Model Evaluation in Python Name: Tashinga Bwanali Company: CODETECH IT SOLUTIONS ID: CTO8DS3491 Domain: Artificial Intelligence Duration: 1 July to 1 August Mentor: Neela Santhosh Kumar

Overview This repository contains Jupyter Notebooks for evaluating NLP and other machine learning models on various datasets. Model evaluation is a crucial step in the development of machine learning applications, ensuring that models perform well and meet the required standards. This project leverages different evaluation techniques and libraries in Python.

Features Data Preprocessing: Techniques to clean and prepare data for model evaluation. Text Vectorization: Methods to convert text data into numerical format suitable for model input. Model Evaluation: Implementation of various metrics and techniques to assess the performance of machine learning models, including: Cross Validation Confusion Matrix Precision, Recall, and F1-Score R-Squared Silhouette Score for clustering models Churn Modeling for customer retention analysis ROC Curve & AUC Grid Search and Randomized Search for hyperparameter tuning Visualization: Plotting results to gain insights from the evaluation process. Structure The repository contains the following files:

1. Cross Validation and its types.ipynb: Notebook explaining cross-validation techniques.
2. XGBoost.ipynb: Notebook for training and evaluating XGBoost models. Churn_Modelling.csv: Dataset for churn modeling. Confusion Matrix, Precision, Recall, F1-Score.ipynb: Notebook for evaluating models using confusion matrix and related metrics. Grid Search and Randomized Search.ipynb: Notebook for hyperparameter tuning. R Squared.ipynb: Notebook explaining R-squared evaluation metric. ROC Curve & AUC.ipynb: Notebook for plotting ROC curves and calculating AUC. Silhouette Distance for Clustering.ipynb: Notebook for evaluating clustering models using silhouette score. Social_Network_Ads.csv: Dataset for evaluating classification models. Data The data directory contains sample datasets used for model evaluation. You can replace these with your own datasets if needed. The notebooks include code to load and preprocess this data.

Usage Follow the steps in the Jupyter Notebooks to:

Load and preprocess the data. Vectorize the text using techniques like TF-IDF. Train machine learning models using various algorithms. Evaluate the performance of models using metrics such as accuracy, precision, recall, F1-score, R-squared, silhouette score, and others. Visualize the evaluation results using plots. Contribution Contributions are welcome! If you have any improvements or suggestions, feel free to fork the repository and create a pull request.

Acknowledgements Special thanks to all the open-source contributors whose libraries and tools made this project possible.

Contact For any questions or inquiries, please contact Tashinga Bwanali at [bwanalitashinga4@gmail.com].