Information Processing and Retrieval Project

PCA vs Autoencoder

The Project uses Pima Indians diabetes data set to predict whether a patient will have diabetes or not based upon patient’s lab test result variables like Glucose, Blood Pressure, etc. using CART decision tree algorithm and K-Nearest Model achieving 75% accuracy. The project involved the use of Python-Scikit Learn, SciPy, Pandas, MatPlotLib and …

"Key-Agricultural Commodities Daily Market Price Predictor" is a Deep Neural Networks Project that predicts minimum, maximum and modal prices of 12 agricultural commodities that are grown in India.

Deriving the Local Outlier Factor Score

This library will help you easily parallelize your python code for all kind of data transformations in MapReduce fashion.

Comparison of classification algorithms in microarray data analysis

Kaggle Competition : Predicting house prices using a collection of advanced regression techniques and data visualization with plotly

Supervised Machine learning - classification, regression, time-series forecast

This project can be widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine.

Now Classify Flower pictures with a Deep Learning Model. Model is trained from transfer learning using VGGnet.

This is an exercise to help understand Logistic Regression and also the interpretation of the model's coefficients!

This project aims to test my data manipulation, data visualization, and basic modelling skills to build linear regression and k-means clustering models.

A machine learning project to predict Customers/Clients into correct segment to provide promotional information or for product advertising.

Using Unsupervised Machine Learning to predict tradable cryptocurrencies for potential customer investments.

finding_donors machine learning model

Predict if a woman will develop breast cancer_Ensemble Techniques_Stacking

It is supervised machine learning model that is trained on the customer's purchased data to predict whether the customer with some attributes like salary, age, gender, etc will buy or not.

This repository contains a comprehensive implementation of gradient descent for linear regression, including visualizations and comparisons with ordinary least squares (OLS) regression. It also includes an additional implementation for multiple linear regression using gradient descent.

Use various techniques to train and evaluate a model based on loan risk. Use a dataset of historical lending activity from a peer-to-peer lending services company to build a model that can identify the creditworthiness of borrowers.