initial Data preparation is done

Kaggle/Titanic/titanic.py

@@ -1,11 +1,12 @@
 # %%
 import pandas as pd
-
-passenger_train = pd.read_csv("train.csv", sep=",")
+path = r"/home/soumyajit/Codes/DL/100DaysOfML/Kaggle/Titanic/train.csv"
+passenger_train = pd.read_csv(path, sep=",")
 passenger_train
 
 # %%
-passenger_test = pd.read_csv("test.csv")
+path = r"/home/soumyajit/Codes/DL/100DaysOfML/Kaggle/Titanic/test.csv"
+passenger_test = pd.read_csv(path)
 passenger_test
 
 # %%
@@ -53,8 +54,7 @@
 
 # %%
 passenger_train_var = passenger_train
-passenger_train_var["Age_Per_Fare"] = passenger_train_var["Age"]
-    /passenger_train_var["Fare"]
+passenger_train_var["Age_Per_Fare"] = passenger_train_var["Age"]/passenger_train_var["Fare"]
 passenger_train_var.plot(kind="scatter", x="Survived", y="Age_Per_Fare",
                          alpha=0.1, figsize=(15, 12))
 
@@ -68,4 +68,52 @@
 passenger_train_label
 
 #%%
+passenger_train_var = passenger_train_var.drop("Name",axis = 1)
+passenger_train_var = passenger_train_var.drop("Cabin",axis = 1)
+passenger_train_var = passenger_train_var.drop("Ticket",axis = 1)
+passenger_train_var = passenger_train_var.drop("PassengerId",axis = 1)
+passenger_train_var = passenger_train_var.drop("Embarked",axis = 1)
+passenger_train_var = passenger_train_var.drop("Age_Per_Fare",axis = 1)
+
+passenger_train_var_num = passenger_train_var.drop("Sex",axis = 1)
+# passenger_train_var_num = passenger_train_var_num.drop("Embarked",axis = 1)
+passenger_train_var_num
 
+#%%
+from sklearn.base import BaseEstimator, TransformerMixin
+class DataFrameSelector(BaseEstimator, TransformerMixin):
+    def __init__(self, attribute_names):
+        self.attribute_names = attribute_names
+    def fit(self, X, y=None):
+        return self
+    def transform(self, X):
+        return X[self.attribute_names].values
+#%%
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import StandardScaler
+from sklearn.preprocessing import Imputer
+from sklearn.pipeline import FeatureUnion
+from sklearn.preprocessing import LabelBinarizer
+
+num_attribs = list(passenger_train_var_num)
+cat_attribs = ["Sex"]
+num_pipeline = Pipeline([
+('selector', DataFrameSelector(num_attribs)),
+('imputer', Imputer(strategy="median")),
+('std_scaler', StandardScaler()),
+])
+cat_pipeline = Pipeline([
+('selector', DataFrameSelector(cat_attribs)),
+('label_binarizer', LabelBinarizer()),
+])
+full_pipeline = FeatureUnion(transformer_list=[
+("num_pipeline", num_pipeline),
+("cat_pipeline", cat_pipeline),
+])
+full_pipeline
+
+#%%
+passenger_train_var
+#%%
+passenger_prepared = full_pipeline.fit_transform(passenger_train_var)
+passenger_prepared