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nstructions to explore this dataset are: 1 . Data Acquisition and Initial Analysis: Retrieve the MNIST dataset. Perform exploratory data analysis to understand the dataset's
nstructions to explore this dataset are:
Data Acquisition and Initial Analysis:
Retrieve the MNIST dataset.
Perform exploratory data analysis to understand the dataset's structure, including
i how many images
ii how many features and the range of feature values eg histogram of the data value
relating it to realworld, such as real images.
iii. how many categorieslabels discrete or continuous type and what they are?
iv visualize at least three randomly selected samples within each category feel the variance
of the data
v visualize more data samples to see whether there are bad data samples need to be
removed. What bad data samples do you think can be
Data Preparation and Manipulation:
Apply dimensionality reduction techniques PCA and tSNE to the MNIST dataset and visualize the
results.
Split the dataset into training samples and testing samples sets.
Machine Learning Model Implementation:
Train a Random Forest classifier on the original dataset and record its performance.
Use PCA to reduce the datasets dimensionality to Train a new Random Forest classifier on the
reduced dataset and see how long it takes. Was training much faster? Then, evaluate the classifier on
the test set. How does it compare to the previous classifier?
Critical Evaluation and Conclusion:
Provide a comprehensive evaluation of the performance of the models.
Summarize findings and insights
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