Provide this code in r wwupscgovin/ex ID Conbad Us - How T Traffic Domination CP-006 Video -147 Search - Visual Stu pyter Heart Disease Predictions Last Checkpoint 39 minutes ago (unsaved changes) Feature Selection In 11 import seaborn as sns get correlations of each features in dataset corrmat =d f corr() topcorrfeatures = corrmatindex pltfigure (figsize =(2020)) plot heat map g=s n s h e a t m a p(d f topcorrfeatures corr ( ) annot=True cmap=RdV1Gn)

Question

Provide this code in r wwupscgovin/ex ID Conbad Us - How T Traffic Domination CP-006 Video -147 Search - Visual Stu pyter Heart Disease Predictions Last Checkpoint 39 minutes ago (unsaved changes) Feature Selection In 11 import seaborn as sns get correlations of each features in dataset corrmat =d f corr() topcorrfeatures = corrmatindex pltfigure (figsize =(2020)) plot heat map g=s n s h e a t m a p(d f topcorrfeatures  corr ( ) annot=True cmap=RdV1Gn)

StudyX AI Fast Model · Accepted Answer

Solution By Steps
Step 1: Import the Seaborn Library
Import the Seaborn library in R using the following command:
```R
library(seaborn)
```

Step 2: Calculate the Correlation Matrix
Calculate the correlation matrix of the dataset using the `cor()` function:
```R
corrmat <- cor(df)
```

Step 3: Identify Top Correlated Features
Identify the top correlated features from the correlation matrix:
```R
top_corr_features <- rownames(corrmat)[order(-corrmat[, "target_feature"]),]
```

Step 4: Create a Heatmap
Create a heatmap to visualize the correlations between features:
```R
plt <- figure(figsize=c(20,20))
g <- sns.heatmap(df[top_corr_features, top_corr_features], annot=TRUE, cmap="RdYlGn")
```

Final Answer
The code provided imports the Seaborn library, calculates the correlation matrix of the dataset, identifies the top correlated features, and creates a heatmap to visualize the correlations between features.

Key Concept
Data Visualization

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