Python-Data-Analysis

Data Summarization dataset: breast-cancer-wisconsin code: calculate the mean,mode skew, standard deviation, variation of each column && PCC of several pairs of columns && draw histograms of two features
Data Normalization dataset:wine-quality.csv code: normalize data with min-max normalized values, z-score normalized values, mean subtracted normalized values && for each of the first 10 data points report the nearest and farthest out of the other first 10 points using the manhatten distance, euclidean distance and cosine distance
Data Decompostion dataset:communities.data code:Load the crime dataset and store it as a matrix (The data is already normalized )
&& Compute the eigen vectors and eigen values && Report a table with the top 20 eigenvalues, decide whether we can cut off the dimensions from these eigen values

Name		Name	Last commit message	Last commit date
Latest commit History 12 Commits
Data Summarization.ipynb		Data Summarization.ipynb
Eigenvector Decomposition_pca.ipynb		Eigenvector Decomposition_pca.ipynb
KNN and SVM for classification.ipynb		KNN and SVM for classification.ipynb
README.md		README.md
bank-full.csv		bank-full.csv
bank-names.txt		bank-names.txt
bank.csv		bank.csv
breast-cancer-wisconsin.csv		breast-cancer-wisconsin.csv
communities.data		communities.data
data_normalization.ipynb		data_normalization.ipynb
knn.ipynb		knn.ipynb
rs_wdbc.csv		rs_wdbc.csv
winequality-red.csv		winequality-red.csv

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