What's included

• Data Collection and Preparation

  Well-organized datasets cleaned, normalized, and ready for model training.

• Exploratory Data Analysis (EDA)

  EDA report with visualizations highlighting key data patterns, distributions, correlations, and potential predictive features.

• Model Development and Training

  Implementation of machine learning models (e.g., regression, classification, clustering) based on project requirements. Hyperparameter tuning, cross-validation, and performance metrics for optimal model accuracy.

• Model Evaluation and Visualization

  Comprehensive evaluation using metrics like accuracy, precision, recall, F1-score, or AUC. Visual explanations of model behavior (e.g., confusion matrices, feature importance graphs, ROC curves).

• Final Reports and Documentation

  Detailed final report with model results, insights, and recommendations for decision-making.

• Consultation and Support

  Guidance on integrating models into existing systems or recommendations for further improvements.