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Python Deep Learning Guided Project Tensorflow -Employment Classification ML as baseline, lvl3, 27m

Updated: Jul 24, 2023


Free, Instruct, instructional, instructional education,free python learn, seaborn, python, project, data analysis project, pandas, analyze, Tensorflow, architecture, neural network, deep learning
Final Neural Network

Follow along with me and code your own Python Tensorfow Project. In this beginner deep learning classification project with Tensorflow's Sequential model. When we start designing the architecture for a deep learning model it is a good idea to set up a base model so we can evaluate our progress. Sklearn's RandomForestClassifier or GradientBoostingClassifier with the default setting work well as base models.



Random Forest just gives good predictions in a few lines of code and sets the baseline of what we should be trying to beat with our Tensorflow Sequential model.


With Sklearn and Tensorflow we'll predict if employees will stay at the company and help the coporate planning team.














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dataGroups:

















Through many experiments using a simple for loop in Python, we determine the correct architecture for our Sequential model in Tensorflow.



Free, Instruct, instructional, instructional education,free python learn, seaborn, python, project, data analysis project, pandas, analyze, Tensorflow, architecture, neural network, deep learning

Complete univariate analysis in for loop with Pandas .plot to quickly understand the distributions in your our employement dataset.


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Univariate Analysis

Next, plot the univariate distributions conditionally color-coded ny our target. In our deep learning classification workflow, we are predicting if an employee with Leave or Not from their current position.


Free, Instruct, instructional, instructional education,free python learn, seaborn, python, project, data analysis project, pandas, analyze, Tensorflow, architecture, neural network, deep learning
One hot encoding

One hot encoding our categories with pandas .get_dummies to use our categories in our predictive deep learning model.


Free, Instruct, instructional, instructional education,free python learn, seaborn, python, project, data analysis project, pandas, analyze, Tensorflow, architecture, neural network, deep learning
Train Test Split in our Deep Learning workflow

Train Test Split using Sklearn. Follow by standardization and principal component again with Sklearn.


Free, Instruct, instructional, instructional education,free python learn, seaborn, python, project, data analysis project, pandas, analyze, Tensorflow, architecture, neural network, deep learning
Sklearn's RandomForest as our baseline model

Use Sklearn's RandomForestClassifier to get our baseline accuracy score.


Free, Instruct, instructional, instructional education,free python learn, seaborn, python, project, data analysis project, pandas, analyze, Tensorflow, architecture, neural network, deep learning
Model build function

But a model_factory function that builds our model so we can run through a series of experiments to get our model to work well.


Free, Instruct, instructional, instructional education,free python learn, seaborn, python, project, data analysis project, pandas, analyze, Tensorflow, architecture, neural network, deep learning
Set up deep learning model experiments to find right architecture


Our deep learning experimental playground to find the best architecture for our Tensorflow model.








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