relataly-public-python-tutorials

Collection of Python notebooks for various machine learning, deep learning and analytics use cases

relataly-public-python-tutorials

Welcome to the GitHub tutorial directory of the relataly.com blog on python machine learning & deep learning!

The files listed in this directory are all Jupyter notebook files.

Each file represents a separate Python project and is described in more detail in a blog post.

Some of the topics covered are:

Time series forecasting for stock market prediction using recurrent neural networks
Computer vision: image classification with convolutional neural networks
Anomaly detection using random isolation forests
Explorative analytics and visualization using different charts and tools, incl. heat maps and geographic maps
Distributed computing with PySpark
Hyperparameter tuning using grid search
Recommender systems using collaborative filtering

For access to blog articles visit www.relataly.com.

Also check out the other relataly repositories: https://github.com/flo7up/relataly-public-python-API-tutorials

Shield:

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Name		Name	Last commit message	Last commit date
Latest commit History 238 Commits
.gitignore		.gitignore
001 Time Series Forecasting - Forecasting US Beer Sales with (auto) ARIMA.ipynb		001 Time Series Forecasting - Forecasting US Beer Sales with (auto) ARIMA.ipynb
003 Time Series Forecasting - Univariate Model using Recurrent Neural Networks.ipynb		003 Time Series Forecasting - Univariate Model using Recurrent Neural Networks.ipynb
004 Time Series Forecasting - Adjusting Prediction Intervals.ipynb		004 Time Series Forecasting - Adjusting Prediction Intervals.ipynb
005 Time Series Forecasting - Multi-step Rolling Forecasting.ipynb		005 Time Series Forecasting - Multi-step Rolling Forecasting.ipynb
006 Time Series Forecasting - Multi-Output Regression.ipynb		006 Time Series Forecasting - Multi-Output Regression.ipynb
007 Time Series Forecasting - Multivariate Time Series Models.ipynb		007 Time Series Forecasting - Multivariate Time Series Models.ipynb
008 Time Series Forecasting - Feature Engineering for Multivariate Models.ipynb		008 Time Series Forecasting - Feature Engineering for Multivariate Models.ipynb
009 Time Series Forecasting - Measuring Model Performance.ipynb		009 Time Series Forecasting - Measuring Model Performance.ipynb
013 NLP - Simple Sentiment Analysis using Bayes and Logistic Regression.ipynb		013 NLP - Simple Sentiment Analysis using Bayes and Logistic Regression.ipynb
015 Hyperparameter Tuning of Regression Models using Random Search.ipynb		015 Hyperparameter Tuning of Regression Models using Random Search.ipynb
016 Hyperparameter Tuning of Random Decision Forests using Grid Search.ipynb		016 Hyperparameter Tuning of Random Decision Forests using Grid Search.ipynb
017 Permutation Feature Importance - Customer Churn Prediction using Random Decision Forest.ipynb		017 Permutation Feature Importance - Customer Churn Prediction using Random Decision Forest.ipynb
018 Classification - Forecasting Criminal Activity in San Francisco.ipynb		018 Classification - Forecasting Criminal Activity in San Francisco.ipynb
019 Classification - Classifying Shopper Buying Intention using Logistic Regression.ipynb		019 Classification - Classifying Shopper Buying Intention using Logistic Regression.ipynb
020 Classification - Measuring Classifier Performance with Confusion Matrix, Error Metrics and ROC Curve.ipynb		020 Classification - Measuring Classifier Performance with Confusion Matrix, Error Metrics and ROC Curve.ipynb
021 Classification - Unsupervised Feature Selection.ipynb		021 Classification - Unsupervised Feature Selection.ipynb
025 Bots - Building a Twitter Bot with Python.ipynb		025 Bots - Building a Twitter Bot with Python.ipynb
026 Anomaly Detection - Using Isolation Forests to fight Credit Card Fraud with Python.ipynb		026 Anomaly Detection - Using Isolation Forests to fight Credit Card Fraud with Python.ipynb
031 Recommender Systems - Movie Recommender using Collaborative Filtering.ipynb		031 Recommender Systems - Movie Recommender using Collaborative Filtering.ipynb
032 Recommender Systems - Movie Recommender using Content-based Filtering.ipynb		032 Recommender Systems - Movie Recommender using Content-based Filtering.ipynb
041 Clustering using K-means with Python.ipynb		041 Clustering using K-means with Python.ipynb
042 Vizualizing Stock Market Structures using Cluster Analysis in Python.ipynb		042 Vizualizing Stock Market Structures using Cluster Analysis in Python.ipynb
050 Regression - Exploratory Feature Preparation for Car Sales Price Forecasting.ipynb		050 Regression - Exploratory Feature Preparation for Car Sales Price Forecasting.ipynb
050 Regression - Feature Selection and Exploration.ipynb		050 Regression - Feature Selection and Exploration.ipynb
070 Data Visualization - Geographic Heatmaps using Python.ipynb		070 Data Visualization - Geographic Heatmaps using Python.ipynb
071 Data Visualization - Color-Coded Cryptocurrency Price Charts.ipynb		071 Data Visualization - Color-Coded Cryptocurrency Price Charts.ipynb
073 Data Vizualization - Seaborn Plots Overview.ipynb		073 Data Vizualization - Seaborn Plots Overview.ipynb
112 Correlation Matrix - COVID-19 and Financial Assets.ipynb		112 Correlation Matrix - COVID-19 and Financial Assets.ipynb
200 Computer Vision - Classifying Cats & Dogs Binary.ipynb		200 Computer Vision - Classifying Cats & Dogs Binary.ipynb
300 Distributed Computing - Analyzing Zurich Weather Data using PySpark.ipynb		300 Distributed Computing - Analyzing Zurich Weather Data using PySpark.ipynb
LICENSE.txt		LICENSE.txt
README.md		README.md

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