A brain inspired stochastic neural network that is used for image classification

🏚🏠 RightHouse is a Real Estate Price prediction webapp.

3 - Personal Project - Data Cleaning - Price Predictor - Brasília Apartments

Django backend with CNN model for sentiment analysis of tweets and price prediction of cryptocurrencies.

To analyze the depreciation value of cars over the years by analyzing the sale prices on resale posts. This analysis will also include analysis on various factors like vehicle type, manufacturer, year and so on.

A Machine Learning Model to predict the house prices based on the square feet living

House Price Prediction using Ridge Regression method

This program receives a crochet pattern as a text, and produce useful information about the crochet project from the pattern.

The Laptop Price Predictor is a machine learning project that uses Random Forest algorithm to predict the price of laptops. The project involves extensive data preprocessing, manipulation and feature engineering, and provides a Jupyter notebook for further exploration.

In this project, I analyzed Crankshaft List data to identify factors affecting car prices. Analyzing countless vehicle ads, we aimed to provide valuable insights to users for informed car buying/selling decisions.

IKEA Product Analysis and Price Prediction Using Linear Regression

Banglore Property Price Prediction - Employing XGBoost regression and advanced data science techniques, I successfully improved the R2score of the base model from negatives to an impressive 75%

This a Sneaker Classification Model built using tensorflow keras

Mercari Price Suggestion Challenge

Predict house prices in Ireland based on location using a database of previous house prices and locations.

Attempt to predict the median price of homes in a given Boston suburb in the mid-1970s, given data points about the suburb at the time, such as the crime rate, the local property tax rate, and so on. Only 506 data points, split between 404 training samples and 102 test samples. Each feature in the input data (for example, the crime rate) has a d…

Data preprocessing of raw airline data and predicting prices through various different regression algorithms. Also dumping the model and reusing it for new data.

In this project, I have used Boston Housing Dataset to train the model & Predict Results i.e. House Price according to model. The model is working absolutely fine with error rate of 10.54% which is totally accepted. The code can be used on various datasets by simple modifications.

A Neural Network LSTM model that predicts price of cryptocurrencies.