Breast Cancer Prediction Machine Learning Project

Breast Cancer Prediction
: Breast cancer is the most common type of cancer in women. When cancers are found early, they can often be cured. There are some devices that detect the breast cancer but many times they lead to false positives, which results is patients undergoing painful, expensive surgeries that were not even necessary. These type of cancers are called
benign
which do not require surgeries and we can reduce these unnecessary surgeries by using Machine Learning. We take a dataset of the previous breast cancer patients and train the model to predict whether the cancer is
benign
or
malignant
. These predictions will help doctors to do surgeries only when the cancer is malignant, thus reducing the unnecessary surgeries for woman.
Models
Logistic Regression model
is developed based on 10 features that classify whether the breast cancer is benign or
malignant
. For classifying the patient, users are requested to submit their data on this following form as per the value range.
Languages Used
Python: language
NumPy: library for numerical calculations
Pandas: library for data manipulation and analysis
SkLearn: library which features various classification, regression and clustering algorithms
Flask: microframework for building web applications using Python.
Installation Steps :-
Install Python 3.7.0
Install all dependencies cmd -
python -m pip install --user -r requirements.txt
Finally run cmd -
python app.py
Latest Machine Learning Project with Source Code

Detailed Project Description

Breast Cancer Prediction Machine Learning Project applies machine learning to a real dataset: it prepares the data, trains and evaluates a model, and exposes the trained model through a simple interface so predictions can be made on new inputs.

How It Works
1. The dataset is collected/loaded, cleaned and explored (missing values, encoding, scaling).
2. Features are engineered and the data split into training and test sets.
3. One or more models are trained and compared using accuracy, precision/recall, F1 or RMSE.
4. The best model is saved and served through a web/GUI form that returns predictions.

Technology Roles
- Python
- Flask
- Machine Learning

Implementation Steps
1. Collect the dataset (public repository or own data) and understand each attribute.
2. Set up the environment (Python, Flask, Machine Learning) and load the data into a notebook.
3. Clean the data: handle missing values, outliers, encoding and scaling; visualise distributions.
4. Split into train/validation/test sets and engineer relevant features.
5. Train several candidate models, tune hyper-parameters and compare metrics.
6. Save the best model and wrap it in a simple web/GUI form for live predictions.
7. Document results with plots, confusion matrix / error charts and conclusions.

Key Features
- Data pre-processing pipeline
- Model comparison
- Evaluation metrics and plots
- Prediction interface

Expected Output
On completion the student will have evaluation results (metrics, confusion matrix or error plots) and a working prediction form.

Deliverables
- Notebook / source code with comments
- Cleaned dataset and trained model file
- Evaluation report with metrics and plots
- Prediction interface