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This assignment requires applying advanced classification algorithms to the MNIST dataset and conducting an in - depth regression analysis on the California housing dataset. Each

This assignment requires applying advanced classification algorithms to the MNIST dataset and conducting an in-
depth regression analysis on the California housing dataset. Each task must demonstrate not only technical
proficiency but also strategic thinking and ethical considerations.
Tasks
1. Advanced Dataset Preparation
Split the MNIST dataset into training and testing sets using scikit-learn functions or your own
custom function. Include detailed comments to explain your process.
Conditions:
Use your first name for the training set and your last name for the testing set variable
names. For instance, If your name is john doe, use john_train and doe_test as your
variable names.
Use the last two digits of your student ID as the random_state for any function that requires
it. For instance, the value for the random_state =[Last two digits of your student ID]
Utilize advanced preprocessing techniques to enhance model performance, such as feature scaling
and dimensionality reduction where appropriate.
2. Advanced kNN Classifier
Set k=10 and utilize the kNN classifier from scikit-learn, providing a detailed explanation of the
function parameters and their implications.
Evaluate the model using advanced metrics. Discuss the rationale behind choosing specific metrics
and their implications on the model evaluation.
Conduct an in-depth analysis of varying k values, including a statistical test to determine if changes
in performance are significant.
3. SVM Classifier with Parameter Optimization
Apply an SVM classifier using both linear and non-linear kernels. Experiment with feature
engineering techniques to improve model accuracy.
Discuss the kernel trick and its impact on the computational complexity and performance of the
model.

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