Introduction
Python for data mining
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What you should know
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Exercise files
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1. Preliminaries
Tools for data mining
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The CRISP-DM data mining model
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Privacy, copyright, and bias
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Validating results
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2. Dimensionality Reduction
Dimensionality reduction overview
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Handwritten digits dataset
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PCA
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LDA
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t-SNE
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Challenge: PCA
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Solution: PCA
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3. Clustering
Clustering overview
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Penguin dataset
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Hierarchical clustering
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K-means
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DBSCAN
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Challenge: K-means
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Solution: K-means
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4. Classification
Classification overview
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Spambase dataset
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KNN
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Naive Bayes
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Decision trees
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Challenge: KNN
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Solution: KNN
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5. Association Analysis
Association analysis overview
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Groceries dataset
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Apriori
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Eclat
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FP-Growth
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Challenge: Apriori
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Solution: Apriori
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6. Time-Series Mining
Time-series mining
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Air Passengers dataset
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Time-Series decomposition
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ARIMA
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MLP
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Challenge: Decomposition
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Solution: Decomposition
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7. Text Mining
Text mining overview
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Iliad dataset
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Sentiment analysis: Binary classification
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Sentiment analysis: Sentiment scoring
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Word pairs
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Challenge: Sentiment scoring
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Solution: Sentiment scoring
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Ex_Files_Data_Mining_Python_R.zip
(1.0 MB)