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Data Science & Big Data Analysis.

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March 28th, 2023

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HW13Tensorflow

Q1 Keras and the MNIST Dataset (Optional)

Summarize all the results for the MNIST Dataset – http://yann.lecun.com/exdb/mnist/

Q2: Analyze the MNIST Dataset with Keras

Show screen shots to show installation Explain your results

Hint – use the following links

https://www.kaggle.com/ritupande/self-tutorial-deep-learning-using-keras/data

Install keras on anaconda – https://anaconda.org/conda-forge/keras

Q3. Redo MNIST Dataset with CNN

Hint – use the following links

https://www.kaggle.com/moghazy/guide-to-cnns-with-data-augmentation-keras

Q4 Use Tensorflow play to provide insights on how Tensorflow works –

•Tensor Flow Playground

Q5. Recommendation system with Tensorflow (Optional)

Go through the following tutorial to do the recommendation system with Tensorflow

https://developers.google.com/machine-learning/recommendation/

Q6 Do the XGBoost Exercise on the Titanic dataset

Install XGBoost on anaconda – https://anaconda.org/conda-forge/xgboost

hint on link –

https://www.kaggle.com/ihopethiswillfi/titanic-survival-prediction-in-python-with-xgboost

Q7 Apply XGBoost to Churn Modelling (same dataset as for ANN from previous week’s exercise)

And compare results to the ANN algorithm

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