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Project 1
import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns auto= pd.read_csv("auto_clean.csv") print(auto) symboling normalized-losses make aspiration num-of-doors \ 0 3 122 alfa-romero std two 1 3 122 alfa-romero std two 2 1 122 alfa-romero std two 3 2 164 audi std four 4 2 164…
Sushant Ovhal
updated on 17 Oct 2022
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
auto= pd.read_csv("auto_clean.csv")
print(auto)
auto.info()
auto[auto == '?']
autoclean = auto.replace('?',np.NaN)
autoclean
auto.isna().sum()
null=auto.isnull().any(axis = 1)
nullvalue=null.index[null.values]
nullvalue
missingrows = auto.iloc[nullvalue,:]
missingrows
auto.isnull().all(axis=0).sum()
auto.isnull().all(axis=1).sum()
round(auto.isnull().sum().sort_values(ascending =False)/len(auto) *100,2)
auto[['normalized-losses','price','peak-rpm','horsepower']] = auto[['normalized-losses','price','peak-rpm','horsepower']].apply(pd.to_numeric)
auto[['normalized-losses','price','peak-rpm','horsepower']].describe()
auto.loc[:,'normalized-losses'].fillna(auto['normalized-losses'].mean(),inplace = True)
round(auto.isnull().sum().sort_values(ascending = False)/len(auto) * 100,2)
df.loc[:,'Price'].fillna(df['Price'].mean(),inplace = True)
df.loc[:,'Stroke'].fillna(df['Stroke'].mean(),inplace = True)
df.loc[:,'Bore'].fillna(df['Bore'].mean(),inplace = True)
df.loc[:,'Peak RPM'].fillna(df['Peak RPM'].mean(),inplace = True)
df.loc[:,'Horsepower'].fillna(df['Horsepower'].mean(),inplace = True)
round(df.isnull().sum().sort_values(ascending = False)/len(df) * 100,2)
auto.loc[:,'price'].fillna(auto['price'].mean(),inplace =True)
auto.loc[:,'stroke'].fillna(auto['stroke'].mean(),inplace= True)
auto.loc[:,'bore'].fillna(auto['bore'].mean(),inplace = True)
auto.loc[:,'peak-rpm'].fillna(auto['peak-rpm'].mean(),inplace =True)
auto.loc[:,'horsepower'].fillna(auto['horsepower'].mean(),inplace = True)
round(auto.isnull().sum().sort_values(ascending = False)/len(auto) * 100,2)
auto[['horsepower-binned']]
auto['horsepower-binned'].unique()
auto['horsepower'].astype('category').value_counts()
df['No. of Doors'].astype('category').value_counts()
df.loc[:,'No. of Doors'].fillna('four',inplace = True)
df['No. of Doors'].astype('category').value_counts()
df.to_csv('clean_auto.csv')
auto.to_csv('Cleandata_auto.csv')
auto.hist(figsize=(30,30))
plt.show()
plt.figure(figsize=(15,10))
sns.heatmap(auto.select_dtypes(include='number').corr(),annot =True,cmap='coolwarm')
plt.title("Numerical features")
plt.show()
plt.figure(figsize=(15,10))
sns.countplot(auto['normalized-losses'])
plt.title("values")
plt.show
auto['normalized-losses'].describe()
sns.displot(auto['normalized-losses'],kde=True)
plt.title("Distribution of losses")
plt.show()
plt.figure(figsize=(10,7))
sns.heatmap(auto.select_dtypes(include='number').corr(),annot = True,cmap='coolwarm')
plt.title("correlation od all number")
plt.show()
auto.drop(['symboling','normalized-losses','compression-ratio','peak-rpm'],axis=1,inplace=True)
plt.figure(figsize=(10,7))
sns.heatmap(auto.select_dtypes(include='number').corr(),annot = True,cmap='coolwarm')
plt.title("correlation od all number")
plt.show()
auto.select_dtypes(exclude='number').head()
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