We have been employed as an ML Engineer in a Hospital (P-Health), upon reaching there we noticed that our company spends a lot of money and resources running tests and examinations on patients to see if they have lung cancer or not.

We have proposed that we can build a ML model that can predict the patient that have higher chances of having cancer, of course we our model won't be 100% sure if someone has or not, but we would create one that predicts if someone that has lung cancer more

• Type of Machine Learning Method
  • Supervised Learning
    • Classification
      • Random Forest
        • Accuracy - 95.16 %
        • F1 Score - 97.35%
        • Recall - 100%
      • SVM
        • Accuracy - 88.71 %
        • F1 Score - 94.02%
      • KNN
        • Accuracy - 91.16 %
        • F1 Score - 95.35%

• Import Dataset
• Exploratory Data Analysis
• Feature Engineering
  • Data Cleaning
  • Missing Data Imputation
  • Feature Encoding
• Model Build
  • Train / Test Data split
  • Model Initiation and Fitting
  • Test predictions
• Model Perfromance
  • MAE
  • R^2 score
  • Case Prediction