Missing Migrants Classifier
This project aims to classify and predict the number of missing or dead migrants based on various features related to migration incidents. It utilizes machine learning techniques to build and evaluate different classification models.

This was also referenced off a similar project, which was extremely helpful:
https://github.com/mukulsinghal001/lead-scoring-model-python/blob/main/Lead%20Scoring%20Classification%20Model%20Diagnosis%20with%20%20Probability%20Calibration%20%26%20ROC_AUC%20%2B%20PR%20Plot.ipynb

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Overview
The project includes the following key components:

1. Data Preprocessing: The dataset is preprocessed, including one-hot encoding of categorical features, handling missing values, and scaling numerical features.
2. Feature Selection: Feature selection is performed using a Random Forest model to identify the most important features for classification.
3. Model Selection: Several classification models are tested, including Random Forest, Adaboost, ExtraTrees, BaggingClassifier, GradientBoosting, DecisionTree, KNN, Logistic Regression, SGD Classifier, MLPClassifier, NaiveBayes, SVM, and CatBoost.
4. Model Evaluation: Models are evaluated using cross-validation and various metrics, including accuracy, error rates, classification reports, and confusion matrices.
5. Top Models: The top-performing models are identified based on accuracy scores, and detailed evaluation results are provided.