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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

MLOps Strategies for Sales Conversion Success

ashish kumar
ashish kumar Last Updated : 08 Jan, 2024
7 min read

Introduction

ChatGPT

In the dynamic landscape of modern business, the intersection of machine learning and operations (MLOps) has emerged as a powerful force, reshaping traditional approaches to sales conversion optimization. The article takes you into the transformative role that MLOps strategies play in revolutionizing sales conversion success. As businesses strive for heightened efficiency and enhanced customer interactions, the integration of machine learning techniques into operations takes center stage. This exploration unveils innovative strategies that leverage MLOps to not only streamline sales processes but also to unlock unprecedented success in converting prospects into loyal customers. Join us on a journey through the intricacies of MLOps and discover how its strategic application is reshaping the landscape of sales conversion.

MLOps strategies | Sales conversion success

Learning Objectives

  • Importance of Sale optimization model
  • Cleaning Data, transforming datasets, and preprocessing datasets
  • Building End-to-End Fraud detection using Kedro and Deepcheck
  • Deploying model using streamlit and huggingface

This article was published as a part of the Data Science Blogathon.

Table of contents

What is Sale Optimization Model?

A Sale optimization model is an end-to-end machine learning model to maximize the sale of products and improve the conversion rate. The model takes several parameters as inputs such as impression, age group, gender, Click-Through Rate, and Cost Per Click. Once you train the model predicts the number of people who will buy the product after seeing the ad.

Necessary Prerequisites

1) Clone the repository

git clone https://github.com/ashishk831/Final-THC.git
cd Final-THC

2) Create and activate the virtual environment

#create a virtual environment
python3 -m venv SOP
#Activate your virtual environment in your project folder
source SOP/bin/activate
pip install -r requirements.txt

4)Install Kedro, Kedro-viz, Streamlit and Deepcheck

pip install streamlit
pip install Deepcheck
pip install Kedro
pip install Kedro-viz

Data Description

Let us perform a fundamental Data analysis using Python implementation on a dataset from Kaggle. To download the dataset, click here.

import pandas as pd
import numpy as np
df = pd.read_csv('KAG_conversion_data.csv')
df.head()
image.png
image.png
Column Description
ad_id An unique ID for each ad
xyz_campaign_id An ID associated with each ad campaign of XYZ company
fb_campaign_id An ID associated with how Facebook tracks each campaign
age Age of the person to whom the ad is shown
gender Gender of the person to whim the add is shown
interest a code specifying the category to which the person’s interest belongs (interests are as mentioned in the person’s Facebook public profile)
Impressions the number of times the ad was shown.
Clicks Number of clicks on for that ad.
Spent Amount paid by company xyz to Facebook, to show that ad
Total
conversion		 Total
number of people who enquired about the product after seeing the ad
Approved
conversion		 Total
number of people who bought the product after seeing the ad

Here the “Approved conversion” is the target column. Our
goal is to design a model which will increase the sale of product once people see
the ad.

Model Development Using Kedro

For building this project end-to-end, we will be using the Kedro tool. Kedro, is an open-source tool used for building a production-ready machine learning model, offering a number of benefits.

  • Handles Complexity: It provides a structure to test data which can be pushed to production after successful testing.
  • Standardization: It provides standard template for project. Making it easier to understand for others.
  • Production-Ready: Code can be easily pushed to production with exploratory code that you can transition to reproducible, maintainable, and modular experiments.

Read More: Walkthrough of Kedro Framework

Pipeline Structure

To create a project in Kedro follow the below steps.

#create project
kedro new

#create pipeline
kedro pipeline create <pipeline-name>

#Run kedro
kedro run

#Visualizing pipeline
kedro viz

Using kedro we will design the end-to-end model pipeline which is shown below.

localhost:Kedro-viz
localhost:Kedro-viz

Data Preprocessing

  • Check for missing values and handle them.
  • Creating two new columns CTR and CPC.
  • Converting column variable into numerical.
import pandas as pd
import numpy as np
from sklearn.preprocessing import LabelEncoder

def preprocessing(data: pd.DataFrame):    
    data.gender = data.gender.apply(lambda x: 1 if x=="M" else 0)
    data['CTR'] = ((data['Clicks']/data['Impressions'])*100)
    data['CPC'] = data['Spent']/data['Clicks']
    data['CPC'] = data['CPC'].replace(np.nan,0)
    encoder=LabelEncoder()
    encoder.fit(data["age"])
    data["age"]=encoder.transform(data["age"])
    #data.Approved_Conversion = data.Approved_Conversion.apply(lambda x: 0 if x==0 else 1)
    preprocessed_data = data.copy()
    
    return preprocessed_data

Split data

import pandas as pd
from sklearn.model_selection import train_test_split

def split_data(processed_data: pd.DataFrame):
    X = processed_data[['ad_id', 'age', 'gender', 'interest', 'Spent', 
    'Total_Conversion','CTR', 'CPC']]
    y = processed_data["Approved_Conversion"]
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, 
    random_state=42)
    return X_train, X_test, y_train, y_test

Above the dataset is divided into train dataset and test dataset for model training purpose.

Model Training

from sklearn.ensemble import RandomForestRegressor


def train_model(X_train, y_train):
    
    model = RandomForestRegressor(n_estimators = 50, random_state = 0, max_samples=0.75)
    
    model.fit(X_train, y_train)
    return model

We will be using the RandomForestRegressor module to train the model. Alone with RandomForestRegressor we are passing other parameter such as n_estimators random_state and max_samples.

Evaluation

import numpy as np
import logging
from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error, max_error


def evaluate_model(model, X_test, y_test):
    y_pred = model.predict(X_test)
    mae=mean_absolute_error(y_test, y_pred)
    mse=mean_squared_error(y_test, y_pred)
    rmse=np.sqrt(mse)
    r2score=r2_score(y_test, y_pred)
    me = max_error(y_test, y_pred)
    print("MAE Of Model is: ",mae)
    print("MSE Of Model is: ",mse)
    print("RMSE Of Model is: ",rmse)
    print("R2_Score Of Model is: ",r2score)
    logger = logging.getLogger(__name__)
    logger.info("Model has a coefficient R^2 of %.3f on test data.", r2score)
    return {"r2_score": r2score, "mae": mae, "max_error": me}

Once model is trained, its evaluated using number of key metrics such as MAE, MSE, RMSE and R2-score.

Experiment Tracker

To track the model performance and select the best model we will be using the experiment tracker. The functionality of the experiment tracker is to save all information about the experiment when the application is run. To enable the experiment tracker in Kedro we can update the catalog.xml file. The parameter versioned need to be set True. Below is the example

model:
  type: pickle.PickleDataSet
  filepath: data/06_models/model.pkl
  backend: pickle
  versioned: True

This helps in tracking the model result and saving the model version. Here, we will be using the experiment tracker at the evaluation step to track the model performance during the development phase.

When the model is executed it will generate different evaluation metrices such as MAE,MSE, RMSE and R2-score for different timestamp as show in image. On the basis of above evaluation metrices best model can be selected.

Experiment Tracker | MLOps strategies | Sales conversion success
Local: Kedro-viz

Deepcheck: For Data and Model Monitoring

When the model is deployed in production there are chances that the data quality is changed over time and due to this model performance can also change. To fix this problem we need to monitor the data in the production environment. For this, we will be using an open-source tool Deepcheck. Deepcheck has inbuilt libraries such as Label-drift and Feature-Drift which can be easily integrated with model code.

  • FeatureDrift: – A drift means a change in the distribution of data over time due to which model performance degrades. FeaturDift means change has occurred in a single feature of the dataset.
  • Labeldrift: – Labeldrift occurs when the ground truth labels for a dataset change over time. It mainly occur due to change in the label criteria.

Integrating Model Prediction and Monitoring with Streamlit

Now we will build a user interface to interact with the model for making prediction on the given input parameters to check the conversion rate.

import streamlit as st
import pandas as pd
import joblib
import numpy as np 

st.sidebar.header("Model Prediction or Report")

selected_report = st.sidebar.selectbox("Select from below", ["Model Prediction",
"Data Integrity","Feature Drift", "Label Drift"])

if selected_report=="Model Prediction":
        st.header("Sales Optimization Model")
        #def predict(ad_id, age, gender, interest, Impressions, Clicks, Spent, 
        #Total_Conversion, CTR, CPC):
        def predict(ad_id, age, gender, interest,  Spent, Total_Conversion, CTR, CPC):
            if gender == 'Male':
                gender = 0
            else:
                gender = 1
            ad_id = int(ad_id)
            age = int(age)
            gender = int(gender)
            interest = int(interest)
            #Impressions = int(Impressions)
            #Clicks = int(Clicks)
            Spent = float(Spent)
            Total_Conversion = int(Total_Conversion)
            CTR = float(CTR*0.000001)
            CPC = float(CPC)
 
            input=np.array([[ad_id, age, gender, interest, Spent, 
            Total_Conversion, CTR, CPC]]).astype(np.float64)
    
        
            model = joblib.load('model/model.pkl')
    # Make prediction
            prediction = model.predict(input)
            prediction= np.round(prediction)
    # Return the predicted value for Approved_Conversion 
            return prediction
        
        ad_id = st.number_input('Enter the advertisement ID',min_value = 0)
        age = st.number_input('Enter the target age stoup',min_value = 0)
        gender = st.radio("Gender",('Male','Female'))
        interest = st.selectbox('Interest', [2, 7, 10, 15, 16, 18, 19, 20, 21, 22, 23,
         24, 25, 
                        26, 27, 28, 29, 30, 31, 32, 36, 63, 64, 65, 66, 100, 101, 102, 
                        103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114])
        #Impressions = st.number_input('Enter the number of impressions',min_value = 0)
        #Clicks = st.number_input('Enter the number of clicks',min_value = 0)
        Spent = st.number_input('Enter the amount spent on the ad',min_value = 0)
        Total_Conversion = st.number_input('Enter the total conversion count',
        min_value = 0)
        CTR = st.number_input('Enter the Click-Through Rate',min_value = 0)
        CPC = st.number_input('Enter the Cost Per Click',min_value = 0)

        if st.button("Predicted Approved Conversion"): 
             output = predict(ad_id, age, gender, interest, Spent, Total_Conversion, 
             CTR, CPC)
             st.success("Approved Conversion Rate  :{}".format(output)) 
else:
    st.header("Sales Model Monitoring Report")
    report_file_name = "report/"+ selected_report.replace(" ", "") + ".html"
    HtmlFile = open(report_file_name, 'r', encoding='utf-8')
    source_code = HtmlFile.read() 
    st.components.v1.html(source_code, width=1200, height=1500, scrolling=True)
HuggingFace App Demo | MLOps strategies | Sales conversion success
Localhost:streamlit

Deployment Using HuggingFace

Now that we have build an end-to-end sale optimization model, we will deploy the model using HuggingFace. In huggingface, we need to configure the README.md file for model deployment. Huggingface take care of CI/CD. As whenever there is change in file, it will track to changes and redeploy the app. Below is the readme.md file configuration.

title: {{Sale-str-opt}}
emoji: {{Sale-str-opt}}
colorFrom: {{colorFrom}}
colorTo: {{colorTo}}
sdk: {{sdk}}
sdk_version: {{sdkVersion}}
app_file: app.py
pinned: false

HuggingFace App Demo

HuggingFace App Demo | MLOps strategies | Sales conversion success
https://huggingface.co/spaces/ashishk566/sales-str-opt

For cloud version click here.

Conclusion

  • Machine learning apps can give the test conversion rate in unknown market helping business to know product demand.
  • Using Sale optimization model business can target their right set of audience.
  • This application helps is increasing the business revenue.
  • Monitoring data in real time can also help in tracking model change and user behaviour change.

Frequently Asked Questions

Q1. What is the purpose of sale optimization model?

A. The purpose of sale optimization model is to prediction the number of customers who will buy the product after seeing the ad.

Q2. Why do we need to monitor the data?

A. Monitoring the data helps in tracking the dataset and model behaviour.

Q3. Is huggingface free to use?

A. Yes, huggingface is free to use with basic feature 2 vCPU,16 GB RAM.

Q4. Are there specific rules for selecting reports at model monitoring stage?

A. There are no strict rules for selecting reports at model monitoring stage, deepcheck have many inbuilt libraries such model drift, distribution drift.

Q5. How streamlit is helpful?

A. Streamlit helps in local deployment, which help in fixing error during development phase.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

blogathondatasetdriftfacebookHuggingFaceMLOpsmodel performanceoptimizationSales conversionstreamlit
ashish kumar
ashish kumar

Data Science enthusiast...Data alchemist, turning numbers into insights. Passionate about using data to create a better future.
I'm a data alchemist, turning numbers into insights and insights into solutions. I'm passionate about using data to solve real-world problems and make a positive impact on the world. I can conjure up data visualizations that illuminate patterns and trends that would otherwise be hidden. I'm also a storyteller, and I can use data to craft compelling narratives that inspire others to take action.
I'm always looking for new challenges, and I'm excited to use my skills to make a difference in the world. Whether it's developing new machine learning algorithms to fight disease, or using data to optimize renewable energy production, I'm up for the task.

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