Learn To Predict Breast Cancer Using Machine Learning

🎉 Learn To Predict Breast Cancer Using Machine Learning 🎓

Headline: Dive into the world of machine learning and predict breast cancer with precision! In this comprehensive course, you'll learn to construct three fundamental machine learning models: Logistic Regression, Decision Tree, and Random Forest Classifier, all from scratch using Scikit-learn. Join instructor Megha Ghosh in this insightful journey tailored for those with a Python programming background and theoretical knowledge of the aforementioned algorithms.

Course Description:

Welcome to an engaging online course where you will learn to harness the power of machine learning to classify breast cancer as either Malignant or Benign. With hands-on experience, you'll be working with the Breast Cancer Wisconsin (Diagnostic) Data Set from Kaggle, a rich resource for your predictive modeling endeavors.

Prerequisite:

• Proficiency in Python programming
• Theoretical understanding of Logistic Regression model, Decision Tree model, and Random Forest Classifier model

Learn Step-By-Step:

1. Loading Dataset:

   • Introduction and Import Libraries 🚀
   • Download Dataset directly from Kaggle 📁
   • 2nd Way To Load Data To Colab 🛠️

2. Exploratory Data Analysis (EDA):

   • Checking The Total Number Of Rows And Columns 🔍
   • Checking The Columns And Their Corresponding Data Types 💻
   • Finding Null Values 🚫
   • Dropping The Column With All Missing Values ⏹️
   • Checking Datatypes 📈

3. Visualization:

   • Display A Count Of Malignant (M) Or Benign (B) Cells 📊
   • Visualizing The Counts Of Both Cells 🤹
   • Perform LabelEncoding - Encode The 'diagnosis' Column 🔑
   • Pair Plot - Plot Pairwise Relationships In A Dataset 🌀
   • Get The Correlation Of The Columns 📈

4. Dataset Manipulation on ML Algorithms:

   • Split the data into Independent and Dependent sets for Feature Scaling ⚖️
   • Scaling The Dataset - Feature Scaling 🔫

5. Create Function For Three Different Models:

   • Building Logistic Regression Classifier 🎯
   • Building Decision Tree Classifier 🌳
   • Building Random Forest Classifier 🌲

6. Evaluate the performance of the model:

   • Printing Accuracy Of Each Model On The Training Dataset 📝
   • Model Accuracy On Confusion Matrix 🧠
   • Prediction 🔮

Conclusion: By completing this course, you will not only be able to build robust classifiers for breast cancer prediction but also gain proficiency in setting up and working with the Google Colab environment. You'll learn the intricacies of cleaning and preparing data for analysis, ensuring a solid foundation in machine learning for future endeavors.

Embark on this enlightening path with Megha Ghosh and turn your passion for machine learning into actionable insights that could change lives. Enroll now and take the first step towards mastering machine learning for predictive healthcare applications! 🚀💪

Note: This course is designed for learners who are comfortable with Python and have a basic understanding of machine learning concepts. It will guide you through every step, from data loading to model evaluation, ensuring a comprehensive learning experience. Get ready to make a real-world impact with your machine learning skills! 🌟