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Reading list

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

World Bank’s Machine Learning Model to Save Lives in Low-Income Areas

Yana Khare
Yana Khare Last Updated : 10 Apr, 2023
4 min read

Combating Inflation Crisis in Precarious Regions: World Bank’s Revolutionary Machine-Learning Solution

Living conditions have been severely affected by the global rise in inflation, particularly in crisis-hit regions, severely impacting households in precarious situations. In low-income nations, where prices can be unpredictable and challenging to measure, a combination of surveys and machine learning predictions can produce estimates that are just as accurate as actual price measures. The World Bank recently released a report about this in the Policy Research Working Paper Series. Following the report, the World Bank has developed a machine learning model to help curtail inflation of food prices as part of the Food Systems 2030 plan.

Bo Pieter Johannes Andree, a data scientist at the Development Economics Data Group of the World Bank, is the brain behind this ingenious model. He developed the methodology as part of a project titled “Building the Evidence on Protracted Forced Displacement: A Multi-Stakeholder Partnership.” The research and model development were based on the monthly price survey data collected by the World Food Program (WFP).

Also Read: What is Machine Learning? A Friendly Introduction for Aspiring Data Scientists and Managers

World Bank to curtail inflation of food prices using machine learning

Source: Pinterest

A Sharp Increase in Household Spending Needed to Meet Basic Needs

When inflation is high, family expenditure to cover essential requirements may climb significantly, necessitating policy action. In more challenging situations, a rise in food costs may be a symptom of regional food shortages. This would signify the beginning or escalation of a food and nutrition crisis. This is a significant problem because while inflation shows an overall increase in price levels over a wide range of items, the prices of certain goods may rise dramatically. Beyond food products, a wide range of goods’ prices must also be observed to measure inflation correctly. Yet, concurrently examining their pricing becomes more challenging as the number of items in the basket increases.

World Bank Using Machine Learning Method to Monitor Inflation

How the World Bank uses machine learning models to monitor Inflation

Source: Pinterest

By building many machine learning models for various price items and connecting them to forecast missing data based on other prices. The World Bank study employs an innovative strategy to overcome this difficulty. This method makes it possible to track food prices in real-time across more than 1200 marketplaces in 25 different countries for more than 40 different food products. The technique estimates unobserved local market prices using surveys from surrounding marketplaces and the costs of associated items. This fills up the gaps in a basket of commodities’ area-specific pricing data. Enabling real-time monitoring of the dynamics of local inflation using patchy and irregular survey data.

Crucial Insights for Decision-Makers in Low-Income and Data-Poor Locations

The findings of this study offer significant guidance for policymakers in low-income and information-poor regions. These regions are challenging to maintain extensive and costly price monitoring programs using conventional consumer price index (CPI) methods to track general price levels for a wide range of consumer goods. The technique can enhance macroeconomic monitoring in areas with inadequate data by acquiring information at a cheaper cost and complementing traditional data collection efforts.

Also Read: AI Revolution in Legal Sector: Chatbots Take Center Stage in Courtrooms

Expanding the Scope of the Monitor

Food Systems 2030 Multi-Donor Trust Fund of the World Bank is expanding the monitor’s purview. The World Bank is now improving the algorithms by using data from the International Food Policy Research Institute (IFPRI). Enabling them to process a greater number of price items and remain resilient even when the data coverage is limited.

Saving Lives in Poorer Nations with World Bank’s Machine Learning Technique

In low-income nations, the World Bank’s machine learning technique saves lives. The present inflation issue makes making ends meet challenging for many people. With regard to more than 40 food categories, the World Bank’s machine learning technology offers decision-makers real-time monitoring of food prices in more than 1200 marketplaces across 25 countries.

Also Read: India’s AI Leap: Homegrown ChatGPT to Empower Millions

Our Say

The World Bank’s machine-learning approach will significantly benefit low-income nations suffering from the present inflation issue. Decision-makers may offer tailored policy responses to assist individuals in crisis-affected areas. They would do this with real-time monitoring of food prices in over 1200 marketplaces across 25 countries. The World Bank’s Food Systems 2030 Multi-Donor Trust Fund’s increase of the monitor’s scope is an encouraging move to enable even more thorough monitoring of inflation dynamics in data-limited countries.

World Bank's Food Systems 2030 Multi-Donor Trust Fund

The application of machine learning in solving problems across various sectors has now reached a global scale. It is inspiring to see world organizations making use of technology, artificial intelligence, and such innovative models to tackle issues. Let us hope that these steps lead us to a better, technologically advanced, sustainable future.

banksFood Systems 2030IFPRIinflationmachine learningMachine Learning Approachmachine learning modelnewsWorld BankWorld Food Program
Yana Khare
Yana Khare

A 23-year-old, pursuing her Master's in English, an avid reader, and a melophile. My all-time favorite quote is by Albus Dumbledore - "Happiness can be found even in the darkest of times if one remembers to turn on the light."

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