Pinned Repositories
Ensemble-Techniques
EDA, Data Preprocessing, Customer Profiling, Bagging Classifiers (Bagging and Random Forest), Boosting Classifier (AdaBoost,Gradient Boosting,XGBoost), Stacking Classifier, Hyperparameter Tuning using GridSearchCV, Business insights
K-means-Hierarchical-Clustering-PCA-Clustering
K-means, Scaling, Silhouette Score, Visual Analysis of Clustering, Hierarchical Clustering, Cophenetic Correlation, Dimensionality Reduction, and Principal Component Analysis
Model-Tuning
Up and downsampling, Regularization, Hyperparameter tuning
Supervised-Learning---Classification
EDA, Data Pre-processing, Logistic regression, Multicollinearity, Finding optimal threshold using AUC-ROC curve, Decision trees, Pruning
Supervised-Learning---Foundations
EDA, Linear Regression, Linear Regression assumptions, Business insights and recommendations
TensorFlow
Business-Statistics
Hypothesis Testing, a/b testing, Data Visualization, Statistical Inference
Unsupervised-Learning
EDA, Kmeans Clustering, Hierarchical Clustering, Cluster Profiling
Auto-Lending-Startup-Case-Study-Data-Science-Leadership
Facebook-s-Prophet
Forecasting Using Facebook’s Prophet Library
john-d-noble's Repositories
john-d-noble/Business-Statistics
Hypothesis Testing, a/b testing, Data Visualization, Statistical Inference
john-d-noble/Unsupervised-Learning
EDA, Kmeans Clustering, Hierarchical Clustering, Cluster Profiling
john-d-noble/Auto-Lending-Startup-Case-Study-Data-Science-Leadership
john-d-noble/Ensemble-Techniques
EDA, Data Preprocessing, Customer Profiling, Bagging Classifiers (Bagging and Random Forest), Boosting Classifier (AdaBoost,Gradient Boosting,XGBoost), Stacking Classifier, Hyperparameter Tuning using GridSearchCV, Business insights
john-d-noble/Facebook-s-Prophet
Forecasting Using Facebook’s Prophet Library
john-d-noble/john-d-noble
Config files for my GitHub profile.
john-d-noble/K-means-Hierarchical-Clustering-PCA-Clustering
K-means, Scaling, Silhouette Score, Visual Analysis of Clustering, Hierarchical Clustering, Cophenetic Correlation, Dimensionality Reduction, and Principal Component Analysis
john-d-noble/Model-Tuning
Up and downsampling, Regularization, Hyperparameter tuning
john-d-noble/Prophet-Vs-SARIMA
john-d-noble/Python---Foundations
Exploratory Data Analysis (Variable Identification, Univariate analysis, Bi-Variate analysis), Python
john-d-noble/StockPrice-Forecasting-Tesla
john-d-noble/Supervised-Learning---Classification
EDA, Data Pre-processing, Logistic regression, Multicollinearity, Finding optimal threshold using AUC-ROC curve, Decision trees, Pruning
john-d-noble/Supervised-Learning---Foundations
EDA, Linear Regression, Linear Regression assumptions, Business insights and recommendations
john-d-noble/TensorFlow