{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Project 2\n", "\n", "# Used Vehicle Price Prediction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction\n", "\n", "- 1.2 Million listings scraped from TrueCar.com - Price, Mileage, Make, Model dataset from Kaggle: [data](https://www.kaggle.com/jpayne/852k-used-car-listings)\n", "- Each observation represents the price of an used car" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data = pd.read_csv('../datasets/dataTrain_carListings.zip')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", " | Price | \n", "Year | \n", "Mileage | \n", "State | \n", "Make | \n", "Model | \n", "
---|---|---|---|---|---|---|
0 | \n", "21490 | \n", "2014 | \n", "31909 | \n", "MD | \n", "Nissan | \n", "MuranoAWD | \n", "
1 | \n", "21250 | \n", "2016 | \n", "25741 | \n", "KY | \n", "Chevrolet | \n", "CamaroCoupe | \n", "
2 | \n", "20925 | \n", "2016 | \n", "24633 | \n", "SC | \n", "Hyundai | \n", "Santa | \n", "
3 | \n", "14500 | \n", "2012 | \n", "84026 | \n", "OK | \n", "Jeep | \n", "Grand | \n", "
4 | \n", "32488 | \n", "2013 | \n", "22816 | \n", "TN | \n", "Jeep | \n", "Wrangler | \n", "