• Background: Solar radiation is an important source for electricity generation. For effective utilization, it is important to precisely know the irradiance amount at different time horizons: minutes, hours, and days. Depending on the horizon, two main classes of methods can be used to forecast the solar radiation: statistical time series forecasting methods for short to midterm horizons and numerical weather prediction methods for medium- to long-term horizons. • Objective: To forecast the next day solar irradiance (measured in W/m2) values using ClimaCell API data (6-hours per day) and real weather station data from a solar plant. 1
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The task is to predict whether individual tracks encountered in a listening session will be skipped by a particular user. In order to do this, complete information about the first half of a user’s listening session is provided, while the prediction is to be carried out on the second half. Participants have access to metadata, as well as acoustic descriptors, for all the tracks encountered in listening sessions. The output of a prediction is a binary variable for each track in the second half of the session indicating if it was skipped or not, with a 1 indicating that the track skipped, and a 0 indicating that the track was not skipped. 1
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The Ames Housing dataset was compiled by Dean De Cock for use in data science education. It's an incredible alternative for data scientists looking for a modernized and expanded version of the often-cited Boston Housing dataset. With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, here, I tried to predict the final price of each home. 1
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The director of SZE bank identified that going through the loan applications to filter the people who can be granted loans or need to be rejected is a tedious and time-consuming process. The idea behind this ML project is to build an ML model and web application that the bank can use to classify if a user can be granted a loan or not. 1
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On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for everyone on board, resulting in the death of 1502 out of 2224 passengers and crew. While there was some element of luck involved in surviving, it seems some groups of people were more likely to survive than others. The aim here is to predict if a person will survive or not based on the input features. 0
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Here is the data set: https://www.kaggle.com/iamaniket/suv-data. It contains a number of features and we have to predict if a customer with a given set of features will purchase the SUV or not. Here I have used multiple algorithms to compare the accuracy output of each algorithm and found that SVM provides the highest accuracy. 0
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The main motive In this challenge is to identify which customers will make a specific transaction in the future, irrespective of the amount of money transacted based on various input features 0
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MachineHack Retail price forecasting hackathon. The main aim was to apply EDA and Feature engineering and make use of the best possible ML model to predict the retail price based on input features. 0
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Iris is the family in the flower which contains several species such as the Setosa, Versicolor, Virginia, etc. The main aim is to Predict the iris variety using flower features. 0
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The dataset is related to red and white variants of the Portuguese "Vinho Verde" wine. Due to privacy and logistic issues, only physicochemical (inputs) and sensory (the output) variables are available (e.g. there is no data about grape types, wine brand, wine selling price, etc.). 0
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Every year a lot of companies hire a number of employees. The companies invest time and money in training those employees, not just this but there are training programs within the companies for their existing employees as well. The aim of these programs is to increase the effectiveness of their employees. But where HR Analytics fit in this? and is it just about improving the performance of employees? 0
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