Car Price Predictor App used to predict the price of the car based on certain input parameters created using python's scikit-learn, fastapi, numpy and joblib packages.

Last update: May 10, 2022

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Deep Learning Pricefy

Overview

Pricefy

Car Price Predictor App used to predict the price of the car based on certain input parameters created using python's scikit-learn, fastapi, numpy and joblib packages.

Dataset Description :-

This dataset contains information about used cars. This data can be used for a lot of purposes such as price prediction to exemplify the use of linear regression in Machine Learning.

The data contains the following columns:

Feature Name	Feature Description
Name	Name of the Car model
Present_Price	Present showroom price of the car
Year	Car Model Year
Kms_Driven	Kilometers driven till now
Owner	No of Owners (0 or 1 or 2 or 3)
Fuel_Type	Type of Fuel (Petrol or Diesel or CNG)
Seller_Type	Whether seller is (Individual or Dealer)
Transmission	Transmission type (Automatic or Manual)
Selling_Price	Used Car selling price (Target) variable

Installation :-

Open Anaconda prompt and create new environment 👇

conda create -n your_env_name python = (any_version_number)

Then Activate the newly created environment 👇

conda activate your_env_name

To install all requirement packages for the app 👇

pip install -r requirements.txt

Then, Run the app 👇

uvicorn main:app --reload

Car Price Predictor App used to predict the price of the car based on certain input parameters created using python's scikit-learn, fastapi, numpy and joblib packages.

Related tags

Overview

Pricefy

Dataset Description :-

Installation :-

📷 Screenshots:-

Home page:-

About section:-

Developer section:-

Swagger UI:-

Redoc UI:-

Demo GIF Image 👇 :-

Owner

Siva Prakash

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Car Price Predictor App used to predict the price of the car based on certain input parameters created using python's scikit-learn, fastapi, numpy and joblib packages.

Related tags

Overview

Pricefy

Dataset Description :-

Installation :-

📷 Screenshots:-

Home page:-

About section:-

Developer section:-

Swagger UI:-

Redoc UI:-

Demo GIF Image 👇 :-

Owner

Siva Prakash

Fully Convolutional Networks for Semantic Segmentation by Jonathan Long*, Evan Shelhamer*, and Trevor Darrell. CVPR 2015 and PAMI 2016.

Implementation of Feedback Transformer in Pytorch

Earth Vision Foundation

Towards Rolling Shutter Correction and Deblurring in Dynamic Scenes (CVPR2021)

[EMNLP 2021] MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations

[CVPR 2021] Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

MoveNet Single Pose on DepthAI

Text2Art is an AI art generator powered with VQGAN + CLIP and CLIPDrawer models

Code for our work "Activation to Saliency: Forming High-Quality Labels for Unsupervised Salient Object Detection".

Resco: A simple python package that report the effect of deep residual learning

Optimizers-visualized - Visualization of different optimizers on local minimas and saddle points.

PyTorch3D is FAIR's library of reusable components for deep learning with 3D data

Demo code for ICCV 2021 paper "Sensor-Guided Optical Flow"

Recurrent Scale Approximation (RSA) for Object Detection

Code for paper Novel View Synthesis via Depth-guided Skip Connections

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Generalized and Efficient Blackbox Optimization System.

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The tl;dr on a few notable transformer/language model papers + other papers (alignment, memorization, etc).

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