Machine-Learning-Projects

(★ 655)

Machine Learning Experiments and Work

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Download Latest Version (.zip)

- - Eight_Puzzle-checkpoint.ipynb
  - Solving the Eight Puzzle with Search Algorithms-checkpoint.ipynb
- - eight_puzzle.cpython-36.pyc
- - 391-F17-P1.pdf
  - Eight_Puzzle.ipynb
  - project_1.xlsx
  - report.html
- - effective_branching.png
  - nodes_generated.png
- - move_display.txt
  - randomize_solve_astar.txt
  - set_state.txt
  - set_state_solve_astar.txt
  - set_state_solve_beam.txt
- eight_puzzle.py
- eight_puzzle.pyc
- play_puzzle.py
- readme.md
- report.ipynb
- report.pdf
- - Deep Neural Network Classifier-checkpoint.ipynb
  - Pre-Trained Google Inception Convolutional Neural Network-checkpoint.ipynb
- - dnn_classifier.cpython-36.pyc
- - titanic.csv
  - titanic.xls
- - checkpoint
  - dnn_1.data-00000-of-00001
  - dnn_1.index
  - dnn_1.meta
  - mnist_grid_best.data-00000-of-00001
  - mnist_grid_best.index
  - mnist_grid_best.meta
  - mnist_random_best_model.data-00000-of-00001
  - mnist_random_best_model.index
  - mnist_random_best_model.meta
- Deep Neural Network Classifier.ipynb
- dnn_classifier.py
- dnn_classifier.pyc
- confusion_matrix_rf.png
- decision_tree_simple.png
- images.txt
- nyc_-74.1_-73.7_40.6_40.85.PNG
- nyc_-74.2_-73.7_40.6_40.9.PNG
- power_scores.png
- roc_auc_rf.png
- roc_auc_single.png
- simple_problem.png
- tree_from_optimized_forest.png
- tree_real_data.png
- - Using Pre-Trained Convolutional Neural Network-checkpoint.ipynb
- - __init__.py
  - BUILD
  - checkpoint
  - download_and_convert_data.py
  - eval_image_classifier.py
  - export_inference_graph.py
  - export_inference_graph_test.py
  - imagenet.py
  - Object Recognition with Google's Convolutional Neural Networks.ipynb
  - README.md
  - setup.py
  - slim_walkthrough.ipynb
  - train_image_classifier.py
  - Training Model for Novel Image Identification.ipynb
  - WORKSPACE
- imagenet_class_names.txt
- - Improving Random Forest Part 1-checkpoint.ipynb
  - Improving Random Forest Part 2-checkpoint.ipynb
  - Random Forest Explained-checkpoint.ipynb
- - .RData
  - .Rhistory
  - hist_averages.csv
  - munge_temps.R
  - raw_temps.csv
  - temp_format.R
  - temps.csv
  - temps_extended.csv
- - actual_and_variables.png
  - basic_plots.png
  - best_tree.dot
  - best_tree.png
  - data_summary.PNG
  - exp_additional_plots.png
  - exp_cumulative_importances.png
  - exp_data.PNG
  - exp_data_summary.PNG
  - exp_temp_plots.png
  - exp_variable_importances.png
  - feature_curves.png
  - feature_importances.png
  - feature_tradeoffs.PNG
  - human_decision_process.PNG
  - hyperparameters.png
  - model_accuracy_error_comparison.png
  - model_comparison.png
  - num_trees_curves.png
  - pair_plots.png
  - pair_plots_small.png
  - radio_tuning.jpeg
  - small_tree.dot
  - small_tree.png
  - small_tree_annotated.PNG
  - temp_actual_predicted.png
  - Temperature Prediction Decision Tree - Page 1.png
  - temperature_prediction_decision_tree.PNG
  - tree.dot
  - tree.png
- Improving Random Forest Part 1.ipynb
- Improving Random Forest Part 2.ipynb
- Random Forest Explained.ipynb
- rf_drawings.docx
.gitattributes
.gitignore
algorithm_comparison.png
Article Work.ipynb
Automated Feature Engineering Advanced.ipynb
Automated Feature Engineering Basic.ipynb
average_sub.csv
Differing Labels.png
Feature Selection.ipynb
feature_relevance.csv
gbm_submission.csv
hyperparameter_improvement.png
intro_to_model_tuning.ipynb
introduction_to_manual_feature_engineering_part_one.ipynb
label_encoding.png
lgb_gbm_submission.csv
manual_feature_engineering_part_1.ipynb
manual_feature_engineering_part_2.ipynb
Model Selection.ipynb
olson.pdf
one_hot_encoding.png
Random Forest Tutorial.ipynb
README.md
tpe_bayesian.PNG

# Installation Guide

1. Get the code

git clone https://github.com/WillKoehrsen/Machine-Learning-Projects

Downloads the entire project code from GitHub to your computer.

cd Machine-Learning-Projects

Moves into the project folder you just downloaded.

2. Python

Easy Recommended

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

⚠️ This is a large repository, so this method may point to an internal sub-package rather than the actual core product. Check the full README as well.

pip install .

Installs the package published on PyPI directly — no need to clone the source.

jupyter notebook

Launches Jupyter in your browser so you can open and run the notebook (.ipynb) files.

✅ If it runs without errors and prints output in the terminal, it worked.