Link to the dataset:
https://www.kaggle.com/c/house-prices-advanced-regression-techniques/overview
- numpy
- pandas
- matplotlib
- seaborn
- spicy
- sklearn
- warnings
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Trends: A trend is defined as a pattern of change. sns.lineplot - Line charts are best to show trends over a period of time, and multiple lines can be used to show trends in more than one group. -
Relationship: There are many different chart types that you can use to understand relationships between variables in your data. -
sns.barplot: Bar charts are useful for comparing quantities corresponding to different groups. -
sns.heatmap: Heatmaps can be used to find color-coded patterns in tables of numbers. -
sns.scatterplot: Scatter plots show the relationship between two continuous variables; if color-coded, we can also show the relationship with a third categorical variable. -
sns.regplot: Including a regression line in the scatter plot makes it easier to see any linear relationship between two variables. -
sns.lmplot: This command is useful for drawing multiple regression lines, if the scatter plot contains multiple, color-coded groups. -
sns.swarmplot: Categorical scatter plots show the relationship between a continuous variable and a categorical variable. -
Distribution: We visualize distributions to show the possible values that we can expect to see in a variable, along with how likely they are. -
sns.distplot: Histograms show the distribution of a single numerical variable. -
sns.kdeplot: KDE plots (or 2D KDE plots) show an estimated, smooth distribution of a single numerical variable (or two numerical variables). -
sns.jointplot: This command is useful for simultaneously displaying a 2D KDE plot with the corresponding KDE plots for each individual variable.