%, Lordstown Motors (RIDE) - Get Reportis down 6.8% and FuelCell Energy (FCEL) - Get Reportis down 18.6%. Tesla (TSLA) - Get Reportis down 2.7%, reducing its year-to-date gains to a mere 580%.
Along with Palantir, a number of other enterprise software names are moving lower today. Salesforce.com (CRM) - Get Reportand Boxs (BOX) - Get Reportearnings reports appear to be playing roles.
Salesforce is down 6.9% after posting October quarter results after the close on Tuesday, issuing guidance for its next two quarters and its next fiscal year, and announcing a $25 billion-plus deal -- widely rumored to be in the works -- to buy Slack Technologies (WORK) - Get Report. While Salesforces top-line numbers (both its results and guidance) were moderately above consensus estimates, pre-earnings expectations were high, and its January quarter EPS guidance was below consensus.
Box, which has been facing stiff competition from Microsoft (MSFT) - Get Reportand others, is down 6.8% after it slightly beat October quarter estimates on Wednesday morning, but issued weaker-than-expected January quarter sales guidance.
Sales data software provider ZoomInfo (ZI) - Get Report, which recently raised more than $550 million through a stock offering, is down 7.6%. Automotive software firm Cerence (CRNC) - Get Reportis down 7.8%, AI software provider Veritone (VERI) - Get Reportis down 4.2%, bill-payment software provider Bill.com (BILL) - Get Reportis down 4% and project management software firm Upland Software (UPLD) - Get Reportis down 7.2%.
The selloff in high-multiple software names comes as cloud data warehousing leader Snowflake (SNOW) - Get Report-- a company that has an exceptionally high valuation even by enterprise software standards -- gets set to report after the bell. Snowflake is down 1.2% in Wednesday trading, but still up more than 150% from its $120 September IPO price.
Salesforce is a holding in Jim Cramers Action Alerts PLUS Charitable Trust Portfolio. Want to be alerted before Cramer buys or sells CRM? Learn more now.
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Data Mining Tools Market to Reflect Impressive Growth Rate Along with Top Leading Players – The Haitian-Caribbean News Network
Data Mining ToolsMarket 2020 Latest Industry Demand Analysis and Business Opportunities across the globe.
The impactful research study on global Data Mining ToolsMarket2020 done by research team and latest research study report added into database of market research vision. The Data Mining Toolsmarket research study describes worldwide Business Opportunities, Important Drivers, Key Challenges, Market Risks in brief.
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Global Data Mining ToolsMarket 2020 research study includes
Some significant activities of the current market size for the worldwide Data Mining Toolsmarket It presents a point by point analysis
The worldwide market for Data Mining Toolsis expected to grow with magnificent CAGR over the next five years, will reach million USD in 2024, from million USD in 2019, according to a New Research study.Global Data Mining ToolsMarket 2020-2026 Answers to your following Questions
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HPE, a touchstone of Silicon Valley, moving headquarters to Houston to save costs, recruit talent – San Francisco Chronicle
A stalwart of Silicon Valley is moving its headquarters to Texas, driven by cost savings and recruiting opportunities as the coronavirus pandemic reshapes the workplace.
Hewlett Packard Enterprise, whose roots date to Bill Hewlett and David Packards 1939 founding of their eponymous company in a Palo Alto garage, is moving to the Houston area.
Antonio Neri, HPEs CEO, wrote in a blog post that in response to this new future of work, we have reevaluated our real estate site strategy to ensure that we are utilizing our workspaces most effectively and positioning our teams and talent in the best interests of our business.
Houston is an attractive market for us to recruit and retain talent, and a great place to do business, Neri wrote. As we look to the future, our business needs, opportunities for cost savings, and team members preferences about the future of work, we have made the decision to relocate HPEs headquarters to the new campus under construction in Spring, Texas, just outside of Houston.
HPE joins a number of major companies that have moved headquarters out of the Bay Area, and lower-cost Texas is a popular destination. Charles Schwab plans to officially move its headquarters from San Francisco to the Dallas area next month after acquiring TD Ameritrade, but previously said it would maintain its local San Francisco workforce. Last year, medical supplies giant McKesson moved from San Francisco to Irving, Texas.
Palantir, the controversial data-mining company that went public in September, moved from Palo Alto to Denver.
Both companies and employees can save significantly on real estate costs by leaving the Bay Area. As the Houston Chronicle reported, housing is significantly cheaper in Houston compared to other major cities, as well as the Bay Area. The median single-family home is $266,685.
The Bay Areas median existing home price was a staggering $1.1 million in October, and despite softening in the rental market, a median one-bedroom San Francisco apartment rents for more than $2,000 per month, according to Apartment List. In Houston, the median one-bedroom goes for $902 per month.
Office rent in Houston is around $30 per square foot annually, about half the cost in Silicon Valley, according to brokerage data. Building regulations in Houston are far less stringent compared to the Bay Area. (Houston famously does not have zoning, though it has other development regulations.)
HPE said its move wouldnt result in layoffs, and Bay Area employees could voluntarily move or remain in San Jose, which will continue to be the companys technical hub. Non-technical corporate jobs including human resources, the legal department and communications will relocate to Texas. HPE plans to consolidate offices in Milpitas and Santa Clara into its San Jose campus.
As of 2017, the company had over 3,000 employees in Houston, and it is the companys largest employment center. It had 61,600 employees as of October 2019.
The business-enterprise-focused tech company spun off from Hewlett-Packard in 2015. A component of the former Hewlett-Packard, its 2002 Compaq acquisition, was based in Houston, and the enterprise hardware and software operation has long had a large presence in the Texas city. HP Inc., which focuses on consumer computers and printers, will remain headquartered in Palo Alto.
Roland Li is a San Francisco Chronicle staff writer. Email: firstname.lastname@example.org
Rising Uptake of Big Data Analytics Software for Business to Propel Big Data and Business Analytics Market Wall Street Call – Reported Times
iCrowdNewswire Nov 30, 202012:00 AM ET
Report Ocean recently added a new study, titled Big Data and Business Analytics Market by Component (Hardware, Software, and Service) Deployment Model (On-premise and Cloud), Analytics Tool (Dashboard & Data Visualization, Data Mining & Warehousing, Self-service Tools, Reporting, and Others), Application (Customer Analytics, Supply Chain Analytics, Marketing Analytics, Pricing Analytics, Spatial Analytics, Workforce Analytics, Risk & Credit Analytics, and Transportation Analytics), and Industry Vertical (BFSI, Manufacturing, Healthcare, Government, Energy & Utilities, Transportation, Retail & E-Commerce, IT & Telecom, Education and Others): Global Opportunity Analysis and Industry Forecast, 20202027, to its vast trajectory.
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The increasing uptake of big data analytics software in enterprises, aspiring to gain improved and faster decision-making capabilities to stay a step ahead of their competitors by analyzing and acting upon information in a timely manner, is significantly propelling the global market.
Going forward, the increasing demand for cloud-based big data analytics software among small and medium enterprises is likely to influence the growth of the market positively in the near future. However, the high implementation cost and the lack of skilled workforce may act as restraints over the next few years.
Impact of Covid 19 on Global Big Data and Business Analytics Market
The ongoing pandemic has been severely damaging for many markets but the global big data and business analytics market is certainly not one of them. In fact, with cloud computing taking the center stage, the need to gain detailed insights into businesses has increased among companies, which is supporting the growth of this market.
Software Emerges as Leading Component Segment
The global big data and business analytics market has been analyzed on the basis of the component, deployment model, analytics tool, application, industry vertical, and the region in this report. In terms of the component, the market has been bifurcated into hardware, software, and services. The software segment is recording a greater progress than other component segments in this market.
Based on the deployment model, it has been classified into on-premise and cloud. In terms of the analytics tool, it has been categorized into dashboard and data visualization, data mining and warehousing, self-service tools, reporting, and others. Customer analytics, supply chain analytics, marketing analytics, pricing analytics, spatial analytics, workforce analytics, risk and credit analytics, and transportation analytics are considered as the prominent application areas of big data and business analytics in this study.
As per industry vertical, the market has been segregated into BFSI, manufacturing, healthcare, government, energy & utilities, transportation, retail and e-commerce, IT and telecom, education and other segments. Geographically, the report assessed the global market across North America, Europe, Asia Pacific, and LAMEA. Among these, North America has acquired the leading position and is expected to continue to do so over the next few years.
The global big data and business analytics market is segmented into:
By Deployment Model
By Analytics Tool
By Industry Vertical
Companies Mentioned in the Report
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Keywords:Big Data and Business Analytics Market, Big Data and Business Analytics Market size, Big Data and Business Analytics Market share, Big Data and Business Analytics Market trends, Global Big Data and Business Analytics Market, Big Data and Business Analytics Market forecast
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Data Quality Tools Market 2026 Growth Forecast Analysis by Manufacturers, Regions, Type and Application – The Market Feed
The Data Quality Tools market research report providesan in-depth analysis of parent market trends, macro-economic indicators, and governing factors, along with market attractiveness as per segment. The report also maps the qualitative impact of various market factors on market segments and geographies.Data Quality Tools Market Research Report is a Professional and In-Depth Study on the Existing State of Data Quality Tools Industry.
This Report Focuses on the Data Quality Tools Definition, Scope, Market Forecast Estimation & Approach, Insights and Growth Relevancy Mapping, Data mining & efficiency, Strategic Analysis, Competition Outlook, Covid19 aftermath Analyst view, Market Dynamics (DROC, PEST Analysis), Market Impacting Trends, Market News & many more. It also Provides Granular Analysis of Market Share, Segmentation, Revenue Forecasts and Regional Analysis till 2026.
Further, Data Quality Tools Market report also covers the development policies and plans, manufacturing processes and cost structures, marketing strategies followed by top players, distributors analysis, marketing channels, potential buyers and Data Quality Tools development history. This report also states import/export, supply, and consumption figures as well as cost, price, revenue and gross margin by regions.
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The Data Quality Tools market report covers major market players like
Data Quality Tools Market is segmented as below:
By Product Type:
Breakup by Application:
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Along with Data Quality Tools Market research analysis, buyer also gets valuable information about global Data Quality Tools Production and its market share, Revenue, Price and Gross Margin, Supply, Consumption, Export, Import volume and values for following Regions:
Impact of COVID-19 on Data Quality Tools Market
The report also contains the effect of the ongoing worldwide pandemic, i.e., COVID-19, on the Data Quality Tools Market and what the future holds for it. It offers an analysis of the impacts of the epidemic on the international Market. The epidemic has immediately interrupted the requirement and supply series. The Data Quality Tools Market report also assesses the economic effect on firms and monetary markets. Futuristic Reports has accumulated advice from several delegates of this business and has engaged from the secondary and primary research to extend the customers with strategies and data to combat industry struggles throughout and after the COVID-19 pandemic.
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Data Quality Tools Market Report Provides Comprehensive Analysis as Following:
Frequently Asked Questions
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Mining Software Market 2020-2026: COVID-19 Impact and Revenue Opportunities after Post Pandemic – Murphy’s Hockey Law
Mining Softwaremarket has been analyzed by utilizing the best combination of secondary sources and in-house methodology along with a unique blend of primary insights. The real-time assessment of the Mining Software market is an integral part of our market sizing and forecasting methodology, wherein our industry experts and team of primary participants helped in compiling the best quality with realistic parametric estimations.
In4Researchs latest market research report on theMining Software market, with the help of a complete viewpoint, provides readers with an estimation of the global market landscape. This report on the Mining Software market analyzes the scenario for the period of 2020 to 2026, wherein, 2019 is the base year. This report enables readers to make important decisions regarding their business, with the help of a variety of information enclosed in the study.
This report on the Mining Software market also provides data on the developments made by important key companies and stakeholders in the market, along with competitive intelligence. The report also covers an understanding of strengths, weaknesses, threats, and opportunities, along with the market trends and restraints in the landscape.
Questions Answered in Mining Software Market Report:
Request for a sample copy of the report to get extensive insights into Mining Software market @https://www.in4research.com/sample-request/10484 Based on Product type, Mining Software market can be segmented as:
Based on Application,Mining Software market can be segmented:
The Mining Software industry study concludes with a list of leading companies/suppliers operating in this industry at different stages of the value chain.
List of key players profiled in the report:
If you are planning to invest into new products or trying to understand this growing market, this report is your starting point.
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Regional Overview & Analysis of Mining Software Market:
Analysis of COVID-19 Impact & Post Pandemic Opportunities in Mining Software Market:The outbreak of COVID-19 has brought along a global recession, which has impacted several industries. Along with this impact COVID Pandemic has also generated few new business opportunities for Mining Software market. Overall competitive landscape and market dynamics of Mining Software has been disrupted due to this pandemic. All these disruptions and impacts has been analysed quantifiably in this report, which is backed by market trends, events and revenue shift analysis. COVID impact analysis also covers strategic adjustments for Tier 1, 2 and 3 players of Mining Software market.
Table of Content: Global Mining Software Market
Chapter 1. Research Objective1.1 Objective, Definition & Scope1.2 Methodology1.2.1 Primary Research1.2.2 Secondary Research1.2.3 Market Forecast Estimation & Approach1.2.4 Assumptions & Assessments1.3 Insights and Growth Relevancy Mapping1.3.1 FABRIC Platform1.4 Data mining & efficiency
Chapter 2. Executive Summary2.1 Mining Software Market Overview2.2 Interconnectivity & Related markets2.3 Ecosystem Map2.4 Mining Software Market Business Segmentation2.5 Mining Software Market Geographic Segmentation2.6 Competition Outlook2.7 Key Statistics
Chapter 3. Strategic Analysis3.1 Mining Software Market Revenue Opportunities3.2 Cost Optimization3.3 Covid19 aftermath Analyst view3.4 Mining Software Market Digital Transformation
Chapter 4. Market Dynamics4.1 DROC4.1.1 Drivers4.1.2 Restraints4.1.3 Opportunities4.1.4 Challenges4.2 PEST Analysis4.2.1 Political4.2.2 Economic4.2.3 Social4.2.4 Technological4.3 Market Impacting Trends4.3.1 Positive Impact Trends4.3.2 Adverse Impact Trends4.4 Porters 5-force Analysis4.5 Market News By Segments4.5.1 Organic News4.5.2 Inorganic News
Chapter 5. Segmentation & Statistics5.1 Segmentation Overview5.2 Demand Forecast & Market Sizing
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The Solution Approach Of The Great Indian Hiring Hackathon: Winners’ Take – Analytics India Magazine
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MachineHack has successfully concluded The Great Indian Hiring Hackathon on 23rd of November 2020, where it collaborated for the first time with 12 companies to help data science professionals land up in a rewarding career. In this hackathon, the MachineHack community was asked to come up with an algorithm to predict the price of retail items belonging to different categories. In participation with companies like Aditya Birla Group, Bridgei2i, Concentrix, Fractal, Genpact, Lowes, MiQ, Piramal, Scienaptic, Vmware, WellsFargo, and Zycus, the hackathon has witnessed an active attendance of whooping 5655 practitioners.
Foretelling the retail price can be a daunting task due to the huge datasets with a variety of attributes ranging from text, numbers (floats, integers), as well as date and time. Also, outliers can be a big problem when dealing with unit prices. Thus this hackathon asked the participants to come out with a solution to forecast retail prices of items of different categories.
With the COVID pandemic dwindling the data science job market, this hackathon was designed to bring out the talent in the industry to the potential recruiters. After various stages of critical evaluation that includes assessing the participants based on their Root Mean Square Error (RMSE) scores and their leaderboard scores, a number of candidates topped the charts. Here we will introduce you to two of those champions of The Great Indian Hiring Hackathon and will describe their approach to solving the problem.
A computer science student, Nilesh Verma while familiar with Python, isnt a professional in the AI and data science field. After completing his masters, one of the subjects he studied was data mining, and that was his first step towards the data science field. In March 2020, Nilesh started working on machine learning projects in his university, where he developed six different types of projects in machine learning, deep learning, natural language processing, and computer vision. Some of his projects were even featured in local newspapers and TV channels.
To solve this problem, Nilesh firstly tried to load the dataset in a pandas data frame, and then started with the Exploratory Data Analysis (EDA) operations on data. With this, Nilesh noted some variances in column quantity, unit price and countries. He also pointed out that the values contained in these columns range smaller compared to other columns. To this, Nilesh started checking the column data types for date-time values and ran it with a simple random forest (RF) model and got a 28% RMSE score.
The aim was to get a lower RMSE score, and that is why Nilesh started with doing feature engaging and extracted five features from date-time columns and five statistical elements, which was again run on the model to get a 23% RMSE score. Once thats done, Nilesh removed the outlier and tried some power transmission of the Unit-Price column, because that is highly skewed, which helped in reaching a 22% RMSE score. To improve the 2-3% RMSE score, Nilesh also tried removing duplicate values. Finally, Nilesh started working on the normal range and pulled some extra data points that were higher than the normal one, which, again, helped to improve the score by 3-4%. With all these, Nilesh managed to get a 16-18% RMSE score.
To further reduce the score, Nilesh started digging more on the data and found some loopholes. While checking the dataset, Nilesh realised that a lot of values were present in test data rows, however, werent present in the training rows. To solve the data-leakage problem, he decided to merge training and test data. Considering there werent any test data labels for this situation, that fill the values with zero, Nilesh overfitted the model with some dummy data. After saving the dataset, Nilesh used an Excel sheet for data manipulation operation, which was time friendly. After doing all the manipulation, i.e. replacing large values, filing zero values to mean, median, etc., he managed to get a 4-6% RMSE score. Further changing minus values to plus in Quantity columns improved the RMSE score 2-3%.
With that being said, the aim was to get an RMSE score of 1% and for that Nilesh started working on the models leveraging Sklearn, and added some popular algorithms like Catboost, Xgboost, etc. Concluding the process, Nilesh noticed that the top two algorithms that are providing the least RMSE score on train data are DecisionTreeRegressor and ExtraTreesRegressor and made the ensemble model. After saving the model score with a different run, Nilesh managed to get a 3-4% RMSE score, but on some runs, it also reached up to 1% RMSE.
With a mechanical engineering background, Harikrishnan V always had an analytical mindset. Being a curious mind, Harikrishnan has always been very passionate about digging deep and finding information. During his early years, Harikrishnan used to collect data and analyse it to answer questions of problems like IIM CAT scoring trends, 2008 recession effect on placements, crime scenario in India etc. Realising that he can do wonders in a data career, he started his formal preparation into data science six months ago by starting an online course and learning from content on the internet as well as practising on self-projects. According to him, the learning process has been intense yet exciting because practising data science has been a very fulfilling journey for Harikrishnan.
Despite being his first data science competition, Harikrishnan managed to top the charts with his brilliant approach. To solve this problem, he started with a thorough exploratory analysis, where he found many patterns within the data. He noticed that most of the rows to predict have a low UnitPrice and very few have exceptionally high values.
There was one extreme outlier and a few other high ones.
On further exploration to understand any pattern the spread of prices in the data, he recognised a pattern by StockCodes.
To this, he grouped the train data by the number of unique prices in a StockCode and found that up to 11 unique prices. The other 5 StockCodes (3678,3680,3679,3683,3681) were the ones with maximum uncertainty and the high-value outliers.
InvoiceDate was converted to a float as days elapsed since 2010-1-1
For the StockCodes with just one unique value, Harikrishnan simply mapped and predicted those values in the test set, which led to a total of 8084 out of 122049 rows. Next, he tried many models and concluded that the XGBRegressor gave the best results on this dataset.
Further, he made nine different models, each with its best hyperparameter settings, for each set of StockCodes with unique values from 2 up to 11, i.e. (2,3,4,5,6,7,8,9,11). He also had a table with the unique prices of each StockCode. After the prediction result from these nine separate models, he used a function and approximated each prediction to the closest unique UnitPrice for that particular StockCode. This managed to total 121423 out of 122049 rows, which were done.
Next, there were 91 rows in the test set with a StockCode not present in train data, and for them, he approximated the UnitPrice to the weighted price of the closest StockCode in train data a total of 121514 out of 122049 rows done. Now only 535 rows remain to be predicted! On further exploration, he found that StockCodes 3678 and 3680 were from only one customer (14096) and that the prices had a strong correlation with the date.
The vertical lines show the position of the test points to predict.
Post this, Harikrishnan created a feature month by combining month number and year number as a string from InvoiceDate. Here, he ran a model on the combined 3678 and 3680 StockCode points from train data and predicted the test values. Quantity and Date were used as numeric and month and StockCode as categorical size rows are done.
Now only 3 StockCodes (3679,3681,3683) and 529 rows remain to be predicted, which were the most tricky and time-consuming. In his exploratory analysis, he observed that Stocks 3679 and 3683 had extreme outliers and that 3679s outlier matched as a pair (+1 & -1 quantity from the same customer in the same day) to a test entry for Stock 3681! So there was a possibility for mixing. It was further observed that a lot of high-value transactions occurred in pairs. Although this could be handpicked, Harikrishnan decided to make a model detect such pairs and predict them accurately.
For this, he made a dataset with the full 3681 data combined with 3679 and 3683 data where the UnitPrice z-score exceeded three and made a custom algorithm combined with an XGBRegressor to predict such pair values. Now there was the requirement for making new features to predict the remaining 3681 data and the other 2 StockCode rows. He calculated monthly customer sales for all non-high-value StockCodes by combining train and test data already predicted and stored in a data frame.
Further, more features were created hour group; month group; weekday group; month start; different country; monthly sales customer; total sales customer; days visited customer; months visited customer; invoice numbers customer; transact numbers customer; average spend per transaction customer; average spend per invoice customer; average spend per day customer, and average spend per month customer. Features of all customers were stored in a data frame.
For StockCode 3679, there was one extreme outlier, and thats why train row was removed, and modelling was done on the 3679 data, and test values predicted. A total of 121548 out of 122049 rows are done. For StockCode 3683, the majority prices were 15,18,28,40. Countries 13 and 14 were predominant and had predominantly price 18. Here also there is one extreme outlier which again has an identical pair in test data in StockCode 3683 itself, which was removed and modelling was done on the data. Predicted values in the range of 12 to 45 were approximated to the closest among [15,18,28,40]. High quantity and predictions less than 0 rows were approximated with median price from train data. Rows of Countries 31 & 32 were capped at their maximum value (40) from train data a total 121904 out of 122049 rows done. Only 120 rows remain to be predicted in StockCode 3681. These are the most unpredictable rows with significant high-value entries.
On exploring the data, the one extreme outlier close to 40000 doesnt have a pair in the test data, and thats why it was removed. The train data used here to capture maximum trends is all rows of categories >=3678 in the train data combined with the high 3681 pair values predicted in the test set with the earlier model. The best model was run, and the values for the remaining 3681 Stock rows were predicted. High variance in prices was only in Quantities -2,-1,1, and other high quantity (+ve and -ve) rows with prediction less than 0 were approximated with corresponding median values from the train data of Stock 3681.
Further, a few customers were identified having low transactions and quantities and who were judged to be with low-value spenders in this category. These peoples transactions were approximated with low values in two groups 1 and 5. 3681 Stock Customers with no negative quantity and high overall quantity with at least one Quantity=1 in the test entry would have a relation with their high quantity spends. Their one quantity transactions were predicted with their average high quantity transaction value with custom code. Customers who ended their transactions with the store with a high proportion of final transactions in StockCode 3681 had a trend of having a maximum earlier monthly sale value as the UnitPrice in this transaction. Such transactions were appropriately predicted with custom code. Customers with a high negative value for total sales would have approximately that value as UnitPrice in their one quantity 3681 entry, to even out the cash flow. Such customers transactions were appropriately predicted. For pairs like mentioned above, which had both members in the test set, their predicted values from my model were averaged and assigned.
In this hackathon, RMSE was the metric, and thats why Harikrishnan wanted to predict as many points as possible in the test set. He further sought out of the box ideas to use the available resources to improve his score using the daily submissions and realised that there were only a few dozen rows in the entire test set of 122049 rows. There were three rows of customers who had no transactions in train data, a pair transaction (+1 & -1 quantity) with both rows in the test set, customers with high sales values, and customers with high occurrence in 3681 Stock in test set etc. These all identified rows amounted to only a few dozens. It was given that the public leader board scoring was being done on 70% test data.
To which, he made a submission with the value of one interesting point changed in the 70% data, which in turn changed the RMSE score. With this, he could predict the value of a point by calculating the difference in the sum of squared errors by using the equation of RMSE. He made a function to do this calculation and tried to predict the value of a few more shortlisted points. Harikrishnan also included his script without the last block of code of the hack.
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Yield10 Bioscience Researcher Dr. Meghna Malik to Present at the 4th CRISPR AgBio Congress 2020 Virtual Event – GlobeNewswire
WOBURN, Mass., Dec. 02, 2020 (GLOBE NEWSWIRE) -- Yield10 Bioscience, Inc. (Nasdaq:YTEN), an agricultural bioscience company, today announced that Meghna Malik, Ph.D., Senior Director, will present at the 4th CRISPR AgBio Congress which is being held December 1-3, 2020 as a virtual event.
Dr. Maliks presentation is titled Yield10 trait development: Using CRISPR to increase seed yield and oil content in Camelina. The presentation will be part of the Expanding the CRISPR scope to more challenging agricultural crops session which is scheduled at 6:00 pm EST on Dec. 2. Dr. Malik will also participate in a Virtual Roundtable titled Analyzing the next generation of promising target traits: Revolutionizing the future of agriculture, which is scheduled at 3:00 pm EST on Dec. 3.
In her presentation, Dr. Malik will discuss the approach taken by Yield10 and its wholly owned subsidiary, Metabolix Oilseeds, to deploy novel traits in the oilseed crops Camelina sativa and canola using CRISPR genome-editing to increase seed yield and oil content. The presentation describes the simultaneous editing of three gene targets (C3008a, C3008b, C3009) designed to reduce the oil turnover during seed maturation. To do this, the researchers simultaneously edited nine genes in Camelina using CRISPR. Different combinations of edits were obtained and characterized. Dr. Malik will present data obtained from a triple edited Line E3902 showing a five percent increase in total oil produced per plant in greenhouse studies and a calculated 15 percent increase in total oil produced per hectare in field tests conducted in 2019.
Dr. Malik will also highlight Yield10s work with the novel oil content trait C3007, which disrupts BADC, a novel negative regulator of acetyl-CoA carboxylase (ACCase), a key enzyme in fatty acid biosynthesis. Yield10 has obtained stable edits for select badc genes and gene combinations deployed in Camelina and canola. In greenhouse studies, certain combinations of CRISPR-edited BADC targets deployed in Camelina have shown an increase in oil produced per plant. In 2020, Yield10 conducted its first field trials of BADC (C3007) edited Camelina lines in the U.S. Yield10 has also produced C3007 canola lines where an increase in oil produced per plant has been observed in greenhouse studies.
Our presentation highlights our success deploying multiple CRISPR edits in a complex genome crop like Camelina and translating the greenhouse research to field testing to obtain the positive outcome of increasing oil content, said Meghna Malik, Ph.D., Senior Director of Metabolix Oilseeds, the Canadian subsidiary of Yield10 Bioscience. Our research with these CRISPR-edited Camelina and canola lines is intended to increase seed oil content to maximize oil yields per acre. We also see the potential to combine or stack these CRISPR edits with oil composition traits, such as Camelina omega-3 (DHA+EPA), to increase yield and hence the economic value of engineered crops. We look forward to reporting further results for these traits as our work continues to progress in 2021.
Yield10 recently announced a collaboration with Rothamsted Research to develop advanced technology for producing omega-3 (DHA+EPA) nutritional oils in Camelina.
Learn more about the conference at the 4th CRISPR AgBio Congress website. A copy of Dr. Maliks slide deck is available on the Yield10 Bioscience website.
About Yield10 Bioscience
Yield10 Bioscience, Inc. is an agricultural bioscience company developing crop innovations for sustainable global food security. The Company uses its Trait Factory including the GRAIN big data mining trait gene discovery tool as well as the Camelina oilseed Fast Field Testing system to develop high value seed traits for the agriculture and food industries. As a path toward commercialization of novel traits, Yield10 is pursuing a partnering approach with major agricultural companies to drive new traits into development for canola, soybean, corn, and other commercial crops. The Company is also developing improved Camelina varieties as a platform crop for the production and commercialization of nutritional oils, proteins, and PHA biomaterials. The Companys expertise in oilseed crops also extends into canola, where it is currently field-testing novel yield traits to generate data to drive additional licensing opportunities. Yield10 is headquartered in Woburn, MA and has an Oilseeds Center of Excellence in Saskatoon, Canada.
For more information about the company, please visit http://www.yield10bio.com, or follow the Company on Twitter, Facebook and LinkedIn.
Safe Harbor for Forward-Looking Statements
This press release contains forward-looking statements which are made pursuant to the safe harbor provisions of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended. The forward-looking statements in this release do not constitute guarantees of future performance. Investors are cautioned that statements in this press release which are not strictly historical, including, without limitation, the use of the Companys technology to successfully identify targets and develop systems using CRISPR genome editing for increasing crop yield and oil content, the timing for reporting of further results, the ability of greenhouse studies to predict yield results in field tests, and progress by Yield10 in driving increases in oil biosynthesis and developing its products, constitute forward-looking statements. Such forward-looking statements are subject to a number of risks and uncertainties that could cause actual results to differ materially from those anticipated, including the risks and uncertainties detailed in Yield10 Bioscience's filings with the Securities and Exchange Commission. Yield10 assumes no obligation to update any forward-looking information contained in this press release or with respect to the matters described herein.
Contacts: Yield10 Bioscience:Lynne H. Brum, (617) 682-4693, LBrum@yield10bio.com
Investor Relations: Bret Shapiro, (561) 479-8566, email@example.comManaging Director, CORE IR
Media Inquiries: Eric Fischgrund, firstname.lastname@example.org FischTank PR
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About the author: Anu Chowdhury is an analytics consultant at Brillio with a history of working in the information technology and services industry, mainly skilled in Data Warehousing and Business Intelligence.
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