We are going to use the same dataset we used in the Personal Cancer Diagnosis artile that was publised earlier We will not be dealing with the exploratory data analysis but will straight go ahead with the machine learning model implementation of the dataset. In Random Forests we have Decision…

Problem statement : — Classify the given genetic variations/mutations based on evidence from text-based clinical literature. Data Source: https://www.kaggle.com/c/msk-redefining-cancer-treatment/ Data: Thanks to Memorial Sloan Kettering Cancer Center (MSKCC) for curating this dataset a great help to humanity. Business Objective and Constraints There is no Latency requirement High accuracy is required as we are dealing with human lives

Accuracy —

What is loss ?

Let start with the assumptions that we need to make The class label Y takes only two outcomes+1, 0 like a coin toss and hence can be thought of as a Benoulli random variable. The first big assumption is that the class label Y has a Bernoulli distribution. We have…

Logisitc regression ==> simple Algorithm Naive Bayes ==> No geometric intuition. Learnt it through Probabilistic technique Logistic Regression can be learnt from three perspectives Geometric intuition probabilistic approach which involves dense mathematics Loss minimization framework Assumption Classes are linearly or almost linearly seperable plane

Seq 2 Seq Models Applications Google Translate Auto Reply Auto Suggestion Seminal paper — ILya Sutskever Machine Translation ==> Seq to Seq Models

Objective This is a two class label(1 — client with payment difficulties, 0 — all other cases) classification problem. Table of Contents 1) Introduction 2) Business problem 3) Mapping to ML problem 4) Introduction to Datasets 5) Existing approaches 6) First Cut Approach 7) Exploratory Data Analysis (EDA) 8) Feature engineering 9) Model Explanation …