bhargavpirates

100DaysOfCode

100DaysOfCode --> Django FrameWork ,AI/ML

Algorithms

Amazon-Fashion-Discovery-Engine

Build a recommendation engine which suggests similar products (apparel) to the given product (apparel) in any e-commerce websites.

Amzon-Fine-Food-Reviews-Using-all-Machine-Learning-Models

Amzon-Fine-Food-Reviews-using-DeepLearning-Model-LSTM

Amzon Fine Food reviews detection using Neural Network Sequence Model LSTM

CICD_DEVELOPMENT

Django_Project

This Project has Django Applications

google_service_framework

HR_Analytics

A large company named XYZ, employs, at any given point of time, around 4000 employees. However, every year, around 15% of its employees leave the company and need to be replaced with the talent pool available in the job market. The management believes that this level of attrition (employees leaving, either on their own or because they got fired) is bad for the company, because of the following reasons - The former employees’ projects get delayed, which makes it difficult to meet timelines, resulting in a reputation loss among consumers and partners A sizeable department has to be maintained, for the purposes of recruiting new talent More often than not, the new employees have to be trained for the job and/or given time to acclimatise themselves to the company Hence, the management has contracted an HR analytics firm to understand what factors they should focus on, in order to curb attrition. In other words, they want to know what changes they should make to their workplace, in order to get most of their employees to stay. Also, they want to know which of these variables is most important and needs to be addressed right away.

Quora-Question-Pairs-Similarity

Predicting whether a pair of questions are duplicates or not using MachineLearning Models

bhargavpirates/HR_Analytics

A large company named XYZ, employs, at any given point of time, around 4000 employees. However, every year, around 15% of its employees leave the company and need to be replaced with the talent pool available in the job market. The management believes that this level of attrition (employees leaving, either on their own or because they got fired) is bad for the company, because of the following reasons - The former employees’ projects get delayed, which makes it difficult to meet timelines, resulting in a reputation loss among consumers and partners A sizeable department has to be maintained, for the purposes of recruiting new talent More often than not, the new employees have to be trained for the job and/or given time to acclimatise themselves to the company Hence, the management has contracted an HR analytics firm to understand what factors they should focus on, in order to curb attrition. In other words, they want to know what changes they should make to their workplace, in order to get most of their employees to stay. Also, they want to know which of these variables is most important and needs to be addressed right away.

bhargavpirates/Amzon-Fine-Food-Reviews-Using-all-Machine-Learning-Models

bhargavpirates/Quora-Question-Pairs-Similarity

Predicting whether a pair of questions are duplicates or not using MachineLearning Models

bhargavpirates/100DaysOfCode

100DaysOfCode --> Django FrameWork ,AI/ML

bhargavpirates/Algorithms

bhargavpirates/Amazon-Fashion-Discovery-Engine

Build a recommendation engine which suggests similar products (apparel) to the given product (apparel) in any e-commerce websites.

bhargavpirates/Amzon-Fine-Food-Reviews-using-DeepLearning-Model-LSTM

Amzon Fine Food reviews detection using Neural Network Sequence Model LSTM

bhargavpirates/CICD_DEVELOPMENT

bhargavpirates/Django_Project

This Project has Django Applications

bhargavpirates/google_service_framework

bhargavpirates/HTML_CSS_Basics

HTML_CSS_Basics for Django

bhargavpirates/HumanActivityRecognition

Humman Activity using Deep Learning Technique LSTM

bhargavpirates/LearningJava

bhargavpirates/Machine-Language-Translation-

Machine language Translation Using DeepLearning Encoder-Decoder Model

bhargavpirates/ML_Models

Problrm delth using ML Techniques along with EDA

bhargavpirates/MNIST-Prediction-using-Deep-learning-Techniques

bhargavpirates/MultiDomainReview_SentimentAnalysis

Sentiment Analysis Performed on the Reviews of Different Domains ie Books,DVD,Electronics,Kitchen using LogisticRegression , DeepLearning

bhargavpirates/Netflix-Recommendation-system

NetFlix Movie Recommendadtions System using ML Models

bhargavpirates/NewyorkCity-Taxi-predications

Predicting Taxi Pickup Densities for the Yellow Cabs in NewYorkCity

bhargavpirates/NLP

Working on all NLP Techniques

bhargavpirates/Pyspark_Personal_Training

Necessary Info about Pyspark Methods

bhargavpirates/PythonMail

bhargavpirates/PythonMailScripts

bhargavpirates/React

Created with StackBlitz ⚡️

bhargavpirates/StackOverFlow-Tag-Prediction

StackOverFlow-Tag-Prediction using ML Models

bhargavpirates/test

tghhhh

bhargavpirates/TSNE-Visualization-on-AmzonFineFoodReview-Data

bhargavpirates/TwitteAPi_Sentimental_Analysis_with_MultiDomain_as_Training_Data

Performed Sentimental Analysis on the Twitter tags ie Considering all the tweets from the selected tag and done sentimental analysis on them.here I consider multi Domain data as the Training data and applied all Machine Learning Models on top of that data and then sent preprocessed tweet data as the predicted data and predicted it sentimental values and latter applied DeepLearning model on the training data and compared both ML and DL models

bhargavpirates/Twitter_Python_Bot

Twitter_bot

bhargavpirates/TwitterAPI-Sentimental-Analysis

Performed Sentimental Analysis on the Twitter tags ie Considering all the tweets from the selected tag and done sentimental analysis on them.here I consider multi Domain data as the Training data and applied all Machine Learning Models on top of that data and then sent preprocessed tweet data as the predicted data and predicted it sentimental values and latter applied DeepLearning model on the training data and compared both ML and DL models