Visualization

Setup

import scipy
from scipy.stats.stats import pearsonr
from datetime import datetime
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

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

Method Name: balance_imbalance_check

def balance_imbalance_check(self, dataframe, target):

Description: This method will be used to plot the balance/imbalance datasets using barplot/countplot
Input Description: data: the input dataframe with target column.
target: target column name.
Output: plot of target variable value count.
On Failure: Raise Exception

x = sns.countplot(target,data=dataframe).set_title("Balance Imbalance Count")
sns.barplot(x = 'is_promoted', y = 'is_promoted' ,data=df, hue  = 'is_promoted', estimator = lambda x: len(x)/len(df) *100).set_title("Balance Imbalance Count")

Method Name: corelation_heatmap

    def corelation_heatmap(self, dataframe):

Description: This method will be used to plot the heatmap to show the corelation among the variables
Input Description: data: the input dataframe with target column.
Output: plot of heatmap that shows the corelation among the variable.
On Failure: Raise Exception

data = dataframe.select_dtypes(include=[np.number])
plt.figure(figsize=(20, 10))
sns.set_palette("PuBuGn_d")
plot = sns.heatmap(data.corr(), annot=True, cmap='RdYlGn')
plot.figure.savefig("static/graphs/correlation.png")