NAME:SAILAJA DONGA
COMPANY:CODTECH IT SOLUTIONS
ID:CT08DS521
DOMAIN:DATASCIENCE
DURATION:SEPTEMBER TO NOVEMBER

Classification Project with Predictive Modeling

This project builds predictive models using various classification algorithms on a labeled dataset. It covers data preprocessing, model training, evaluation, and performance comparison to determine the most suitable model for the task. The classification algorithms explored include Logistic Regression, Decision Trees, Random Forests, and Support Vector Machines (SVM).

Project Workflow

1. Data Preprocessing

• Cleaning: Handling missing values and removing duplicates.
• Encoding: Transforming categorical features into numerical values.
• Scaling: Normalizing or standardizing data to enhance model performance.

2. Data Splitting

• Dividing the dataset into training and testing sets to validate model effectiveness.

3. Model Training

• Applying several classification algorithms to the training set:
  • Logistic Regression
  • Decision Tree Classifier
  • Random Forest Classifier * Support Vector Machine (SVM)

4. Model Evaluation

• Accuracy: Measures the proportion of correctly predicted instances.
• Precision: Indicates how many of the predicted positive cases are true positives.
• Recall: Shows how many actual positive cases were captured by the model.
• F1-Score: Balances precision and recall, particularly useful for imbalanced datasets.

5. Model Comparison

• Comparing the models’ performance based on evaluation metrics to identify the best model for this dataset.

Results and Conclusion

• The project includes a comparison of model performance, and the model with the highest accuracy, precision, recall, and F1-score is identified as the most suitable for the task.

Project Structure

• Data: Contains the dataset used for modeling.
• Documentation: Includes project information and guidelines for use.
• Evaluation Metrics: A summary of results for each model, providing insights into their performance. #Usage

1. Preprocess the Data: Clean, encode, and scale the dataset.
2. Train Models: Apply classification algorithms to build predictive models.
3. Evaluate and Compare: Use evaluation metrics to assess model performance and select the best-performing model.

Contributing

Contributions are welcome. Please feel free to fork this repository, make changes, and submit pull requests for review.