This repository contains several machine learning projects done in Jupyter Notebooks

This is GEM repo, as it has all the Hands-On ML notebooks

- Notebook for car price prediction using regression models.

This repository contains introductory notebooks for random forest.

Different ML Algorithms both in scripts & Jupyter Notebooks

Jupyter notebook containing Python code for predicting exoplanet orbital obliquities using machine learning random forest regression models.

This Jupyter notebook serves as a machine learning template to quickly make predictions and analyse feature importance in a dataset.

A repo containing a few exploratory notebooks for statistical (ARIMA) and supervised ML (random forest, KNN) approaches to time series analysis of monthly retail sales (sourced from St. Louis Fed). Notebooks also explore the use of MLFlow for experiment tracking, model registration, and deployment/inference.

In this notebook, I have done Data Cleaning, Data Wrangling, EDA and Feature Engineering. After that I trained the dataset using Machine Learning Algorithm Random Forest Regressor.

This project comprises two interconnected objectives, each represented by a separate Jupyter Notebook. Both objectives aim to apply machine learning methodologies to solve practical problems in predictive analytics.

A trained ML model for prediction of house prices in IOWA using Random Forest Regression technique

This Notebook kernel deals with a dataset from 2017 of three 3 MW windturbines. It covers an EDA and Data Visualization of the Turbine and and the comparison of different regression Models including Decision Tree, Random Forrest, Support Vector Machine and ANN.