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Itsbeulah/README.md

import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score #load the data from csv file to PandasDataFrame titanic_data = pd.read_csv("/content/train.csv") #printing first five rows of the dataframe titanic_data.head() titanic_data.shape titanic_data.info() titanic_data.isnull().sum() titanic_data = titanic_data.drop(columns='Cabin',axis=1) titanic_data['Age'].fillna(titanic_data['Age'].mean(), inplace=True) print(titanic_data['Embarked'].mode()) print(titanic_data['Embarked'].mode()[0]) #replacing the missing values in 'Embarked' column with mode value titanic_data['Embarked'].fillna(titanic_data['Embarked'].mode()[0], inplace=True) titanic_data.describe() #finding the no.of people survived and not survived titanic_data['Survived'].value_counts() sns.set() making a countplot for "Survived"coloumn sns.countplot(x='Survived', data=titanic_data)

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