Data Science
Predicting House Prices Based on Size
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The dataset contains housing prices in a city. The task is to predict the price of a new house in the city, given its size (in square feet).
Determining the Best Function to Approximate a Set of Training Data
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The problem is to find the best function to approximate a given set of training data. This is an important task in machine learning and data science, as it can help improve the accuracy of predictions . There are many ways to approach this problem, and it is often difficult to determine the best function to use. However, there are some methods that can be used to find a good approximation.
Predicting Housing Prices
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The goal is to predict the price of a new house given its features. The dataset contains information on housing prices, so we can use this to train a machine learning model. Once the model is trained, we can use it to predict the price of a new house.
Price Prediction for New Houses in a City
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The goal is to predict the price of a new house in a city, given a dataset of housing prices in the city. This is a supervised learning problem, where the dataset is used to train a model that can make predictions about new houses.
Implementing K-Nearest Neighbor from scratch in Python.
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This article explains how to implement a K-Nearest Neighbor algorithm in python from scratch without using any machine learning libraries.
Identifying Outliers in a Dataset
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To identify outliers in a dataset, you can use a variety of methods, such as visual inspection, statistical tests, or machine learning algorithms.
Supervised Learning with a Large Number of Features
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The best approach to take when working with a large number of features in a supervised learning problem is to use a random forest. This will allow you to build a model that can accurately predict a binary outcome.
Predicting Housing Prices with Machine Learning
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A machine learning model can be used to predict the sale price of a new home based on a set of features.
