Car Price Prediction

pip install numpy

import numpy

import pandas as pd 

import numpy as np

import matplotlib.pyplot as plt

import seaborn as sns

import scipy as sp

df = pd.read_csv('output.csv') 

df.head()

headers = ["symboling", "normalized-losses", "make", 

           "fuel-type", "aspiration","num-of-doors",

           "body-style","drive-wheels", "engine-location",

           "wheel-base","length", "width","height", "curb-weight",

           "engine-type","num-of-cylinders", "engine-size", 

           "fuel-system","bore","stroke", "compression-ratio",

           "horsepower", "peak-rpm","city-mpg","highway-mpg","price"]

df.columns=headers

df.head()

data = df

data.isna().any()

data.isnull().any() 

data['city-mpg'] = 235 / df['city-mpg']

data.rename(columns = {'city_mpg': "city-L / 100km"}, inplace = True)

print(data.columns)

data.dtypes 

data.price.unique()

data = data[data.price != '?']

data['price'] = data['price'].astype(int)

data.dtypes

data['length'] = data['length']/data['length'].max()

data['width'] = data['width']/data['width'].max()

data['height'] = data['height']/data['height'].max()

bins = np.linspace(min(data['price']), max(data['price']), 4) 

group_names = ['Low', 'Medium', 'High']

data['price-binned'] = pd.cut(data['price'], bins, 

                              labels = group_names, 

                              include_lowest = True)

print(data['price-binned'])

plt.hist(data['price-binned'])

plt.show()

pd.get_dummies(data['fuel-type']).head()

data.describe()

plt.boxplot(data['price'])

sns.boxplot(x ='drive-wheels', y ='price', data = data)

plt.scatter(data['engine-size'], data['price'])

plt.title('Scatterplot of Enginesize vs Price')

plt.xlabel('Engine size')

plt.ylabel('Price')

plt.grid()

plt.show()

test = data[['drive-wheels', 'body-style', 'price']]

data_grp = test.groupby(['drive-wheels', 'body-style'], 

                         as_index = False).mean()

data_grp

data_pivot = data_grp.pivot(index = 'drive-wheels',

                            columns = 'body-style')

data_pivot

plt.pcolor(data_pivot, cmap ='RdBu')

plt.colorbar()

plt.show()

data_annova = data[['make', 'price']]

grouped_annova = data_annova.groupby(['make'])

annova_results_l = sp.stats.f_oneway(

                             grouped_annova.get_group('honda')['price'],

                             grouped_annova.get_group('subaru')['price']

                                    )

print(annova_results_l)

sns.regplot(x ='engine-size', y ='price', data = data)

plt.ylim(0, )