Machine Learning & Deep Learning practice code.4

• read files and save as df

df = pd.read_csv('data_v1.csv')

df

• EDA(Exploratory Data Analysis)

df.head()

df.tail()

df.info()

df.index

df.columns

df.values

• check if null data exists

df.isnull().sum()

• get the information of statistics

df.describe()

<Data Preprocessing>

• grasp the structure of the data

df.info()

• delete the columns

df.drop('customerID', axis=1, inplace=True)

df.info()

• change of the types of columns

df['TotalCharges']

• change the colomn type to float

df['TotalCharges'].astype(float) ---> WRONG

• search with boolean indexing

(df[TotalCharges'] == '') | (df[TotalCharges'] == ' ')

cond = (df['TotalCharges'] == '') | (df[Totalcharges'] == ' ')
df[cond]

• change df['TotalCharges'] to Zero

df['TotalCharges'].replace([' '], ['0'], inplace=True)

• check change of TotalCharges column to float

df['TotalCharges'] = df['TotalCharges'].astype(float)

cond = (df['TotalCharges'] == '') | (df['TotalCharges'] == ' ')
df[cond]

• checking

df.info()

• change column 'Churn's value format to numbers

df['Churn'].value_counts()

• change 'Churn's Yes, No to 1, 0

df['churn'].replace[replace([Yes', 'No'], [1, 0], inplace=True)

• check the column's distribution

df['Churn'].value_counts()

check existence of null data

df.isnull().sum()

• delete columns that has many null data with drop

df.drop('DeviceProtection', axis=1, inplace=True)
df.dropna(inplace=True)

• check if there's another null

df.isnull().sum()

df.info()

\< Visualization>

import matplotlib.pyplot as plt
%matplotlib inline

df['gender'].value_counts()

df['gender'].value_counts().plot(kind='bar')

• column patner's distribution. bar chart

df['Partner'].value_counts().plot(kind='bar')

• make a bar chart of 'object'column at once using select_dtype() function

df.select_dtypes('O').head(3)

• select only Object column names

df.select_dtypes('O').columns.values

• draw bar chart of object column one by one
  dependents, phoneService -> has unbalance -> delete needed

object_list = df.select_dtypes('object').columns.values

for col in object_list:
	df[col].value_counts().plot(kind='bar')
    plt.title(col)
    plt.show()

• deleting unbalanced columns

df.drop('PhoneService', axis=1, inplace=True)

• visualize columns that has number type(int, float)

df.select_dtypes('number').head(3)

• checkgin Churn column

df['Churn'].value_counts()

• checking bar chart of 'Churn' column

df['Churn'].value_counts().plot(kind='bar')

• same process for 'SeniorCitizen' Column

df['SeniorCitizen'].value_counts()

df['SeniorCitizen'].value_counts().plot(kind='bar')

df.drop('SeniorCitizen', axis=1, inplace=True)

df.info()

• Histogram

sns.histplot(data=df, x='tenure')

sns.histplot(data=df, x='tenure', hue='Churn')

• make it curve gragh

sns.kdeplot(data=df, x='tenure', hue='Churn')

sns.histplot(data=df, x='TotalCharges')

sns.kdeplot(data=df, x='TotalCharges', hue='Churn')

sns.countplot(data=df, x='MultipleLines', hue='Churn')

• Heatmap
• correalation between columns

df[['tenure', 'MonthlyCharges', 'TotalCharges']].corr()

sns.heatmap(df[['tenure', 'MonthlyCharges', 'TotalCharges']].corr(), annot=True)

-Boxplot

sns.boxplot(data=df, x='Churn', y='TotalCharges')

• restore the result as csv file

df.to_csv('data_v1_save.csv', index=False)

pd.read_csv('data_v1_save.csv').head()