Finding the Best ML Classifier

Evaluating ML Classifiers for Loan Case Prediction

In this repository, you can check out the Machine Learning classifiers I built to predict whether a loan case will be paid off or not. Find out more in the following sections!

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Background

In this project, I loaded a historical dataset from previous loan applications, cleaned the data, and applied different classification algorithms on the data in order to predict whether a loan case will be paid off or not.

I used the following algorithms to build my models:

• k-Nearest Neighbor
• Decision Tree
• Support Vector Machine
• Logistic Regression

And I used the following metrics to evaluate my models in my report:

• Jaccard Index
• F1-score
• Log Loss

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Technologies & Usage

This project leverages Python 3.9, Numpy, Matplotlib, Scikit-Learn, Pandas, and Seaborn with the following requirements and dependencies:

• import itertools
• import numpy as np
• import matplotlib.pyplot as plt
• from matplotlib.ticker import NullFormatter
• import pandas as pd
• import numpy as np
• import matplotlib.ticker as ticker
• from sklearn import preprocessing
• %matplotlib inline
• import warnings
• import seaborn as sns
• from sklearn.model_selection import train_test_split
• from sklearn.neighbors import KNeighborsClassifier
• from sklearn import metrics
• from sklearn.tree import DecisionTreeClassifier
• import sklearn.tree as tree
• from sklearn import svm
• from sklearn.linear_model import LogisticRegression
• from sklearn.metrics import confusion_matrix, jaccard_score, f1_score, logg_loss