Yogesh1108-Predicting-Common-Sports-Injuries-of-Track(Team-G-17)

Detailed Technical Description of Files

1. app.py

The app.py script is designed to create a web application interface using Streamlit, a popular framework for building interactive web apps in Python. The primary functionality of this app is to provide a user-friendly interface for predicting outcomes based on the provided dataset.

Key Components and Functionality:

• Importing Libraries: The script imports essential libraries such as streamlit, pandas, numpy, and joblib. These libraries are crucial for data manipulation, numerical computations, and loading machine learning models.

• Loading Data and Model: The script loads the preprocessed dataset and the trained machine learning model. It uses the joblib library to load the serialized model file, ensuring efficient loading and prediction.

• User Interface Creation: The script utilizes Streamlit's functions (st.title, st.text_input, st.number_input, etc.) to create an interactive web interface. Users can input various features required for prediction.

• Prediction Logic: Based on the user inputs, the script processes the data and uses the loaded model to make predictions. The results are displayed back to the user through the web interface.

• Additional Features: The script includes additional features like displaying dataset information, providing user instructions, and integrating visual elements for better user engagement.

2. predict.py

The predict.py script is focused on handling the prediction logic independently from the web interface. This modular approach allows for more flexible use, such as batch predictions or integration with other systems.

Key Components and Functionality:

• Importing Libraries: Similar to app.py, this script imports necessary libraries for data manipulation and model loading.

• Loading Model: The trained machine learning model is loaded using joblib, ensuring quick and efficient prediction capabilities.

• Data Preprocessing: The script includes functions to preprocess input data, ensuring it matches the format and scale expected by the model. This step is crucial for maintaining prediction accuracy.

• Prediction Function: The core function takes preprocessed data as input and returns the model's prediction. This function is designed to be reusable in various contexts, such as batch processing or API endpoints.

3. train.py

The train.py script is responsible for training the machine learning model. It includes data loading, preprocessing, model training, and evaluation steps.

Key Components and Functionality:

• Importing Libraries: The script imports libraries for data manipulation (pandas, numpy), machine learning (scikit-learn), and job serialization (joblib).

• Loading Data: The script loads the dataset from a specified file path and performs initial exploratory data analysis to understand the dataset's structure and content.

• Data Preprocessing: This step includes handling missing values, encoding categorical variables, scaling numerical features, and splitting the data into training and testing sets.

• Model Training: The script defines the machine learning model (e.g., Decision Tree, KNN, SVM) and trains it using the preprocessed training data. It also includes hyperparameter tuning to optimize model performance.

• Model Evaluation: The trained model is evaluated using various metrics such as accuracy, precision, recall, and F1-score. This step ensures the model's performance is adequate before deployment.

• Saving Model: The trained model is serialized and saved to a file using joblib, making it easy to load and use for predictions in other scripts.

Dataset Information

The dataset top_10_features_dataset.csv contains several features related to physical training sessions. Here is a breakdown of the key features:

• nr. rest days: Number of rest days in a given period.
• nr. strength trainings: Number of strength training sessions.
• nr. tough sessions (effort in Z5, T1, or T2): Number of tough training sessions.
• Date: The date of the record.
• total hours alternative training: Total hours spent in alternative training.
• nr. days with interval session: Number of days with interval training sessions.
• injury: Indicator of injury (binary value).

Each row in the dataset represents a specific training period, capturing various metrics related to physical training and injury occurrences. The dataset is used to train a machine learning model to predict potential injuries based on training patterns and other factors.

Overall Workflow

1. Data Collection and Preprocessing: Collect and preprocess the dataset to ensure it is clean and suitable for model training. This includes handling missing values, encoding categorical features, and scaling numerical features.

2. Model Training: Train a machine learning model using the preprocessed dataset. Evaluate the model's performance using various metrics to ensure its accuracy and reliability.

3. Model Deployment: Deploy the trained model using a web application (Streamlit) to provide an interactive interface for users to input data and receive predictions.

4. Prediction and Evaluation: Continuously monitor and evaluate the model's performance in the deployed environment. Make necessary adjustments to improve accuracy and user experience.

Conclusion

This project demonstrates the complete workflow of developing a machine learning model, from data collection and preprocessing to model training, deployment, and prediction. By leveraging Streamlit for the web interface and adhering to best practices in machine learning, the project ensures a robust and user-friendly application for predicting outcomes based on physical training data.