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machine learning basics/titanic_data_prediction.py

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+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+
+a = pd.read_csv("titanic_data.csv")
+a.head()
+
+def impute_age(cols):
+   Age = cols[0]
+   Pclass = cols[1]
+   if pd.isnull(Age):
+       if Pclass == 1:
+           return 37
+       elif Pclass == 2:
+           return 29
+       else:
+           return 24
+   else:
+       return Age
+
+a['Age'] = a[['Age', 'Pclass']].apply(impute_age, axis = 1)
+
+def impute_cabin(col):
+   Cabin = col[0]
+   if type(Cabin) == str:
+       return 1
+   else:
+       return 0
+
+a['Cabin'] = a[['Cabin']].apply(impute_cabin, axis = 1)
+
+
+dataset = a
+
+sex = pd.get_dummies(dataset['Sex'],drop_first=True)
+embark = pd.get_dummies(dataset['Embarked'],drop_first=True)
+dataset.drop(['Sex','Embarked','Name','Ticket'],axis=1,inplace=True)
+dataset = pd.concat([dataset,sex,embark],axis=1)
+
+
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import accuracy_score
+X_train, X_test, y_train, y_test = train_test_split(dataset.drop('Survived',axis=1),dataset['Survived'], test_size=0.2,random_state=45)
+
+regressor = LogisticRegression()
+regressor.fit(X_train, y_train)
+pred = regressor.predict(X_test)
+
+print(accuracy_score(y_test, pred))
+