Customer Churn Prediction Using Machine Learning Powerpoint Presentation Slides ML CD

Slide 1: The slide introduces Customer Churn Prediction Using Machine Learning. State Your Company name and begin.
Slide 2: This is an Agenda slide. State your agendas here.
Slide 3: The slide displays Table of contents for the presentation.
Slide 4: The slide renders Table of contents further.
Slide 5: This slide demonstrates the graphical representation of the increasing user customer churn rate.
Slide 6: This slide covers the company’s underperforming metrics, such as customer satisfaction score, net promoter score, average revenue per user, etc.
Slide 7: This slide highlights key problems faced by the company due to rising user churn, such as declining revenue, increased marketing costs, etc.
Slide 8: The slide again shows Title of contents.
Slide 9: This slide covers the major reasons for implementing customer churn prediction using machine learning.
Slide 10: The slide shows another Title of contents.
Slide 11: This slide contains a brief overview of machine learning for making predictions without explicit programming.
Slide 12: This slide highlights key advantages of machine learning for churn prediction such as identify at-risk customers, optimize products and services, etc.
Slide 13: The slide renders another Title of contents.
Slide 14: This slide covers major steps for churn prediction, such as defining the problem, explanatory data analysis, data cleaning, preprocessing, etc.
Slide 15: The slide represents Title of contents further.
Slide 16: This slide contains the process of defining the timeframe and the business context for customer churn prediction.
Slide 17: The slide depicts Title of contents further.
Slide 18: This slide contains a brief defines classification as the process of categorizing a given set of data into classes.
Slide 19: This slide briefly defines regression in machine learning.
Slide 20: This slide covers key parameters to consider before selecting an ML algorithm, such as the nature of the problem, size and complexity, etc.
Slide 21: This slide highlights approaches for customer churn prediction, such as classification and regression.
Slide 22: The slide displays Title of contents further.
Slide 23: This slide provides a brief introduction to data collection in machine learning.
Slide 24: This slide contains key machine learning trends for data gathering such as synthetic data generation, active learning, transfer learning, etc.
Slide 25: This slide displays major data-gathering technologies for machine learning such as web scraping, application programming interfaces (APIs), etc.
Slide 26: This slide covers major data collection sources for user attrition rate forecasts.
Slide 27: The slide depicts Title of contents further.
Slide 28: This slide provides a brief introduction to exploratory data analysis in machine learning.
Slide 29: This slide covers elements of exploratory data analysis for churn prediction.
Slide 30: The slide again shows Title of contents.
Slide 31: This slide covers the need for data preprocessing in machine learning, such as improving data quality, dealing with missing values, normalizing and scaling.
Slide 32: This slide highlights the process of data preprocessing in machine learning.
Slide 33: This slide contains major data integration challenges faced during user churn forecasts.
Slide 34: The slide shows Title of contents further.
Slide 35: This slide covers a brief introduction to feature engineering in ML.
Slide 36: This slide contains the feature engineering steps in machine learning.
Slide 37: This slide highlights key feature engineering tactics such as one-hot encoding, binning, scaling, feature split, and text data preprocessing.
Slide 38: This slide covers the major features for churn prediction, such as customer characteristics, product characteristics, transaction history, etc.
Slide 39: This slide renders key ways for determining features during churn prediction such as Permutation Importance, ELI5 Python Package, and SHAP.
Slide 40: The slide shows another Title of contents.
Slide 41: This slide covers a brief introduction to a non-parametric supervised learning algorithm.
Slide 42: This slide contains the process flow of customer churn prediction using the decision tree algorithm.
Slide 43: The slide again displays Title of contents.
Slide 44: This slide covers a brief overview of logistic regression analysis.
Slide 45: This slide renders assumptions for churn prediction related to the binary-dependent variable, independence, no multicollinearity, linearity, and sample size.
Slide 46: The slide shows Title of contents which is to be discussed further.
Slide 47: This slide gives a brief introduction to random forest algorithms.
Slide 48: This slide contains the random forest model creation steps, such as creating dummy variables, creating datasets with predictor variables, etc.
Slide 49: The slide again shows Title of contents.
Slide 50: This slide covers key elements to consider for churn prediction such as complexity of issue, data availability & quality, interpretability, etc.
Slide 51: This slide renders methods of choosing the right churn prediction model, such as train-test split, cross-validation, model averaging, and model performance.
Slide 52: The slide illustrates Title of contents further.
Slide 53: This slide covers various elements of model training for churn prediction such as feeding engineered data, parametrized ML algorithm, etc.
Slide 54: The slide displays another Title of contents.
Slide 55: This slide covers major methods of machine learning model optimization, such as exhaustive search, gradient descent, and genetics.
Slide 56: The slide depicts Title of contents which is to be discussed further.
Slide 57: This slide gives a brief introduction to hyperparameters and hyperparameter tuning.
Slide 58: This slide covers methods of hyperparameter tuning such as grid search, random search, and Bayesian optimization.
Slide 59: The slide renders Title of contents further.
Slide 60: This slide covers machine learning model implementation methods such as API deployment, batch deployment, and model export.
Slide 61: The slide again represents Title of contents.
Slide 62: This slide covers significant business processes for integrating prediction models such as CRM systems, marketing automation platforms, etc.
Slide 63: The slide continues Title of contents.
Slide 64: This slide covers elements such as model performance monitoring, data quality and distribution, model retraining and updates, etc.
Slide 65: The slide shows another Title of contents.
Slide 66: This slide covers customer retention strategies based on machine learning outcomes.
Slide 67: The slide again represents Ttitel of contents.
Slide 68: This slide highlights the graphical representation of the decreasing user customer churn rate.
Slide 69: This slide covers improved company metrics, such as customer satisfaction score, net promoter score, average revenue per user, customer lifetime value, etc.
Slide 70: The slide illustrates Title of contents.
Slide 71: This slide covers a dashboard for evaluating customer churn metrics such as subscribers churning, revenue lost, and churn-to-retention proportion, etc.
Slide 72: This slide shows all the icons included in the presentation.
Slide 73: This slide is titled as Additional Slides for moving forward.
Slide 74: The slide describes Major reasons for customer churn across various sectors.
Slide 75: The sldie displays Major types of customer churn.
Slide 76: This slide covers machine learning applications across telecom, retail, banking, and marketing.
Slide 77: The slide shows the Difference between regression and classification in ML.
Slide 78: This is our mission, vision and goal slide. State your firm's goal here.
Slide 79: This is Our Team slide with names and designation.
Slide 80: This is an Idea Generation slide to state a new idea or highlight information, specifications etc.
Slide 81: This slide describes Line chart with two products comparison.
Slide 82: This slide presents Roadmap with additional textboxes.
Slide 83: This is a Thank You slide with address, contact numbers and email address.