Solved end-to-end deep learning projects
In this deep learning project, we will predict customer churn using Artificial Neural Networks and learn how to model an ANN in R with the keras deep learning package.
Description:
Customer churn refers to the situation when a customer ends their relationship with a company, and its a costly problem. Customers are the fuel that powers a business.
Loss of customers impacts sales. Further, it’s much more difficult and costly to gain new customers than it is to retain existing customers. As a result, organizations need to focus on reducing customer churn.The good news is that machine learning can help. For many businesses that offer subscription-based services, its critical to both predict customer churn and explain what features relate to customer churn.
Older techniques such as logistic regression can be less accurate than newer techniques such as deep learning, which is why we are going to show you how to model an ANN in R with the keras package.
Keras, Churn prediction, Lime, and Feature Importance.
In this data science project, you will work with German credit dataset using classification techniques like Decision Tree, Neural Networks etc to classify loan applications using R.
Description:
The German credit dataset contains information on 1000 loan applicants. Each applicant is described by a set of 20 different attributes. Of these 20 attributes, seventeen attributes are discrete while three are continuous. The main idea is to use techniques from the field of information theory to select a set of important attributes that can be used to classify tuples. In this data science project, you will train a neural network using these attributes; the neural network is then used to classify tuples.
- Application of Logistic Regression
- Decision Tree based rules
- Neural Network
- Benchmarking
- Feature selection
- Feature Engineering
In this deep learning project, you will build a classification system where to precisely identify human fitness activities.
Application of classification algorithms Compare multiple algorithms Deployment of Keras Deep learning algorithm Deployment of SVM and Adaboost Comparison of models
Description The Human Activity Recognition dataset was built from the recordings of 30 study participants performing activities of daily living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors. The objective is to classify activities into one of the six activities performed.Each person performed six activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) wearing a smartphone (Samsung Galaxy S II) on the waist.
Using its embedded accelerometer and gyroscope, we captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The experiments have been video-recorded to label the data manually. The obtained dataset has been randomly partitioned into two sets, where 70% of the volunteers were selected for generating the training data and 30% the test data.
In this project, we are going to work on Deep Learning using H2O to predict Census income.
- Apply deep learning models
- Use of H2O
- Compare the DL models
- Grid Search on DL models
- Tuning the accuracy in DL models
Description This data was extracted from the census bureau database found at: http://www.census.gov/ftp/pub/DES/www/welcome.html
- Split into train-test using MLC++ GenCVFiles (2/3, 1/3 random).
- 48842 instances, mix of continuous and discrete (train=32561, test=16281)
- 45222 if instances with unknown values are removed (train=30162, test=15060)
- Duplicate or conflicting instances : 6
Class probabilities for adult.all file
- Probability for the label '>50K' : 23.93% / 24.78% (without unknowns)
- Probability for the label '<=50K' : 76.07% / 75.22% (without unknowns)
Extraction was done by Barry Becker from the 1994 Census database.
A set of reasonably clean records was extracted using the following conditions:
((AAGE>16) && (AGI>100) && (AFNLWGT>1)&& (HRSWK>0))
Prediction task is to determine whether a person makes over 50K a year.
In this project, we are going to talk about H2O and functionality in terms of building Machine Learning models.
- Data cleaning using H2O
- Model Training using H2O
- Model scalability using H2O in Hadoop environment
- Driverless AI using H2O
Description:
H2O.ai is focused on bringing AI to businesses through software.
H2O includes many common Machine Learning algorithms, such as generalized linear modeling (linear regression, logistic regression, etc.), Naive Bayes, principal components analysis, k-means clustering, and word2vec. H2O implements best-in-class algorithms at scale, such as distributed random forest, gradient boosting, and deep learning. H2O also includes a Stacked Ensembles method, which finds the optimal combination of a collection of prediction algorithms using a process known as stacking.
Apply deep learning paradigm to forecast univariate time series data.
Description:
Deep learning is an upcoming field, where we are seeing a lot of implementations in the day to day business operations, including segmentation, clustering, forecasting, prediction or recommendation etc. Deep learning architecture has many branches and one of them is the recurrent neural network (RNN), the method that we are going to analyze in this deep learning project is about Long Short Term Memory Network (LSTM) to perform time series forecasting for univariate time series data.
- Develop a baseline of performance for a forecast problem.
- Design a robust test harness for one-step time series forecasting.
- Prepare data for LSTM recurrent neural network python model
- Develop LSTM python model
- Evaluate an LSTM recurrent neural network for time series forecasting.