BREAST CANCER DIAGNOSIS

MOTIVATION:

• In India one in every 28 women has a possiblity of getting Breast Cancer.
• If left un diagnoised it could be fatal.
• So efficient diagnositc methods are required.
• In this project we will try to make diagnois using Machine Learning.

TASK:

• So in this project we try to predict whether a lump is benign or Malignant.

DATASET:

• The dataset can be downloaded here
• 

FEATURES:

FEATURES	DESCRIPTION
* MEAN_RADIUS	Radius of the lump formed
* MEAN_TEXTURE	The texture of the lump formed
* MEAN_PERIMETER	perimeter of the lump
* MEAN_AREA	Total area occupied by the lump
* MEAN_SMOOTHNESS	Smoothness of the lump

LABEL:

• Given the above features we have to diagnois whether the lump is cancerous or not.
• Benign(0) or Malignant(1).

MODELS USED:

• Logistic Regression
• Decision Tree
• Support Vector Machine
• Random Forest
• Boosting(using decision tree)
• MLP Classfier

F1_SCORE COMPARISION

• This barplot helps us find the best model.
• 
• The best model is random forest.

MODEL SELECTION:

• The model with the best F1_score was Boosting using decision tree.
• However the chosen model is Logistic Regression.
• The reason is that the F1_score of Logistic regression is that it is way faster than that of the Boosting.
• As well as the F1_score of the Logistic Regression is nearly equal to that of the Boosting.

CLASSIFICATION REPORT

•