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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

How AI & Mobile Data Can Be Used to Fight Poverty

Yana Khare
Yana Khare Last Updated : 25 Apr, 2023
3 min read
Using AI and Mobile Data to reduce poverty

A recent study emphasizes the possibility of employing machine learning and data from mobile phones to target and reduce high poverty rates. Mobile devices produce massive volumes of data. Local residents can learn important details about their socioeconomic standing by using them. Tailored programs that aim to eradicate poverty may be created using this information. The paper presents a case study demonstrating how researchers utilized mobile phone data like CDR to identify areas in India with high poverty levels. In addition, the paper also showed how this knowledge was used to develop targeted interventions. The author also emphasizes how machine learning can analyze big datasets & generate insights that can be utilized to reduce poverty.

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

A Promising Solution for Identifying Ultra-Poor Households

Using CDR or call detail records to help combat poverty using AI and machine learning

The author presented the paper at the World Bank and UC Berkeley. Researchers showed how the government’s anti-poverty program could target extremely low-income households successfully. The government could use machine learning on non-traditional administrative data, like call detail records (CDRs) from a significant mobile phone operator in Afghanistan. CDRs include details on, among other things, phone numbers, communication patterns, network contacts, and recharging habits.

A supervised machine learning model trained on CDR data, an asset-based wealth index, and a consumption metric frequently used as a proxy to assess poverty in low- and middle-income countries are all evaluated and compared in this research as three ways of correctly identifying ultra-poor families. The gradient boosting model trained 797 behavioral indicators computed from CDR data for the supervised ML algorithm, outperforming other common machine learning algorithms.

Comparing the Accuracy of the CDR-Based Method and Alternatives to Reduce Poverty

Using CDR through mobile phone to identify people who live in poverty and help reduce it using AI and machine learning

The study discovered that the CDR-based strategy, which achieved precision and recall of 42%, was equally accurate as the other two methods in identifying ultra-poor homes. The results of the area under the curve (AUC) tests also revealed similarities between the techniques. With an AUC of 0.78, the combined approach, which employed logistic regression to classify homes into ultra-poor and non-ultra-poor categories by combining all three methods, outperformed the separate techniques using one or two data sources. However, given the impossibility of gathering consumption data for sizable populations, an eclectic approach employing solely CDR and asset data could be the most practical choice.

Addressing Ethical Concerns and Limitations of Using CDR to Reduce Poverty

Addressing the ethical issues and constraints brought up by using CDR data to reduce poverty is essential. Having access to phone data is vital. If data is unavailable to particular sections of the population, it will affect targeting precision. Additionally, CDR-based targeting entails accessing confidential and sensitive data, calling for detailed privacy requirements and informed permission. Data reduction may be one way to reduce privacy threats, but it may also reduce the effectiveness of targeting. Finally, using CDR data for program eligibility may encourage people who want to game the system to act strategically.

Our Say

Balancing Potential Benefits and Ethical Constraints of Using CDR/ AI/ Machine learning for reducing poverty

Enhancing current survey-based methodologies, merging ML with CDR data, & lowering costs can potentially revolutionize targeting economic interventions or aid programs. Thus, helping in reducing and combating poverty in low-income countries like India. However, we must consider practical and moral considerations, including accessing data, addressing privacy concerns, and preventing possible data manipulation. It is critical to balance these limitations with the potential advantages of CDR-based targeting in each unique situation. It is crucial to approach machine learning applications wisely and responsibly. Additionally, one must ensure that they align with ethical principles and prioritize the welfare of people and communities as technology continues to develop and change the world.

Also Read: Ethics in Data Science and Proper Privacy and Usage of Data

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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