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# # Auto Mileage Per Gallon Prediction

# The data concerns city-cycle fuel consumption in miles per gallon, to be predicted in terms of 3 multivalued discrete and 5 continuous attributes.
#
# **Source**: https://archive.ics.uci.edu/ml/datasets/auto+mpg

# <img width="1000" src="https://carused.jp/blog/wp-content/uploads/2019/03/how-to-detect-a-mileage-rollback.jpg">

# ## Goals:
#
# ### Understand the dataset
# - How the features are related to each other?
# - Is there redundant features?
# - Is there outliers?
# - Is there missing data?
# - Are data types adequate for analysis?
#
# ### Understand the problem
# - What features are correlated to the target feature?
# - Is it possible to create new features that are correlated to the target feature?
# - Answer [questions](https://en.wikipedia.org/wiki/Data_analysis#Analytical_activities_of_data_users) using data 

# +
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt

# %matplotlib inline
# -

pd.set_option("display.max_columns", None)

df = pd.read_csv("data/auto_mpg.csv")



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