Predict

Data Visualization:

This class shall be used to include all Data Visualization techniques to be feed to the Machine Learning Models

Balance imbalance check

This method will be used to plot the balance/imbalance datasets using barplot/countplot

Input: data: the input dataframe with target column.

Target: target column name.

Output: plot of target variable value count.

def balance_imbalance_check(self, dataframe, target):
        sns.barplot(x='is_promoted', y='is_promoted', data=dataframe,
                    estimator=lambda x: len(x) / len(dataframe) * 100)
        plt.xlabel('ispromoted')
        plt.ylabel('percentage')
        plt.title('Balance Imbalance Count')
        plt.savefig("static/graphs/imbalance.png")  

Correlation Heatmap

This method will be used to generate interactive heatmap plot to show the pairwise correlation of input variables

Input Description: data: the input dataframe with target column.

Output: returns json file with correlation information that can be used by plotly to generate interactive plots.

def correlation_heatmap(self, dataframe):
          data = dataframe.select_dtypes(include=[np.number])

          plotdata = [go.Heatmap(
              z=data.corr(),
              x=list(data.columns),
              y=list(data.columns)
              ,dx=1,dy=1
              )]
          graphJSON = json.dumps(plotdata, cls=plotly.utils.PlotlyJSONEncoder)

          return graphJSON