What's included

• Data Collection and Cleaning:

  Cleaned and preprocessed dataset ready for analysis, ensuring data integrity and reliability.

• Exploratory Data Analysis (EDA) Report:

  In-depth analysis of the dataset, including visualizations and insights, to understand key patterns and trends.

• Feature Engineering:

  Identification and creation of relevant features that contribute to the predictive power of the machine learning model.

• Model Building:

  Trained machine learning models using suitable algorithms, demonstrating the ability to predict outcomes based on input data.

• Model Evaluation Metrics:

  Performance metrics such as accuracy, precision, recall, and F1-score to assess the model's effectiveness.

• Hyperparameter Tuning:

  Fine-tuned model parameters to optimize performance and enhance predictive accuracy.

• Final Report and Presentation:

  A comprehensive final report summarizing the entire project, including methodologies, results, and recommendations. A presentation may also be included for a detailed walkthrough.