commit-live-students · Abhimanyu22 · Nov 30, 2018 · Nov 30, 2018 · Nov 30, 2018 · Dec 2, 2018
diff --git a/__pycache__/__init__.cpython-36.pyc b/__pycache__/__init__.cpython-36.pyc
diff --git a/q01_outlier_removal/__pycache__/__init__.cpython-36.pyc b/q01_outlier_removal/__pycache__/__init__.cpython-36.pyc
diff --git a/q01_outlier_removal/__pycache__/build.cpython-36.pyc b/q01_outlier_removal/__pycache__/build.cpython-36.pyc
diff --git a/q01_outlier_removal/build.py b/q01_outlier_removal/build.py
@@ -1,8 +1,25 @@
+
 # Default imports
 import pandas as pd
 
 loan_data = pd.read_csv('data/loan_prediction_uncleaned.csv')
 loan_data = loan_data.drop('Loan_ID', 1)
+loan_data_numerical = loan_data[['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount']]
+
+def outlier_removal(data):
+    df = data
+    qlt = df.quantile(q=0.95)
+
+    df = df.drop(df[(df['ApplicantIncome']>qlt[0])].index)
+    df = df.drop(df[(df['CoapplicantIncome']>qlt[1])].index)
+    df = df.drop(df[(df['LoanAmount']>qlt[2])].index)
+
+    return df
+
+outlier_removal(loan_data)
+
+
+
+
 
 
-# Write your Solution here:
diff --git a/q01_outlier_removal/tests/__pycache__/__init__.cpython-36.pyc b/q01_outlier_removal/tests/__pycache__/__init__.cpython-36.pyc
diff --git a/q01_outlier_removal/tests/__pycache__/test_q01_outlier_removal.cpython-36.pyc b/q01_outlier_removal/tests/__pycache__/test_q01_outlier_removal.cpython-36.pyc
diff --git a/q02_data_cleaning_all/__pycache__/__init__.cpython-36.pyc b/q02_data_cleaning_all/__pycache__/__init__.cpython-36.pyc
diff --git a/q02_data_cleaning_all/__pycache__/build.cpython-36.pyc b/q02_data_cleaning_all/__pycache__/build.cpython-36.pyc
diff --git a/q02_data_cleaning_all/build.py b/q02_data_cleaning_all/build.py
@@ -1,14 +1,33 @@
+# %load q02_data_cleaning_all/build.py
 # Default Imports
 import sys, os
 sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname('__file__'))))
 import pandas as pd
 import numpy as np
+from sklearn.preprocessing import Imputer
 from sklearn.model_selection import train_test_split
 from greyatomlib.logistic_regression_project.q01_outlier_removal.build import outlier_removal
 
 loan_data = pd.read_csv('data/loan_prediction_uncleaned.csv')
 loan_data = loan_data.drop('Loan_ID', 1)
 loan_data = outlier_removal(loan_data)
 
+def data_cleaning(data):
+    df = data
+    imputer_mean = Imputer(missing_values='NaN', strategy='mean')
+    imputer_mean.fit(df[['LoanAmount']])
+    df['LoanAmount'] = imputer_mean.transform(df[['LoanAmount']])
+    cat_features = ['Gender', 'Married', 'Dependents', 'Self_Employed', 'Loan_Amount_Term', 'Credit_History']
+    for feature in cat_features:
+        df[feature] = df[feature].fillna(df[feature].mode()[0])
+    X = df.iloc[:,:-1]
+    y = df.iloc[:,-1]
+
+    np.random.seed(9)
+    X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.25, train_size=0.75)
+    return X, y, X_train, X_test, y_train, y_test
+
+data_cleaning(loan_data)
+
+
 
-# Write your solution here :
diff --git a/q02_data_cleaning_all/tests/__pycache__/__init__.cpython-36.pyc b/q02_data_cleaning_all/tests/__pycache__/__init__.cpython-36.pyc
diff --git a/q02_data_cleaning_all/tests/__pycache__/test_q02_data_cleaning.cpython-36.pyc b/q02_data_cleaning_all/tests/__pycache__/test_q02_data_cleaning.cpython-36.pyc
diff --git a/q02_data_cleaning_all_2/__pycache__/__init__.cpython-36.pyc b/q02_data_cleaning_all_2/__pycache__/__init__.cpython-36.pyc
diff --git a/q02_data_cleaning_all_2/__pycache__/build.cpython-36.pyc b/q02_data_cleaning_all_2/__pycache__/build.cpython-36.pyc
diff --git a/q02_data_cleaning_all_2/build.py b/q02_data_cleaning_all_2/build.py
@@ -1,3 +1,4 @@
+# %load q02_data_cleaning_all_2/build.py
 # Default Imports
 import pandas as pd
 import numpy as np
@@ -9,5 +10,25 @@
 loan_data = outlier_removal(loan_data)
 X, y, X_train, X_test, y_train, y_test = data_cleaning(loan_data)
 
+def data_cleaning_2(X_train, X_test, y_train, y_test):
+
+    numeric_feature = ['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', 'Loan_Amount_Term','Credit_History']
+    cat_features = ['Gender', 'Married', 'Dependents', 'Education', 'Self_Employed', 'Property_Area']
+
+    for feature in numeric_feature:
+        X_train[feature] = np.sqrt(X_train[feature])
+        X_test[feature] = np.sqrt(X_test[feature])
+
+    X_train_dummy = pd.get_dummies(X_train[cat_features], drop_first=True)
+    X_test_dummy = pd.get_dummies(X_test[cat_features], drop_first=True)
+    X_train = X_train[numeric_feature].join(X_train_dummy)
+    X_test = X_test[numeric_feature].join(X_test_dummy)
+    y_train, y_test = y_train, y_test
+
+    return X_train, X_test, y_train, y_test
+
+data_cleaning_2(X_train, X_test, y_train, y_test)
+
+
+
 
-# Write your solution here :
diff --git a/q02_data_cleaning_all_2/tests/__pycache__/__init__.cpython-36.pyc b/q02_data_cleaning_all_2/tests/__pycache__/__init__.cpython-36.pyc
diff --git a/q02_data_cleaning_all_2/tests/__pycache__/q02_test_data_cleaning_2.cpython-36.pyc b/q02_data_cleaning_all_2/tests/__pycache__/q02_test_data_cleaning_2.cpython-36.pyc
diff --git a/q03_logistic_regression/__pycache__/__init__.cpython-36.pyc b/q03_logistic_regression/__pycache__/__init__.cpython-36.pyc
diff --git a/q03_logistic_regression/__pycache__/build.cpython-36.pyc b/q03_logistic_regression/__pycache__/build.cpython-36.pyc
diff --git a/q03_logistic_regression/build.py b/q03_logistic_regression/build.py
@@ -1,5 +1,7 @@
+# %load q03_logistic_regression/build.py
 # Default Imports
 import pandas as pd
+import numpy as np
 from sklearn.preprocessing import StandardScaler
 from sklearn.linear_model import LogisticRegression
 from sklearn.metrics import confusion_matrix
@@ -13,6 +15,24 @@
 X, y, X_train, X_test, y_train, y_test = data_cleaning(loan_data)
 X_train, X_test, y_train, y_test = data_cleaning_2(X_train, X_test, y_train, y_test)
 
+def logistic_regression(X_train, X_test, y_train, y_test):
+    column_transform = ['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount']
+
+    stand_scale = StandardScaler()
+    X_train.loc[:, column_transform] = stand_scale.fit_transform(X_train.loc[:, column_transform])
+    X_test.loc[:, column_transform] = stand_scale.fit_transform(X_test.loc[:, column_transform])
+
+    lr = LogisticRegression(random_state=9)
+    lr.fit(X_train, y_train)
+
+    y_pred = lr.predict(X_test)
+    cm = confusion_matrix(y_test,y_pred)
+
+    return cm
+
+
+
+
+
 
-# Write your solution code here:
 
diff --git a/q03_logistic_regression/tests/__pycache__/__init__.cpython-36.pyc b/q03_logistic_regression/tests/__pycache__/__init__.cpython-36.pyc
diff --git a/q03_logistic_regression/tests/__pycache__/test_q03_logistic_regression.cpython-36.pyc b/q03_logistic_regression/tests/__pycache__/test_q03_logistic_regression.cpython-36.pyc