commit-live-students · makrand12 · Oct 23, 2018 · Oct 23, 2018 · Oct 23, 2018 · Oct 23, 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
@@ -1,3 +1,4 @@
+# %load q01_outlier_removal/build.py
 # Default imports
 import pandas as pd
 
@@ -6,3 +7,11 @@
 
 
 # Write your Solution here:
+def outlier_removal(loan_data):
+    loan = loan_data[['ApplicantIncome','CoapplicantIncome','LoanAmount']]
+    q = loan_data.quantile(0.95)
+    for l in loan:
+        loan_data = loan_data.drop(loan_data[loan_data[l] > q[l]].index)
+    return loan_data
+outlier_removal(loan_data).shape
+
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 +1,39 @@
+# %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.model_selection import train_test_split
+from sklearn.preprocessing import Imputer
 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)
-
+np.random.seed(9)
 
 # Write your solution here :
+def data_cleaning(loan_data):
+    #Computing mean of the numerical variable
+    impu_mean = Imputer(missing_values = 'NaN', strategy='mean')
+    impu_mean.fit(loan_data[['LoanAmount']])
+    loan_data[['LoanAmount']] = impu_mean.transform(loan_data[['LoanAmount']])
+
+    #Computing mode of the categorical variable
+    cate_col = ['Gender', 'Married', 'Dependents', 'Self_Employed', 'Loan_Amount_Term', 'Credit_History']
+    for col in cate_col:
+        loan_data['LoanAmount'].fillna(loan_data[col].mode(), inplace = True)
+
+    #seperate the features and target variable
+    X = loan_data.iloc[:,:-1]
+    y = loan_data.iloc[:,-1]
+
+    #train test split 
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 9)
+    return X, y, X_train, X_test, y_train, y_test
+data_cleaning(loan_data)
+
+
+
+
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
@@ -11,3 +12,13 @@
 
 
 # Write your solution here :
+def data_cleaning_2(X_train,X_test,y_train,y_test):
+    cols=X_train.select_dtypes(include=['object']).columns.tolist()
+    X_train['ApplicantIncome'] = np.sqrt(X_train['ApplicantIncome'])
+    X_test['ApplicantIncome']= np.sqrt(X_test['ApplicantIncome'])
+    X_train=pd.get_dummies(X_train,columns=cols,drop_first=True)
+    X_test=pd.get_dummies(X_test,columns=cols,drop_first=True)
+    return X_train,X_test,y_train,y_test
+data_cleaning_2(X_train,X_test,y_train,y_test)
+
+
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
@@ -1,3 +1,4 @@
+# %load q03_logistic_regression/build.py
 # Default Imports
 import pandas as pd
 from sklearn.preprocessing import StandardScaler
@@ -15,4 +16,15 @@
 
 
 # Write your solution code here:
+def logistic_regression(X_train,X_test,y_train,y_test):
+     scalar=StandardScaler()
+     scalar.fit(X_train[['ApplicantIncome','CoapplicantIncome','LoanAmount']])
+     modl = LogisticRegression(random_state=9)
+     modl.fit(X_train,y_train)
+     y_pred=modl.predict(X_test)
+     cm = confusion_matrix(y_test,y_pred)
+     return cm
+logistic_regression(X_train,X_test,y_train,y_test)
+
+
 
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