CONTENTS

• This contains all my projects for Data Science, NLP and Machine Learning.
• Done with Jupyter notebook.
• And PYTHON is my favourite language.

Basic Stats

• Frequently used stats : [StatsExplained] (http://nbviewer.jupyter.org/github/sam2015/Machine_Learning/blob/master/StatsExplained/StatsExplained.ipynb)

Dealing with imbalanced data

• Undersampling, Oversampling and SMOTE : [imbalanceData] (http://nbviewer.jupyter.org/github/sam2015/Machine_Learning/blob/master/imbalancedDATA/Imbalanced%20Data.ipynb)

Dimensionality reduction (PCA, T-SNE)

• PCA : [PCA-logic] (http://nbviewer.jupyter.org/github/sam2015/Machine_Learning/blob/master/DimensionallityReduction/PCA/PCA_intiution.ipynb)
• Recduce using T-SNE : [T-sne] (http://nbviewer.jupyter.org/github/sam2015/Machine_Learning/blob/master/DimensionallityReduction/Untitled.ipynb)

Linear Regression

• Linear regression

Logistic Regression

• Logistic

Clustering (Kmeans)

• Steps to simple Kmeans-Clustering : [K-means] (http://nbviewer.jupyter.org/github/sam2015/Machine_Learning/blob/master/CLustering/K-means/Kmeans%20Clustering.ipynb)
• Hierarchical Clustering : [MeanShift-clustering] (http://nbviewer.jupyter.org/github/sam2015/Machine_Learning/blob/master/CLustering/Hierarchical%20clustering/H_clustering.ipynb)
• Silhouette analysis for Kmeans : [Silhouette] (http://nbviewer.jupyter.org/github/sam2015/Machine_Learning/blob/master/CLustering/ClusteringProject/Insurance_clustering.ipynb)

Time-Series

• Starting with time-series : [Airdata-time-series] (http://nbviewer.jupyter.org/github/sam2015/Machine_Learning/blob/master/Time%20series/Time%20Series/Untitled.ipynb)
• Time-series : [Tractor-data-time-series] (https://github.com/sam2015/Machine_Learning/blob/master/Time%20series/Tractor_Ivy_Time%20series/Untitled.ipynb)

Cohort-analysis

• Analysing with food-odering data : [Cohort] (http://nbviewer.jupyter.org/github/sam2015/Machine_Learning/blob/master/Cohort%20Analysis/Untitled.ipynb)