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Learn To Predict Breast Cancer Using Machine Learning

Learn to build three Machine Learning models (Logistic regression, Decision Tree, Random Forest) from scratch

Learn To Predict Breast Cancer Using Machine Learning Learn to build three Machine Learning models (Logistic regression, Decision Tree, Random Forest) from scratch
Learn To Predict Breast Cancer Using Machine Learning Learn to build three Machine Learning models (Logistic regression, Decision Tree, Random Forest) from scratch

Use Python for Machine Learning to classify breast cancer as either Malignant or Benign.
Implement Machine Learning Algorithms
Exploratory Data Analysis
Learn to use Pandas for Data Analysis
Learn to use NumPy for Numerical Data
Learn to use Matplotlib for Python Plotting
Use Plotly for interactive dynamic visualizations
Learn to use Seaborn for Python Graphical Representation
Logistic Regression
Random Forest and Decision Trees

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Requirements
Basics of Python
Some high school mathematics level.
Some programming experience
Description
Here you will learn to build three models that are Logistic regression model, the Decision Tree model, and Random Forest Classifier model using Scikit-learn to classify breast cancer as either Malignant or Benign.

We will use the Breast Cancer Wisconsin (Diagnostic) Data Set from Kaggle.

Prerequisite

You should be familiar with the Python Programming language and you should have a theoretical understanding of the three algorithms that is Logistic regression model, Decision Tree model, and Random Forest Classifier model.

Learn Step-By-Step

In this course you will be taught through these steps:

Section 1: Loading Dataset

Introduction and Import Libraries

Download Dataset directly from Kaggle

2nd Way To Load Data To Colab

Section 2: EDA – Exploratory Data Analysis

Checking The Total Number Of Rows And Columns

Checking The Columns And Their Corresponding Data Types (Along With Finding Whether They Contain Null Values Or Not)

2nd Way To Check For Null Values

Dropping The Column With All Missing Values

Checking Datatypes

Section 3: Visualization

Display A Count Of Malignant (M) Or Benign (B) Cells

Visualizing The Counts Of Both Cells

Perform LabelEncoding – Encode The ‘diagnosis’ Column Or Categorical Data Values

Pair Plot – Plot Pairwise Relationships In A Dataset

Get The Correlation Of The Columns -> How One Column Can Influence The Other Visualizing The Correlation

Section 4: Dataset Manipulation on ML Algorithms

Split the data into Independent and Dependent sets to perform Feature Scaling

Scaling The Dataset – Feature Scaling

Section 5: Create Function For Three Different Models

Building Logistic Regression Classifier

Building Decision Tree Classifier

Building Random Forest Classifier

Section 6: Evaluate the performance of the model

Printing Accuracy Of Each Model On The Training Dataset

Model Accuracy On Confusion Matrix

2nd Way To Get Metrics

Prediction

Conclusion

By the end of this project, you will be able to build three classifiers to classify cancerous and noncancerous patients. You will also be able to set up and work with the Google colab environment. Additionally, you will also be able to clean and prepare data for analysis.

Who this course is for:
Interested in the field of Machine Learning? Then this course is for you!
This course had been designed to be your guide to learning how to use the power of Python to analyze data, create some good beautiful visualization for better understanding and use some powerful machine learning algorithms.
This course will also give you a hands-on walk through step-by-step into the world of machine learning and how amazing it is to make prediction on some serious real-life problems. This course will not only help you develop new skills and improve your understanding but also grow confidence in you.

We will be happy to hear your thoughts

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