Bhargav's repositories
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
100DaysOfCode
100DaysOfCode --> Django FrameWork ,AI/ML
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-DeepLearning-Model-LSTM
Amzon Fine Food reviews detection using Neural Network Sequence Model LSTM
Django_Project
This Project has Django Applications
HTML_CSS_Basics
HTML_CSS_Basics for Django
HumanActivityRecognition
Humman Activity using Deep Learning Technique LSTM
Machine-Language-Translation-
Machine language Translation Using DeepLearning Encoder-Decoder Model
ML_Models
Problrm delth using ML Techniques along with EDA
MultiDomainReview_SentimentAnalysis
Sentiment Analysis Performed on the Reviews of Different Domains ie Books,DVD,Electronics,Kitchen using LogisticRegression , DeepLearning
Netflix-Recommendation-system
NetFlix Movie Recommendadtions System using ML Models
NewyorkCity-Taxi-predications
Predicting Taxi Pickup Densities for the Yellow Cabs in NewYorkCity
Pyspark_Personal_Training
Necessary Info about Pyspark Methods
React
Created with StackBlitz ⚡️
StackOverFlow-Tag-Prediction
StackOverFlow-Tag-Prediction using ML Models
test
tghhhh
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
Twitter_Python_Bot
Twitter_bot
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