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Name		Name	Last commit message	Last commit date
Latest commit   History 199 Commits 199 Commits
Final_Capstone		Final_Capstone
Unit 3		Unit 3
Unit 5		Unit 5
1.1.4 - translate question to rigorous study.ipynb		1.1.4 - translate question to rigorous study.ipynb
1.2.3 - SQL Exercise.ipynb		1.2.3 - SQL Exercise.ipynb
1.2.4 - SQL grouping and aggregating.ipynb		1.2.4 - SQL grouping and aggregating.ipynb
1.2.5 - SQL Joins and CTE's.ipynb		1.2.5 - SQL Joins and CTE's.ipynb
1.2.5_SQL_Joins_and_CTE's.ipynb		1.2.5_SQL_Joins_and_CTE's.ipynb
1.2.6 - Airbnb SQL Questions.ipynb		1.2.6 - Airbnb SQL Questions.ipynb
1.2.6_Airbnb_SQL_Questions.ipynb		1.2.6_Airbnb_SQL_Questions.ipynb
1.3.3 - Seaborn exercise.ipynb		1.3.3 - Seaborn exercise.ipynb
1.3.6 - Data Cleaning and Validation.ipynb		1.3.6 - Data Cleaning and Validation.ipynb
1.4.1 - A_B Tests.ipynb		1.4.1 - A_B Tests.ipynb
1.4.3 - Bias and A_A Testing.ipynb		1.4.3 - Bias and A_A Testing.ipynb
1.4.4 - Evaluate an Experiment Analysis.ipynb		1.4.4 - Evaluate an Experiment Analysis.ipynb
1.4.5 - The Research Proposal.ipynb		1.4.5 - The Research Proposal.ipynb
2.1.3 - Formulating Good Research Questions.ipynb		2.1.3 - Formulating Good Research Questions.ipynb
2.1.5 - Feature engineering drill.ipynb		2.1.5 - Feature engineering drill.ipynb
2.1.8 - Preparing a data set for modeling.ipynb		2.1.8 - Preparing a data set for modeling.ipynb
2.2.3 - Regression or classification.ipynb		2.2.3 - Regression or classification.ipynb
2.2.4 (2).ipynb		2.2.4 (2).ipynb
2.2.4.ipynb		2.2.4.ipynb
2.2.7 - Naive Bayes - sentiment labeled sendences.ipynb		2.2.7 - Naive Bayes - sentiment labeled sendences.ipynb
2.3.4 - iterate and evaluate classifier.ipynb		2.3.4 - iterate and evaluate classifier.ipynb
2.4.2 - Prepare data for MV Regression.ipynb		2.4.2 - Prepare data for MV Regression.ipynb
2.4.3 - Assumptions of linear regression.ipynb		2.4.3 - Assumptions of linear regression.ipynb
2.4.4 - Property crime prediction model.ipynb		2.4.4 - Property crime prediction model.ipynb
2.5.2 - Validating a linear regression.ipynb		2.5.2 - Validating a linear regression.ipynb
2.5.3 - Dimensionality Reduction.ipynb		2.5.3 - Dimensionality Reduction.ipynb
3.1.1 - NN algorithm.ipynb		3.1.1 - NN algorithm.ipynb
3.1.3 - Validating KNN.ipynb		3.1.3 - Validating KNN.ipynb
3.1.4 - OLS and KNN model comparison.ipynb		3.1.4 - OLS and KNN model comparison.ipynb
3.1.5.ipynb		3.1.5.ipynb
3.2.2 - KNN example methodoligy.ipynb		3.2.2 - KNN example methodoligy.ipynb
3.2.5 - Random forest example.ipynb		3.2.5 - Random forest example.ipynb
3.2.6 - Decision Tree and Random Forest model comparison.ipynb		3.2.6 - Decision Tree and Random Forest model comparison.ipynb
3.2.7.ipynb		3.2.7.ipynb
3.3.3 - Regularization parameter .ipynb		3.3.3 - Regularization parameter .ipynb
3.3.4 - Advanced regression.ipynb		3.3.4 - Advanced regression.ipynb
3.3.4 - [6 Distributions].ipynb		3.3.4 - [6 Distributions].ipynb
3.3.7 - Central Limit Theorem.ipynb		3.3.7 - Central Limit Theorem.ipynb
3.4.4 - Support vector machines.ipynb		3.4.4 - Support vector machines.ipynb
3.5.3 - Gradient boost model (copy 1).ipynb		3.5.3 - Gradient boost model (copy 1).ipynb
3.5.3 - Gradient boost model.ipynb		3.5.3 - Gradient boost model.ipynb
3.5.5 - Model identification.ipynb		3.5.5 - Model identification.ipynb
3.6.2 - Airline Arrivals.ipynb		3.6.2 - Airline Arrivals.ipynb
4.1.3 - unsupervised or supervised.ipynb		4.1.3 - unsupervised or supervised.ipynb
4.2.2 - K-Means.ipynb		4.2.2 - K-Means.ipynb
4.2.3 - Clustering.ipynb		4.2.3 - Clustering.ipynb
4.2.5_Boston_marathon_clusters.ipynb		4.2.5_Boston_marathon_clusters.ipynb
4.3.4_supervised_neural_nets.ipynb		4.3.4_supervised_neural_nets.ipynb
4.3.6_graduate_admissions_NN_classifier.ipynb		4.3.6_graduate_admissions_NN_classifier.ipynb
4.3.6_graduate_admissions_neural_network.ipynb		4.3.6_graduate_admissions_neural_network.ipynb
4.4.2_NLP.ipynb		4.4.2_NLP.ipynb
4.4.2_NLP_Alice_v_Emma.ipynb		4.4.2_NLP_Alice_v_Emma.ipynb
4.4.2_NLP_macbeth_v_moby_dick.ipynb		4.4.2_NLP_macbeth_v_moby_dick.ipynb
4.4.3_NLP_unsupervised.ipynb		4.4.3_NLP_unsupervised.ipynb
4.4.5_NLP_caesar_v_hamlet.ipynb		4.4.5_NLP_caesar_v_hamlet.ipynb
5.2.4_yahoo_finance.ipynb		5.2.4_yahoo_finance.ipynb
5.5.4_what_test.ipynb		5.5.4_what_test.ipynb
6.1_time_challenge.ipynb		6.1_time_challenge.ipynb
6.1_time_series_analysis.ipynb		6.1_time_series_analysis.ipynb
AI_challenge.ipynb		AI_challenge.ipynb
Capstone Data Analysis Report.ipynb		Capstone Data Analysis Report.ipynb
Euler.ipynb		Euler.ipynb
FUR.ipynb		FUR.ipynb
FUR_final.ipynb		FUR_final.ipynb
FUR_multiply.ipynb		FUR_multiply.ipynb
KNN_demo.ipynb		KNN_demo.ipynb
OMP - NN algorithm.ipynb		OMP - NN algorithm.ipynb
Santader - extra_tree.ipynb		Santader - extra_tree.ipynb
Santander - extra_tree - PCA.ipynb		Santander - extra_tree - PCA.ipynb
Santander - extra_tree.ipynb		Santander - extra_tree.ipynb
Santander_Bank_supervised_learning_capstone.ipynb		Santander_Bank_supervised_learning_capstone.ipynb
Unit 1 - Capstone.ipynb		Unit 1 - Capstone.ipynb
Unsupervised_Capstone.ipynb		Unsupervised_Capstone.ipynb
binary_tree.ipynb		binary_tree.ipynb
btc.ipynb		btc.ipynb
donate_within_5.ipynb		donate_within_5.ipynb
final_capstone_Fred_Etter.ipynb		final_capstone_Fred_Etter.ipynb
gold.ipynb		gold.ipynb
metrics.ipynb		metrics.ipynb
non_to_vol.ipynb		non_to_vol.ipynb
plane_crashes.ipynb		plane_crashes.ipynb
snow.ipynb		snow.ipynb
store_item_sales_forecasting_Fred_Etter.ipynb		store_item_sales_forecasting_Fred_Etter.ipynb
supervised_learning_capstone_Fred_Etter.ipynb		supervised_learning_capstone_Fred_Etter.ipynb
tech_exercise.ipynb		tech_exercise.ipynb
test_naive_bayes.ipynb		test_naive_bayes.ipynb
vol_to_non.ipynb		vol_to_non.ipynb
volunteer.ipynb		volunteer.ipynb
volunteer_plus.ipynb		volunteer_plus.ipynb
volunteer_plus_12_13.ipynb		volunteer_plus_12_13.ipynb
yield_curve.ipynb		yield_curve.ipynb
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