What will the following code generate?plt.figure(figsize = (10, 10))sns.heatmap(df.corr(), annot = True)A.Heatmap showing correlations between features with annotationsB.Heatmap showing correlations between features with annotations and color barC.Heatmap showing correlations between features without annotations

The given plot is generated by the following code.plt.figure(figsize=(3,3))sns.heatmap(dataset.corr(), annot=True, cmap='jet')plt.show()What can you do to fix the plot so that you are able to see names of the attributes.You can increase the size of the plotYou should change the color of the plotThis problem cannot be fixed as there are too many items to show in this plotYou should use 'plt.display()' function

Which function is used to create a heatmap in Seaborn?1 pointsns.heatmap()sns.scatterplot()sns.lineplot()sns.barplot()

What will the following code generate?fig = px.scatter(admission_df, x = "GRE Score", y = "Chance of Admit")fig.show()A.A static scatterplot for the “admission_df” pandas DataFrame showing the GRE Score on the x-axis and Chance of Admission on the y-axis.B.An interactive scatterplot for the “admission_df” pandas DataFrame showing the GRE Score on the x-axis and Chance of Admission on the y-axis.C.An interactive scatterplot for the “admission_df” pandas DataFrame showing the GRE Score on the y-axis and Chance of Admission on the x-axis.

Question 4The Python library we used to plot the chart in video/lab is1 pointPandasMatPlotLibPlotlyPyCoinGecko

This image is a heatmap that visualizes missing values in a dataset. Here's a detailed explanation: ### Structure: - **Rows**: Each row represents an individual data entry or record. - **Columns**: Each column represents a different feature or variable in the dataset. The names of these features are listed at the bottom of the heatmap. - **Colors**: - **Yellow**: Indicates missing values in the dataset. - **Purple**: Indicates non-missing (present) values in the dataset. ### Interpretation: - **Vertical Stripes**: If a column has a vertical stripe of yellow, it means that particular feature has missing values for those records. - **Horizontal Stripes**: If a row has a horizontal stripe of yellow, it means that particular record has missing values for those features. - **Dense Yellow Areas**: Columns with a lot of yellow indicate features with a high proportion of missing values. - **Dense Purple Areas**: Columns with a lot of purple indicate features with a low proportion of missing values. ### Observations: - Some features have a significant amount of missing data (e.g., the columns towards the right side of the heatmap). - Some features have very few or no missing values (e.g., the columns towards the left side of the heatmap). - The pattern of missing values can help in understanding the data quality and deciding on strategies for handling missing data, such as imputation or removal of certain features or records. ### Use Cases: - **Data Cleaning**: Identifying which features or records need attention for missing data. - **Data Analysis**: Understanding the distribution of missing values can help in making informed decisions about data preprocessing. - **Modeling**: Ensuring that the handling of missing data is appropriate for the type of analysis or machine learning model being used. This heatmap is a useful tool for quickly assessing the completeness of a dataset and planning the next steps in data preprocessing.