{"id":24804989,"url":"https://github.com/R-Mahesh45/HR---Resume-Text-Classification","last_synced_at":"2025-10-13T06:32:04.790Z","repository":{"id":244501925,"uuid":"815430059","full_name":"R-Mahesh45/HR---Resume-Text-Classification","owner":"R-Mahesh45","description":"Text Classification for Resumes: Conducted Exploratory Data Analysis (EDA) on a vast collection of resumes. Organized the data using Bag of Words (BoW) and TF-IDF techniques. Built and evaluated multiple models, with Logistic Regression delivering standout performance. Created Word Clouds and Histograms.","archived":false,"fork":false,"pushed_at":"2025-02-05T18:01:26.000Z","size":11740,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-09-12T21:21:01.895Z","etag":null,"topics":["data","datacleaning","extract-transform-load","feature-extraction","nlp","nltk-tokenizer","text-mining","text-processing"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/R-Mahesh45.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2024-06-15T06:21:03.000Z","updated_at":"2025-05-09T09:10:52.000Z","dependencies_parsed_at":"2024-06-15T07:32:02.249Z","dependency_job_id":"6031ab07-90ee-4014-8c16-fe968a0a04f1","html_url":"https://github.com/R-Mahesh45/HR---Resume-Text-Classification","commit_stats":null,"previous_names":["r-mahesh45/internship-text-mining-resume-classification-","r-mahesh45/resume-text-classification-and-data-visualization","r-mahesh45/hr---resume-text-classification"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/R-Mahesh45/HR---Resume-Text-Classification","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/R-Mahesh45%2FHR---Resume-Text-Classification","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/R-Mahesh45%2FHR---Resume-Text-Classification/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/R-Mahesh45%2FHR---Resume-Text-Classification/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/R-Mahesh45%2FHR---Resume-Text-Classification/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/R-Mahesh45","download_url":"https://codeload.github.com/R-Mahesh45/HR---Resume-Text-Classification/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/R-Mahesh45%2FHR---Resume-Text-Classification/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":279013968,"owners_count":26085429,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-10-13T02:00:06.723Z","response_time":61,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data","datacleaning","extract-transform-load","feature-extraction","nlp","nltk-tokenizer","text-mining","text-processing"],"created_at":"2025-01-30T07:16:16.362Z","updated_at":"2025-10-13T06:32:04.775Z","avatar_url":"https://github.com/R-Mahesh45.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"### Step-by-Step Explanation 🌟\n\n#### 1. Data Preparation 📋\n- **Objective**: Load the dataset and prepare it for analysis.\n- **Actions**: \n  - Create a sample DataFrame containing document information such as file names, text content, and categories.\n  - This DataFrame will be used for further processing.\n\n#### 2. Text Preprocessing 🧹\n- **Objective**: Clean and transform the raw text data into a format suitable for analysis.\n- **Actions**:\n  - **Convert to Lowercase**: Convert all text to lowercase to ensure uniformity.\n  - **Tokenize**: Split the text into individual words.\n  - **Remove Stopwords**: Remove common words (e.g., \"and\", \"the\") that do not carry significant meaning.\n  - **Stemming**: Reduce words to their root forms (e.g., \"running\" to \"run\").\n  - **Lemmatization**: Further reduce words to their base forms (e.g., \"better\" to \"good\").\n\n#### 3. Feature Extraction 🔍\n- **Objective**: Convert the processed text data into numerical features using TF-IDF.\n- **Actions**:\n  - **TF-IDF Vectorization**: Transform the text data into a matrix of TF-IDF features. TF-IDF (Term Frequency-Inverse Document Frequency) captures the importance of each word in the document relative to the entire corpus.\n\n#### 4. Encode the Target Variable 🔢\n- **Objective**: Convert the categorical target variable (document category) into numerical values.\n- **Actions**:\n  - Use `LabelEncoder` to encode the categories into numerical values.\n\n#### 5. Split the Data ✂️\n- **Objective**: Split the dataset into training and testing sets for model evaluation.\n- **Actions**:\n  - Use `train_test_split` to split the data into training (80%) and testing (20%) sets.\n\n#### 6. Model Training 🧠\n- **Objective**: Train a machine learning model on the training data.\n- **Actions**:\n  - **Model Selection**: Use a Logistic Regression model.\n  - **Training**: Fit the model on the training data.\n\n#### 7. Model Evaluation 📊\n- **Objective**: Evaluate the model's performance on both the training and testing sets.\n- **Actions**:\n  - **Predictions**: Make predictions on the training and testing sets.\n  - **Accuracy**: Calculate the accuracy of the model on both sets.\n  - **Classification Report**: Generate a classification report to provide detailed metrics (precision, recall, F1-score) for each class.\n  - **Bar Plot**: Visualize the accuracy on the training and testing sets using a bar plot.\n\n#### 8. Create Word Cloud ☁️\n- **Objective**: Visualize the most important words in the dataset based on their TF-IDF scores.\n- **Actions**:\n  - **TF-IDF Means**: Calculate the average TF-IDF scores for each feature (word).\n  - **Word Cloud Generation**: Generate a word cloud where the size of each word indicates its importance (TF-IDF score).\n\n### Summary 🌟\n\nThis step-by-step explanation guides you through the process of preparing text data, extracting features, training and evaluating a machine learning model, and visualizing important words using a word cloud. This comprehensive approach provides insights into the importance of different words in your dataset and helps you understand the performance of your classification model.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FR-Mahesh45%2FHR---Resume-Text-Classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FR-Mahesh45%2FHR---Resume-Text-Classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FR-Mahesh45%2FHR---Resume-Text-Classification/lists"}