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[Free] Learn To Predict Breast Cancer Using Machine Learning

Learn to construct three Machine Learning fashions (Logistic regression, Determination Tree, Random Forest) from scratch – Free Course

What you’ll be taught

  • Use Python for Machine Learning to categorise breast most cancers as both Malignant or Benign.
  • Implement Machine Learning Algorithms
  • Exploratory Information Evaluation
  • Learn to make use of Pandas for Information Evaluation
  • Learn to make use of NumPy for Numerical Information
  • Learn to make use of Matplotlib for Python Plotting
  • Use Plotly for interactive dynamic visualizations
  • Learn to make use of Seaborn for Python Graphical Illustration
  • Logistic Regression
  • Random Forest and Determination Timber


  • Fundamentals of Python
  • Some highschool arithmetic degree.
  • Some programming expertise


Right here you’ll be taught to construct three fashions which are Logistic regression mannequin, the Determination Tree mannequin, and Random Forest Classifier mannequin utilizing Scikit-learn to categorise breast most cancers as both Malignant or Benign.

We’ll use the Breast Cancer Wisconsin (Diagnostic) Information Set from Kaggle.


Try to be acquainted with the Python Programming language and you must have a theoretical understanding of the three algorithms that’s Logistic regression mannequin, Determination Tree mannequin, and Random Forest Classifier mannequin.

Learn Step-By-Step

On this course you can be taught by means of these steps:

  • Part 1: Loading Dataset
    • Introduction and Import Libraries
    • Obtain Dataset straight from Kaggle
    • 2nd Method To Load Information To Colab
  • Part 2: EDA – Exploratory Information Evaluation
    • Checking The Complete Quantity Of Rows And Columns
    • Checking The Columns And Their Corresponding Information Sorts (Alongside With Discovering Whether or not They Include Null Values Or Not)
    • 2nd Method To Examine For Null Values
    • Dropping The Column With All Lacking Values
    • Checking Datatypes
  • Part 3: Visualization
    • Show A Rely Of Malignant (M) Or Benign (B) Cells
    • Visualizing The Counts Of Each Cells
    • Carry out LabelEncoding – Encode The ‘analysis’ Column Or Categorical Information Values
    • Pair Plot – Plot Pairwise Relationships In A Dataset
    • Get The Correlation Of The Columns -> How One Column Can Affect The Different Visualizing The Correlation
  • Part 4: Dataset Manipulation on ML Algorithms
    • Break up the info into Impartial and Dependent units to carry out Function Scaling
    • Scaling The Dataset – Function Scaling
  • Part 5: Create Perform For Three Totally different Fashions
    • Constructing Logistic Regression Classifier
    • Constructing Determination Tree Classifier
    • Constructing Random Forest Classifier
  • Part 6: Consider the efficiency of the mannequin
    • Printing Accuracy Of Every Mannequin On The Coaching Dataset
    • Mannequin Accuracy On Confusion Matrix
      • 2nd Method To Get Metrics
    • Prediction


By the tip of this challenge, it is possible for you to to construct three classifiers to categorise cancerous and noncancerous sufferers. Additionally, you will be capable of arrange and work with the Google colab atmosphere. Moreover, additionally, you will be capable of clear and put together knowledge for evaluation.

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