# Documentation

Dataset Understanding:
- Loaded the medical transcription dataset ('mtsamples.csv').
- Displayed basic information about the dataset, including features and labels.
- Displayed basic statistics about the numerical columns.
- Displayed unique values in the 'medical_specialty' column.

Data Preprocessing:
- Cleaned text by removing special characters, lowercasing, and tokenization.
- Handled missing values and duplicates.
- Split the data into training and validation sets.

Train/Fine-tune on given domain-specific dataset:
- Extracted features using TF-IDF.
- Trained a RandomForestClassifier.

 Incorporate a language model:
- Incorporated spaCy for tokenization.

Evaluate the effectiveness:
- Evaluated the model using the classification report.

EDA on the train data and test results:
- Explored the distribution of medical specialties.
- Visualized the most common words in transcriptions.
- Visualized the confusion matrix.

Challenges and Solutions:
- Handled missing values and duplicates to ensure clean data.
- Adapted the code to handle variations in the dataset structure.
- have trouble with the spacy library.

Results:
- Achieved insights into the distribution of medical specialties and common words.
- Evaluated the model's performance using the confusion matrix.

Future Considerations:
- Further fine-tuning of the model for improved performance.
- Exploration of advanced language models for better feature extraction.