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We’re excited to share that DGTL mart will be showcasing at IETF 2025 AI India!

We’re thrilled to announce that DGTLmart will be showcasing at IETF 2025 AI India! This is an incredible opportunity for us to present our cutting-edge innovations and solutions in the AI and digital marketing space. Join us as we connect with industry leaders, exchange ideas, and explore the future of AI technology. Stay tuned for more updates and come visit our booth to discover how DGTLmart is transforming businesses with AI-powered strategies!

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Alternative Revenue Cross Selling, Reselling & Upselling Do You Know? Probability of selling to an existing customer is 60-70%,While the pro

Alternative Revenue Cross Selling, Reselling & Upselling Do You Know? Probability of selling to an existing customer is 60-70%,While the probability of selling to a new prospect is only 5-20% Why? You should invest in Upselling Why? You should invest in Upselling Do You Know? Increasing customer retention rates by just 5% increases profits up

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Running a Random Forecast

from pandas import Series, DataFrame import pandas as pd import numpy as np import os import matplotlib.pylab as plt from sklearn.cross_validation import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import classification_report import sklearn.metrics # Feature Importance from sklearn import datasets from sklearn.ensemble import ExtraTreesClassifier

os.chdir("C:\Users\ASUS\Downloads")

Load the dataset

AH_data = pd.read_csv("tree_addhealth.csv") data_clean = AH_data.dropna()

data_clean.dtypes data_clean.describe()

Split into training and testing sets

predictors = data_clean[['BIO_SEX','HISPANIC','WHITE','BLACK','NAMERICAN','ASIAN','age', 'ALCEVR1','ALCPROBS1','marever1','cocever1','inhever1','cigavail','DEP1','ESTEEM1','VIOL1', 'PASSIST','DEVIANT1','SCHCONN1','GPA1','EXPEL1','FAMCONCT','PARACTV','PARPRES']]

targets = data_clean.TREG1

pred_train, pred_test, tar_train, tar_test = train_test_split(predictors, targets, test_size=.4)

pred_train.shape pred_test.shape tar_train.shape tar_test.shape

Build model on training data

from sklearn.ensemble import RandomForestClassifier

classifier=RandomForestClassifier(n_estimators=25) classifier=classifier.fit(pred_train,tar_train)

predictions=classifier.predict(pred_test)

sklearn.metrics.confusion_matrix(tar_test,predictions) sklearn.metrics.accuracy_score(tar_test, predictions)

fit an Extra Trees model to the data

model = ExtraTreesClassifier() model.fit(pred_train,tar_train)

display the relative importance of each attribute

print(model.feature_importances_)

""" Running a different number of trees and see the effect of that on the accuracy of the prediction """

trees=range(25) accuracy=np.zeros(25)

for idx in range(len(trees)): classifier=RandomForestClassifier(n_estimators=idx + 1) classifier=classifier.fit(pred_train,tar_train) predictions=classifier.predict(pred_test) accuracy[idx]=sklearn.metrics.accuracy_score(tar_test, predictions)

plt.cla() plt.plot(trees, accuracy)

OUTPUT:

[0.02674519 0.01452508 0.02383281 0.01552068 0.00653599 0.00626536 0.06307556 0.05160827 0.04981202 0.10180787 0.01907461 0.01705372 0.02688986 0.05987085 0.05729702 0.04918319 0.01754836 0.07740653 0.06704035 0.07006239 0.01263347 0.05856173 0.05589433 0.05175476]

The most correlated variable is marijuana use, while the smallest correlation is obtained from being American.

The accuracy mostly goes up to 85%. Thus, a limited number of trees with slightly lower accuracy, 83%, is enough for this model.

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Classification Tree

import matplotlib.pylab as pltfrom sklearn.tree import plot_treefrom sklearn.datasets import load_irisfrom sklearn.model_selection import train_test_splitfrom sklearn.tree import DecisionTreeClassifierfrom sklearn.metrics import accuracy_score

# Load a dataset as an exampleiris = load_iris()

X, y = iris.data, iris.target

# Split data into training and test sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)

# Create the Decision Tree Model

clf = DecisionTreeClassifier()

# Train the modelclf.fit(X_train, y_train)

# Predict responses for the test dataset

y_pred = clf.predict(X_test)

# Calculate the accuracy of the model

accuracy = accuracy_score(y_test, y_pred)

print(f'Model accuracy: {accuracy}')

plt.figure(figsize=(20,10))

plot_tree(clf, filled=True)

plt.show()