<jupyter_start><jupyter_text>Project 2Due date: July 27, 2021, 11:59 pm- The result will be evaluated from a report in Jupyter, which must be found in a public GitHub repository.- The project must be carried out in the groups assigned in class.- Use clear and rigorous procedures.- Due date: July 27, 2021, 11:59 pm, through Bloque Neón + (Upload repository link)- Projects will not be received after the delivery time or by any other means than the one established. Used Vehicle Price Prediction  Introduction- 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)- Each observation represents the price of an used car<jupyter_code>%matplotlib inline
import pandas as pd
data = pd.read_csv('../datasets/dataTrain_carListings.zip')
data.head()
data.shape
data.Price.describe()
data.plot(kind='scatter', y='Price', x='Year')
data.plot(kind='scatter', y='Price', x='Mileage')
data.columns<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10196078431372549, 'fraction_numerical': 0.025490196078431372, 'mean_word_length': 5.38125, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 1, 'lorem ipsum': 0, 'www.': 1, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '453671', 'license': 'MIT', 'n_tokens_mistral': 330, 'n_tokens_neox': 297, 'n_words': 120, 'path': 'Exercises/P2-UsedVehiclePricePrediction.ipynb', 'repo_name': 'jega1228/Modelos_analiticos_avanzados_2', 'size': '1020'}