This Flask app predicts house prices using a RandomForestRegressor model trained on a housing dataset. It includes data pre-processing with pipelines and imputers, stratified train-test splitting, and a user input form. Predictions are displayed on the web page, making it ideal for learning basic machine learning deployment with Flask.

flood forecasting system - prediction of extent of the inundation, impact analysis & damage estimation

ML Supervised-Regression : Used Car Price Prediction

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.

Air quality prediction in Africa using Light Gradient Boost Model and Random Forest Regressor

Tools & skills Used: Python, MATLAB, Seaborn, ML models

Validation RMSLE obtained: 0.21163 which is less than the RMSLE score (0.22909) that won the Kaggle Competition.

Data analysis project on a marketing campaign database using Python

THIS IS OUR FIRST PROJECT SUBMITTED IN GITHUB. "Wonderful Learning Experience"

This is the proof of concept, how a relatively unsophisticated statistical model trained on the large MPDS dataset predicts physical properties from the only crystalline structure (POSCAR or CIF).

Rusty Bargain is a used car buying and selling company that is developing an app to attract new buyers. My job as data science is to create a model that can determine the market value of a car.

It is an e-commerce web portal for farmers and customers. Farmers can list there crops with quantity and base price. Customers can bid on a crop with there prices. Farmer can sell there crop to best bid. Framer can also predict the production of the crop of a particular season, year, weather, and area.

I was just applying things that i learnt.(End-to-End).Well it does basically what the name says with 97% accuracy ig.

Taxi Fare predictor using Random Forest and GridSearchCV for Hyperparameter Tuning

This is a end to end Data Science project where the task is to predict the Fare of the flights (Indian Only). Data is in the form of Excel spreadsheets, one is for training purpose and the other is for testing.

This project develops a machine learning model to predict the salaries of baseball players based on their past performance.

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.

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.

“StockPulse:- Customized Portfolio Forecasting with Small Cap Stocks: - A Comparative Study and Model building with reference to Indian Stock Market with Machine Learning and Deep Learning Models.”

📈 Bitcoin Price Prediction using Random Forest Regressor 🧠