The dataset represents policyholder information for ABC Insurance Company, containing 500+ records of customers. It includes demographic details, policy types, premium amounts, claim history, and customer satisfaction ratings. The dataset is structured as follows:

1. Customer Demographics Customer_ID: Unique identifier for each policyholder. Age: Age of the customer (ranging from 18 to 80 years). Gender: Categorical variable (Male, Female, M, F, Other). Region: Geographic location of the customer (North, South, East, West).
2. Policy Information Policy_Type: The type of insurance policy (Auto, Home, Life). Premium: Monthly premium paid by the customer (varies between $50 and $500). Claim_Count: Number of claims filed in the last year (ranges from 0 to 4).
3. Engagement & Satisfaction Date_Joined: The date when the customer enrolled in an insurance policy. Customer_Satisfaction: Survey rating (scale of 1-10) reflecting customer experience.

Potential Data Issues Missing values in categorical fields like Gender and numerical fields like Premium. Inconsistent categorical entries, such as variations in Gender representation. Duplicate records, which can lead to misleading insights. Outliers in Premium and Claim_Count affecting data accuracy.

Context

Your client is an Insurance company that has provided Health Insurance to its customers now they need your help in building a model to predict whether the policyholders (customers) from the past year will also be interested in Vehicle Insurance provided by the company.

An insurance policy is an arrangement by which a company undertakes to provide a guarantee of compensation for specified loss, damage, illness, or death in return for the payment of a specified premium. A premium is a sum of money that the customer needs to pay regularly to an insurance company for this guarantee.

For example, you may pay a premium of Rs. 5000 each year for a health insurance cover of Rs. 200,000/- so that if, God forbid, you fall ill and need to be hospitalized in that year, the insurance provider company will bear the cost of hospitalization etc. for upto Rs. 200,000. Now if you are wondering how can company bear such high hospitalisation cost when it charges a premium of only Rs. 5000/-, that is where the concept of probabilities comes in picture. For example, like you, there may be 100 customers who would be paying a premium of Rs. 5000 every year, but only a few of them (say 2-3) would get hospitalised that year and not everyone. This way everyone shares the risk of everyone else.

Just like medical insurance, there is vehicle insurance where every year customer needs to pay a premium of certain amount to insurance provider company so that in case of unfortunate accident by the vehicle, the insurance provider company will provide a compensation (called ‘sum assured’) to the customer.

Building a model to predict whether a customer would be interested in Vehicle Insurance is extremely helpful for the company because it can then accordingly plan its communication strategy to reach out to those customers and optimise its business model and revenue.

Now, in order to predict, whether the customer would be interested in Vehicle insurance, you have information about demographics (gender, age, region code type), Vehicles (Vehicle Age, Damage), Policy (Premium, sourcing channel) etc.

Data Description

train.csv

test.csv | Variable | Definition| |--- |--- | |id| Unique ID for the customer| |Gender| Gender of the customer| |Age|Age of the customer |Driving_License| 0 : Customer does not have DL, 1 : Customer already has DL| |Region_Code| Unique code for the region of the customer| |Previously_Insured| 1 : Customer already has Vehicle Insurance, 0 : Customer doesn't have Vehicle Insurance| |Vehicle_Age| Age of the Vehicle| |Vehicle_Damage|1 : Customer got his/her vehicle damaged in the past.0 : Customer didn't get his/her vehicle damaged in the past.| |Annual_Premium| The amount customer needs to pay as premium in the year| |Policy_Sales_Channel| Anonymised Code for the channel of outreaching to the customer ie. Different Agents, Over Mail, Over Phone, In Person, etc.| |Vintage| Number of Days, Customer has been associated with the company|

sample_submission.csv |Variable |Definition | |--- |--- | | id | Unique ID | | Response|Probability of Customer being interested in Vehicle Loan|

Health Insurance Lead Prediction

Your Client FinMan is a financial services company that provides various financial services like loan, investment funds, insurance etc. to its customers. FinMan wishes to cross-sell health insurance to the existing customers who may or may not hold insurance policies with the company. The company recommend health insurance to it's customers based on their profile once these customers land on the website. Customers might browse the recommended health insurance policy and consequently fill up a form to apply. When these customers fill-up the form, their Response towards the policy is considered positive and they are classified as a lead.

Once these leads are acquired, the sales advisors approach them to convert and thus the company can sell proposed health insurance to these leads in a more efficient manner.

Now the company needs your help in building a model to predict whether the person will be interested in their proposed Health plan/policy given the information about:

Demographics (city, age, region etc.) Information regarding holding policies of the customer Recommended Policy Information

Link for More: https://datahack.analyticsvidhya.com/contest/job-a-thon/

Context

Our client is an Insurance company that has provided Health Insurance to its customers now they need your help in building a model to predict whether the policyholders (customers) from past year will also be interested in Vehicle Insurance provided by the company.

An insurance policy is an arrangement by which a company undertakes to provide a guarantee of compensation for specified loss, damage, illness, or death in return for the payment of a specified premium. A premium is a sum of money that the customer needs to pay regularly to an insurance company for this guarantee.

For example, you may pay a premium of Rs. 5000 each year for a health insurance cover of Rs. 200,000/- so that if, God forbid, you fall ill and need to be hospitalised in that year, the insurance provider company will bear the cost of hospitalisation etc. for upto Rs. 200,000. Now if you are wondering how can company bear such high hospitalisation cost when it charges a premium of only Rs. 5000/-, that is where the concept of probabilities comes in picture. For example, like you, there may be 100 customers who would be paying a premium of Rs. 5000 every year, but only a few of them (say 2-3) would get hospitalised that year and not everyone. This way everyone shares the risk of everyone else.

Just like medical insurance, there is vehicle insurance where every year customer needs to pay a premium of certain amount to insurance provider company so that in case of unfortunate accident by the vehicle, the insurance provider company will provide a compensation (called ‘sum assured’) to the customer.

Building a model to predict whether a customer would be interested in Vehicle Insurance is extremely helpful for the company because it can then accordingly plan its communication strategy to reach out to those customers and optimise its business model and revenue.

Now, in order to predict, whether the customer would be interested in Vehicle insurance, you have information about demographics (gender, age, region code type), Vehicles (Vehicle Age, Damage), Policy (Premium, sourcing channel) etc.

Data Description

• Train Data

• Test Data

• Submission

Evaluation Metric

The evaluation metric for this hackathon is ROC_AUC score.

Public and Private split

The public leaderboard is based on 40% of test data, while final rank would be decided on remaining 60% of test data (which is private leaderboard)

Guidelines for Final Submission

Please ensure that your final submission includes the following:

1. Solution file containing the predicted response of the customer (Probability of response 1)
2. Code file for reproducing the submission, note that it is mandatory to submit your code for a valid final submission

Home Health Agency Insurance Market Outlook

According to our latest research, the global Home Health Agency Insurance market size reached USD 3.6 billion in 2024, with a robust growth trajectory driven by the increasing demand for home-based healthcare services and stringent regulatory compliance requirements. The market is expected to grow at a CAGR of 8.2% during the forecast period, reaching USD 7.1 billion by 2033. This healthy expansion is primarily fueled by the rising aging population, the surge in chronic illnesses necessitating home care, and the growing awareness among home health agencies regarding the importance of comprehensive insurance coverage to mitigate operational risks.

One of the most significant growth factors for the Home Health Agency Insurance market is the rapidly increasing geriatric population worldwide. As more individuals age and require long-term care, the preference for receiving medical services in the comfort of one's home has grown substantially. This demographic shift is compelling home health agencies to expand their offerings, which in turn amplifies their exposure to various operational, professional, and legal risks. Consequently, these agencies are increasingly recognizing the necessity of robust insurance solutions, including general liability, professional liability, and workers’ compensation insurance, to safeguard against potential lawsuits, employee injuries, and other unforeseen incidents. The heightened focus on quality care and patient safety further accentuates the need for comprehensive insurance coverage, propelling market growth.

Another pivotal driver is the evolving regulatory landscape governing the home healthcare sector. Governments and regulatory bodies across regions are instituting stricter compliance mandates for home health agencies, particularly regarding employee safety, patient data protection, and service quality. These regulatory requirements often include mandatory insurance coverage, such as workers’ compensation and professional liability insurance, as a prerequisite for licensing and operation. This compliance-driven demand is particularly pronounced in developed markets like North America and Europe, where regulatory scrutiny is intense. Moreover, the increasing frequency of claims related to medical malpractice, property damage, and business interruptions has underscored the importance of specialized insurance products tailored for home health agencies, thereby reinforcing market expansion.

Technological advancements and digital transformation in the insurance industry are also playing a transformative role in the growth of the Home Health Agency Insurance market. The integration of digital platforms and insurtech solutions has streamlined the insurance purchasing process, making it easier for home health agencies to compare, customize, and purchase policies that best suit their needs. Additionally, the use of data analytics and artificial intelligence is enabling insurers to offer more accurate risk assessments and personalized coverage options, thereby enhancing the overall value proposition for home health agencies. This digital shift is not only improving operational efficiency but is also fostering greater transparency and trust between insurers and their clients, further accelerating market adoption.

From a regional perspective, North America continues to dominate the Home Health Agency Insurance market, accounting for the largest share in 2024, driven by a mature home healthcare sector, high insurance penetration, and stringent regulatory frameworks. However, the Asia Pacific region is emerging as the fastest-growing market, fueled by increasing healthcare expenditure, rising awareness about insurance, and rapid expansion of home health services in countries such as China, India, and Japan. Meanwhile, Europe maintains a steady growth trajectory, supported by well-established healthcare infrastructure and proactive regulatory policies. Latin America and the Middle East & Africa are also witnessing gradual market development, primarily due to improving healthcare access and growing investments in home health services.

Snapshot img

Greece Life and Non Life Insurance Market Size 2024-2028

The life and non life insurance market in Greece size is forecast to increase by USD 1.33 billion at a CAGR of 4.6% between 2023 and 2028. The market is driven by the digitalization of the insurance industry, with Insurance enterprises integrating IT and analytic solutions to enhance customer experience and streamline operations. The economy and the banking system serve as the main drivers of the market's growth. The integration of digital technology is a significant trend in the market, with insurers like Ethniki and NN Hellenic investing in advanced technologies to improve efficiency and competitiveness. However, data privacy and security concerns pose challenges to the market's growth, as insurers must ensure the protection of sensitive customer information in the digital age. Overall, the Greek insurance market is poised for growth, with digitalization and data security being key areas of focus.

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The life insurance market in Greece has witnessed significant growth over the past few years. The market, on the other hand, recorded gross written premium of €7.5 billion during the same period. The loss ratio for life insurance stood at 65.5%, while non-life insurance recorded a loss ratio of 72%. The Greek insurance industry is regulated by the Hellenic Financial Stability Fund and industry associations such as Ethniki and NN Hellenic. The economy and banking system play a crucial role in the insurance market's growth. The industry's digital transformation is gaining momentum, with insurers embracing digital insurance to enhance customer experience and streamline operations.

Furthermore, the life insurance industry's major product categories include individual and group life, health, and pension insurance. The penetration rate for life insurance is relatively low at 2.6%, presenting significant growth opportunities. Premium ceded to reinsurers stood at 15% for life insurance and 30% for non-life insurance, with cession rates varying among insurers. Demographics and segment dynamics significantly impact the Greek insurance market. The aging population and increasing awareness of the need for insurance products are driving the growth of the life insurance sector. Competitive advantages include customized solutions, innovative products, and excellent customer service. Data from the National Statistic Offices and the Hellénic Statistical Authority provide valuable insights into the Greek insurance market's trends and developments. The industry's future growth is expected to be driven by a focus on innovation, digitalization, and customer-centricity.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2017-2022 for the following segments.

Type

  Life insurance
  Non-life insurance


Distribution Channel

  Agency
  Direct
  Banks


Geography

  Greece

By Type Insights

The Life insurance segment is estimated to witness significant growth during the forecast period.The life insurance industry in Greece has experienced notable growth in the product category of life insurance policies. This trend can be attributed to the growing recognition of the importance of securing financial security for oneself and one's family. Life insurance policies serve as a vital safety net, providing financial assistance to policyholders' dependents in the unfortunate event of the policyholder's demise. The protection offered by life insurance policies extends to covering outstanding debts such as mortgages, financing children's education, and meeting other financial obligations that can place a significant burden on the family. Furthermore, some life insurance policies offer savings or investment components, enabling policyholders to accumulate wealth over time.

Furthermore, the segment dynamics of the life insurance market in Greece are influenced by various demographic factors and competitive advantages of insurers. Premiums ceded and cession rates continue to shape the market landscape, making it an intriguing area for investment and growth.

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The Life insurance segment was valued at USD 2.34 billion in 2018 and showed a gradual increase during the forecast period.

Our market researchers analyzed the data with 2023 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing strategies to gain a competitive advantage.

Market Driver

Digitalization of insurance industry is the key driver of the market. The insurance sector in Greece has undergone substantial transformation due to digitalization, leading to enhanced convenience, efficiency, and personalized services

Health Insurance Lead Prediction

Your Client FinMan is a financial services company that provides various financial services like loans, investment funds, insurance etc. to its customers. FinMan wishes to cross-sell health insurance to the existing customers who may or may not hold insurance policies with the company. The company recommends health insurance to it's customers based on their profile once these customers land on the website. Customers might browse the recommended health insurance policy and consequently fill up a form to apply. When these customers fill-up the form, their Response towards the policy is considered positive and they are classified as a lead.

Once these leads are acquired, the sales advisors approach them to convert and thus the company can sell proposed health insurance to these leads in a more efficient manner.

Now the company needs your help in building a model to predict whether the person will be interested in their proposed Health plan/policy given the information about:

Demographics (city, age, region etc.) Information regarding holding policies of the customer Recommended Policy Information

Basic, mandatory health insurance in the Netherlands costed on average almost ***** euros per year per person in 2024, an increase of almost 100 euros compared to the previous year. This number only covers health insurance under the so-called Dutch Health Insurance Act (or Zvw), and excludes private, additional health insurance. Also note that this is an average, as premiums vary per person. Some people can receive a discount, for example by taking on a voluntary excess (in Dutch: vrijwillig eigen risico) on top of their already existing mandatory excess (or verplicht eigen risico) in exchange for a lower premium.

A survival of the fittest?

Health insurance in the Netherlands is a hybrid, of sorts. On the one hand, the Dutch government is responsible for the contents of the health insurance package. It is then, however, up to the ** health insurance groups to offer this product via the free market. These insurance groups are obliged to accept all residents of the Netherlands, regardless of age or state of health. To add to the competitiveness within Dutch health insurance, clients can switch insurance provider at the end of every year. If consumers find a health insurance which, for example, is cheaper or provides a better service, they can end their current contract and choose a new company. For example, in 2023, *** percent of the individuals cancelled their health insurance and switched to another company.

Curbing costs

The idea behind this system is two-fold. First, by means of the competitive free market, it is a way for the Dutch government to make healthcare as affordable as it can be. Whether this works is difficult to assess as national healthcare expenses in the Netherlands have grown at a faster rate in recent years, exceeding 100 billion euros since 2020. Second, the system is meant to make consumers more aware of how expensive healthcare is. This is where the aforementioned verplicht eigen risico or mandatory excess comes in. From the age of **, it is compulsory to pay this set amount of money on healthcare during a year before the health insurance starts to reimburse.

According to cognitive market research, the global burial insurance market size was USD 245158.2 million in 2024. It will expand at a compound annual growth rate (CAGR) of 6.50% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 98063.28 million in 2024 and will grow at a compound annual growth rate (CAGR) of 4.7% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 73547.46 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 56386.39 million in 2024 and will grow at a compound annual growth rate (CAGR) of 8.5% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 12257.91 million in 2024 and will grow at a compound annual growth rate (CAGR) of 5.9% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 4903.16 million in 2024 and will grow at a compound annual growth rate (CAGR) of 6.2% from 2024 to 2031.
The level death benefit held the highest burial insurance market revenue share in 2024.

Market Dynamics of Burial Insurance Market

Key Drivers for Burial Insurance Market

Rise in the Number of Elderly People to Increase the Demand Globally

The burial insurance market has experienced growth due to a rise in the number of older adults. The aging population significantly influences the market for burial insurance. There is a growing need for funeral and burial services as the population ages. People can plan and pay for these costs with burial insurance, preventing their loved ones from having to shoulder the weight of debt after they pass away. Furthermore, burial insurance providers provide customizable coverage choices to accommodate a range of individual requirements. This enables clients to select a plan that fits their unique needs by offering a variety of benefit amounts and policy lengths. Additionally, some burial insurance companies form alliances to provide advantages like preferred provider networks and subsidized services, which improve the clientele's experience in general. For example, USAA Life Insurance Company announced in April 2021 that it has partnered with Mutual of Omaha Insurance Company, a top life insurance provider, to offer a guaranteed issue whole life insurance solution. The new offering gives USAA members access to a guaranteed issue whole life insurance plan intended to assist in paying for funeral or burial costs.

Personalization, Adaptability, and Simplicity of Use to Propel Market Growth

The burial insurance market has witnessed steady growth, driven by personalization, adaptability, and simplicity of use. When it comes to customization and versatility, burial insurance policies surpass standard life insurance policies. They particularly cover funeral and burial expenses, and policyholders can customize the coverage amount and length to suit their requirements. Burial insurance is becoming more and more popular among consumers due to its customizable policy options, which are propelling the market's expansion. Moreover, underwriting procedures for burial insurance plans have been streamlined, increasing their accessibility to a wider spectrum of people. These plans are easier to obtain for elderly persons or those with pre-existing medical conditions who might have trouble acquiring regular life insurance since they have simpler health questionnaires and lower face-value coverage. As a result, these elements supported the market expansion for burial insurance.

Restraint Factor for the Burial Insurance Market

Complexities to Limit the Sales

Benefits from burial insurance are paid out in a convoluted manner. Certain companies provide an extensive selection exclusively for accidental deaths. In contrast, in the event of a natural death, they only pay the applicant back for the entire amount paid, plus interest. If the individual lives longer, the majority of the burial insurance premiums will need to be paid. As a result, assets lose value, which prevents the market from growing. Digital burial insurance needs to be approved by state regulations before it can be sold. Regulators at the national level oversee burial insurance providers. An important part of funeral ...

The global seniors travel insurance market, valued at $4920.3 million in 2025, is projected to experience steady growth, driven by several key factors. The increasing aging population globally, coupled with a rise in disposable income among seniors and a growing desire for travel experiences later in life, are significantly fueling market expansion. The market is segmented by age group (50-60, 60-70, and above 70 years old), reflecting differing needs and risk profiles. Single-trip and annual multi-trip coverage options cater to varying travel patterns. Strong competition exists among major players like Allianz, AIG, Zurich, and others, leading to product innovation and competitive pricing. The market's growth, however, may face some restraints, including concerns about pre-existing medical conditions and the complexities of international healthcare systems potentially leading to higher premiums and limited coverage for certain health issues. Geographic variations in market penetration are expected, with North America and Europe likely maintaining a larger market share due to higher senior populations and greater travel frequency within these regions. The market is predicted to be influenced by technological advancements in claims processing and customer service, improving efficiency and accessibility for senior travellers. The forecast period of 2025-2033 anticipates a continuation of this growth trend, albeit at a moderate pace, reflecting the relatively stable nature of the insurance sector. Market expansion will likely be driven by increased awareness of the importance of travel insurance among seniors, personalized product offerings tailored to the specific needs of this demographic, and strategic partnerships between insurance providers and travel agencies targeting senior travellers. Further segmentation based on specific health needs or travel styles might emerge to cater to the diverse needs within the senior traveller segment. Regulatory changes affecting insurance coverage for older adults could also influence market dynamics in the coming years. Focus on digital platforms and telehealth integration are likely to play a vital role in market expansion, making it more convenient for seniors to access and utilize travel insurance services.

This is Health insurance Data to analyze Sales , internal operations and market size of a health insurance company . To analyze the sales, internal operations, and market size of a health insurance company, you would need access to relevant data. While I don't have real-time data, I can provide you with a general outline of the types of data you may need to analyze these aspects. Here are some key data points to consider:

Sales Analysis:

Monthly/quarterly/annual premium revenue Number of policies sold Premiums by product types (e.g., individual, family, group) Sales channels (e.g., agents, brokers, online) Internal Operations Analysis:

Claims data: Number of claims filed, paid, and denied Claim settlement time and ratios Customer service metrics (e.g., response time, satisfaction ratings) Underwriting metrics (e.g., policy acceptance rate, risk assessment) Market Analysis:

Market share: Percentage of the total health insurance market held by the company Competition analysis: Market share of competitors, their product offerings, and pricing Demographics: Age, income, location, and other relevant demographic information of policyholders Regulatory factors: Changes in regulations or laws affecting the health insurance industry Other data points that could be useful for analysis include customer retention rates, profitability analysis, marketing expenditure, and customer feedback.

Keep in mind that this is a general overview, and the specific data requirements may vary based on your company's unique goals and objectives. Additionally, it's important to handle and analyze this data in compliance with relevant privacy and data protection laws.

Home care providers support the overall health and well-being of millions in the US annually. This number has been growing fast, expanding the scale and scope of home care providers in recent years. A rising number of adults 65 and older has been the primary driver behind this, as older adults are at a higher risk of developing a condition or experiencing an injury that limits their ability to perform tasks they once did independently. While changing demographic trends are an overarching trend impacting the health sector, the pandemic has permanently altered the industry's trajectory. Widespread outbreaks at residential facilities in the first year of the pandemic led more people to value remaining in their homes as they age; the interest in aging-in-place has only grown even as pandemic concerns have dissipated, as older adults look for options that provide safety and independence. In all, revenue has been expanding at a CAGR of 3.7% to an estimated $155.9 billion over the past five years, including expected growth of 3.2% in 2025. The mounting need for home care services and a shortage of home health aides create a mismatch between supply and demand that limits revenue growth. Shortages, preexisting the pandemic, have worsened as caregivers seek more flexible jobs with higher pay, creating increasingly high turnover that pressures providers to raise wages. Medicare and Medicaid reimbursements to home health agencies have been declining for several years, preventing home health agencies from raising salaries despite shortages. Clients eligible for home care services through insurance face long waits, leading more people to opt for self-directed care, where family members or friends work as caregivers. Too few caregivers prevent the industry from fully benefiting from rising demand and curtail profit growth. Trends driving growth in recent years will continue, providing various opportunities for home care providers. How home care providers capitalize on these trends will depend on insurer reimbursements and workforce development. Technology, ranging from wearables to telehealth, will have a more prominent role in the industry as providers look for ways to improve patient care while lessening the burden on staff. Regulatory and financial pressures will maintain consolidation activity, with private equity investment likely to expand. A significant headwind facing the industry will be the future of Medicare policies, the extent to which they cover home health and how states will react to Medicaid cuts in the Trump Administration's Big Beautiful Bill. Revenue will grow at a CAGR of 2.9% to an estimated $179.8 billion over the next five years.

Will you take vehicle insurance?

An insurance policy is an arrangement by which a company undertakes to provide a guarantee of compensation for specified loss, damage, illness, or death in return for the payment of a specified premium. A premium is a sum of money that the customer needs to pay regularly to an insurance company for this guarantee.

For example, you may pay a premium of Rs. 5000 each year for a health insurance cover of Rs. 200,000/- so that if, God forbid, you fall ill and need to be hospitalised in that year, the insurance provider company will bear the cost of hospitalisation etc. for upto Rs. 200,000. Now if you are wondering how can company bear such high hospitalisation cost when it charges a premium of only Rs. 5000/-, that is where the concept of probabilities comes in picture. For example, like you, there may be 100 customers who would be paying a premium of Rs. 5000 every year, but only a few of them (say 2-3) would get hospitalised that year and not everyone. This way everyone shares the risk of everyone else.

Just like medical insurance, there is vehicle insurance where every year customer needs to pay a premium of a certain amount to its insurance provider company so that in case of an unfortunate accident by the vehicle, the insurance provider company will provide compensation (called ‘sum assured’) to the customer.

Building a model to predict whether a customer would be interested in Vehicle Insurance is extremely helpful for the company because it can then accordingly plan its communication strategy to reach out to those customers and optimise its business model and revenue.

Now, in order to predict, whether the customer would be interested in Vehicle insurance, you have information about demographics (gender, age, region code type), Vehicles (Vehicle Age, Damage), Policy (Premium, sourcing channel) etc.

Content

id: Unique ID for the customer Gender: Gender of the customer Age :: Age of the customer driving license: 0 :: Customer does not have DL, 1 : Customer already has DL RegionCode: Unique code for the region of the customer PreviouslyInsured 1 : Customer already has Vehicle Insurance, 0 : Customer doesn't have Vehicle Insurance VehicleAge: Age of the Vehicle VehicleDamage: 1 : Customer got his/her vehicle damaged in the past, 0 : Customer: Customer didn't get his/her vehicle damaged in the past. AnnualPremium: The amount customer needs to pay as premium in the year PolicySalesChannel: Anonymised Code for the channel of outreaching to the customer ie. Different Agents, Over Mail, Over Phone, In Person, etc. Vintage: Number of Days, Customer has been associated with the company Response: 1: Customer is interested, 0 : Customer is not interested

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Original DatabSource Analytics Vidhya: https://datahack.analyticsvidhya.com/contest/janatahack-cross-sell-prediction/#About

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Context Your Client FinMan is a financial services company that provides various financial services like loan, investment funds, insurance etc. to its customers. FinMan wishes to cross-sell health insurance to the existing customers who may or may not hold insurance policies with the company. The company recommend health insurance to it's customers based on their profile once these customers land on the website. Customers might browse the recommended health insurance policy and consequently fill up a form to apply. When these customers fill-up the form, their Response towards the policy is considered positive and they are classified as a lead.

Once these leads are acquired, the sales advisors approach them to convert and thus the company can sell proposed health insurance to these leads in a more efficient manner.

Content Demographics (city, age, region etc.) Information regarding holding policies of the customer Recommended Policy Information

Acknowledgements This is dataset is released as part of a hackathon conducted by Analytics Vidhya. Visit https://datahack.analyticsvidhya.com/contest/job-a-thon/#ProblemStatement for more information.

Problem Statement

Your client is an Insurance company and they need your help in building a model to predict whether the policyholder (customer) will pay next premium on time or not. An insurance policy is an arrangement by which a company undertakes to provide a guarantee of compensation for specified loss, damage, illness, or death in return for the payment of a specified premium. A premium is a sum of money that you pay regularly to an insurance company for this guarantee.

For example, you may pay a premium of Rs. 5000 each year for a medical insurance cover of Rs. 200,000/- so that if, God forbid, you fall ill and need to be hospitalized in that year, the insurance provider company will bear the cost of hospitalization etc. for upto Rs. 200,000. Now if you are wondering how can company bear such high hospitalization cost when it charges a premium of only Rs. 5000/-, that is where the concept of probabilities comes in picture. For example, like you, there may be 100 customers who would be paying a premium of Rs. 5000 every year, but only a few of them (say 2-3) would get hospitalized that year and not everyone. This way everyone shares the risk of everyone else. Just like medical insurance, there is life insurance where every year you pay a premium of certain amount to insurance provider company so that in case of unfortunate event of your death, the insurance provider company will provide a compensation (called ‘sum assured’) to your immediate family. Similarly, there can be a variety of insurance products for different kinds of risks. As you can imagine, if a large number of customers do not pay the premium on time, it might disrupt the cash flow and smooth operation for the company. A customer may stop making regular premium payments for a variety of reasons - some may forget, some may find it expensive and not worth the value, some may not have money to pay the premium etc.

What you have to do...

Build a model to predict whether a customer would make the premium payment can be extremely helpful for the company because it can then accordingly plan its communication strategy to reach out to those customers who are less likely to pay and convince them to continue making timely payment.

Given

Now, in order to predict whether the customer would pay the next premium or not, you have information about past premium payment history for the policyholders along with their demographics (age, monthly income, area type) and sourcing channel etc.

Context

Jantahack: Cross-sell Prediction

Cross-selling identifies products or services that satisfy additional, complementary needs that are unfulfilled by the original product that a customer possesses. As an example, a mouse could be cross-sold to a customer purchasing a keyboard. Oftentimes, cross-selling points users to products they would have purchased anyways; by showing them at the right time, a store ensures they make the sale.

Cross-selling is prevalent in various domains and industries including banks. For example, credit cards are cross-sold to people registering a savings account. In ecommerce, cross-selling is often utilized on product pages, during the checkout process, and in lifecycle campaigns. It is a highly-effective tactic for generating repeat purchases, demonstrating the breadth of a catalog to customers. Cross-selling can alert users to products they didn't previously know you offered, further earning their confidence as the best retailer to satisfy a particular need.

This weekend we invite you to participate in another Janatahack with the theme of Cross-sell prediction. Stay tuned for the problem statement and datasets this Friday and get a chance to work on a real industry case study along with 250 AV points at stake.

Content

Your client is an Insurance company that has provided Health Insurance to its customers now they need your help in building a model to predict whether the policyholders (customers) from past year will also be interested in Vehicle Insurance provided by the company.

An insurance policy is an arrangement by which a company undertakes to provide a guarantee of compensation for specified loss, damage, illness, or death in return for the payment of a specified premium. A premium is a sum of money that the customer needs to pay regularly to an insurance company for this guarantee.

For example, you may pay a premium of Rs. 5000 each year for a health insurance cover of Rs. 200,000/- so that if, God forbid, you fall ill and need to be hospitalised in that year, the insurance provider company will bear the cost of hospitalisation etc. for upto Rs. 200,000. Now if you are wondering how can company bear such high hospitalisation cost when it charges a premium of only Rs. 5000/-, that is where the concept of probabilities comes in picture. For example, like you, there may be 100 customers who would be paying a premium of Rs. 5000 every year, but only a few of them (say 2-3) would get hospitalised that year and not everyone. This way everyone shares the risk of everyone else.

Just like medical insurance, there is vehicle insurance where every year customer needs to pay a premium of certain amount to insurance provider company so that in case of unfortunate accident by the vehicle, the insurance provider company will provide a compensation (called ‘sum assured’) to the customer.

Building a model to predict whether a customer would be interested in Vehicle Insurance is extremely helpful for the company because it can then accordingly plan its communication strategy to reach out to those customers and optimise its business model and revenue.

Now, in order to predict, whether the customer would be interested in Vehicle insurance, you have information about demographics (gender, age, region code type), Vehicles (Vehicle Age, Damage), Policy (Premium, sourcing channel) etc.

Acknowledgements

train.csv Variable Definition id Unique ID for the customer Gender Gender of the customer Age Age of the customer Driving_License 0 : Customer does not have DL, 1 : Customer already has DL Region_Code Unique code for the region of the customer Previously_Insured 1 : Customer already has Vehicle Insurance, 0 : Customer doesn't have Vehicle Insurance Vehicle_Age Age of the Vehicle Vehicle_Damage 1 : Customer got his/her vehicle damaged in the past. 0 : Customer didn't get his/her vehicle damaged in the past. Annual_Premium The amount customer needs to pay as premium in the year Policy_Sales_Channel Anonymised Code for the channel of outreaching to the customer ie. Different Agents, Over Mail, Over Phone, In Person, etc. Vintage Number of Days, Customer has been associated with the company Response 1 : Customer is interested, 0 : Customer is not interested

test.csv Variable Definition id Unique ID for the customer Gender Gender of the customer Age Age of the customer Driving_License 0 : Customer does not have DL, 1 : Customer already has DL Region_Code Unique code for the region of the customer Previously_Insured 1 : Customer already has Vehicle Insurance, 0 : Customer doesn't have Vehicle Insurance Vehicle_Age Age of the Vehicle Vehicle_Damage 1 : Customer got his/her vehicle damaged in the past. 0 : Customer didn't get his/her vehicle damaged in the past. Annual_Premium The amount customer needs to pay as premium in the year Policy_Sales_Channel Anonymised Code f...

Context

Insurance companies that sell life, health, and property and casualty insurance are using machine learning (ML) to drive improvements in customer service, fraud detection, and operational efficiency. The data provided by an Insurance company which is not excluded from other companies to getting advantage of ML. This company provides Health Insurance to its customers. We can build a model to predict whether the policyholders (customers) from past year will also be interested in Vehicle Insurance provided by the company.

An insurance policy is an arrangement by which a company undertakes to provide a guarantee of compensation for specified loss, damage, illness, or death in return for the payment of a specified premium. A premium is a sum of money that the customer needs to pay regularly to an insurance company for this guarantee.

For example, you may pay a premium of Rs. 5000 each year for a health insurance cover of Rs. 200,000/- so that if, God forbid, you fall ill and need to be hospitalized in that year, the insurance provider company will bear the cost of hospitalization etc. for up to Rs. 200,000. Now if you are wondering how can company bear such high hospitalization cost when it charges a premium of only Rs. 5000/-, that is where the concept of probabilities comes in picture. For example, like you, there may be 100 customers who would be paying a premium of Rs. 5000 every year, but only a few of them (say 2-3) would get hospitalized that year and not everyone. This way everyone shares the risk of everyone else.

Just like medical insurance, there is vehicle insurance where every year customer needs to pay a premium of certain amount to insurance provider company so that in case of unfortunate accident by the vehicle, the insurance provider company will provide a compensation (called ‘sum assured’) to the customer.

Content

Building a model to predict whether a customer would be interested in Vehicle Insurance is extremely helpful for the company because it can then accordingly plan its communication strategy to reach out to those customers and optimize its business model and revenue.

We have information about: - Demographics (gender, age, region code type), - Vehicles (Vehicle Age, Damage), - Policy (Premium, sourcing channel) etc.

Update: Test data target values has been added. To evaluate your models more precisely you can use: https://www.kaggle.com/arashnic/answer

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Moreover the supplemental goal is to practice learning imbalanced data and verify how the results can help in real operational process. The Response feature (target) is highly imbalanced.

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0: 319594 1: 62531 Name: Response, dtype: int64

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Practicing some techniques like resampling is useful to verify impacts on validation results and confusion matrix. #
https://miro.medium.com/max/640/1*KxFmI15rxhvKRVl-febp-Q.png"> figure. Under-sampling: Tomek links # #

Starter Kernel(s)

• Quick EDA and LGB ,XGB
• Handling Imbalanced: Resampling the right way

Inspiration

Predict whether a customer would be interested in Vehicle Insurance

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MORE DATASETs ...

Advanced Business Analytics - Power of Predictive Modeling Course ID: 226161-D, course type: lab Project 2, deadline: 27th January 2023 You are employed as a data scientist at a nonlife insurance company with a focus on motor insurance business. Your manager has requested you to analyze the data on claims reported by the clients in the first quarter of 2023 in order to perform customer segmentation and determine the key risk drivers of claims severity. The main purpose of the analysis is to allow the actuarial department to better assess the risk for the given line of business as well as to identify potential frauds requiring further investigation by claims handling department of the company.

Descirptions on table Feature name; Description cust_age; Age of the customer policy_id; Insurance policy ID coverage_start_date; Insurance coverage start date cust_region; Customer region sum_assured_group; Sum assured group (low, medium or high sum assured) ins_deductible; Insurance deductible (amount which is deducted from the sum paid by the insurer in case claim is accepted) annual_prem; Annual premium zip_code; Postal code insured_sex; Gender edu_lvl; Education level marital_status; Marital status claim_incurred_date; Date when claim has been incurred claim_type; Incident type acc_type; Accident type (if applicable) emg_services_notified; Emergency services notified about the incident incident_city; City where the incident occurred incident_hour; Hour when the incident occurred num_vehicles_involved; Number of vehicles involved property_damage; Variable indicating whether a property damage (other than car damage) has occurred bodily_injuries; Number of people affected by bodily injuries due to the incident witnesses; Number of witnesses of the incident police_report_avlbl; Variable indicating whether police report on the incident is available total_claim_amount; Total claim amount injury_claim; Amount of claim related to bodily injury property_claim; Property claim amount vehicle_claim; Vehicle claim amount car_brand; Car brand car_model; Car model production_year; Car production year

Task description: Based on abovementioned dataset prepare the following tasks: 1. Exploratory data analysis and feature engineering Conduct exploratory data analysis (i.a. missing values, descriptive statistics of characteristics and their distributions, etc.). Create new features that can be used to obtain additional information about the analyzed customer portfolio. Analyze the relationships between the features and generate appropriate visualizations. Based on the analyzes performed, select the variables that you will use to build the segmentation model and briefly justify your choice. 2. Building the segmentation model Using the selected variables (make relevant transformations if necessary), build a segmentation model using the K-means method. Briefly justify the choice of the optimal number of clusters, as well as the choice of optimal cluster initialization points. 3. Business analysis Describe the groups of insured persons selected on the basis of the model and interpret the statistics for individual segments and their business characteristics. Visualize the segments. 4. Anomaly detection* Using any anomaly detection algorithm, try to identify unusual claims/damage reports that may be an attempt to extort compensation from the insurance company. Indicate a maximum of 5 observations identified in this way and briefly justify your choice. Score to obtain: 10 points maximum, out of which: Correctness of results and interpretation: 3 (weight: x2) Programming (possibility of reproducing results, code readability, comments): 2 Aesthetics of work and completeness of materials: 1 Innovation of the proposed solution: 1 Possibility of obtaining additional 2 points for an extra task (*), i.e. max 12/10. Task submission: Please upload your solution to the MS Teams group. The solution should include: a. Text file with the solution to the task, analysis results, model parameter estimates and visualizations along with description and conclusions (.pdf). All authors should be listed at the very beginning of the report. b. Program code (SAS, R or Python) containing the definition of the SAS library (if applicable)/working directory and the libraries used (if applicable) at the beginning of the program code to enable reproduction of results after changing the working directory/input data path. c. A printout containing the default set of results obtained using the prepared program code.

Context

Cross-selling identifies products or services that satisfy additional, complementary needs that are unfulfilled by the original product that a customer possesses. As an example, a mouse could be cross-sold to a customer purchasing a keyboard. Oftentimes, cross-selling points users to products they would have purchased anyway; by showing them at the right time, a store ensures they make the sale. Cross-selling is prevalent in various domains and industries including banks. For example, credit cards are cross-sold to people registering a savings account. In e-commerce, cross-selling is often utilized on product pages, during the checkout process, and in lifecycle campaigns. It is a highly-effective tactic for generating repeat purchases, demonstrating the breadth of a catalog to customers. Cross-selling can alert users to products they didn't previously know you offered, further earning their confidence as the best retailer to satisfy a particular need.

Content

Your client is an Insurance company that has provided Health Insurance to its customers now they need your help in building a model to predict whether the policyholders (customers) from the past year will also be interested in Vehicle Insurance provided by the company.

An insurance policy is an arrangement by which a company undertakes to provide a guarantee of compensation for specified loss, damage, illness, or death in return for the payment of a specified premium. A premium is a sum of money that the customer needs to pay regularly to an insurance company for this guarantee.

For example, you may pay a premium of Rs. 5000 each year for a health insurance cover of Rs. 200,000/- so that if God forbid, you fall ill and need to be hospitalized in that year, the insurance provider company will bear the cost of hospitalization, etc. for up to Rs. 200,000. Now if you are wondering how can the company bear such high hospitalization cost when it charges a premium of only Rs. 5000/-, that is where the concept of probabilities comes in picture. For example, like you, there may be 100 customers who would be paying a premium of Rs. 5000 every year, but only a few of them (say 2-3) would get hospitalized that year and not everyone. This way everyone shares the risk of everyone else.

Just like medical insurance, there is vehicle insurance where every year customer needs to pay a premium of a certain amount to the insurance provider company so that in case of an unfortunate accident by the vehicle, the insurance provider company will provide compensation (called ‘sum assured’) to the customer.

Building a model to predict whether a customer would be interested in Vehicle Insurance is extremely helpful for the company because it can then accordingly plan its communication strategy to reach out to those customers and optimize its business model and revenue.

Now, in order to predict, whether the customer would be interested in Vehicle insurance, you have information about demographics (gender, age, region code type), Vehicles (Vehicle Age, Damage), Policy (Premium, sourcing channel), etc.

Acknowledgements

Thanks Analytics Vidhya for providing yet another exciting dataset for the weekend hackathon.