Chart types and when to use them

Transcription

1 APPENDIX A Chart types and when to use them Pie chart Figure illustration of pie chart 2.3 % 4.5 % Browser Usage for April % 38.3 % Internet Explorer Firefox Chrome Safari Opera 35.8 % Pie chart is best to use when trying to compare parts of a whole. If is suitable for analyzing categorical variables. Danish Haroon 2017 D. Haroon, Python Machine Learning Case Studies, DOI /

2 Appendix A Chart types and when to use them Bar graph Figure illustration of a bar graph number of people What kind of pet do you own? Rabbit Dog Cat Goldfish Hamster Bar graph is used to compare things between different groups or track changes over time (i.e. when changes are large). Bar graph is suitable for categorical and interval variables. Histogram Figure illustration of a histogram Histogram is suitable for continuous variables. It is used to plot frequency distributions with or without classes. Changing the class intervals will change the underlying distribution. 198

3 Appendix A Chart types and when to use them Stem and Leaf plot Figure illustration of a Stem and Leaf plot 15, 16, 21, 23, 23, 26, 26, 30, 32, 41 Stem Leaf how to place 32 Stem and Leaf plot is suitable for discrete interval variables, not that much in frequency. In other words Stem and Leaf plot can be inferred to as the transpose of a histogram. Contrary to histogram, we can reconstruct the original data from a Stem and Leaf plot. Box plot Figure illustration of a Box plot Outliers Lower Quartile (Q1) Median (Q2) Upper Quartile (Q2) Outliers Minimum Maximum Lower Limit 1.5 IQR 1.5 IQR IQR Upper Limit Data Range Box plot is a transformed version of histogram which can help understand the median, variance and skewness of the data distribution. Line in the center is represented by the median, and lines on both ends are referred to as whiskers. Edges of the whiskers represent the first and second quartile with the difference between those referred to as an Inter Quartile Range. Points lying outside this range are considered to as outliers. 199

4 Index A Autocorrelation ACF, 113 Durbin Watson (see Durbin Watson statistic) PACF, 114 Autocorrelation function (ACF), 113 Auto-regressive integrated moving averages (ARIMA) ARMA, combined model, 122 linear function, 120 moving average, 121 Auto-regressive moving averages (ARMA), 119 B Bayesian Gaussian mixture model, C Center of measure center statistics, mean arithmetic, 21 geometric, 21 median, 22 mode, 22 normal distribution, outliers (see Outliers) skewness, 26 standard deviation, 23 variance, Central limit theorem, 40 Classification model confusion matrix, 181 cross-validation, 184 dataset, 162, 164, 166 decision trees, spam filtering, 196 feature representations, features, 178, 180 image classification, 196 insurance, 196 music, 196 ROC, 182 Clustering Bayesian Gaussian mixture (see Bayesian Gaussian mixture model) BIC score, 141 dataset, data transformation, demographic-based customer segmentation, 159 Elbow method, 138 Gaussian mixture (see Gaussian mixture models) K means, , PCA (see Principle component analysis (PCA)) requirements, 134 search engines, 159 Silhouette score, supervised vs. unsupervised learning, 133 techniques, 134 variance, Concrete comprehensive strength, Continuous/quantitative variables, 6 Danish Haroon 2017 D. Haroon, Python Machine Learning Case Studies, DOI /

5 INDEX Correlation dataset, 63 Kendall rank, 34 negative, 61 pair-wise Pearson, 61 Pearson R, 34 positive, 61 response and exploratory variables, 58, 60, 62 Spearman rank, D Data transformation data frame transformation, 135 matrix, Data wrangling, , Demographic variable, 8 Dependent and independent variables, 8 9 Dickey-Fuller test, Discrete variables, 8 Durbin Watson statistic, E ElasticNet, Elbow method, 138 Exploratory data analysis (EDA), 99 continuous/quantitative (see Continuous/quantitative variables) correlation, 173 dataset, 4 5 discrete variables, 7 multivariate (see Multivariate analysis) status, 175, 177 time series components, univariate (see Univariate analysis) variables demographic, 8 dependent and independent, 8 9 discrete, 7 lurking, 8 F Forecasts linear regression model, 126 sales, 127 time series, weather, 127 G Gaussian mixture models covariance, function, 152 keywords, 155 K means, 151 objects, 154 Gradient boosting regression multiple, 85 non-linear flexible regression technique, 82 single, Grid search, 75 H, I, J Hypothesis testing null, 37 t distributions and sample size, t statistics, 37 K Kernel approximation bagging, 189 boosting, 190 ensemble method, 189 SGD classifier, L Lasso regression definition, 79 multiple, 80 Linear regression multiple, single, Lurking variable, 8 M Mean absolute error (MAE), 68 Mean squared error (MSE), 68 Multicollinearity and singularity, Multivariate analysis,

6 INDEX N Normal distribution, O Outliers center of measures, 31 interval of values, 28 trip duration, 29, 30, 32 values, 30 Overfitting. See Underfitting P, Q Partial autocorrelation function (PACF), 114 Principle component analysis (PCA) data frame, 146 keywords, orthogonal transformation, 144 two-dimensional space, 145 R Random forest classification accuracy, 192 boosting, definition, 191 Receiver operating characteristic (ROC) FPR, 182 TPR, 182 Regression agriculture, 91 call center, 91 cases-to-independent variables (IVs), 55 concrete compressive strength, 45, 47 correlation coefficients (see Correlation) dataset, 57 extrapolation, 48 insurance companies, 91 interpolation, 48 least squares, 50 linear, 49 metrics explained variance score, 68 MAE, 68 MSE, residual, 69 residual plot, 70 RSS, 70 R 2, 69 missing data, 55 multicollinearity and singularity, multiple, 51 name mapping, 57 polynomial, predict bonds value, 90 predicting salary, 91 predicting sales, rate of inflation, real estate industry, stepwise, Residual sum of squares (RSS), 70 Ridge regression alpha values, 77 linear least squares, 75 multicollinearity, 75 multiple, 76 representation, 76 S Skewness, 26 Sklearn.metrics, 67 Statistics and probability actuarial science, 42 astrostatistics, 42 biostatistics, 42 business analytics, 42 center of measure (see Center of measure) correlation (see Correlation) cycle sharing scheme, 2 3 econometrics, 43 EDA (see Exploratory data analysis (EDA)) elections, 43 machine learning, 43 statistical signal processing, 43 Support vector machines hyperplane, 86 multiple, 88 single, T Time series components cyclic pattern, 18 seasonal pattern, 18 trend,

7 INDEX Time series object dataset, 96 decomposition, Dickey-Fuller test, differencing, disease outbreak, 128 exploratory data analysis, 99 exponential smoothing, forecast (see Forecasts) memory, moving average smoothing, 106, 108 properties, 99 sales forecasting, 127 stock market prediction, 128 tests, 116, transformations log, square root, trend and remove, 106 unemployment estimates, 127 weather forecasting, 127 U, V, W, X, Y, Z Underfitting cross-validation, high bias, high variance, 66 non-linear line, Univariate analysis dataset, 9 distributions, 11, 13 user types, 10 11,