Tathagat Banerjee's repositories
Food-Sound
Food Sound
Breast_Cancer_Augmentation_With_TL
Breast Cancer Deep Learning
EEG-Motor-Imagery-Classification-CNNs-TensorFlow
EEG Motor Imagery Tasks Classification (by Channels) via Convolutional Neural Networks (CNNs) based on TensorFlow
Algorithms
A collection of algorithms and data structures
image_generator
DCGAN image generator 🖼️.
U-2-Net
Modified fork of NathanUA's U-2-Net Repository. Used for demonstration purposes.
qml-mooc
Lecture notebooks and coding assignments for the quantum machine learning MOOC created by Peter Wittek on EdX in the Spring 2019
Machines-Can-Draw-Neural-Style-Transfer
Neural Style Transfer (NST) refers to a class of software algorithms that manipulate digital images, or videos, in order to adopt the appearance or visual style of another image. NST algorithms are characterized by their use of deep neural networks for the sake of image transformation.
Python_Based_Live_Object_Detection
Machine Learning Web hosted face detector
covid19india-flutter
Flutter app for tracking Covid19 impact in India. Inspired by https://www.covid19india.org/
-QSAR-fish-bioconcentration-factor
UCI repository on ”QSAR fish bioconcentration factor , with excel plots as well as python seaborn , pandas use
CoronaVirusTrackerFlutter
Corona Virus Tracker & Advices App with Flutter
Plant-Disease-Analysis-using-Transfer-Learning
Acknowledgements This dataset was gotten from spMohanty's GitHub Repo Inspiration This dataset was created for use in my Plant Disease detection System
Fundamental-Of-Financial-Analytics
Fundamental Of Financial Analytics - Tbills
Technical-Stock-Analytics
Financial analytics is a field that gives different views of a company's financial data. It helps to gain in depth knowledge and take action against it to improve the performance of your business. Financial analytics has its effect on all parts of your business.
Airline-Selection
Airline-Selection-Analysis
Introduction-to-Genomic-Data-Sciences---Breast-cancer-Detection
# Breast-cancer-risk-prediction > Necessity, who is the mother of invention. – Plato* ## Welcome to my GitHub repository on Using Predictive Analytics model to diagnose breast cancer. --- ### Objective: The repository is a learning exercise to: * Apply the fundamental concepts of machine learning from an available dataset * Evaluate and interpret my results and justify my interpretation based on observed data set * Create notebooks that serve as computational records and document my thought process. The analysis is divided into four sections, saved in juypter notebooks in this repository 1. Identifying the problem and Data Sources 2. Exploratory Data Analysis 3. Pre-Processing the Data 4. Build model to predict whether breast cell tissue is malignant or Benign ### [Notebook 1](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB1_IdentifyProblem%2BDataClean.ipynb): Identifying the problem and Getting data. **Notebook goal:Identify the types of information contained in our data set** In this notebook I used Python modules to import external data sets for the purpose of getting to know/familiarize myself with the data to get a good grasp of the data and think about how to handle the data in different ways. ### [Notebook 2](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB2_ExploratoryDataAnalysis.ipynb) Exploratory Data Analysis **Notebook goal: Explore the variables to assess how they relate to the response variable** In this notebook, I am getting familiar with the data using data exploration and visualization techniques using python libraries (Pandas, matplotlib, seaborn. Familiarity with the data is important which will provide useful knowledge for data pre-processing) ### [Notebook 3](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB3_DataPreprocesing.ipynb) Pre-Processing the data **Notebook goal:Find the most predictive features of the data and filter it so it will enhance the predictive power of the analytics model.** In this notebook I use feature selection to reduce high-dimension data, feature extraction and transformation for dimensionality reduction. This is essential in preparing the data before predictive models are developed. ### [Notebook 4](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB4_PredictiveModelUsingSVM.ipynb) Predictive model using Support Vector Machine (svm) **Notebook goal: Construct predictive models to predict the diagnosis of a breast tumor.** In this notebook, I construct a predictive model using SVM machine learning algorithm to predict the diagnosis of a breast tumor. The diagnosis of a breast tumor is a binary variable (benign or malignant). I also evaluate the model using confusion matrix the receiver operating curves (ROC), which are essential in assessing and interpreting the fitted model. ### [Notebook 5](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB_5%20OptimizingSVMClassifier.ipynb): Optimizing the Support Vector Classifier **Notebook goal: Construct predictive models to predict the diagnosis of a breast tumor.** In this notebook, I aim to tune parameters of the SVM Classification model using scikit-learn.
Chest-Pneumonia
Chest Pneumonia