Solved end-to-end Data Science Projects in Telecommunications

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Data Science Projects in Telecommunications

Azure Deep Learning-Deploy RNN CNN models for TimeSeries

In this Azure MLOps Project, you will learn to perform docker-based deployment of RNN and CNN Models for Time Series Forecasting on Azure Cloud.

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Build Multi Class Text Classification Models with RNN and LSTM

In this Deep Learning Project, you will use the customer complaints data about consumer financial products to build multi-class text classification models using RNN and LSTM.

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Skip Gram Model Python Implementation for Word Embeddings

Skip-Gram Model word2vec Example -Learn how to implement the skip gram algorithm in NLP for word embeddings on a set of documents.

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Deep Learning Project for Time Series Forecasting in Python

Deep Learning for Time Series Forecasting in Python -A Hands-On Approach to Build Deep Learning Models (MLP, CNN, LSTM, and a Hybrid Model CNN-LSTM) on Time Series Data.

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GCP MLOps Project to Deploy ARIMA Model using uWSGI Flask

Build an end-to-end MLOps Pipeline to deploy a Time Series ARIMA Model on GCP using uWSGI and Flask

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Predict Churn for a Telecom company using Logistic Regression

Machine Learning Project in R- Predict the customer churn of telecom sector and find out the key drivers that lead to churn. Learn how the logistic regression model using R can be used to identify the customer churn in telecom dataset.

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NLP Project for Multi Class Text Classification using BERT Model

In this NLP Project, you will learn how to build a multi-class text classification model using using the pre-trained BERT model.

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Build a Text Classification Model with Attention Mechanism NLP

In this NLP Project, you will learn to build a multi class text classification model with attention mechanism.

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FEAST Feature Store Example for Scaling Machine Learning

FEAST Feature Store Example- Learn to use FEAST Feature Store to manage, store, and discover features for customer churn prediction machine learning project.

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Time Series Forecasting Project-Building ARIMA Model in Python

Build a time series ARIMA model in Python to forecast the use of arrival rate density to support staffing decisions at call centres.

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Time Series Project to Build a Multiple Linear Regression Model

Learn to build a Multiple linear regression model in Python on Time Series Data

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Build a Multi ClassText Classification Model using Naive Bayes

Implement the Naive Bayes Algorithm to build a multi class text classification model in Python.

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Build Time Series Models for Gaussian Processes in Python

Time Series Project - A hands-on approach to Gaussian Processes for Time Series Modelling in Python

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Build ARCH and GARCH Models in Time Series using Python

In this Project we will build an ARCH and a GARCH model using Python

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AWS MLOps Project to Deploy Multiple Linear Regression Model

Build and Deploy a Multiple Linear Regression Model in Python on AWS

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AWS MLOps Project for Gaussian Process Time Series Modeling

MLOps Project to Build and Deploy a Gaussian Process Time Series Model in Python on AWS

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AWS MLOps Project for ARCH and GARCH Time Series Models

Build and deploy ARCH and GARCH time series forecasting models in Python on AWS .

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Time Series Analysis with Facebook Prophet Python and Cesium

Time Series Analysis Project - Use the Facebook Prophet and Cesium Open Source Library for Time Series Forecasting in Python

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Churn Prediction in Telecom using Machine Learning in R

Estimating churners before they discontinue using a product or service is extremely important. In this ML project, you will develop a churn prediction model in telecom to predict customers who are most likely subject to churn.

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Jingwei Li

Graduate Research assistance at Stony Brook University

I think that they are fantastic. I attended Yale and Stanford and have worked at Honeywell,Oracle, and Arthur Andersen(Accenture) in the US. I have taken Big Data and Hadoop,NoSQL, Spark, Hadoop Admin, Hadoop projects. I have been happy with every project. They have really brought me into the forefront of Data Science and Big data. I would recommend this to everyone. It is more than worth the price. After working with them I feel so much more employable for current projects.

Ray han

Tech Leader | Stanford / Yale University

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Anand Kumpatla

Sr Data Scientist @ Doubleslash Software Solutions Pvt Ltd

Having worked in the field of Data Science, I wanted to explore how I can implement projects in other domains, So I thought of connecting with ProjectPro. A project that helped me absorb this topic was "Credit Risk Modelling". To understand other domains, it is important to wear a thinking cap and that's where ProjectPro helped me. I also got a chance to talk to experts who have worked on these domains - they helped me by walking through the project. Kudos to the ProjectPro team!

Gautam Vermani

Data Consultant at Confidential

I come from a background in Marketing and Analytics and when I developed an interest in Machine Learning algorithms, I did multiple in-class courses from reputed institutions though I got good theoretical knowledge, the practical approach, real word application, and deployment knowledge were missing. ProjectPro helped me bridge that gap. ProjectPro has real-time projects that helped me improve my skills. What I liked most is that I get exposure to so many projects, given the work nature I wouldn't have gotten exposure to such a variety of projects and their approaches. It is helping me apply knowledge to other projects too. I highly recommend ProjectPro to everyone who wants to excel in their DataScience career.

Ameeruddin Mohammed

ETL (Abintio) developer at IBM

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Abhinav Agarwal

Graduate Student at Northwestern University

I am the Director of Data Analytics with over 10+ years of IT experience. I have a background in SQL, Python, and Big Data working with Accenture, IBM, and Infosys. I am looking to enhance my skills in Data Engineering/Science and hoping to find real-world projects fortunately, I came across Project Pro. Project Pro helped me by providing an in-depth explanation of the end-to-end real-world data engineering projects. From data extraction, transformation, and storage up to data visualization. I learned more about Kafka, AWS, NI-FI, and Spark. Thru the help of the knowledge I gained from Project Pro, I was able to do well in the coding exams, interview and helped me land a job at EY. I will recommend every aspiring data professional as well as existing data science/engineer expert to try Project Pro to enhance their knowledge.

Ed Godalle

Director Data Analytics at EY / EY Tech

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Savvy Sahai

Data Science Intern, Capgemini