https://github.com/abhinav330/ml-implementation-on-classified-data
This repository implements a K-Nearest Neighbors (KNN) model in Python for predicting loan defaults. It analyzes the KNN_Project_Data dataset (features & binary target 'TARGET CLASS').
https://github.com/abhinav330/ml-implementation-on-classified-data
cluster-analysis clustering clustering-algorithm data-science datascience knearest-neighbor-algorithm knn knn-classification machine-learning machine-learning-algorithms machinelearning sklearn supervised-learning
Last synced: 22 days ago
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This repository implements a K-Nearest Neighbors (KNN) model in Python for predicting loan defaults. It analyzes the KNN_Project_Data dataset (features & binary target 'TARGET CLASS').
- Host: GitHub
- URL: https://github.com/abhinav330/ml-implementation-on-classified-data
- Owner: Abhinav330
- Created: 2024-08-25T23:01:17.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-08-31T02:47:52.000Z (almost 2 years ago)
- Last Synced: 2025-03-03T08:16:35.107Z (over 1 year ago)
- Topics: cluster-analysis, clustering, clustering-algorithm, data-science, datascience, knearest-neighbor-algorithm, knn, knn-classification, machine-learning, machine-learning-algorithms, machinelearning, sklearn, supervised-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 4.24 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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# ML-implementation-on-Classified-data
This repository implements a K-Nearest Neighbors (KNN) model in Python for predicting loan defaults. It analyzes the KNN_Project_Data dataset (features & binary target 'TARGET CLASS').
# Code Summary
This Python script demonstrates the use of the K-Nearest Neighbors (KNN) classification algorithm for a binary classification task. It imports various libraries, loads a dataset named 'KNN_Project_Data', scales the data, and trains a KNN model to predict the 'TARGET CLASS'. The code also performs model evaluation and hyperparameter tuning.
## Data Loading and Exploration
The code begins by importing necessary libraries, loading the dataset 'KNN_Project_Data' using pandas, and displaying dataset information using `df.info()`. The dataset is assumed to contain features and a binary target variable ('TARGET CLASS').
## Data Preprocessing
Data preprocessing steps include:
- Scaling the feature data using StandardScaler from scikit-learn to ensure that all features have the same scale.
- Splitting the dataset into training and testing sets using `train_test_split()`.
## Model Building
The code then proceeds to build a K-Nearest Neighbors (KNN) classifier model with the following steps:
- Importing KNeighborsClassifier from scikit-learn.
- Initializing a KNN model with a specified number of neighbors (in this case, `n_neighbors=1`).
- Fitting the model to the training data.
- Making predictions on the test data.
## Model Evaluation
The code evaluates the KNN model by:
- Importing classification_report, confusion_matrix, and accuracy_score from scikit-learn.
- Printing the confusion matrix and classification report, which includes metrics such as precision, recall, and F1-score.
- Performing hyperparameter tuning by iterating through different values of 'n_neighbors' and collecting error values. This helps to find an optimal 'n_neighbors' value.
- Plotting the error values against 'n_neighbors' to visualize the trade-off between bias and variance.
- Rebuilding the KNN model with the optimal 'n_neighbors' value found during hyperparameter tuning.
- Printing the final confusion matrix, classification report, and accuracy score.
The code aims to find the best 'n_neighbors' value that maximizes the model's performance.