animeshsinghrajput

3_7_2017_Data_Team

AI_And_DataScience

Machine Learning & Deep Learning and Data science

Amazon-Food-Reviews-Analysis-and-Modelling

[Machine Learning | Data Analysis] Data Analysis on Amazon Fine Food Reviews dataset.

Analyzing-customer-reviews.

Predicting the rating based on customer review.

Automatic_Ticket_Classification_NLP

awesome-mlops

A curated list of references for MLOps

Azure-AI-ML-Workathon

This series of workshops on various services is created to help you gain expertise on Azure cognitive and ML Services. You will be able to explore each functionality offered by the service through the GUI & REST APIs and observe the outcomes. We have also shared sample datasets related to each service to replicate what we have built in our workshops. Once you complete these labs, you’ll go from Zero to Hero on the respective service and should be able to Demo, Develop and Deploy your own custom use cases.

Chat-bot

This is the code for a LSTM Chat bot

code_snippets

Credit_EDA_Case_Study

• This case study aims to identify patterns which indicate if a client has difficulty paying their installments which may be used for taking actions such as denying the loan, reducing the amount of loan, lending (to risky applicants) at a higher interest rate, etc. • This will ensure that the consumers capable of repaying the loan are not rejected. Identification of such applicants using EDA is the aim of this case study.

animeshsinghrajput/AI_And_DataScience

Machine Learning & Deep Learning and Data science

animeshsinghrajput/Amazon-Food-Reviews-Analysis-and-Modelling

[Machine Learning | Data Analysis] Data Analysis on Amazon Fine Food Reviews dataset.

animeshsinghrajput/Automatic_Ticket_Classification_NLP

animeshsinghrajput/awesome-mlops

A curated list of references for MLOps

animeshsinghrajput/Azure-AI-ML-Workathon

This series of workshops on various services is created to help you gain expertise on Azure cognitive and ML Services. You will be able to explore each functionality offered by the service through the GUI & REST APIs and observe the outcomes. We have also shared sample datasets related to each service to replicate what we have built in our workshops. Once you complete these labs, you’ll go from Zero to Hero on the respective service and should be able to Demo, Develop and Deploy your own custom use cases.

animeshsinghrajput/code_snippets

animeshsinghrajput/Credit_EDA_Case_Study

• This case study aims to identify patterns which indicate if a client has difficulty paying their installments which may be used for taking actions such as denying the loan, reducing the amount of loan, lending (to risky applicants) at a higher interest rate, etc. • This will ensure that the consumers capable of repaying the loan are not rejected. Identification of such applicants using EDA is the aim of this case study.

animeshsinghrajput/Customer-Review-Prediction

animeshsinghrajput/Data-Science

Data Science Concepts

animeshsinghrajput/Data-Science--Cheat-Sheet

Cheat Sheets

animeshsinghrajput/Data-Science-Cheatsheet

animeshsinghrajput/deep-learning-drizzle

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

animeshsinghrajput/Fraud-Analysis

Insurance fraud claims analysis project

animeshsinghrajput/healthcareai-py

Python tools for healthcare machine learning

animeshsinghrajput/HR-Analytics

animeshsinghrajput/Lead_Scoring_Case_Study

• Goal of case study to build a logistic regression model to assign a lead score between 0 and 100 to each of the leads which can be used by the company to target potential leads. • A higher score would mean that the lead is hot, i.e. is most likely to convert whereas a lower score would mean that the lead is cold and will mostly not get converted. • Have been provided with a leads dataset from the past with around 9000 data points. This dataset consists of various attributes such as Lead Source, Total Time Spent on Website, Total Visits, Last Activity, etc. which may or may not be useful in ultimately deciding whether a lead will be converted or not. • The target variable, in this case, is the column ‘Converted’ which tells whether a past lead was converted or not wherein 1 means it was converted and 0 means it wasn’t converted.

animeshsinghrajput/LetsUpgrade-Assignments

These are ASSIGNMENTS and NOTES of LetsUpgrade - Free Coding Python from ZERO - HERO

animeshsinghrajput/Machine_learning

Basic machine learning algorithm implementation

animeshsinghrajput/ML_Car_Evaluation

Created a model to evaluate cars according to their characteristics. Dataset used in thie project is included as car.csv was obtained from UCI Machine Learning Repository

animeshsinghrajput/MobilePrice_Classification

animeshsinghrajput/practicalAI

📚 A practical approach to learning and using machine learning.

animeshsinghrajput/Repo-2017

Python codes in Machine Learning, NLP, Deep Learning and Reinforcement Learning with Keras and Theano

animeshsinghrajput/saleor

A modular, high performance e-commerce storefront built with Python, GraphQL, Django, and ReactJS.

animeshsinghrajput/sensor_fault_predictor

animeshsinghrajput/spam-classifier

Performed NLP analysis on the spam-ham dataset to classify texts as spam or ham using different ML classifiers. The dataset is taken from the link: https://www.kaggle.com/uciml/sms-spam-collection-dataset. The dataset contains 5,574 SMS messages in English tagged as ham (legitimate) or spam.

animeshsinghrajput/stanford-cs-230-deep-learning

VIP cheatsheets for Stanford's CS 230 Deep Learning

animeshsinghrajput/Telecom_Churn_Case_Study

• In this project, we have analyze customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn and identify the main indicators of churn. • The dataset contains customer-level information for a span of four consecutive months - June, July, August and September. The months are encoded as 6, 7, 8 and 9, respectively. • The business objective is to predict the churn in the last (i.e. the ninth) month using the data (features) from the first three months. To do this task well, understanding the typical customer behavior during churn will be helpful.

animeshsinghrajput/upGrad_Darshan

animeshsinghrajput/XGBoost-Tutorial-for-Beginners

One of the most common questions we get on Data science is: How can we provide better solutions than other machine learning algorithms? If you get confused and ask experts what should you learn at this stage, most of them would suggest / agree that you go ahead with ensemble learning?

animeshsinghrajput/yihui

Personal website of Yihui Xie