Abstract: This Project mainly focuses on using different Models to estimate covariance matrix on fixed
income securities to maximize return and minimize risk for portfolio optimization and Index Replication.
The Models are based on research in the field of, quantitative financial theory and statistics theories,
which are Sample Covariance Matrix Estimation, Weighted Sample Covariance Matrix Estimation, and Shrinkage
Estimation for Covariance Matrices.
Abstract: The price of a house can’t be predicted by studying the number of bedrooms and bathrooms, the
height of the ceiling, the age of the property or even the extensive study of the neighborhood. A
house that becomes a home of a family needs to tick off all the boxes. A house buyer considers a
lot more than the statistics of the property before investing his/her hard earned money into it.
This project attempts to expand the house prediction model to predict the sale price of a house in
Ames, Iowa by taking into account various parameters a house buyer is most likely to consider. We
are using the Ames housing dataset that consists of 79 explanatory variables i.e, 37 quantitative
and 42 qualitative that describe almost every possible aspect of residential homes.
Severity Detection in mammograms
Abstract: Classifiction of Severity in mammograms using i) Decision Tree, ii) Random Forest, iii) KNN and iv) Naive Bayes
Algorithms. This data contains 961 instances of masses detected in mammograms, and contains the following
attributes:
1. BI-RADS assessment: 1 to 5 (ordinal)
2. Age: patient's age in years (integer)
3. Shape: mass shape: round=1 oval=2 lobular=3 irregular=4 (nominal)
4. Margin: mass margin: circumscribed=1 microlobulated=2 obscured=3 ill-defined=4
spiculated=5 (nominal)
5. Density: mass density high=1 iso=2 low=3 fat-containing=4 (ordinal)
6. Severity: benign=No or malignant=Yes (binary)
BI-RADS is an assessment of how confident the severity classification is; it is not a "predictive"
attribute and so I will discard it. The age, shape, margin, and density attributes are the features
that I will build our model with, and "severity" is the classification I will attempt to predict
based on those attributes.
