Name: Fakhre Alam
Type: User
Company: Iris Software Inc
Bio: A data scientist and a R and Python programmer, with an insatiable intellectual curiosity, and the ability to mine hidden gems located within large sets.
Location: Noida
Blog: https://www.irissoftware.com/
Fakhre Alam's Projects
Sentiment Analysis
Neural Network
data_wrangling
Deep Learnig for Image Processing and Retrieval
Python Basics with Numpy (optional assignment) Welcome to your first assignment. This exercise gives you a brief introduction to Python. Even if you've used Python before, this will help familiarize you with functions we'll need. Instructions: You will be using Python 3. Avoid using for-loops and while-loops, unless you are explicitly told to do so. Do not modify the (# GRADED FUNCTION [function name]) comment in some cells. Your work would not be graded if you change this. Each cell containing that comment should only contain one function. After coding your function, run the cell right below it to check if your result is correct. After this assignment you will: Be able to use iPython Notebooks Be able to use numpy functions and numpy matrix/vector operations Understand the concept of "broadcasting" Be able to vectorize code Let's get started!
Using Deep learnig for Image Classification
Document Retrival Analysis
Projects
Proceesing Images in Python
KNN CLASSIFIER ON MNIST DATA
Data Analysis Report
Projects
Seminar held By Cdac and Manipal
Introduction The intention of this notebook is to utilize tensorflow to build a neural network that helps to predict default likelihood, and to visualize some of the insights generated from the study. This kernel will evolve over time as I continue to add features and study the Lending Club data
Projects
Repository for Programming Assignment 2 for R Programming on Coursera
A library for common Qlikview Scripting tasks
Song Recommender Analysis
Using News Article to Predict Stock Market Movements
This kernel may (or may not) be helpful in your long and often tedious machine learning journey. This kernel is easily understandable to the beginner like me. This verbosity tries to explain everything I could possibly know. Once you get through the notebook, you can find this useful and straightforward. I attempted to explain things as simple as possible. In this kernel, I'm going to attempt the only Machine learning Algorithms to predict if a passenger survived from the sinking Titanic or not. So it's a binary classification problem. Keep Learning, Fakhre Alam
This notebook is a very basic and simple introductory primer to the method of ensembling models, in particular the variant of ensembling known as Stacking. In a nutshell stacking uses as a first-level (base), the predictions of a few basic machine learning models (classifiers) and then uses another model at the second-level to predict the output from the earlier first-level predictions. The Titanic dataset is a prime candidate for introducing this concept as many newcomers to Kaggle start out here. Furthermore even though stacking has been responsible for many a team winning Kaggle competitions there seems to be a dearth of kernels on this topic so I hope this notebook can fill somewhat of that void. I myself am quite a newcomer to the Kaggle scene as well and the first proper ensembling/stacking script that I managed to chance upon and study was one written in the AllState Severity Claims competition by the great Faron. The material in this notebook borrows heavily from Faron's script although ported to factor in ensembles of classifiers whilst his was ensembles of regressors. Anyway please check out his script here: Stacking Starter : by Faron Now onto the notebook at hand and I hope that it manages to do justice and convey the concept of ensembling in an intuitive and concise manner. My other standalone Kaggle script which implements exactly the same ensembling steps (albeit with different parameters) discussed below gives a Public LB score of 0.808 which is good enough to get to the top 9% and runs just under 4 minutes. Therefore I am pretty sure there is a lot of room to improve and add on to that script. Anyways please feel free to leave me any comments with regards to how I can improve
I am trying to predict loan outcomes (0, 1) using an unweighted soft voting ensemble classifier (sklearn's VotingClassifier class with voting='soft'). For a given sample, this outputs the class label with highest averaged probability predicted by the component classifiers.
voting classifier