Machine Learning
Averaging Numbers
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This program will take in a list of numbers and output the average.
K-Nearest Neighbors Classifier
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A k-nearest neighbors classifier is a machine learning algorithm that can be used to classify data points into k categories. The algorithm works by taking a set of points in d-dimensional space and finding the k nearest neighbors to each point. The points are then classified into k categories based on the categories of their nearest neighbors.
Binary Classification with Machine Learning
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This problem is about training a machine learning algorithm to do binary classification, i.e. to predict whether a new data point belongs to one class or the other. The data for training is given as a set of pairs (x, y), where x is a feature vector and y is the corresponding label (0 or 1). The goal is to design an algorithm that can learn from this data and correctly classify new data points.
Find the Median Value in an Array of Integers
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This technical problem deals with finding the median value in an array of integers. The proposed solution is to design a function that takes in an array of integers and returns the median value.
Predicting Customer Purchases
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The goal is to predict whether a customer will make a purchase in the next month, based on their name, gender, age, and purchase history. A machine learning model will be trained on customer records to make these predictions.
Feature Selection for Machine Learning
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A machine learning algorithm is used to identify which features in a dataset are most predictive of the target variable. This can be used to reduce the dimensionality of the data and improve the performance of the machine learning models.
Predicting Loan Default
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This problem asks for a machine learning model that can predict whether or not a loan will default.
Predicting E-Commerce Customer Purchases
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The goal is to predict which items will be purchased by a customer given their ID, using a large dataset of e-commerce customer data. Each customer has an ID, name, and a list of items they have purchased. Some customers have purchased the same item multiple times.
