fakhre-alam Goto Github PK

analyzing-product-sentiment-analyisis

Sentiment Analysis

building-a-neural-net-to-predict-loan

Neural Network

data-wrangling

data_wrangling

deep-features-for-image-retrieval-imag-processing

Deep Learnig for Image Processing and Retrieval

deep-learning

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!

deep-learning-for-image-classification-image-processing-

Using Deep learnig for Image Classification

document-retrival-

Document Retrival Analysis

end-to-end-machine-learning-project-with-mlflow

genai-question-ansering-app

Banking Domain Questions Answer Bot

hpc

identifying-safe-loans-with-decision-trees

Projects

image-processing-in-python

Proceesing Images in Python

iris-api

knn_classifier_mnist_data

KNN CLASSIFIER ON MNIST DATA

loan-data-analysis-report

Data Analysis Report

loan_data_analysis

Projects

machine-learning-automated-pipeline-prediction

This project is a comprehensive machine learning solution designed to automate the prediction of optimal pipelines for data processing and model training. It streamlines the end-to-end machine learning workflow, from data preprocessing to model deployment, utilizing automated techniques to enhance efficiency and performance.

metadata-management

Seminar held By Cdac and Manipal

mlflow-pipeline

MLflow is an open-source platform designed to manage the end-to-end machine learning lifecycle. It provides tools for tracking experiments, packaging code into reproducible runs, and sharing and deploying models

neural-network

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

predicting-house-price

Projects

programmingassignment2

Repository for Programming Assignment 2 for R Programming on Coursera

qlikview-components

A library for common Qlikview Scripting tasks

song-recommender

Song Recommender Analysis

stock-market-prediction

Using News Article to Predict Stock Market Movements

text-data-pre_processing

tf-idf_document_search

TF_IDF_Document search functionality using streamlit

time-series

Time series data analysis

titanic-classification-comprehensive-modeling

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

titanic-data-analysis

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