- a) Create a XOR dataset and plot
b) Create a linear perception model, fit the dataset you create and Plot the decision region
c) Create a Random Forest model, fit the dataset you create and Plot the decision region
e) Create a MLPClassifier model, fit the dataset you create and Plot the decision region. use: hidden_layer_sizes=(50,50 ). Hint: "from sklearn.neural_network import MLPClassifier" Also check the website:https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html
- a) Read the California Housing dataset from: https:// download.mlcc.google.com/mledu-datasets/california_housing_train.csv
b) Sort by the 'median_house_value' column. Assign the ones above average to be y = 1 and the ones below the average to be y = 0. This is a supervised classification task.
c) Loop thru classifiers and determine which is the best by accuracy use 5-fold cross validation with stratisfied sampling. Try to increase your accuracy with any feature engineering techniques/ideas. Can you use the lat/long to bring in any other interesting data? Do it if you have time.
d) Plot the decision regions.
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