This map presents the full data available on the MLTSD GeoHub, and maps several of the key variables reflected by the Second Career Program of ETD.The Second Career program provides training to unemployed or laid-off individuals to help them find employment in high demand occupations in Ontario. The intention of the SC program is to return individuals to employment by the most cost effective path. Second Career provides up to $28,000 to assist laid-off workers with training-related costs such as tuition, books, transportation, and basic living expenses, based on individual need. Additional allowances may be available for people with disabilities, and for clients needing help with the costs of dependent care, living away from home and literacy and basic skills upgrading, also based on individual need. People with disabilities may also be given extensions on training and upgrading durations, to meet their specific needs. Clients may be required to contribute to their skills training, based on the client’s total annual gross household income and the number of household members.About This DatasetThis dataset contains data on SC clients for each of the twenty-six Local Board (LB) areas in Ontario for the 2015/16 fiscal year, based on data provided to Local Boards and Local Employment Planning Councils (LEPC) in June 2016 (see below for details on Local Boards). These clients have been distributed across Local Board areas based on the client’s home address, not the address of their training institution(s).Different variables in this dataset cover different groups of Second Career clients, as follows:Demographic and skills training variables are composed of all SC clients that started in 2015/16.At exit outcome variables are composed of all SC clients that completed their program in 2015/16.12-month outcome variables are composed of all SC clients that completed a 12-month survey in 2015/16.The specific variables that fall into each of the above categories are detailed in the Technical Dictionary. As a result of these differences, not all variables in this dataset are comparable to the other variables in this dataset; for example, the outcomes at exit data is not the outcomes for the clients described by the demographic variables.About Local BoardsLocal Boards are independent not-for-profit corporations sponsored by the Ministry of Labour, Training and Skills Development to improve the condition of the labour market in their specified region. These organizations are led by business and labour representatives, and include representation from constituencies including educators, trainers, women, Francophones, persons with disabilities, visible minorities, youth, Indigenous community members, and others. For the 2015/16 fiscal year there were twenty-six Local Boards, which collectively covered all of the province of Ontario. The primary role of Local Boards is to help improve the conditions of their local labour market by:engaging communities in a locally-driven process to identify and respond to the key trends, opportunities and priorities that prevail in their local labour markets;facilitating a local planning process where community organizations and institutions agree to initiate and/or implement joint actions to address local labour market issues of common interest;creating opportunities for partnership development activities and projects that respond to more complex and/or pressing local labour market challenges; andorganizing events and undertaking activities that promote the importance of education, training and skills upgrading to youth, parents, employers, employed and unemployed workers, and the public in general.In December 2015, the government of Ontario launched an eighteen-month Local Employment Planning Council pilot program, which established LEPCs in eight regions in the province formerly covered by Local Boards. LEPCs expand on the activities of existing Local Boards, leveraging additional resources and a stronger, more integrated approach to local planning and workforce development to fund community-based projects that support innovative approaches to local labour market issues, provide more accurate and detailed labour market information, and develop detailed knowledge of local service delivery beyond Employment Ontario (EO).Eight existing Local Boards were awarded LEPC contracts that were effective as of January 1st, 2016. As such, from January 1st, 2016 to March 31st, 2016, these eight Local Boards were simultaneously Local Employment Planning Councils. The eight Local Boards awarded contracts were:Durham Workforce AuthorityPeel-Halton Workforce Development GroupWorkforce Development Board - Peterborough, Kawartha Lakes, Northumberland, HaliburtonOttawa Integrated Local Labour Market PlanningFar Northeast Training BoardNorth Superior Workforce Planning BoardElgin Middlesex Oxford Workforce Planning & Development BoardWorkforce Windsor-EssexMLTSD has provided Local Boards and LEPCs with demographic and outcome data for clients of Employment Ontario (EO) programs delivered by service providers across the province on an annual basis since June 2013. This was done to assist Local Boards in understanding local labour market conditions. These datasets may be used to facilitate and inform evidence-based discussions about local service issues – gaps, overlaps and under-served populations - with EO service providers and other organizations as appropriate to the local context.Data on the following EO programs for the 2015/16 fiscal year was made available to Local Boards and LEPCs in June 2016: Employment Services (ES)Literacy and Basic Skills (LBS) Second Career (SC) ApprenticeshipThis dataset contains the 2015/16 SC data that was sent to Local Boards and LEPCs. Datasets covering past fiscal years will be released in the future.Terms and Definitions

NOC – The National Organizational Classification (NOC) is an occupational classification system developed by Statistics Canada and Human Resources and Skills Development Canada to provide a standard lexicon to describe and group occupations in Canada primarily on the basis of the work being performed in the occupation. It is a comprehensive system that encompasses all occupations in Canada in a hierarchical structure. At the highest level are ten broad occupational categories, each of which has a unique one-digit identifier. These broad occupational categories are further divided into forty major groups (two-digit codes), 140 minor groups (three-digit codes), and 500 unit groups (four-digit codes). This dataset uses four-digit NOC codes from the 2011 edition to identify the training programs of Second Career clients.Notes

Data reporting on 5 individuals or less has been suppressed to protect the privacy of those individuals.Data published: Feb 1, 2017Publisher: Ministry of Labour, Training and Skills Development (MLTSD)Update frequency: Yearly Geographical coverage: Ontario

US Industry Data by State, by Industry

Number of Establishments, Sales, Payroll, and Employees

By Gary Hoover [source]

About this dataset

This data set provides a detailed look into the US economy. It includes information on establishments and nonemployer businesses, as well as sales revenue, payrolls, and the number of employees. Gleaned from the Economic Census done every five years, this data is a valuable resource to anyone curious about where the nation was economically at the time. With columns including geographic area name, North American Industry Classification System (NAICS) codes for industries, descriptions of those codes meaning of operation or tax status, and annual payroll, this information-rich dataset contains all you need to track economic trends over time. Whether you’re a researcher studying industry patterns or an entrepreneur looking for market insight — this dataset has what you’re looking for!

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How to use the dataset

This dataset provides detailed US industry data by state, including the number of establishments, value of sales, payroll, and number of employees. All the data is based on the North American Industry Classification System (NAICS) code for each specific industry. This will allow you to easily analyze and compare industries across different states or regions.

Research Ideas

• Analyzing the economic impact of a new business or industry trends in different states: Comparing the change in the number of establishments, payroll, and employees over time can give insight into how a state is affected by a new industry trend or introduction of a new service or product.
• Estimating customer sales potential for businesses: This dataset can be used to estimate the potential customer base for businesses in different geographic areas. By analyzing total business done by non-employers in an area along with its estimated population can help estimate how much overall sales potential exists for a given region.
• Tracking competitor performance: By looking at shipments, receipts, and value of business done across industries in different regions or even cities, companies can track their competitors’ performance and compare it to their own to better assess their strategies going forward

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: 2012 Industry Data by Industry and State.csv | Column name | Description | |:----------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------| | Geographic area name | The name of the geographic area the data is for. (String) | | NAICS code | The North American Industry Classification System (NAICS) code for the industry. (String) | | Meaning of NAICS code | The description of the NAICS code. (String) | | Meaning of Type of operation or tax status code | The description of the type of operation or tax status code. (String) ...

Allegation Disposition (Findings): Valid. Based on the standard of preponderance of the evidence, it is more likely than not that the maltreatment occurred. Invalid. Based on the standard of preponderance of the evidence, it is more likely than not that the maltreatment did not occur. Unable to Determine. A preponderance of the available evidence is insufficient to support a finding of Valid or Invalid. Other. The allegation disposition Other is used when an investigation of the allegation was not completed for some reason, e.g. clients died or cases were misclassified. Family Violence is indicated when a validated investigation has a relative perpetrator, excluding those where financial exploitation is the only confirmed allegation. Beginning in Fiscal Year 2015, services provided during the investigation are documented in the investigation stage and not in a separate service stage. The "Other" Disposition category refers to those investigations that workers could not complete, e.g. clients died or cases were misclassified. The population totals do not match prior DFPS Data Books, printed or online. Past population estimates are adjusted based on the U.S. Census data as it becomes available. This is important to keep the data in line with current best practices, but will cause some past counts, such as Abuse/Neglect Victims per 1,000 Texas Children, to be recalculated. Population Data Source - Population Estimates and Projections Program, Texas State Data Center, Office of the State Demographer and the Institute for Demographic and Socioeconomic Research, The University of Texas at San Antonio. Current population estimates and projections for all years from 2014 to 2023 as of December 2020. Visit dfps.state.tx.us for information on all DFPS programs.

APS may provide non-purchased services or purchased services. The chart compares these two types of services. Non-purchased services can include services provided directly by APS staff or services provided by other entities, including social casework, legal actions, or services provided by other government agencies or community organizations. APS may purchase services for clients using Purchased Client Services (PCS) funds. Purchased services may include short-term shelter, food, medication, health services, financial help with rent or utilities, transportation, and minor home repair. All other available resources must be used where feasible before purchased client services are initiated. Visit dfps.state.tx.us for information on all DFPS programs.

This dataset contains detailed information about 500 Walmart store records, providing insights into various store characteristics. The data is focused on store-related attributes, which are crucial for analyzing store performance and customer demographics.

Columns in Store Data Sheet:

Discount_Rate: The discount rate offered by the store (in percentage). Customer_Age: The average age of customers visiting the store. Store_Size: The size of the store (in square feet). Inventory_Level: The level of inventory available in the store. Number_of_Employees: The number of employees working in the store. Marketing_Spend: The amount spent on marketing (in USD). Family: Indicates if the store targets family customers (Yes/No). Kids: Indicates if the store targets customers with kids (Yes/No). Weekend: Indicates if the data was collected on a weekend (Yes/No). Holiday: Indicates if the data was collected during a holiday (Yes/No). Foot_Traffic: The number of customers visiting the store. Average_Transaction_Value: The average value of transactions (in USD). Online_Sales: The online sales generated by the store (in USD). Purpose: The dataset is designed to help analyze various factors that influence store performance at Walmart. It can be used for statistical analysis, machine learning, and business strategy development to optimize store operations and marketing efforts.

Usage: Researchers, analysts, and business strategists can leverage this dataset to:

Identify patterns and trends in store performance. Develop predictive models for understanding customer demographics. Evaluate the impact of different variables on store operations. Optimize inventory and staffing levels based on customer behavior.

This dataset simulates data for a retail business, designed for Business Intelligence (BI) analysis, data visualization, and machine learning applications. The data covers multiple aspects of a retail environment, including sales, customer behavior, employee performance, inventory management, marketing campaigns, and operational costs.

It is ideal for exploring topics like sales forecasting, customer segmentation, inventory optimization, campaign ROI analysis, and performance evaluation.

Features: The dataset is structured into multiple tables, each representing a key entity in the retail business:

Lojas (Stores):

Loja_ID: Unique identifier for the store. Nome: Name of the store. Regiao: Region where the store is located. Cidade: City where the store is located. Tipo: Type of store (e.g., physical, online). Produtos (Products):

Produto_ID: Unique identifier for the product. Nome: Name of the product. Categoria: Category of the product. Preco: Price of the product. Custo_Aquisicao: Acquisition cost. Clientes (Customers):

Cliente_ID: Unique identifier for the customer. Nome: Name of the customer. Idade: Age of the customer. Genero: Gender. Cidade: City of residence. Canal_Compra: Preferred purchase channel. Total_Compras: Total spending. Vendas (Sales):

Venda_ID: Unique identifier for the sale. Loja_ID: Store where the sale occurred. Produto_ID: Product sold. Cliente_ID: Customer making the purchase. Colaborador_ID: Employee involved in the sale. Quantidade: Quantity sold. Preco_Unitario: Price per unit. Data: Date of sale. Canal: Sales channel (e.g., online, in-store). Colaboradores (Employees):

Colaborador_ID: Unique identifier for the employee. Loja_ID: Store where the employee works. Nome: Employee's name. Funcao: Job role. Horas_Trabalhadas_Semanais: Weekly working hours. Avaliacao_Desempenho: Performance rating. Vendas_Realizadas: Sales completed by the employee. Naturalidade: Place of origin. Campanhas (Marketing Campaigns):

Campanha_ID: Unique identifier for the campaign. Nome: Campaign name. Canal: Marketing channel. Investimento: Investment made in the campaign. Vendas_Geradas: Sales generated by the campaign. Data_Inicio: Start date. Data_Fim: End date. Stock (Inventory):

Produto_ID: Product identifier. Quantidade: Current stock level. Max: Maximum stock level. Min: Minimum stock level. Tempo_Entrega: Delivery time. Devolucoes (Returns):

Devolucao_ID: Unique identifier for the return. Venda_ID: Sale associated with the return. Produto_ID: Product being returned. Cliente_ID: Customer making the return. Quantidade: Quantity returned. Motivo_Devolucao: Reason for return. Data_Devolucao: Return date. Custos_Operacionais (Operational Costs):

Custo_ID: Unique identifier for the cost. Loja_ID: Store associated with the cost. Tipo_Custo: Type of cost (e.g., rent, utilities). Valor_Mensal: Monthly cost amount. Data: Cost recording date. Product Reviews:

Review_ID: Unique review identifier. Produto_ID: Reviewed product. Avaliacao: Rating (e.g., 1-5 stars). Comentario: Customer comment. Data: Date of the review. Use Cases: Data Visualization: Create dashboards for tracking sales, inventory, and employee performance. Machine Learning: Build models for predicting sales, identifying customer churn, or optimizing stock levels. Statistical Analysis: Analyze customer demographics, product performance, or campaign ROI. Scenario Simulation: Explore the impact of marketing campaigns or inventory changes on sales. Data Format: All tables are provided as CSV files. Each table is normalized to reflect relational database structures, with foreign keys linking related tables. Additional Notes: All data is synthetic and generated using Python scripts with libraries like Faker and pandas. The dataset does not represent real-world entities or behaviors but is modeled to closely mimic actual retail operations.

ABOUT THIS CHART Allegation Disposition (Findings) Codes:

Valid. Based on the standard of preponderance of the evidence, it is more likely than not that the maltreatment occurred.

Invalid. Based on the standard of preponderance of the evidence, it is more likely than not that the maltreatment did not occur.

Unable to Determine. A preponderance of the available evidence is insufficient to support a finding of Valid or Invalid.

Other. The allegation disposition Other is used when an investigation of the allegation was not completed for some reason, e.g. clients died or cases were misclassified.

1. Family Violence is indicated when a validated investigation has a relative perpetrator, excluding those where financial exploitation is the only confirmed allegation.

2. Beginning in Fiscal Year 2015, services provided during the investigation are documented in the investigation stage and not in a separate service stage.

3. The "Other" Disposition category refers to those investigations that workers could not complete, e.g. clients died or cases were misclassified.

4. The population totals do not match prior DFPS Data Books, printed or online. Past population estimates are adjusted based on the U.S. Census data as it becomes available. This is important to keep the data in line with current best practices, but will cause some past counts, such as Abuse/Neglect Victims per 1,000 Texas Children, to be recalculated.

Population Data Source - Population Estimates and Projections Program, Texas State Data Center, Office of the State Demographer and the Institute for Demographic and Socioeconomic Research, The University of Texas at San Antonio.

Current population estimates and projections for all years from 2014 to 2023 as of December 2023.

Visit dfps.state.tx.us for information on all DFPS programs.

Structural business statistics (SBS) describe the structure, conduct and performance of economic activities, down to the most detailed activity level (several hundred economic sectors).
The EU Member States transmit SBS annually to the European Commission (Eurostat) based on European legislation.
SBS covers all activities of the business economy with the exception of agricultural activities, public administration and largely non-market services such as education and health. The data is provided by all EU Member States, Iceland, Norway and Switzerland, some candidate and potential candidate countries.
Most data is collected by National Statistical Institutes (NSIs) through statistical surveys, business registers or various administrative sources. Regulatory or controlling national offices for financial institutions or central banks often provide the information required for the financial sector (NACE Rev 2 Section K).
Member States apply various statistical methods - such as grossing up, model based estimation or different forms of imputation - according to the data source to ensure the quality of SBS produced.

Main characteristics (variables) of the SBS data category:

• "Business Demographic" variables (e.g. Number of active enterprises)
• "Output related" variables (e.g. Net turnover, Value added)
• "Input related" variables: labour input (e.g. Employment, Hours worked); goods and services input (e.g. Purchases of goods and services); capital input (e.g. Gross investments)

All SBS characteristics are published on Eurostat’s website in tables. An example of the existent tables is presented below:

• Annual enterprise statistics broken down by size classes: SBS data is published by country and employment size class (0-9; 10-19; 20-49; 50-249; 250 persons employed or more), again broken down by detailed economic activity, group level (3-digits). Additional size classes 0-1 and 2-9 persons employed are available for NACE Rev 2 Sections F to J, L to N and P to R and divisions S95 and S96 only for variables "Number of active enterprises" and "Number of employees and self-employed persons". For trade (NACE Rev 2 Section G) a supplementary breakdown by size class of turnover is available.
• Annual enterprise statistics: SBS data is published by country and detailed economic activity (NACE Rev 2) class level (4-digits) and special aggregates.
• Annual/bi-annual business services statistics: "Net turnover by product" and "Net turnover by residence of client" are published by country and detailed down to NACE Rev 2 group level (3-digits). The statistics on “Net turnover by product” permit analysis of products' relative importance in the turnover, consistency of product level statistics and product specialisation. On the other hand, information on “Turnover by residence of client” enable analysis of the type and location of clients and client specialisation.
• Annual regional statistics: Three characteristics are published by NUTS-2 country regions and detailed on NACE Rev 2 division level (2-digits).

Starting from the reference year 2023, voluntary data in new size classes called Small-Mid Caps (i.e. 250-499 and 500 and more persons employed) of selected SBS variables are transmitted by Member States and published together with legal size classes in a new Eurostat table.

More information on the contents of different tables and the detail level and breakdowns required starting with the reference year 2021, is defined in Commission Regulation 2019/2152 (‘EBS Regulation’) and Regulation (EU) 2020/1197 (‘EBS General Implementing Act’) concerning European Business Statistics.

Several important derived indicators are generated in the form of ratios of certain monetary characteristics or per head values. A list with the available derived indicators is available in the Annexes.

The data contains marketing information collected by direct phone calls to evaluate whether clients subscribe to a bank term deposit for a Portuguese banking institution.

File information

1. bank-additional-full.csv with all examples, ordered by date (from May 2008 to November 2010).
2. bank-additional.csv with 10% of the examples, randomly selected from bank-additional-full.csv.
3. bank-additional-balanced.csv with 10% of the examples selected with 50% of successful responses.
4. bank-additional-names.txt with detailed information on attributes, source of the dataset, and citation.

Column descriptors

Demographics:

• age: Customer's age (numeric)
• job: Type of job (categorical: 'admin.', 'services', ...)
• marital: Marital status (categorical: 'married', 'single', ...)
• education: Level of education (categorical: 'basic.4y', 'high.school', ...)

Past customer events:

• default: Has credit in default? (categorical: 'no', 'unknown', ...)
• housing: Has housing loan? (categorical: 'no', 'yes', ...)
• loan: Has personal loan? (categorical: 'no', 'yes', ...)

Past direct marketing contacts:

• contact: Contact communication type (categorical: 'cellular', 'telephone', ...)
• month: Last contact month of year (categorical: 'may', 'nov', ...)
• day_of_week: Last contact day of the week (categorical: 'mon', 'fri', ...)
• duration: Last contact duration, in seconds (numeric). Important note: If duration = 0 then y = 'no'.

Campaign information:

• campaign: Number of contacts performed during this campaign and for this client (numeric, includes the last contact)
• pdays: Number of days that passed by after the client was last contacted from a previous campaign (numeric)
• previous: Number of contacts performed before this campaign and for this client (numeric)
• poutcome: Outcome of the previous marketing campaign (categorical: nonexistent, success, ...)

Socioeconomic factors:

• emp.var.rate: Employment variation rate - quarterly indicator (numeric)
• cons.price.idx: Consumer price index - monthly indicator (numeric)
• cons.conf.idx: Consumer confidence index - monthly indicator (numeric)
• euribor3m: Euribor 3 month rate - daily indicator (numeric)
• nr.employed: Number of employees - quarterly indicator (numeric)

Target variable:

The dataset can be used to train a classifier to predict if a client will subscribe (yes/no) to a bank term deposit. Thus, y is whether the client subscribed to a term deposit (binary: 'yes', 'no')

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This map presents the full data available on the MLTSD GeoHub, and maps several of the key variables reflected by the Employment Services Program of ETD.Employment Services are a suite of services delivered to the public to help Ontarians find sustainable employment. The services are delivered by third-party service providers at service delivery sites (SDS) across Ontario on behalf of the Ministry of Labour, Training and Skills Development (MLTSD). The services are tailored to meet the individual needs of each client and can be provided one-on-one or in a group format. Employment Services fall into two broad categories: unassisted and assisted services.

Unassisted services include the following components:resources and information on all aspects of employment including detailed facts on the local labour marketresources on how to conduct a job search.assistance in registering for additional schoolinghelp with career planningreference to other Employment and government programs.

Unassisted services are available to all Ontarians without reference to eligibility criteria. These unassisted services can be delivered through structured orientation or information sessions (on or off site), e-learning sessions, or one-to-one sessions up to two days in duration. Employers can also use unassisted services to access information on post-employment opportunities and supports available for recruitment and workplace training.

The second category is assisted services, and it includes the following components:assistance with the job search (including individualized assistance in career goal setting, skills assessment, and interview preparation) job matching, placement and incentives (which match client skills and interested with employment opportunities, and include placement into employment, on-the-job training opportunities, and incentives to employers to hire ES clients), and job training/retention (which supports longer-term attachment to or advancement in the labour market or completion of training)For every assisted services client a service plan is maintained by the service provider, which gives details on the types of assisted services the client has accessed. To be eligible for assisted services, clients must be unemployed (defined as working less than twenty hours a week) and not participating in full-time education or training. Clients are also assessed on a number of suitability indicators covering economic, social and other barriers to employment, and service providers are to prioritize serving those clients with multiple suitability indicators.

About This Dataset

This dataset contains data on ES clients for each of the twenty-six Local Board (LB) areas in Ontario for the 2015/16 fiscal year, based on data provided to Local Boards and Local Employment Planning Councils (LEPC) in June 2016 (see below for details on Local Boards). This includes all assisted services clients whose service plan was closed in the 2015/16 fiscal year and all unassisted services clients who accessed unassisted services in the 2015/16 fiscal year. These clients have been distributed across Local Board areas based on the address of each client’s service delivery site, not the client’s home address. Note that clients who had multiple service plans close in the 2015/16 fiscal year (i.e. more than one distinct period during which the client was accessing assisted services) will be counted multiple times in this dataset (once for each closed service plan). Assisted services clients who also accessed unassisted services either before or after accessing assisted services would also be included in the count of unassisted clients (in addition to their assisted services data).

Demographic data on ES assisted services clients, including a client’s suitability indicators and barriers to employment, are collected by the service provider when a client registers for ES (i.e. at intake). Outcomes data on ES assisted services clients is collected through surveys at exit (i.e. when the client has completed accessing ES services and the client’s service plan is closed) and at three, six, and twelve months after exit. As demographic and outcomes data is only collected for assisted services clients, all fields in this dataset contain data only on assisted services clients except for the ‘Number of Clients – Unassisted R&I Clients’ field.

Note that ES is the gateway for other Employment Ontario programs and services; the majority of Second Career (SC) clients, some apprentices, and some Literacy and Basic Skills (LBS) clients have also accessed ES. It is standard procedure for SC, LBS and apprenticeship client and outcome data to be entered as ES data if the program is part of ES service plan. However, for this dataset, SC client and outcomes data has been separated from ES, which as a result lowers the client and outcome counts for ES.

About Local Boards

Local Boards are independent not-for-profit corporations sponsored by the Ministry of Labour, Training and Skills Development to improve the condition of the labour market in their specified region. These organizations are led by business and labour representatives, and include representation from constituencies including educators, trainers, women, Francophones, persons with disabilities, visible minorities, youth, Indigenous community members, and others. For the 2015/16 fiscal year there were twenty-six Local Boards, which collectively covered all of the province of Ontario.

The primary role of Local Boards is to help improve the conditions of their local labour market by:engaging communities in a locally-driven process to identify and respond to the key trends, opportunities and priorities that prevail in their local labour markets;facilitating a local planning process where community organizations and institutions agree to initiate and/or implement joint actions to address local labour market issues of common interest; creating opportunities for partnership development activities and projects that respond to more complex and/or pressing local labour market challenges; and organizing events and undertaking activities that promote the importance of education, training and skills upgrading to youth, parents, employers, employed and unemployed workers, and the public in general.

In December 2015, the government of Ontario launched an eighteen-month Local Employment Planning Council pilot program, which established LEPCs in eight regions in the province formerly covered by Local Boards. LEPCs expand on the activities of existing Local Boards, leveraging additional resources and a stronger, more integrated approach to local planning and workforce development to fund community-based projects that support innovative approaches to local labour market issues, provide more accurate and detailed labour market information, and develop detailed knowledge of local service delivery beyond Employment Ontario (EO).

Eight existing Local Boards were awarded LEPC contracts that were effective as of January 1st, 2016. As such, from January 1st, 2016 to March 31st, 2016, these eight Local Boards were simultaneously Local Employment Planning Councils. The eight Local Boards awarded contracts were:Durham Workforce Authority Peel-Halton Workforce Development GroupWorkforce Development Board - Peterborough, Kawartha Lakes, Northumberland, HaliburtonOttawa Integrated Local Labour Market PlanningFar Northeast Training BoardNorth Superior Workforce Planning Board Elgin Middlesex Oxford Workforce Planning & Development BoardWorkforce Windsor-Essex

MLTSD has provided Local Boards and LEPCs with demographic and outcome data for clients of Employment Ontario (EO) programs delivered by service providers across the province on an annual basis since June 2013. This was done to assist Local Boards in understanding local labour market conditions. These datasets may be used to facilitate and inform evidence-based discussions about local service issues – gaps, overlaps and under-served populations - with EO service providers and other organizations as appropriate to the local context.

Data on the following EO programs for the 2015/16 fiscal year was made available to Local Boards and LEPCs in June 2016:Employment Services (ES)Literacy and Basic Skills (LBS) Second Career (SC) Apprenticeship

This dataset contains the 2015/16 ES data that was sent to Local Boards and LEPCs. Datasets covering past fiscal years will be released in the future.

Notes and Definitions

NAICS – The North American Industry Classification System (NAICS) is an industry classification system developed by the statistical agencies of Canada, the United States, and Mexico against the backdrop of the North American Free Trade Agreement. It is a comprehensive system that encompasses all economic activities in a hierarchical structure. At the highest level, it divides economic activity into twenty sectors, each of which has a unique two-digit identifier. These sectors are further divided into subsectors (three-digit codes), industry groups (four-digit codes), and industries (five-digit codes). This dataset uses two-digit NAICS codes from the 2007 edition to identify the sector of the economy an Employment Services client is employed in prior to and after participation in ES.

NOC – The National Organizational Classification (NOC) is an occupational classification system developed by Statistics Canada and Human Resources and Skills Development Canada to provide a standard lexicon to describe and group occupations in Canada primarily on the basis of the work being performed in the occupation. It is a comprehensive system that encompasses all occupations in Canada in a hierarchical structure. At the highest level are ten broad occupational categories, each of which has a unique one-digit identifier. These broad occupational categories are further divided into forty major groups (two-digit codes), 140 minor groups

🧠 Dataset Overview This dataset simulates a realistic corporate HR environment, allowing practitioners to explore employee productivity, attrition risk, and work-life balance analytics.

It includes detailed information about employees across departments like IT, Sales, HR, and Support, enriched with engineered features such as:

Leave behavior (last leave day, leave frequency)

Work-life balance score

Customer satisfaction

Promotion history

Training hours

Phone number with country code

🔍 Use Cases Employee performance classification

Attrition risk prediction

Work-life imbalance detection

Cross-country HR comparison

Advanced EDA and dashboarding

🧩 Dataset Structure Column Description EmployeeID Unique employee identifier Name Randomly generated full name Gender, Age Demographics Department, JobRole Department and specific job title EducationLevel 1 (High School) to 5 (Doctorate) JoiningDate Date the employee joined the company Country, PhoneNumber, CountryCode Simulated global representation OvertimeHoursPerMonth Avg monthly overtime LeavesTaken Annual leaves taken LastLeaveDate Date of last recorded leave LeaveDayName Day of the week of that leave ProjectsHandled Number of completed projects TrainingHours Training received in hours CustomerSatisfaction Score out of 10 (client-facing roles only) LastPromotionYear Last year of promotion YearsAtCompany Derived from joining year WorkLifeBalanceScore Derived from overtime and leave PerformanceRating Final rating (1–5) AttritionRisk Whether the employee is at attrition risk (Yes/No)

📦 File Info Format: CSV

Rows: 500

Columns: 24

📘 Inspiration Inspired by real HR datasets like IBM Attrition, but designed with more behavioral and temporal features to simulate dynamic workplace patterns. This dataset is great for practicing ML pipelines, EDA dashboards, and HR analytics case studies. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20856454%2Fff3d7ab7f826edbfd33a3037830b84b4%2FScreenshot%202025-08-02%20104124.png?generation=1754111595669294&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20856454%2F3ea5a6f57a5d0748b439d0d386d43cba%2FScreenshot%202025-08-02%20104156.png?generation=1754111640882700&alt=media" alt="">

Bank marketing campaigns dataset analysis # Opening a Term Deposit

It is a dataset that describing Portugal bank marketing campaigns results. Conducted campaigns were based mostly on direct phone calls, offering bank client to place a term deposit. If after all marking afforts client had agreed to place deposit - target variable marked 'yes', otherwise 'no'

Sourse of the data https://archive.ics.uci.edu/ml/datasets/bank+marketing

Citation Request:

This dataset is public available for research. The details are described in S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014

1. Title: Bank Marketing (with social/economic context)

2. Sources Created by: Sérgio Moro (ISCTE-IUL), Paulo Cortez (Univ. Minho) and Paulo Rita (ISCTE-IUL) @ 2014

3. Past Usage:

   The full dataset (bank-additional-full.csv) was described and analyzed in:

   S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems (2014), doi:10.1016/j.dss.2014.03.001.

4. Relevant Information:

   This dataset is based on "Bank Marketing" UCI dataset (please check the description at: http://archive.ics.uci.edu/ml/datasets/Bank+Marketing). The data is enriched by the addition of five new social and economic features/attributes (national wide indicators from a ~10M population country), published by the Banco de Portugal and publicly available at: https://www.bportugal.pt/estatisticasweb. This dataset is almost identical to the one used in Moro et al., 2014. Using the rminer package and R tool (http://cran.r-project.org/web/packages/rminer/), we found that the addition of the five new social and economic attributes (made available here) lead to substantial improvement in the prediction of a success, even when the duration of the call is not included. Note: the file can be read in R using: d=read.table("bank-additional-full.csv",header=TRUE,sep=";")

The binary classification goal is to predict if the client will subscribe a bank term deposit (variable y).

1. Number of Instances: 41188 for bank-additional-full.csv

2. Number of Attributes: 20 + output attribute.

3. Attribute information:

   For more information, read [Moro et al., 2014].

   Input variables:

   bank client data:

   *1 - age (numeric)

   *2 - job : type of job (categorical: "admin.","blue-collar","entrepreneur","housemaid","management","retired","self-employed","services","student","technician","unemployed","unknown")

   *3 - marital : marital status (categorical: "divorced","married","single","unknown"; note: "divorced" means divorced or widowed)

   *4 - education (categorical: "basic.4y","basic.6y","basic.9y","high.school","illiterate","professional.course","university.degree","unknown")

   • 5 - default: has credit in default? (categorical: "no","yes","unknown")

   • 6 - housing: has housing loan? (categorical: "no","yes","unknown")

   • 7 - loan: has personal loan? (categorical: "no","yes","unknown")

   related with the last contact of the current campaign:

   • 8 - contact: contact communication type (categorical: "cellular","telephone")

   *9 - month: last contact month of year (categorical: "jan", "feb", "mar", ..., "nov", "dec")

   *10 - day_of_week: last contact day of the week (categorical: "mon","tue","wed","thu","fri")

   *11 - duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y="no"). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.

   other attributes:

   *12 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)

   *13 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted)

   *14 - previous: number of contacts performed before this campaign and for this client (numeric)

   1515 - poutcome: outcome of the previous marketing campaign (categorical: "failure","nonexistent","success")

   social and economic context attributes

   *16 - emp.var.rate: employment variation rate - quarterly indicator (numeric)

   *17 - cons.price.idx: consumer price index - monthly indicator (numeric)

   *18 - cons.conf.idx: consumer confidence index - monthly indicator (numeric)

   *19 - euribor3m: euribor 3 month rate - daily indicator (numeric)

   • 20 - nr.employed: number of employees - quarterly indicator (numeric)

   Output variable (desired target): * 21 - y - h...

1. Create a model to predict the credit card charged amount using the variables in the dataset. You are free to use any variables available in the dataset (we will suggest using numerical data). Were you able to build an accurate and reliable model? Which variables are (or are not) relevant in predicting the amount of credit card charged?
2. Create a clustering model with three (3) clusters using the following variables. Explain the characteristics of each cluster. Where do most Loans ‘R Us customers come from/located? What recommendation(s) can you make from the clusters to increase the number of Loans ‘R Us customers?
3. To help the frontline workers assess credit card fraud, you will need to create a classification model (K nearest neighbor, Naïve Bayes, etc.) based on the dataset available to you. You are free to use any variables in the dataset. One of your employees suggests splitting the dataset into two (training and testing) to create this classification model. Due to the unevenness of the data, a stratified approach to dividing the dataset may be needed. a. Explain what variables you used from the dataset, the classification method utilized, and how the dataset was divided into training and test data. b. How good is your classification model? i. How many were predicted as fraud was actually fraud ii. How many were predicted as fraud was actually not a fraud iii. How many were predicted as not a fraud was actually not a fraud iv. How many were predicted as not a fraud was actually fraud c. Explain how you would use this newly created model to help frontline workers make decisions based on the prediction made by the model.

Column Info trans_date_trans_time: Transaction DateTime merchant: Merchant Name category: Category of Merchant amt: Amount of Transaction city: City of Credit Card Holder state: State of Credit Card Holder lat: Latitude Location of Purchase long: Longitude Location of Purchase city_pop: Credit Card Holder's City Population job: Job of Credit Card Holder dob: Date of Birth of Credit Card Holder transmun: Transaction Number merch_lat: Latitude Location of Merchant merch_long: Longitude Location of Merchant is_fraud: Whether Transaction is Fraud (1) or Not (0)

Context

HR analytics is revolutionising the way human resources departments operate, leading to higher efficiency and better results overall. Human resources has been using analytics for years. However, the collection, processing and analysis of data has been largely manual, and given the nature of human resources dynamics and HR KPIs, the approach has been constraining HR. Therefore, it is surprising that HR departments woke up to the utility of machine learning so late in the game. Here is an opportunity to try predictive analytics in identifying the employees most likely to get promoted.

Content

Your client is a large MNC and they have 9 broad verticals across the organisation. One of the problem your client is facing is around identifying the right people for promotion (only for manager position and below) and prepare them in time. Currently the process, they are following is:

They first identify a set of employees based on recommendations/ past performance Selected employees go through the separate training and evaluation program for each vertical. These programs are based on the required skill of each vertical At the end of the program, based on various factors such as training performance, KPI completion (only employees with KPIs completed greater than 60% are considered) etc., employee gets promotion.

For above mentioned process, the final promotions are only announced after the evaluation and this leads to delay in transition to their new roles. Hence, company needs your help in identifying the eligible candidates at a particular checkpoint so that they can expedite the entire promotion cycle.

They have provided multiple attributes around Employee's past and current performance along with demographics. Now, The task is to predict whether a potential promotee at checkpoint in the test set will be promoted or not after the evaluation process.

Acknowledgements

This data set has been scraped from a contest held by https://www.analyticsvidhya.com/