Learning to create Machine Learning Algorithms

The project aimed to implement Deep NN / RNN based solution in order to develop flexible methods that are able to adaptively fillin, backfill, and predict time-series using a large number of heterogeneous training datasets.

This project uses a machine learning approach in order to predict the number of goals scored by two teams in a match and then calculates the winning team

Machine Learning Software that predicts planets based on their distance from the sun, number of satellites and various properties

I have used Multinomial Naive Bayes, Random Trees Embedding, Random Forest Regressor, Random Forest Classifier, Multinomial Logistic Regression, Linear Support Vector Classifier, Linear Regression, Extra Tree Regressor, Extra Tree Classifier, Decision Tree Classifier, Binary Logistic Regression and calculated accuracy score, confusion matrix and…

A Random Forest Regressor-based framework for compensating sensor drift in MOS gas sensors over 36 months. Designed for industrial IoT, it achieves <10% MAE, reduces recalibration by 40%, and runs in 5 ms on edge devices.

Project aims to forecast potato prices in India using LSTM, KNN, and Random Forest Regression, integrating historical data on prices, regional stats, and rainfall patterns. Targeting agricultural stakeholders for informed decision-making.

The Zomato Delivery Time Prediction Application is a machine learning-driven Flask web application designed to predict the estimated delivery time for food orders placed on the Zomato platform.

The Revolving Credit Behavior Modeling project analyzes revolving credit to facilitate flexible access to funds within a credit limit, assisting financial institutions in setting accurate pricing strategies by addressing risk factors like inflation and interest rates.

A machine learning application aimed at predicting employee salaries based on various features such as experience, education level, location, etc. By using different models and techniques, the project seeks to present an optimized model for salary predictions.

This is a repository for ML based Electricity Demand Prediction

Predict sales prices and practice feature engineering, RFs, and gradient boosting

This repository focuses on building a random forest classifier and regressor as well as a gradient boosted regressor, building them from scratch using only NumPy for faster array processing.

Ranking Reviews Based on their utility using Advanced NLP Techniques

Predict next day's close price for a stock like NSEI, NYA, HSI, IXIC, TWII, etc...!

Applying data mining algorithms to the Stack Overflow Developer Survey dataset using Python

Database management and data analytics from a car-sharing dataset. The dataset contains information about the customers' demand rate between January 2017 and August 2018.

Applying random forest regression algorithm on the model to estimate the excitation current of the synchronous machine.

This code includes reading the data file, data visualization, variable splitting, model building, prediction and different metrics calculation using random forests.

Data science practice project from "Hands-On Machine Learning with SciKit-Learn and TensorFlow"