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.

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.

Easy statistical testing on the web

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.

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.

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This GitHub repository contains code for a student performance prediction model utilizing linear regression. The model is built using relevant student data features and implements one-hot encoding for categorical variables. The repository includes data preprocessing, model training, evaluation, and prediction visualization using matplotlib.

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

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

This was my first step into machine learning, Its not new, I am just uploading it on Github now. Its a llinear regression model

project by team sudo-solve in kodekochikode hackathon

Forcasting the volume of DATA 2G, 3G and 4G stream

This project aims to solve the Vehicle Routing Problem (VRP) by addressing missing data using linear regression and predicting the time required for particular orders. Additionally, it predicts the optimal set of routes for a fleet of vehicles to traverse to deliver to a given set of customers efficiently.

Medical Insurance Cost Prediction

This project involves parsing bike-sharing data from the CityBikes API and merging it with location-based details from Foursquare and Yelp APIs, conducting data exploration, visualization, and regression modeling to examine the correlation between bike station characteristics and adjacent points of interest.