Uthappizza

Prediction of House prices

Machine learning model created to prerdict house prices, used Multiple linear Regression and compared performance of multiple models using test data. Further optimized the model using Ridge and Lasso regression techniques. Total 21612 datapoints each with 21 attributed were used and important features were found to be squarefeet living, numebr of bedrooms, number of bathrooms, number of floors.

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Court Counter

Prediction of Credit Risk

Created the risk model which aims at minimizing the credit risk incurred in the bank. The dataset contained the details about the 65535 customers with each row having 23 attributes of their loan details. Used Decision tree classifier to train the model, implementation was done using the scikit-learn library.

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Kmeans

Clustering of countries for expansion of a firm in foreign markets

Built clustering model using food and beverages sales dataset of countries, which makes easier for a frim to take business decisions. Used Hierarchical clustering and K-means to create the clusters.

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movies page

Web and Mobile Applications

Created web application which finds brief information about any famous street of the world using Wikipedia API and Google streetview API. Designed web-page which lists my favorite movies and show their trailers. Created page to display Restaurant Menu through the Angular JS framework. Built an android app to keep track of score of the basketball match.

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equity research initiative

Equity Research Initiaive

Analyzed Tata Motors Ltd. for 2 months by performing Fundamental, Technical and Ratio Analysis, also constructed Valuation models for the company

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face recognition

Biometrics : Facial Recognition

Cretaed a facial recognition biometric system which identifies the unlabeled set of face images from the given labeled dataset. Implemented using Fisher Face method which uses LDA algorithm. The accuracy obtained was around 98%. Prepared in partial fulfillment of the Laboratory Project EEE F366 Course.

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