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analysing-haberman-dataset

Objective: Analyse the data from Haberman's Survival Dataset,that: 1)How many patients are live morethan 5years and also live within 5years in agewise. 2)How many patients are survived more than 5years after the operation was done. 3)How many patients are died within 5years one operation is done. 4)How many patients are live more than 5years eventhough they have lots of symptoms(lymph_nodes) 5)How many patients are died within 5years eventhough they have less no of symptoms (lymph_nodes) 6)And also we have to find the finest Analysis of particular dataset(Haberman's Survival Dataset) 1)Univaraite analysis(PDF, CDF, Boxplot, Voilin plots) 2)Bi-variate analysis (scatter plots, pair-plots) 3)High level statics of dataset[mean,median,SD,Quantiles]

cnn-rnn

machine-learning-models

Using various model for Amazon Fine Food Dataset

personalized-cancer-diagnosis

Its been divided as Four different Task with same dataset.1)Applying only Tfidf Vectorizer2)Used only 1000 top most words 3)Applying BOW with Logistic regression 4)Used Feature engineering to get best result That means less MSE(misclassified error)

practice-web

python_for_microscopists

https://www.youtube.com/channel/UC34rW-HtPJulxr5wp2Xa04w?sub_confirmation=1

sentimental-analysis-ml

Opinion mining (sometimes known as sentiment analysis or emotion AI) refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine.

tensorflow-and-keras

TensorFlow: