To perform EDA on the given data set.
The primary aim with exploratory analysis is to examine the data for distribution, outliers and anomalies to direct specific testing of your hypothesis.
Import the required packages(pandas,numpy,seaborn).
Read and Load the Dataset.
Remove the null values from the data and remove the outliers.
Remove the non-numerical data columns using drop() method.
Return object containing counts of unique values using (value_counts()).
Plot the counts in the form of Histogram or Bar Graph.
Find the pairwise correlation of all columns in the dataframe(.corr()).
Save the final data set into the file.
df.isnull().sum()
df.drop("Cabin",axis=1,inplace=True)
df.isnull().sum()
df['Age']=df['Age'].fillna(df['Age'].median())
df['Embarked']=df['Embarked'].fillna(df['Embarked'].mode()[0])
df.isnull().sum()
df.boxplot()
df["Embarked"].value_counts()
df["Pclass"].value_counts()
df["Survived"].value_counts()
pd.crosstab(df["Pclass"],df["Age"])
df.corr()
sns.countplot(x="Survived",data=df)
sns.countplot(x="Pclass",data=df)
sns.countplot(x="Sex",data=df)
sns.displot(df["Age"])
sns.displot(df["Fare"])
sns.countplot(x="Pclass",hue="Survived",data=df)
sns.countplot(x="Age",hue="Sex",data=df)
sns.countplot(x="Age",hue="Survived",data=df)
sns.displot(df[df['Survived']==0]["Age"])
sns.displot(df[df['Survived']==1]["Age"])
sns.heatmap(df.corr(),annot=True)
Finally, the given data's were analyzed successfully.