Machine Learning
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.
Selecting a model for accurate prediction
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There are many ways to select a model for accurate prediction, but some common methods include using a cross-validation set or a hold-out set. You can also use a variety of metrics to evaluate the performance of your model, such as accuracy, precision, recall, and F1 score.
Determining Clusters and Outliers in a Set of Data
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Given a set of data points, this problem deals with determining which points are clustered together and which points are outliers. Outliers are points that are not close to other points in the data set.
Handling Missing Data in Machine Learning
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There are a few different ways to handle missing data when training a machine learning model, including imputation, deletion, and model-based methods.
Testing the accuracy of your spam email predictive model
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To test the accuracy of your spam email predictive model, you can use a holdout dataset or cross-validation. With a holdout dataset, you would split your data into a training set and a test set. You would train your model on the training set and then evaluate it on the test set. With cross-validation, you would split your data into a number of folds . For each fold, you would train your model on the training data and then evaluate it on the test data. You can then average the accuracy of your model across all the folds.
Email Spam Classification
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The goal is to develop a machine learning algorithm that can take in a set of emails and correctly classify them as spam or not spam.
Designing a machine learning algorithm to predict customer purchase behavior.
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This technical problem deals with designing a machine learning algorithm that can predict whether or not a customer will purchase a product, given their past purchase history.
Identifying faces in images using machine learning
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The goal is to train a machine learning model to identify faces in new images, given a dataset of images of faces that have been labeled with the name of the person in the image.
