Machine Learning_review_day3
World Happiness Report
https://www.kaggle.com/datasets/ajaypalsinghlo/world-happiness-report-2021
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Visualize as a responsive graphs (treemap(), sunburst(), choropleth())
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treemap: data_frame=dataframe object, path=[parent column, child column], values=column property, color=column property
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sunburst: data_frame=dataframe object, path=[parent column, child column], values=column property, color=column property
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choropleth: data_frame=dataframe object, locations='column names', locationmode='country names', color='column names'
< By Year >
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heatmap(im.show): px.imshow(dataframe object.corr( ), text_auto = True), text_auto=True: real value, correlation visualization
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scattermatricx: px.scatter_matrix(dataframe object, dimensions=['property name'], color='property name'
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- The degree to which the value of another attribute (Ladder score) changes together with the value of the attribute (Logged GDP per capita) is strong, with a high positive correlation with a correlation coefficient of about 0.79.
- The degree to which the value of another attribute (Ladder score) changes together with the value of the attribute (Freedom to make life choices) is moderate, with a moderate positive correlation with a correlation coefficient of about 0.61.
- Since the correlation coefficient is about -0.02, the value of other attributes (Ladder score) hardly changes according to the value of the attribute (Generosity).
- It is a normal negative correlation with a correlation coefficient of about -0.42, and the extent to which the value of another attribute (Ladder score) changes together with the value of the attribute (Perceptions of corruption) is normal.
- scatterplot: Dataframe object, x='x-axis property name', y='y-axis property name', size='property name', trendline='regression line type', trendline_color_override='color
- trendline = {choose among "ols", "lowess", "rolling", "ewm", "expanding" }
<Linear Regression Model>
CO2 Emissions Forecast
https://www.kaggle.com/datasets/debajyotipodder/co2-emission-by-vehicles
