https://github.com/r-mahesh45/hr---resume-text-classification

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.
https://github.com/r-mahesh45/hr---resume-text-classification

data datacleaning extract-transform-load feature-extraction nlp nltk-tokenizer text-mining text-processing

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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.

• Host: GitHub
• URL: https://github.com/r-mahesh45/hr---resume-text-classification
• Owner: R-Mahesh45
• Created: 2024-06-15T06:21:03.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-02-05T18:01:26.000Z (8 months ago)
• Last Synced: 2025-06-08T08:03:52.584Z (4 months ago)
• Topics: data, datacleaning, extract-transform-load, feature-extraction, nlp, nltk-tokenizer, text-mining, text-processing
• Language: Jupyter Notebook
• Homepage:
• Size: 11.2 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
### Step-by-Step Explanation 🌟

#### 1. Data Preparation 📋

- **Objective**: Load the dataset and prepare it for analysis.

- **Actions**: 

  - Create a sample DataFrame containing document information such as file names, text content, and categories.

  - This DataFrame will be used for further processing.

#### 2. Text Preprocessing 🧹

- **Objective**: Clean and transform the raw text data into a format suitable for analysis.

- **Actions**:

  - **Convert to Lowercase**: Convert all text to lowercase to ensure uniformity.

  - **Tokenize**: Split the text into individual words.

  - **Remove Stopwords**: Remove common words (e.g., "and", "the") that do not carry significant meaning.

  - **Stemming**: Reduce words to their root forms (e.g., "running" to "run").

  - **Lemmatization**: Further reduce words to their base forms (e.g., "better" to "good").

#### 3. Feature Extraction 🔍

- **Objective**: Convert the processed text data into numerical features using TF-IDF.

- **Actions**:

  - **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.

#### 4. Encode the Target Variable 🔢

- **Objective**: Convert the categorical target variable (document category) into numerical values.

- **Actions**:

  - Use `LabelEncoder` to encode the categories into numerical values.

#### 5. Split the Data ✂️

- **Objective**: Split the dataset into training and testing sets for model evaluation.

- **Actions**:

  - Use `train_test_split` to split the data into training (80%) and testing (20%) sets.

#### 6. Model Training 🧠

- **Objective**: Train a machine learning model on the training data.

- **Actions**:

  - **Model Selection**: Use a Logistic Regression model.

  - **Training**: Fit the model on the training data.

#### 7. Model Evaluation 📊

- **Objective**: Evaluate the model's performance on both the training and testing sets.

- **Actions**:

  - **Predictions**: Make predictions on the training and testing sets.

  - **Accuracy**: Calculate the accuracy of the model on both sets.

  - **Classification Report**: Generate a classification report to provide detailed metrics (precision, recall, F1-score) for each class.

  - **Bar Plot**: Visualize the accuracy on the training and testing sets using a bar plot.

#### 8. Create Word Cloud ☁️

- **Objective**: Visualize the most important words in the dataset based on their TF-IDF scores.

- **Actions**:

  - **TF-IDF Means**: Calculate the average TF-IDF scores for each feature (word).

  - **Word Cloud Generation**: Generate a word cloud where the size of each word indicates its importance (TF-IDF score).

### Summary 🌟

This 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.