type: ML Classification

instruction: Use the dataset to make sentiment predictions on test.csv. Write the predicted labels ("Negative", "Positive") into result.csv as per the provided template.

hardness: Medium


type: ML Classification

instruction: Predict user response for test.csv using the Health Insurance Cross Sell Prediction dataset. Write the results into `submission.csv` following the format of `sample_submission.csv.`

hardness: Hard


type: ML Classification

instruction: Predict the sentiment categories for the texts in twitter_validation.csv. Save your predictions in prediction.csv using the provided template.

hardness: Easy


type: ML Classification

instruction: Predict whether passengers in test.csv were transported to an alternate dimension based on the Spaceship Titanic dataset. Save your predictions in submission.csv using the template from sample_submission.csv.

hardness: Easy


type: ML Classification

instruction: Predict the tumor diagnosis result (B or M) for the data in test.csv using the Breast Cancer Wisconsin (Diagnostic) Data Set. Save your predictions in label.csv with the column name 'result'.

hardness: Hard


type: ML Classification

instruction: Predict the authenticity of the news in validation.csv using the dataset containing real and fake news. Save your predictions in result.csv with the column name 'result'.

hardness: Easy


type: ML Classification

instruction: Predict whether customers will churn using the dataset in customer_churn_dataset-testing-master.csv. Save your predictions in result.csv with the column name 'result'.

hardness: Easy


type: ML Classification

instruction: Predict the loan approval status (Y or N) for the data in test.csv using the loan approval dataset. Save your predictions in result.csv with the column name 'Loan_Status'.

hardness: Medium


type: ML Classification

instruction: Predict passenger satisfaction in test.csv using the  dataset. Save the prediction results into result.csv with the column name 'satisfaction'.

hardness: Medium


type: ML Classification

instruction: Predict the likelihood of a heart attack for individuals in test.csv using the heart attack dataset. Save your predictions in result.csv using the provided template.

hardness: Easy


type: ML Classification

instruction: Predict whether individuals in Test.csv have an income above the income limit using the income prediction dataset. Save your predictions in results.csv with columns "ID" (user id) and "income_above_limit" (your predicted value).

hardness: Easy


type: ML Classification

instruction: Predict the sentiment of the comments in test.csv. Save your prediction results into sentiment.csv, using the column name 'emotion' to represent the sentiment.

hardness: Medium


type: ML Classification

instruction: Predict whether each turbine will fail based on the data provided in Test.csv. Save your predictions in target.csv with a single column labeled "Target", formatted according to the example in sample_target.csv.

hardness: Medium


type: ML Classification

instruction: Predict whether the users in test.csv will churn based on the customer churn prediction dataset. Save your predictions in churn.csv with the column name "Churn".

hardness: Easy


type: ML Classification

instruction: Predict the categories based on the Genetic Variant Classifications dataset using the information in test.csv. Save your predictions in class.csv with the column name "CLASS".

hardness: Medium


type: ML Classification

instruction: Predict whether credit card clients in test.csv will default based on the Credit Card Default dataset. Save your predictions in defaulter.csv with the column name "IsDefaulter".

hardness: Hard


type: ML Classification

instruction: Predict whether it will rain the next day for each row in test.csv using the Rain in Australia dataset. Save your predictions in tomorrow.csv with the column name "RainTomorrow".

hardness: Hard


type: ML Classification

instruction: Predict whether individuals in test.csv will have a stroke based on the Stroke Prediction dataset. Save your predictions in stroke.csv with the column name "stroke".

hardness: Medium


type: ML Classification

instruction: Predict whether mushrooms in test.csv are poisonous based on the Mushroom dataset. Save your predictions in class.csv with the column name "class".

hardness: Easy


type: ML Classification

instruction: Predict whether individuals in test.csv have heart disease based on the Cardiovascular Diseases Risk Prediction dataset. Save your predictions in disease.csv with the column name "Heart_Disease".

hardness: Medium


type: ML Classification

instruction: Predict whether individuals in test.csv have diabetes based on the Diabetes Prediction dataset. Save your predictions in diabetes.csv with the column name "diabetes".

hardness: Easy


type: ML Classification

instruction: Predict whether the company in test.csv will go bankrupt based on the Company Bankruptcy Prediction dataset. Save your predictions in bankrupt.csv with the column name "Bankrupt?".

hardness: Easy


type: ML Classification

instruction: You are tasked with designing a method to predict the Response variable using the Health Insurance Cross Sell dataset. Predict the responses for entries in test.csv and save the results in submission.csv, ensuring they follow the format provided in sample_submission.csv.

hardness: Medium


type: ML Classification

instruction: You are tasked with designing a method to predict reservation cancellations using the Hotel Reservations dataset. Predict whether users will cancel their reservations based on test.csv data and save the results in result.csv, ensuring they follow the format in sample_result.csv.

hardness: Medium


type: ML Classification

instruction: As a contestant in the Bank customer data churn prediction competition, you need to predict the data in test.csv according to the competition requirements. Write the results into submission.csv following the format specified in sample_submission.csv.

hardness: Hard


type: ML Classification

instruction: As a contestant in the health indicators and smoking status prediction competition, you need to design a method to predict the data in test.csv, incorporating the additional dataset provided, according to the competition requirements. Write the results into submission.csv following the format specified in sample_submission.csv.

hardness: Hard


type: ML Classification

instruction: As a contestant in the health features and medical condition prediction competition, you are tasked with predicting the data in test.csv using the provided datasets according to competition requirements. Please ensure your predictions are formatted and saved in submission.csv as specified in sample_submission.csv.

hardness: Hard


type: ML Classification

instruction: Predict defects in steel plates using the dataset provided. Your task is to design a method to predict test.csv based on competition guidelines. Save results in submission.csv as per sample_submission.csv format.

hardness: Medium


type: ML Classification

instruction: Develop a prediction model for cirrhosis outcomes using the patient data dataset. Your task is to predict test.csv according to competition rules. Ensure your predictions are saved in submission.csv format, matching the structure of sample_submission.csv.

hardness: Hard


type: ML Classification

instruction: Design a method to predict wine quality using the provided dataset. Your task is to generate predictions for test.csv based on competition guidelines. Save your results in submission.csv following the format specified in sample_submission.csv.

hardness: Hard


type: ML Classification

instruction: Predict machine failures in the dataset for Binary Classification competition. Your task is to develop a method to predict test.csv as per competition guidelines. Save your predictions in submission.csv in the format specified by sample_submission.csv.

hardness: Medium


type: ML Classification

instruction: Your task is to develop a method to detect AI-generated text using the dataset provided. Generate predictions for test_essays.csv based on competition rules. Format your results in submission.csv following the structure of sample_submission.csv.

hardness: Medium


type: ML Classification

instruction: Predict obesity risk using the provided dataset. Develop a method to predict test.csv according to competition guidelines. Save your predictions in submission.csv following the format specified in sample_submission.csv.

hardness: Easy


type: ML Classification

instruction: Predict health outcomes of horses using the provided dataset. Develop a method to predict test.csv according to competition guidelines. Save your predictions in submission.csv following the format specified in sample_submission.csv.

hardness: Hard


type: ML Classification

instruction: Classify human activities using the given dataset. Predict the activity categories for activity_test.csv. Write your predictions in activity.csv with the column name "Activity".

hardness: Easy


type: ML Classification

instruction: Predict the status and type of cars using the provided dataset. Use test.csv to generate your predictions. Save your results in result.csv with the column name "new&used".

hardness: Easy


type: ML Classification

instruction: Predict the experience level of job positions using the provided dataset. Use test.csv to generate your predictions. Save your results in result.csv with the column name "formatted_experience_level".

hardness: Hard


type: ML Classification

instruction: Predict the adaptability level of students in online education using the provided dataset. Use test.csv to generate your predictions. Save your results in level.csv with the column name "Adaptivity Level".

hardness: Easy


type: ML Classification

instruction: Classify emotions based on the provided NLP dataset. Predict the emotions for text data in test.txt. Write your predictions in emotions.csv with the column name "emotion".

hardness: Medium


type: ML Classification

instruction: Classify credit scores based on the provided dataset. Predict the credit scores for data in test.csv. Write your predictions inscore.csv with the column name "Credit_Score".

hardness: Medium


type: ML Classification

instruction: Predict fruit categories using the provided dataset. Use test.csv to generate your predictions. Save your results in class.csv with the column name "Class".

hardness: Easy


type: ML Classification

instruction: Predict the categories based on the text information in test.csv for the News Aggregator Dataset competition. Save the predicted results into a file named category.csv, with the column name "CATEGORY".

hardness: Hard


type: ML Classification

instruction: You are tasked with designing a method to predict body performance data using the Body Performance Data dataset. Predict the values for entries in test.csv and save the results in result.csv.

hardness: Medium


type: ML Classification

instruction: Develop a model to predict body performance using the provided dataset. Generate predictions for test.csv. Save your results in class.csv with the column name "class".

hardness: Medium


type: ML Classification

instruction: Classify the sentiment of tweets based on the provided dataset. Predict the sentiment for text data in Corona_NLP_test.csv. Write your predictions in sentiment.csv with the column name "Sentiment".

hardness: Hard


type: ML Classification

instruction: Predict the damage status of buildings using the earthquake-affected dataset. Use incomplete.csv to generate your predictions. Save the results in prediction.csv with the columns "building_id" and "damage_grade".

hardness: Medium


type: ML Clustering

instruction: Perform clustering analysis on annual income and spending scores using the provided dataset. Save the clustering results in cluster.csv with columns "Feature_i" (the ith value of the processed feature vector) and "Cluster" (the clustering label).

hardness: Easy


type: ML Clustering

instruction: Perform clustering on the wine chemical components dataset stored in wine-clustering.csv. Save the clustering results into result.csv, with columns 'Feature_i' (representing the ith value of the processed feature vector) and 'Cluster' (displaying the clustering labels).

hardness: Medium


type: ML Clustering

instruction: Cluster the New York car data based on the provided dataset. Write the clustering results in cluster.csv with columns "Feature_i" (the ith value in the feature vector) and "Cluster" (the cluster label).

hardness: Easy


type: ML Clustering

instruction: Cluster Facebook interactions based on the provided dataset. Write the clustering results in cluster.csv with columns "Feature_i" (the ith value in the feature vector) and "Cluster" (the cluster label).

hardness: Easy


type: ML Clustering

instruction: Perform clustering on the dataset blob_dataset.csv. Save the results in cluster_blob.csv with columns "Feature_i" (the ith value in the feature vector) and "Cluster" (the cluster label).

hardness: Medium


type: ML Clustering

instruction: Perform clustering on the Patient Dataset. Write the clustering results into cluster.csv. Ensure the column names are "Feature_i" (where i signifies the ith value in the feature vector) and "Cluster" (indicating the cluster label).

hardness: Hard


type: ML Clustering

instruction: Perform clustering on the dataset. Write the clustering results into cluster.csv with columns "Feature_i" (the ith value in the feature vector) and "Cluster" (the label for each cluster).

hardness: Medium


type: ML Clustering

instruction: Cluster user activity records from the real estate system dataset (property.csv) into 4 clusters. Save the clustering results in cluster.csv with columns "Feature_i" (i denotes the ith value in the feature vector) and "Cluster" (representing the cluster label).

hardness: Easy


type: ML Clustering

instruction: Perform a clustering task on the global countries dataset, dividing the data into 3 clusters. Save the results in cluster.csv with the column names "Feature_i" (where i represents the ith value in the feature vector) and "Cluster" (the cluster label).

hardness: Hard


type: ML Clustering

instruction: Perform a clustering task on the Forest Cover Type Dataset, dividing the data into an appropriate number of clusters. Save the clustering results in cluster.csv with the columns named "Feature_i" (where i indicates the ith value of the feature vector) and "Cluster" (the label of the cluster).

hardness: Hard


type: ML Clustering

instruction: Perform clustering on the Online Retail dataset to identify distinct groups. Output the clustering results into cluster.csv with columns "Feature_i" (the ith value in the feature vector) and "Cluster" (representing the assigned cluster label).

hardness: Easy


type: ML Clustering

instruction: Cluster the Customer Segmentation dataset to find distinct groups. Save the clustering results in cluster.csv with columns "Feature_i" (i indicating the ith value in the feature vector) and "Cluster" (showing the cluster label).

hardness: Easy


type: ML Clustering

instruction: Perform a clustering task on the dataset of socioeconomic and health factors for various countries, dividing the data into an appropriate number of clusters. Save the clustering results in a file named cluster.csv with the column names "Feature_i" (where i represents the ith value of the feature vector) and "Cluster" (the label of the cluster).

hardness: Hard


type: ML Clustering

instruction: Cluster the dataset of customers' personal and purchase data to discover distinct groups. Save the clustering outcomes in cluster.csv with columns "Feature_i" (where i denotes the ith value in the feature vector) and "Cluster" (representing the cluster label obtained from clustering).

hardness: Medium


type: ML Clustering

instruction: Analyze the Breast Cancer Proteomes dataset to perform a clustering task. Divide the data into suitable clusters and save the results in cluster.csv with columns "Feature_i" (where i denotes the ith value in the feature vector) and "Cluster"(indicating the assigned cluster label).

hardness: Easy


type: ML Clustering

instruction: Perform a clustering task on the Online Retail II UCI dataset to segment the customers into an appropriate number of clusters. Save the clustering results in a file named cluster.csv with the column names "Feature_i" (where i represents the ith value of the feature vector) and "Cluster" (the label of the cluster).

hardness: Hard


type: ML Clustering

instruction: Segment the bank customers in the provided dataset using clustering. Write the results to cluster.csv, including columns "Feature_i" (where i is the ith value in the feature vector) and "Cluster" (the resulting cluster label).

hardness: Easy


type: ML Clustering

instruction: Use the Online Retail dataset to perform clustering and divide customers into appropriate clusters. Save the results in cluster.csv with columns named "Feature_i" (i indicates the ith value of the feature vector) and "Cluster" (the cluster label).

hardness: Easy


type: ML Clustering

instruction: Cluster different types of customer groups from the Online Retail II Data Set dataset (using data from 2009-2010) into an appropriate number of clusters. Save the clustering results in a file named cluster.csv with the column names "Feature_i" (where i represents the ith value of the feature vector) and "Cluster" (the label of the cluster).

hardness: Hard


type: ML Clustering

instruction: Perform clustering on the Iris Flower Dataset. Write the results to cluster.csv with columns "Feature_i" (where i represents the ith value of the feature vector) and "Cluster" (the cluster label).

hardness: Easy


type: ML Clustering

instruction: You are tasked with designing a clustering method using the Market Segmentation dataset. Cluster the users and write the results into cluster.csv, with the column for cluster labels named 'Cluster' and the feature vector columns named 'Feature_i'.

hardness: Medium


type: ML Regression

instruction: Based on the flight booking information dataset, predict the prices for the flights in test.csv. Save the predictions in result.csv with the column name 'price'.

hardness: Easy


type: ML Regression

instruction: Predict electricity prices in test.csv based on your dataset on Spanish electricity and weather. Save your prediction results in result.csv, using the column name "price actual".

hardness: Hard


type: ML Regression

instruction: Predict the weekly sales using the Walmart sales dataset. Save the prediction results in submission.csv with the column name "Weekly_Sales".

hardness: Medium


type: ML Regression

instruction: Predict the popularity of the songs in test.csv using the pop music dataset. Save the prediction results in popularity.csv with the column name "Popularity".

hardness: Hard


type: ML Regression

instruction: Using the income prediction dataset, predict the incomes of individuals in test.csv. Save the results in price.csv with the column name "price".

hardness: Medium


type: ML Regression

instruction: Using the Lego sets and price dataset, predict the star ratings for the entries in test.csv. Save the predictions to result.csv with the column name "Star rating".

hardness: Hard


type: ML Regression

instruction: Based on the Eurostat dataset, predict the average job satisfaction for each entry in test.csv. Write the results to job_satisfaction.csv with the column name "AVG_JOB_SATISFACTION".

hardness: Medium


type: ML Regression

instruction: Predict the product sales based on the shoe product information in the test.csv. Write the prediction results into quantity.csv with the column name "quantity_sold" using the data from test.csv.

hardness: Hard


type: ML Regression

instruction: Based on the Airbnb listing dataset for Barcelona, predict the prices of the entries in test.csv. Write the predictions to price.csv with the column name "price".

hardness: Easy


type: ML Regression

instruction: Utilize the comprehensive drug ratings dataset to predict the usefulness of drugs listed in drugsComTest_raw.csv. Write the predictions into "Usefulness.csv" with the column name "usefulness".

hardness: Hard


type: ML Regression

instruction: Utilize the biogas dataset to forecast the Biogas Generation Estimate (cu-ft/day) for entries in test.csv. Save the predictions as 'biogas_generation_estimate_cuftday' in result.csv.

hardness: Hard


type: ML Regression

instruction: Based on the dataset of vehicle information in New York, predict the Mileage for vehicles in test.csv. Write the results into result.csv under the column name "Mileage".

hardness: Hard


type: ML Regression

instruction: Predict the prices of vehicles in test.csv using the provided Vehicle dataset. Save the predictions in price.csv with the column name "Selling_Price".

hardness: Medium


type: ML Regression

instruction: Predict the car prices in test.csv using the Car Prices Poland dataset. Save the predicted prices in price.csv with the column name "price".

hardness: Hard


type: ML Regression

instruction: Predict the appliance energy consumption using the Appliances Energy Prediction dataset based on test.csv. Save the predictions in appliance.csv with the column name "Appliances".

hardness: Hard


type: ML Regression

instruction: Using the Airfare ML dataset, forecast the flight fares for the entries in test.csv. Save your predictions in fare.csv under the column name "Fare".

hardness: Easy


type: ML Regression

instruction: Based on the Steam Releases dataset, determine the ratings for the games in test.csv. Record your predictions in rating.csv, with the column named "rating".

hardness: Easy


type: ML Regression

instruction: Predict food orders using the Food Demand Forecasting dataset based on the information in test.csv. Write the predictions to submission.csv following the format of sample_submission.csv.

hardness: Medium


type: ML Regression

instruction: Based on the sensor data in test.csv from the Electric Motor Temperature dataset, determine the rotor temperature. Record your predictions in pm.csv with the column name "pm".

hardness: Medium


type: ML Regression

instruction: Predict the required delivery time using the Food Delivery Dataset based on the information in test.csv. Write the results into submission.csv following the format of sample_submission.csv.

hardness: Medium


type: ML Regression

instruction: Using the Used Cars Price Prediction dataset, predict the prices of vehicles based on the information in test-data.csv. Write the predictions into price.csv with the column name "price."

hardness: Easy


type: ML Regression

instruction: You are tasked with designing a method to predict vehicle sales using the Vehicle Sales Data dataset. Predict the sales for entries in test.csv and fill  the results into result.csv.

hardness: Medium


type: ML Regression

instruction: Design a method to predict the median house value using the California Housing Prices dataset. Predict the values for test.csv and fill the results in result.csv.

hardness: Hard


type: ML Regression

instruction: Perform house price predictions and record the results in submission.csv. Follow the format specified in sample_submission.csv.

hardness: Medium


type: ML Regression

instruction: Participate in a flood prediction competition using the provided dataset. Your task is to develop a predictive model to forecast the likelihood of floods in test.csv. Ensure your predictions are saved in submission.csv, following the format outlined in the sample_submission.csv template.

hardness: Hard


type: ML Regression

instruction: Design a solution for the abalone dataset competition described in README.md. Your task is to develop a predictive model and submit the prediction results into submission.csv, adhering to the format specified in sample_submission.csv.

hardness: Hard


type: ML Regression

instruction: Participate in the crab age prediction competition and leverage the additional dataset of crab ages to improve your method. Develop and refine your approach to achieve higher accuracy in predicting crab ages. Ensure your prediction results are saved in submission.csv, formatted according to 'sample_submission.csv'.

hardness: Hard


type: ML Regression

instruction: Participate in the wild blueberry yield prediction competition and leverage the additional dataset to improve your prediction method. Design and optimize your approach to achieve accurate yield predictions. Ensure your prediction results are saved in submission.csv, formatted according to "sample_submission.csv".

hardness: Medium


type: ML Regression

instruction: Design your method to predict CO2 emissions in the competition described in README.md. As a contestant, optimize your approach to accurately predict the data in test.csv. Submit your prediction results into submission.csv, following the format specified in sample_submission.csv.

hardness: Easy


type: ML Regression

instruction: Participate in the sales volume prediction competition and formulate a strategy to predict the data provided in test.csv. Your goal as a contestant is to optimize your prediction method and output the results into submission.csv, adhering to the format outlined in sample_submission.csv.

hardness: Hard


type: ML Regression

instruction: Join the competition focusing on U.S. county-level monthly microbusiness data. Your task as a participant is to create a predictive model for the data in test.csv. Optimize your approach to achieve accurate predictions and format the results into submission.csv following the structure outlined in sample_submission.csv.

hardness: Medium


type: ML Regression

instruction: Participate in the Mineral properties data for hardness prediction competition. Design and optimize a method to predict the data from test.csv and submit your results in submission.csv. Follow the specified format outlined in sample_submission.csv to ensure your predictions meet the competition's requirements.

hardness: Easy


type: ML Regression

instruction: Generate predictions for the data in test.csv as part of the Automated Essay Scoring competition. Save your results in a submission file named submission.csv, following the format specified in sample_submission.csv.

hardness: Hard