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.

learning purpose(End-to-End)

Taxi Fare predictor using Random Forest and GridSearchCV for Hyperparameter Tuning

ML Supervised-Regression : Used Car Price Prediction

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 🧠

The goal of this project is to predict the age of abalone using various physical measurements. The dataset used for this task is the Abalone dataset from Kaggle. The age of abalone is determined by the number of rings, which is the dependent variable in this study.

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.

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).

A real estate market analyzer used to identify recently listed homes that could be undervalued.

The feature engineering techniques discussed are - dimensionality reduction(pca), scaling(standard scaler, normalizer, minmaxscaler), categorical encoding(one hot/dummy), binning, clustering, feature selection. These are techniques performed on a dataset consisting of Californian House Prices.

Predicting breast cancer survival using machine learning models

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

This Project deals with determining the product prices based on the historical retail store sales data. After generating the predictions, our model will help the retail store to decide the price of the products to earn more profits.

Algorithmic Trading Model Development for BTC/USDT Crypto Market. Crafting models that outperform benchmarks, balancing returns and risk management in the dynamic BTC/USDT market.