Prediction of Breast Cancer using Machine Learning | Python Final Year IEEE Project

Prediction of Breast Cancer using Machine Learning | Python Final Year IEEE Project.
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📌IEEE Base Paper Title: Prediction of Breast Cancer, Comparative Review of Machine Learning Techniques, and Their Analysis.
🔬Our Proposed Project Title: Prediction of Breast Cancer using Machine Learning.
💡Implementation Code: Python.
🚀Algorithm / Model Used: Logistics regression.
🌐Web Framework: Flask.
🖥️Frontend: HTML, CSS, JavaScript.
💰Cost (In Indian Rupees): Rs.3000/.
IEEE Base paper Abstract:
Breast cancer is type of tumor that occurs in the tissues of the breast. It is most common type of cancer found in women around the world and it is among the leading causes of deaths in women. This article presents the comparative analysis of machine learning, deep learning and data mining techniques being used for the prediction of breast cancer. Many researchers have put their efforts on breast cancer diagnoses and prognoses, every technique has different accuracy rate and it varies for different situations, tools and datasets being used. Our main focus is to comparatively analyze different existing Machine Learning and Data Mining techniques in order to find out the most appropriate method that will support the large dataset with good accuracy of prediction. The main purpose of this review is to highlight all the previous studies of machine learning algorithms that are being used for breast cancer prediction and this article provides the all necessary information to the beginners who want to analyze the machine learning algorithms to gain the base of deep learning.
REFERENCE:
NOREEN FATIMA, LI LIU, SHA HONG, AND HAROON AHMED, “Prediction of Breast Cancer, Comparative Review of Machine Learning Techniques, and Their Analysis”, IEEE ACCESS, 2020.
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[@tanishsakdeo1681]

Out of all the columns in the dataset, how did you decide which features to use and ask their input from the user?

[@jpinfotechprojects]

If you're using a machine learning algorithm, some models provide feature importance scores. These scores indicate how much each feature contributes to the model's predictive power. Features with high importance scores are more likely to be useful.
The goal of feature selection is to find the most relevant and informative subset of features that will lead to accurate and robust predictions or insights from the data. Different models and problems may require different sets of features, so it's essential to iterate and experiment to find the most suitable features for the specific task at hand.

[@priyanshgupta4464]

which languages were used , was html,css ,js ,mern stack was used?

[@jpinfotechprojects]

FrontEnd: HTML, CSS, JS, Flask.
BackEnd: Python.

[@MrBaik123]

hi sir, what algorithm use for this project? is it random forest?

[@jpinfotechprojects]

Logistic regression

[@bintehawa7712]

Where did u add a AI model link in web code,?

[@jpinfotechprojects]

After the model is developed then we need to move it into the production-ready environment, the first step is to save it into a .h5 or .pkl file using a library like pickle. Make sure you have pickle installed in your environment. Next, let’s import the module and dump the model into . pkl file. Link to the web code

[@SwarnaliMollickA]

Would you please give me the dataset link?

[@jpinfotechprojects]

The dataset used in this Breast Cancer dataset taken from UCI Machine Learning Link: archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29

[@DevilKing-kh7lb]

Can i upload any data set ?

[@jpinfotechprojects]

Sorry, no.

[@dico3250]

please give me the code source ..

[@jpinfotechprojects]

It's a Paid project. If you need it, you can get the details from our email ID: [e-mail] the

[@vitthalvikash1985]

plz give source code

[@jpinfotechprojects]

It's a paid project. If you need it, you can email to: [e-mail]

[@nikhildhande6611]

Sir from where will get these value area mean and all

[@jpinfotechprojects]

Kaggle