27/10/2023
How does the inner workings of a Machine Learning model work?
INCOME and LOAN are the two essential variables we explore with this model.
The goal is to predict the income of individuals based on various input features, including age, s*x, and a score, along with their loan status.
Here's a step-by-step breakdown of how we build the model:
Initial Dataset: We start with a dataset containing information about individuals, including their income, loan status, age, s*x, and score.
Exploratory Data Analysis (EDA): We perform thorough EDA to gain insights into the data, spot patterns, and identify potential challenges.
Data Cleaning: We ensure the dataset is free from errors, inconsistencies, and missing values.
Data Curation: Redundant features are removed to streamline the dataset and enhance model performance.
Pre-Processed Dataset: We preprocess the data using techniques like PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) to reduce dimensionality and extract meaningful features.
Use as Training Set: After preprocessing, we split the dataset into a training set and a test set. The training set is used to teach the model patterns and relationships in the data.
Learning Algorithms & Hyperparameter Optimization: We apply various learning algorithms, such as SVM (Support Vector Machine), LR (Logistic Regression), KNN (K-Nearest Neighbors), DT (Decision Trees), and RF (Random Forest). The hyperparameters of these models are fine-tuned using grid search to achieve optimal performance.
Feature Selection: We select the most relevant features that significantly impact the outcome to avoid overfitting and improve interpretability.
Cross-Validation Model: To validate the model's robustness, we employ cross-validation techniques to measure its generalization performance.
Trained Model & Predicted Y values: Once the model is trained, we use the test set to make predictions on unseen data.
Evaluation Metrics: We evaluate the model's performance using various metrics like classification accuracy, sensitivity, specificity, MCC (Matthews Correlation Coefficient), RMSE (Root Mean Squared Error), MSE (Mean Squared Error), and Rยฒ (R-squared) for regression tasks.
Regression: We explore the relationship between input features and income through regression analysis.
Evaluate Model Performances: We assess how well our model performs on the given dataset and make necessary adjustments if needed.
Additional Models: We experimented with Random Search and Gradient Boosting (GB) to compare their performances with RF.
The final model is ready to make predictions on new data, helping us gain insights into the income levels of individuals and their loan status.
Machine Learning offers limitless opportunities, and we are thrilled to leverage this model for meaningful real-world applications!
