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.

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…

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

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

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.

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.

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

📈 Bitcoin Price Prediction using Random Forest Regressor 🧠

Ranking Reviews Based on their utility using Advanced NLP Techniques

Density Estimation, HMM Signature Verification, K-Means and Gap Statistics, Random Forests

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

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.

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"

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

Based on historical data predict total runs scored by the batting team, at the end of 6 overs for IPL 2021.

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.