Resume classification is the task of automatically categorizing resumes into pre-defined classes or job positions based on the information contained in the resumes. This is typically accomplished using machine learning algorithms such as Support Vector Machines (SVMs), Decision Trees, or Neural Networks. The input to the classification algorithm is typically the text of the resume, which may be preprocessed to extract relevant features such as skills, education, and work experience. The output of the classification algorithm is the predicted job position for the resume. Resume classification is commonly used in the human resources and recruitment industries to streamline the resume screening and shortlisting process.
1D Convolutional Neural Network (1D CNN) is a type of Convolutional Neural Network that is used for processing 1-dimensional data, such as signals and sequences. It is commonly used for tasks such as speech recognition, audio classification, and natural language processing. 1D CNN applies filters to local regions of the input, and slides the filters along the input to learn local patterns. The use of convolutional layers allows 1D CNN to maintain the spatial information in the input data while reducing the number of parameters in the model, resulting in a more efficient and effective learning process. A 1D Convolutional Neural Network (1D CNN) architecture consists of several key components:
Input layer: The input layer accepts the 1-dimensional data that the network will process.
Convolutional layer: One or more convolutional layers apply filters to local regions of the input data and produce feature maps. The filters slide along the input data and learn local patterns in the data.
Pooling layer: One or more pooling layers down-sample the feature maps produced by the convolutional layer, reducing their spatial dimensions and preserving the most important information.
Fully connected layer: One or more fully connected layers use the output of the pooling layer as input to make predictions based on the learned features.
Output layer: The output layer produces the final prediction for the input data, typically in the form of a probability distribution over the possible classes.
The architecture of a 1D CNN can vary depending on the specific task and the size and complexity of the input data. Larger and more complex architectures may include additional convolutional, pooling, and fully connected layers, as well as dropout layers to prevent overfitting. The choice of the architecture will depend on the specifics of the problem and the available computational resources.
In Natural Language Processing (NLP), 1D Convolutional Neural Network (1D CNN) is used for tasks such as text classification and sentiment analysis. In these tasks, the input to the 1D CNN is typically a sequence of word embeddings, which represent the words in a text as high-dimensional vectors. The 1D CNN then applies filters to the word embeddings to learn local patterns in the text. This allows the 1D CNN to effectively capture the relationships between words and their context, which is important for NLP tasks such as sentiment analysis. Additionally, 1D CNN can be combined with other NLP techniques, such as Recurrent Neural Networks (RNNs) and Transformers, to create powerful models for NLP tasks.
- Model.ipynb: Includes code for data preprocessing, 1D-CNN model and code to save the model in h5py.
- ResumeDataSet.csv (main)
- cv.pdf (test)
Model accuracy is: 0.9584774971008301
Training and validation graphs are visual representations of the performance of a machine learning model during the training and validation phases. These graphs are useful for monitoring the progress of training and for assessing the quality of the model.
Loss plot: The loss plot shows the value of the loss function over each epoch of training. The loss function measures how well the model is performing on the training data, and a decreasing trend in the loss plot indicates that the model is improving over time.
Accuracy plot: The accuracy plot shows the accuracy of the model on the training or validation data over each epoch of training. A rising trend in the accuracy plot indicates that the model is improving over time, while a declining trend may indicate overfitting or a poorly trained model.
