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<!DOCTYPE html>
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<h1 class="breadcrumbs-title mb-17 sm-mb-10">Enigma</h1>
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<div class="sub-title primary-color">Contest Details</div>
<p class="desc2 mb-20">
<br><strong>Assessment Criteria:</strong>
<br>Each team's data handling, visualization, manipulation along with their ability to understand and analyse the data correctly to apply the most suitable algorithms on the given data is put to test.<br> • <strong>Visualizations and Illustrations:</strong> “One picture can speak a thousand words”<br> • <strong>Insights you have drawn from your analysis:</strong> "Insights" are clues or patterns in information to help you understand the data better and make informed choices.<br> •
<strong> Conclusive, informed,
data-driven decision making at scale.</strong>
</p>
<div class="sub-title primary-color">Task 1</div>
<p class="desc2 mb-20">
<strong>Problem Statement:</strong><br>The Australian city of Melbourne is experiencing a surge in real estate prices, making it difficult for potential buyers to find reasonably priced 2-bedroom units. In order to gain an edge
in the competitive real estate market and predict the next big trend, a hackathon challenge has been launched. The challenge requires participants to analyze data related to real estate prices and trends in Melbourne, and provide
insights and predictions that can help potential buyers to make informed decisions. The challenge will test the participants' skills in data analysis, visualization, and prediction, and will help them to gain valuable experience
in the field of real estate analytics.
<br><strong>Data Description:</strong>
<br>• <strong>'Id'</strong>: A unique identifier for each property in the dataset.
<br>• <strong>'Location'</strong>: The general area or neighborhood where the property is located.
<br>• <strong>'Area'</strong>: The size of the property in square meters.
<br>• <strong>'Region'</strong>: The region where the property is located.
<br>• <strong>'Number_of_Rooms'</strong>: The number of rooms in the property.
<br>• <strong>'House_Type'</strong>: The type of property (e.g. apartment, house, villa, etc.).
<br>• <strong>'House_Price'</strong>: The price of the property in the local currency.
<br>• <strong>'isSold'</strong>: A binary column indicating whether the property has been sold or not.
<br>• <strong>'Sold_Date'</strong>: The date when the property was sold (if applicable).
<br>• <strong>'Distance_from_City'</strong>: The distance from the property to the city center in kilometers.
<br>• <strong>'Seller'</strong>: The person or company selling the property.
<br>• <strong>'PostalCode'</strong>: The postal code of the property.
<br>• <strong>'Number_of_Bedrooms'</strong>: The number of bedrooms in the property.
<br>• <strong>'Number_of_Bathrooms'</strong>: The number of bathrooms in the property.
<br>• <strong>'Number_of_CarParkings'</strong>: The number of car parkings available with the property.
<br>• <strong>'Area_of_Land_inMeters'</strong>: The total area of land in square meters.
<br>• <strong>'Height_of_Building_inMeters'</strong>: The height of the building in meters.
<br>• <strong>'Year_Built'</strong>: The year when the property was built.
<br>• <strong>'Latitude'</strong>: The latitude coordinate of the property.
<br>• <strong>'Longitude'</strong>: The longitude coordinate of the property.
<br><strong>Task Description:</strong>
<br>The objective of this task is to successfully develop a machine learning model which will help real estate agents, buyers and sellers to make informed decisions based on the predicted sale price of a property. Additionally,
it can be applied to improve residential property price strategies and support the assessment procedure. The goal is to develop a model that can accurately predict the target you have assumed based on its features, and to identify
the key factors that contribute to the sale price.
<!-- <br><strong>Assessment Criteria:</strong>
<br>Each team's data handling, visualization, manipulation along with their ability to understand and analyse the data correctly to apply the most suitable algorithms on the given data is put to test.<br> • <strong>Visualizations and Illustrations:</strong> “One picture can speak a thousand words”<br> • <strong>Insights you have drawn from your analysis:</strong> "Insights" are clues or patterns in information to help you understand the data better and make informed choices.<br> •
<strong> Conclusive, informed,
data-driven decision making at scale.</strong> -->
</p>
<div class=" responsive-div ">
<a href="task1.tsv" class="btn ">Data for Task 1 is here</a>
</div>
<div class=" responsive-div ">
<a href="https://forms.gle/VKEYg9J6aZTVVcsF6" class="btn ">Submit Solution for Task 1 here</a>
</div>
<div class="sub-title primary-color ">Task 2</div>
<p class="desc2 mb-20 ">
<strong>Problem Statement:</strong>
<br>Mental health is an important issue that affects individuals and workplaces worldwide. A major organization conducted a survey to measure attitudes towards mental health and the frequency of mental health disorders in the world.
Participants were asked a variety of questions related to their mental health, peer-to-peer engagement and work environment. In order to gain insights into the current state of mental health and spread awareness about it, a dataset
such as this has been put forth.
<br><strong>Data Description:</strong>
<br>• <strong>'have_youMentalHealthBenefits'</strong>: A binary column indicating whether the respondent has mental health benefits provided by their employer or not.
<br>• <strong>'Country'</strong>: The country where the respondent is located.
<br>• <strong>'Mental_Consequences'</strong>: A binary column indicating whether the respondent believes their mental health condition would have negative consequences on their work.
<br>• <strong>'isCompany_Technical'</strong>: A binary column indicating whether the respondent's employer is in the tech industry or not.
<br>• <strong>'discuss_Family'</strong>: A binary column indicating whether the respondent is comfortable discussing their mental health condition with family members.
<br>• <strong>'isConsequenceSerious'</strong>: A binary column indicating whether the respondent’s mental health condition would have serious consequences on their work/life.
<br>• <strong>'Age'</strong>: The age of the respondent.
<br>• <strong>'isSelfEmployed'</strong>: A binary column indicating whether the respondent is self-employed or not.
<br>• <strong>have_you_PhysicalHealthConversations'</strong>: A binary column indicating whether the respondent is comfortable discussing their physical health condition with their peers.
<br>• <strong>'isAnonymous'</strong>: A binary column indicating whether the respondent feels comfortable with the anonymity of seeking help for a mental health condition.
<br>• <strong>'have_you_MentalHealthConversations'</strong>: A binary column indicating whether the respondent is comfortable discussing their mental health condition with their peers.
<br>• <strong>'have_you_seeked_MentalHealthHelp'</strong>: A binary column indicating whether the respondent has sought help for a mental health condition or not.
<br>• <strong>'feel_Recovering'</strong>: A categorical column indicating how the respondent felt recovering from their condition.
<br>• <strong>'isPart_of_MentalHealthProgram'</strong>: A binary column indicating whether the respondent has been a part of a mental health program or not.
<br>• <strong>'know_you_MentalCareOptions'</strong>: A binary column indicating whether the respondent knows the mental health care options available to them.
<br>• <strong>'isMental_asGoodas_Physical'</strong>: A binary column indicating whether the respondent believes that mental health is given the same importance as physical health.
<br>• <strong>'isMentalIllness_family'</strong>: A binary column indicating whether the respondent has a family history of mental illness or not.
<br>• <strong>'discuss_Peers'</strong>: A categorical column indicating whether the respondent is comfortable discussing their mental health condition with coworkers.
<br>• <strong>'iswork_disturbed'</strong>: A binary column indicating the extent to which a mental health condition has interfered with the respondent's work.
<br>• <strong>'Physical_Consequences'</strong>: A binary column indicating whether the respondent believes discussing a physical health condition would have negative consequences amongst their peer group.
<br>• <strong>'istreatment_required'</strong>: A binary column indicating whether the respondent believes that they require treatment for a mental health condition or not.
<br>• <strong>'Additional_Data'</strong>: An open-ended text column where the respondent can provide additional comments or feedback.
<br>• <strong>'isWork_Remote'</strong>: A binary column indicating whether the respondent has the option to work remotely or not.
<br>• <strong>'Approx_Acquaintances'</strong>: A categorical column indicating the number of acquaintances the respondent has.
<br>• <strong>'Gender'</strong>: The gender of the respondent.
<br><strong>Task Description:</strong>
<br>The objective of this task is to build a machine learning model using the given SQL file of mental health to predict the likelihood of an employee experiencing a mental health issue. The dataset contains information about the
demographics of people, their peer-to-peer interactions and work environment, and their attitudes towards mental health, as well as whether they have experienced a mental health issue in the past. Making use of these attributes
build a classical ML model that can predict the value of the variable that you think is the target.
</p>
<div class="responsive-div ">
<a href="task2.sql " class="btn ">Data for Task 2 is here</a>
</div>
<div class=" responsive-div ">
<a href="https://forms.gle/m1ZnYtQ7attDQYqj9" class="btn ">Submit Solution for Task 2 here</a>
</div>
<div class="sub-title primary-color ">Task 3</div>
<p class="desc2 mb-20 ">
<strong>Problem Statement:</strong>
<br>This challenge has been launched to help farmers in a particular region of the world optimize their irrigation systems based on weather data. Participants are required to analyze the provided weather data and identify patterns
and correlations between the different weather variables and the amount of rainfall. The challenge also requires participants to predict whether it will rain or not on the following day based on the weather observations. The goal
is to provide farmers with accurate and reliable information that they can use to optimize their irrigation systems and improve their crop yield.
<br><strong>Data Description:</strong>
<br>• <strong>'Date'</strong>: The date of the weather observations.
<br>• <strong>'Location'</strong>': The location of the weather station.
<br>• <strong>'minimum_temperature'</strong>: The minimum temperature recorded in degrees Celsius.
<br>• <strong>'maximum_temperature'</strong>: The maximum temperature recorded in degrees Celsius.
<br>• <strong>'Rainfall'</strong>: The amount of rainfall recorded in millimeters.
<br>• <strong>'Evaporation'</strong>: The amount of water evaporated in millimeters.
<br>• <strong>'Sunshine'</strong>: The number of hours of sunshine recorded.
<br>• <strong>'wind_gust_direction'</strong>: The direction of the wind gusts.
<br>• <strong>'wind_gust_speed'</strong>: The speed of the wind gusts.
<br>• <strong>'wind_direction_morning'</strong>: The direction of the wind in the morning.
<br>• <strong>'wind_direction_afternoon'</strong>: The direction of the wind in the afternoon.
<br>• <strong>'wind_speed_morning'</strong>: The speed of the wind in the morning.
<br>• <strong>'wind_speed_afternoon'</strong>: The speed of the wind in the afternoon.
<br>• <strong>'humidity_morning'</strong>: The relative humidity in the morning.
<br>• <strong>'humidity_afternoon'</strong>: The relative humidity in the afternoon.
<br>• <strong>'pressure_morning'</strong>: The atmospheric pressure in the morning.
<br>• <strong>'pressure_afternoon'</strong>: The atmospheric pressure in the afternoon.
<br>• <strong>'clouds_morning'</strong>: The cloud cover in the morning.
<br>• <strong>'cloud_afternoon'</strong>: The cloud cover in the afternoon.
<br>• <strong>'temperature_morning'</strong>: The temperature in the morning in degrees Celsius.
<br>• <strong>'temperature_afternoon'</strong>: The temperature in the afternoon in degree Celsius.
<br>• <strong>'rainfall_today'</strong>: The amount of rainfall recorded on the current day.
<br>• <strong>'rain_tomorrow'</strong>: Whether it rained or not on the following day.
<br><strong>Task Description:</strong>
<br>The objective of this task is to build a machine learning model which will enable farmers, weather forecasters, and water resource management authorities to make informed decisions based on the predicted amount of rainfall
in a particular region. It can also help to optimize crop yields, manage water resources, and prevent natural disasters caused by heavy rainfall.
</p>
<div class="responsive-div ">
<a href="task3.parquet" class="btn ">Data for Task 3 is here</a>
</div>
<div class=" responsive-div ">
<a href="https://forms.gle/HB66pNzNk6jYcRgi9" class="btn ">Submit Solution for Task 3 here</a>
</div>
<div class="sub-title primary-color ">Wild Card 1</div>
<p class="desc2 mb-20 ">
<strong>Problem Statement:</strong>
<br>Sales, a crucial factor in the world of business, has the power to make or break a company. The ability to accurately predict sales is a game-changer, which not only will enable the businesses to optimize their inventory and
pricing strategies to increase their overall revenue but also, allowing them to make informed decisions about inventory, marketing, and overall strategy. With the help of machine learning, sales prediction has become more accurate
than ever before, revolutionizing the way businesses operate and paving the way for a more efficient and successful market. The goal of this project is to predict the sales of items sold at various outlets based on a set of input
features.
<br><strong>Data Description:</strong>
<br>• <strong>'Identifier_of_Item'</strong>: unique identifier for each item sold
<br>• <strong>'Weight_of_item'</strong>': weight of the item in grams
<br>• <strong>'Fat_content_of_item'</strong>: whether the item is low fat or regular
<br>• <strong>'Visibility_of_Item'</strong>: percentage of total display area of all products in a store allocated to the particular item
<br>• <strong>'Item_Type'</strong>: the category of the item
<br>• <strong>'MRP'</strong>: maximum retail price of the item
<br>• <strong>'Identifier_of_outlet'</strong>: unique identifier for each outlet
<br>• <strong>'Year_of_Establishment'</strong>: the year in which the outlet was established
<br>• <strong>'Size_of_outlet'</strong>: the size of the outlet in terms of ground area covered
<br>• <strong>'Location_type'</strong>: the type of location in which the outlet is situated (e.g. urban, rural)
<br>• <strong>'Outlet_Type'</strong>: the type of outlet (e.g. supermarket, grocery store)
<br>• <strong>'Sales_of_Item_Outlet'</strong>: the sales of the particular item in the particular outlet
<br><strong>Task Description:</strong>
<br>The task of this project is to build a machine learning model to predict the sales of items sold at various outlets, based on the provided features. This will involve data preprocessing, exploratory data analysis, feature engineering,
model selection and evaluation, and finally deploying the model for use in real-world applications. The user's goals in this project will be to accurately predict sales in order to optimize inventory and pricing strategies for
each outlet, and thereby increase overall revenue.
</p>
<div class="responsive-div ">
<a href="wild1.csv" class="btn ">Data for Wild Card 1 is here</a>
<a href="https://docs.google.com/forms/d/e/1FAIpQLSf06gcZCrA9CynxzIzwgO1S63e-CJlrqx1JrCQOKh9Z2jL5-g/viewform" class="btn ">Submit Solution for Wild 1 here</a>
</div>
<div class="sub-title primary-color ">Wild Card 2</div>
<p class="desc2 mb-20 ">
<strong>Problem Statement:</strong>
<br>Salaries are a decider in attracting and retaining talented employees, and determining fair compensation is a key concern for organizations across the world. Machine learning-based salary prediction models can help businesses
make informed decisions about employee compensation, taking into account factors such as age, gender, education, occupation, and work experience. By leveraging predictive analytics to estimate salaries, organizations can ensure
that they are offering competitive compensation, while also avoiding issues related to pay discrimination and turnover.
<br><strong>Data Description:</strong>
<br>• <strong>'workers_country'</strong>: The country where the worker belongs to
<br>• <strong>'Worker_Age'</strong>': The age of the worker
<br>• <strong>'worker_martial_status'</strong>: The marital status of the worker
<br>• <strong>'Type_of_work'</strong>: The type of work that the worker does
<br>• <strong>'final_weight'</strong>: The final weight of the observation
<br>• <strong>'worker_hours_per_week'</strong>: The number of hours the worker works per week
<br>• <strong>'gain_of_capital'</strong>: The capital gains of the worker
<br>• <strong>'loss_of_capital'</strong>: The capital losses of the worker
<br>• <strong>'Workers_salary'</strong>: The salary of the worker
<br>• <strong>'worker_gender'</strong>: The gender of the worker
<br>• <strong>'worker_relationship'</strong>: The relationship of the worker in the family
<br>• <strong>'worker_race'</strong>: The race of the worker
<br>• <strong>'education_num'</strong>: The number of years of education of the worker
<br>• <strong>'Worker_Education'</strong>: The level of education of the worker
<br>• <strong>'worker_occupation'</strong>: The occupation of the worker
<br><strong>Task Description:</strong>
<br>The goal of this project is to develop a machine learning model that can predict if a worker's salary is more than $50K or less, based on their demographic, work, and education-related features. The model will help to understand
the factors that affect a worker's earnings and provide insights for policymakers and organizations to improve the economic situation of the workers. The user needs to preprocess the data, perform exploratory data analysis, and
build a predictive model using appropriate machine learning algorithms.
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