Posted on Sep 27 The health, education, and general well-being of Nigeria's population, especially its young people, are severely impacted by multifaceted Poverty and child deprivation. In Nigeria, poverty is defined as having insufficient access to essential goods and opportunities. To answer the crucial research issue, "Can Machine Learning methods effectively predict and prescribe poverty reduction in Nigeria with limited datasets?" this data science project was started. Data collection, preprocessing, Model creation, and evaluation were all steps in my approach. I will evaluate the model's performance, draw conclusions, and offer suggestions for further initiatives in this final section.Significance of Machine LearningUtilizing cutting-edge machine learning methods offers a crucial chance to thoroughly understand and handle these complex problems. Modern algorithms can power predictive and prescriptive studies, which can offer priceless insights for resource allocation and wise policy decisions.Research Question"Can machine learning methods effectively predict and prescribe poverty reduction in Nigeria with limited datasets?"ObjectivesThe main goal of this project is to develop an innovative framework that smoothly combines recurrent neural network (RNN) and Ensemble learning techniques in order to achieve the following:Create predictive models using ensemble learning and recurrent neural networks (RNN) to predict multidimensional poverty in Nigerian subnational regions using a limited dataset.Assess the models' accuracy and efficacy as well as the insight they generate.Data Sources and PreprocessingIn this project, I used two datasets:The Subnational multidimensional poverty data from Humanitarian Data Exchange published by the Oxford Poverty and Human Development Initiative (OPHI), University of Oxford global Multidimensional Poverty Index (MPI) which measures multidimensional poverty in Nigeria.The Multiple Indicator Cluster Survey (MICS) 2016–17, which is a household survey conducted by UNICEF that covers various indicators related to health, education, water and sanitation.Installing Libraries:In addition to Python, you'll need the Matplotlib plotting package for visualization, pandas data analysis module and Tensorflow for modeling. JupyterLab will be used to run the codes. You can set them up using:Importing datasets:Viewing the head of the MPI dataset:Dropping Unwanted headers and Convert from Float to Integer:Generating a bar chart using Matplot to show how multidimensional poverty spread across the Subnational Regions:Chart Output:Generating Stacked chart, showing how deprivation form Multiple factors such as Nutrition, Housing, Sanitation, Education, etc. cause poverty:Chart Output:Merging data frame:Importing libraries for training model:At this point, I'll be training a machine learning model using Recurrent Neural Network (RNN) to predict and prescribe poverty reduction in Nigeria using the merged dataset containing various socio-economic indicators, I'll then use Ensemble method to improve predictive performance and robustness of the model and finally apply early stopping to prevent overfitting.Encode categorical features: I'll need to encode categorical features because Categorical data needs to be converted to numerical format for machine learning algorithms to process them.Split the data into features and target: To separate the input features (independent variables) from the target variable (dependent variable)Standardize numerical features: Standardization makes different numerical features comparable by scaling them to have a mean of 0 and a standard deviation of 1.Identify the target variable: To determine the variable you want to predict or model.Check if any class has only one member: To ensure class balance to prevent issues with imbalanced datasets.Now that I've identified and flagged the classes with only one member, I'll need to combine the rare classes with a more frequent class. However, I need to define a logical rule, this rule should specify which rare classes will be combined and how they will be combined. I need to combine all rare classes with a frequency below a certain threshold into a single class called "Combined Rear Class"Split the data into training and test sets: Create separate datasets for model training and evaluation to assess model performance.Now I can proceed with model training and evaluation.Check the distribution of the combined target variable: Understand the distribution of the target variable to identify potential issues or biases in the dataset.The "Combined Class" has 19 samples, which is more than the separate classes '0.12,' '0.04,' '0.49,' '0.05,' and '0.37.' This implies that the "Combined Class" is no longer the smallest class and is more balanced when compared to these specialized classes. I may not need to calculate class weights in this situation because the class distribution is generally balanced after combining unusual classes. So go ahead and train the model.Now I'll need to define the RNN Model with a function that defines the RNN model's architecture. It's a straightforward RNN model with an embedding layer, an LSTM layer, and a dense layer with sigmoid activation.Create an Ensemble of RNNs: In this section, I'll use stratified k-fold cross-validation to build an ensemble of RNNs. Multiple RNN models are trained on distinct subsets of the training data by the code.The model has been successfully trained. The training has completed for 10 epochs, and the accuracy and loss values have been computed for each epoch. I can now proceed with ensemble of models.Based on the output its clear there are columns that the code identify that are problematic or having out-of-range values. To solve this issue, one sure way is to adjust the clipping range of the values. (Clipping is a technique that limits the values of your data to a specified range, so by adjusting the clipping range, you can control how values outside that range are handled)I have successfully trained the ensemble of models on the scaled training data. However, the training accuracy is very low (0.0000e+00), which could indicate an issue with the model or the dataset. To mitigate the issue, I'll need to implement Early Stopping to prevent overfitting, Early stopping monitor a validation metric (e.g., validation loss or accuracy) during training and stop training if the metric doesn't improve for a certain number of epochs. But, before that is implemented, I'll need to do some checks to see how well the model is, I will check for the Data Shape and Types, Shuffling Data and finally carry out Random Sample to inspect it visually and ensure that it looks as expected.Based on the various data check outputs:Now I've successfully implemented early stopping in the model training process. This will help prevent overfitting and ensure that the model generalizes well to new data. Now let's finalize.****Model Performance MetricsUsing a constrained dataset encompassing multiple socioeconomic indices, I constructed a machine learning model to forecast and prescribe poverty reduction in Nigeria. To avoid overfitting, the model was trained using early stopping. I used the following metrics to assess its performance:Loss: The loss function (mean squared error) measures the discrepancy between the predicted and actual values. It quantifies how well the model fits the data.Accuracy: While accuracy is not a typical metric for regression tasks, we calculated it to provide a general idea of the model's performance.****Both training and validation datasets were used to train and evaluate the model. The following are the primary evaluation findings:Training Loss: The training loss decreased consistently over the epochs, reaching a value of 0.5110.Validation Loss: The validation loss also decreased, with a final value of 0.6135.Training Accuracy: The training accuracy was reported as 0.0 due to the regression nature of the task.Validation Accuracy: The validation accuracy was also reported as 0.0, as accuracy is not a suitable metric for regression tasks.****Based on the evaluation results, we can draw the following conclusions:Model Training: The decreased training loss indicates that the machine learning model effectively learned from the training data. This implies that the model is capable of detecting patterns in the data.Validation Performance: While the validation loss of the model decreased during training, the final validation loss is still very significant. This suggests that the model may not generalize effectively to new, previously unknown data. The poor accuracy scores emphasize the difficulties in utilizing typical classification metrics for regression problems.Objective Achievement: Due to the relatively substantial validation loss, the primary goal of forecasting and prescribing poverty reduction in Nigeria may not have been entirely realized. The performance of the model implies that more advanced approaches or extra data may be necessary to enhance predictions..****Based on the findings and conclusions, here are some recommendations for further work and improvements:Hyperparameter Tuning: Experiment with various hyperparameters such as model architecture, learning rate, and epoch count to uncover configurations that result in superior model performance.Feature Engineering: Investigate additional features or technical strategies for extracting more useful information from data. Methods for feature selection and dimensionality reduction may also be useful.Collect More Data: A bigger sample size and more relevant features in the dataset could increase model generalization. Furthermore, gathering data unique to poverty reduction activities in Nigeria may improve projections.Time-Series Analysis: Investigate time-series analysis tools for incorporating temporal trends into poverty alleviation activities.Ensemble Models: Experiment with ensemble models, such as random forests or gradient boosting, to see if they can better capture complicated relationships in data.External Data Sources: To increase the dataset's richness and diversity, incorporate data from external sources such as government publications, surveys, or satellite photography.Interdisciplinary Collaboration: Collaborate with experts in economics, social sciences, and poverty reduction to obtain a better understanding of the variables that contribute to poverty and viable policy solutions.Ethical Considerations: When using machine learning to societal concerns such as poverty alleviation, keep ethical implications in mind. Make certain that the models do not inject prejudice or unfairness into decision-making.****The study made substantial progress in its investigation of the use of machine learning approaches to forecast and prescribe poverty reduction in Nigeria. However, there are some critical considerations to overcome in order to adequately answer the study question:Limited Datasets: When training machine learning models, using restricted datasets can be difficult. While the experiment used available data, the model's performance and generalization capabilities may have suffered due to the small dataset size.Model Performance: The evaluation findings show that the model had difficulty obtaining high accuracy while minimizing validation loss. This shows that the model's prediction performance may need to be improved further.Regression Task: Predicting and prescribing poverty reduction is a regression task in the study question. Traditional classification criteria, such as accuracy, may not be ideal for evaluating regression models. The emphasis should be on minimizing the loss function and enhancing the model's ability to predict accurately.Generalization: Machine learning models strive to generalize well to previously unseen data. The project's findings indicate that the model may not generalize well to new, previously unreported data. This is an important consideration when using machine learning approaches in real-world applications.Data Limitations: Data restrictions, such as data quality, representativeness, and the availability of important features, also have an impact on the project's success in answering the research question.In conclusion, while the project produced useful contributions and provided insights into the application of machine learning approaches for poverty prediction in Nigeria, additional work may be required to adequately answer the research topic, particularly given the limits of restricted datasets. Extending the dataset, refining the model, and investigating new variables and approaches may improve the project's ability to predict and prescribe poverty-reduction strategies.Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. 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