ReadMe.txt

The Following Folders contains the List of Case Studies, Assignments and workshops done by me During an Online Machine Learning Course

1. Case Studies -

	A. Facebook Friend Recommendation
		Given a directed social graph, have to predict missing links to recommend users

	B. Netflix Prize
		Predict the rating that a user would give to a movie that he has not rated yet

	C. New york taxi
		To find number of pickups, given location co ordinate and time, in the query region
	
	D. Personalized Cancer Diagnosis
		Classify the given genetic variations/mutations based on evidence from text based clinical literature

	E. Quera Question Pair
		Identify which questions asked on Quora are duplicates of questions that have already been asked

	F. Stack Overflow
		Suggest the tags based on the content that was there in the question posted on Stack Overflow

2. Exploratory Data Analysis -
	EDA is performed on Haberman survival dataset

3. Pandas Practice -
	Practice question on Pandas is solved

4. Python Practice -
	Practice question on Python is solved

5. SQL -
	Practice question on SQL is solved

6. Supervised Algorithm -
	
	A. Decision Tree
		Decision Tree is performed on DonorsChoose Dataset to predict whether Proposed Project is Approved or Not

	B. KNN
		K-Nearest neighbour is performed on DonorsChoose Dataset to predict whether Proposed Project is Approved or Not

	C. LinearSVM
		Linear Support Vector Machine is performed on DonorsChoose Dataset to predict whether Proposed Project is Approved or Not

	D. Logistic Regression
		Logistic Regression is performed on DonorsChoose Dataset to predict whether Proposed Project is Approved or Not

	E. Naive Bayes
		Naive Bayes is performed on DonorsChoose Dataset to predict whether Proposed Project is Approved or Not

	F. RandomForest and GBDT
		RandomForest and GBDT is is performed on DonorsChoose Dataset to predict whether Proposed Project is Approved or Not

	G. Stochaistic Gradient Descent
		Implemented Stochaistic Gradient Descent for Linear Regression on Boston Pricing dataset to predict the House prices
 
	H. XGBoost and Truncated SVD
		Truncated SVD is applied to reduce the dimensions and then XGBoost is applied on DonorsChoose Dataset to predict whether Proposed Project is Approved or Not

7. Unsupervised Algorithm -

	A. K-means, Agglomerative, DBSCAN
		K-means, Agglomerative, DBSCAN methods are applied on DonorsChoose Dataset to find the right number of clusters in the Dataset

	B. T-SNE
		EDA is performed on DonorsChoose Dataset and T-SNE is applied to visualize the Two clusters i.e. (Project Approved points and Project Not Approved points) in Two Dimensions

8. Workshop -

	A. Amazon apparel Recommendation
		Online workshop attended demonstrating how similar products are shown to Users based on Text description and Image of Product

	B. Microsoft Malware Detection
		Online workshop attended demonstrating how to identify whether a given piece of file/software is a Malware or Not.