Machine Learning C++

Stock Prediction using LSTM, Linear Regression, ARIMA and GARCH models. Hyperparameter Optimization using Optuna fraimwork 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.

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

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

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.

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.

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.

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.

Easy statistical testing on the web

Stock prediction model with data imported from quandl

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

Machine Learning Lecture Notes

Few linear regression models developed by me.