Machine Learning C++

Stock Prediction using LSTM, Linear Regression, ARIMA and GARCH models. Hyperparameter Optimization using Optuna framework for LSTM variants.

In this project, I applied different regression models for rmse and mae on antenna dataset for predict signal strength.

Linear Regression From Scratch

Stock Market Forecasting with CoreML in Swift

Implementation of Linear regression on Boston House Pricing and Diabetes data sets using python.

A set of projects I worked on as part of my PG Diploma in Data Science Program

This repository contains our project on Stock Market Price prediction Using Historical Data

Open Deep Learning and Reinforcement Learning lectures from top Universities like Stanford University, MIT, UC Berkeley. And an intent classifier which can classifies a query into one of the 21 given intents.

Apache Spark machine learning project using pyspark

In this project I have implemented 14 different types of regression algorithms including Linear Regression, KNN Regressor, Decision Tree Regressor, RandomForest Regressor, XGBoost, CatBoost., LightGBM, etc. Along with it I have also performed Hyper Paramter Optimization & Cross Validation.

Stock prediction model with data imported from quandl

Easy statistical testing on the web

Dummy variables can be really helpful while creating multiple linear regression models.

This is Flask integrated Machine Learning model which uses Linear Regression to Predict the CO2 Emission of Vehicle

This code demonstrates how to integrate PySpark with datasets and perform simple data transformations. It loads a sample dataset using PySpark's built-in functionalities or reads data from external sources and converts it into a PySpark DataFrame for distributed processing and manipulation.

Simple regression problem where by pre-loaded data I have shown different regression algorithm and their performance on data.

The objective is to build a ML-based solution (linear regression model) to develop a dynamic pricing strategy for used and refurbished smartphones, identifying factors that significantly influence it.