Create titatanic_basic

titatanic_basic

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+from sklearn.metrics import accuracy_score, recall_score, precision_score
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.impute import SimpleImputer
+import pandas as pd_df
+import numpy as np
+
+v_error_tol = 1
+
+
+# defining  reading CSV file
+def load_analysis_data(v_in_file):
+    return pd_df.read_csv(v_in_file)
+
+
+def clean_input_data(v_in_df_data, v_data_type):
+    if v_data_type == 'Testset':
+        v_in_df_data['Survived'] = v_in_df_data["Sex"].apply(lambda x: 0 if x == 'male' else 0)
+
+    # Column Sex Analysis and Cleaning
+    V_Null_Count_Sex = v_in_df_data['Sex'].isnull().sum()
+    V_total_Count_Sex = sum(v_in_df_data.value_counts(v_in_df_data['Sex']))
+    V_delta_count_Sex = V_total_Count_Sex - V_Null_Count_Sex
+    V_delta_count_Sex_percentage = (V_Null_Count_Sex / V_total_Count_Sex) * 100
+
+    # Column Embarked Analysis and Cleaning
+    V_Null_Count_Embarked = v_in_df_data['Embarked'].isnull().sum()
+    V_total_Count_Embarked = sum(v_in_df_data.value_counts(v_in_df_data['Embarked']))
+    #print(V_total_Count_Embarked)
+    # V_delta_count_Embarked = V_total_Count_Embarked - V_Null_Count_Embarked
+    V_delta_count_Embarked_percentage = (V_Null_Count_Embarked / V_total_Count_Embarked) * 100
+    #print(V_delta_count_Embarked_percentage)
+
+    # transforming categorical to Numerical for Sex column
+    if V_Null_Count_Sex == 0 or V_delta_count_Sex_percentage < v_error_tol:
+        if V_delta_count_Sex_percentage < v_error_tol:
+            v_in_df_data = v_in_df_data.dropna(subset=["Sex"])
+        v_in_df_data['gender'] = v_in_df_data["Sex"].apply(lambda x: 1 if x == 'male' else 0)
+    else:
+        print('Please review Data set for column Sex')
+
+    ########## transforming categorical to Embarked
+    if V_Null_Count_Embarked == 0 or V_delta_count_Embarked_percentage < v_error_tol:
+
+        if V_delta_count_Embarked_percentage < v_error_tol:
+            v_in_df_data = v_in_df_data.dropna(subset=["Embarked"])
+            condition_one = (v_in_df_data["Embarked"] == 'S')
+            condition_two = (v_in_df_data["Embarked"] == 'C')
+            condition_three = (v_in_df_data["Embarked"] == 'Q')
+            conditions = [condition_one, condition_two, condition_three]
+            choices = [1, 2, 3]
+            v_in_df_data["Embarked_val"] = np.select(conditions, choices)
+    else:
+        print('Please review Data set for column Embarked')
+
+    v_in_df_data_clean = pd_df.DataFrame(v_in_df_data, columns=['PassengerId', 'Survived', 'Pclass', 'gender',
+                                                                'Age', 'SibSp', 'Parch', 'Fare', 'Embarked_val'])
+
+    ########Filling Median value for rest of the NA column
+    median_impute = SimpleImputer(strategy="median")
+    X_val = median_impute.fit_transform(v_in_df_data_clean)
+    v_in_df_data_clean = pd_df.DataFrame(X_val, columns=v_in_df_data_clean.columns)
+    return v_in_df_data_clean
+
+
+def split_data(in_df_clean_base, in_file_process_flag):
+    if in_file_process_flag == 'Trainset':
+        X_train, X_test, y_train, y_test = train_test_split(in_df_clean_base, in_df_clean_base['Survived'],
+                                                            test_size=0.2, stratify=in_df_clean_base['Pclass'])
+        X_train = pd_df.DataFrame(X_train, columns=['PassengerId', 'Pclass', 'gender', 'Age', 'Fare', 'Embarked_val'])
+        X_test = pd_df.DataFrame(X_test, columns=['PassengerId', 'Pclass', 'gender', 'Age', 'Fare', 'Embarked_val'])
+    else:
+        X_test = pd_df.DataFrame(in_df_clean_base,
+                                 columns=['PassengerId', 'Pclass', 'gender', 'Age', 'Fare', 'Embarked_val'])
+        y_test = pd_df.DataFrame(in_df_clean_base,
+                                 columns=['PassengerId', 'Survived'])
+        X_train = [0]
+        y_train = [0]
+    return X_train, X_test, y_train, y_test
+
+
+def build_model(in_x_train, in_y_train, in_X_test, in_n_estimators, in_max_leaf_nodes):
+    # Model Build and Test-- Random Forest Classifier
+    rnd_clf = RandomForestClassifier(n_estimators=in_n_estimators, max_leaf_nodes=in_max_leaf_nodes, n_jobs=-1)
+    rnd_clf.fit(in_x_train, in_y_train)
+    y_final_rf = rnd_clf.predict(in_X_test)
+    return rnd_clf, y_final_rf
+
+
+def model_metrics(in_y_test, in_y_final_rf):
+    # Model Metrics
+    print('accuracy_score->' + str(accuracy_score(in_y_test, in_y_final_rf)))
+    print('recall_score->' + str(recall_score(in_y_test, in_y_final_rf)))
+    print('precision_score->' + str(precision_score(in_y_test, in_y_final_rf)))
+
+
+########reading file for train data
+V_file = 'train - Titanic.csv'
+V_file_process_flag = 'Trainset'
+V_n_estimators = 500
+V_max_leaf_nodes = 16
+titanic_base = load_analysis_data(V_file)
+titanic_clean_base = clean_input_data(titanic_base, V_file_process_flag)
+V_X_train, V_X_test, V_y_train, V_y_test = split_data(titanic_clean_base, V_file_process_flag)
+# build model and test accuracy of the model
+rnd_clf_model, y_final_test_rf = build_model(V_X_train, V_y_train, V_X_test, V_n_estimators, V_max_leaf_nodes)
+model_metrics(V_y_test, y_final_test_rf)
+
+## testing of model
+V_file = 'test - Titanic.csv'
+V_file_process_flag = 'Testset'
+V_n_estimators = 500
+V_max_leaf_nodes = 16
+titanic_base = load_analysis_data(V_file)
+titanic_clean_base = clean_input_data(titanic_base, V_file_process_flag)
+V_X_train, V_X_test, V_y_train, V_y_test = split_data(titanic_clean_base, V_file_process_flag)
+y_final_value = rnd_clf_model.predict(V_X_test)
+#print(y_final_value.to_csv)
+
+submit = pd_df.DataFrame({"PassengerId": V_X_test.PassengerId, 'Survived': y_final_value})
+submit.to_csv("Titanic_final_submission.csv", index=False)