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Jeet1994/Ensemble-Methods-Python.ipynb

Created November 2, 2019 08:16
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Ensemble-Methods-Python.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [],
"source": [
"train = pd.read_csv(\"train.csv\") "
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [],
"source": [
"test = pd.read_csv(\"test.csv\") "
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [],
"source": [
"#used for splitting dataset if not already split, \n",
"from sklearn.model_selection import train_test_split\n",
"#x = data.iloc[:, :-1].values\n",
"#y = data.iloc[:, 1].values\n",
"#x_train, x_test, y_train, y_test = train_test_split(x, y,test_size=1/3, random_state=0)"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Loan_ID 0\n",
"Gender 13\n",
"Married 3\n",
"Dependents 15\n",
"Education 0\n",
"Self_Employed 32\n",
"ApplicantIncome 0\n",
"CoapplicantIncome 0\n",
"LoanAmount 22\n",
"Loan_Amount_Term 14\n",
"Credit_History 50\n",
"Property_Area 0\n",
"Loan_Status 0\n",
"dtype: int64"
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.apply(lambda x: sum(x.isnull()),axis=0) # checking missing values in each column of train dataset"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Loan_ID 0\n",
"Gender 11\n",
"Married 0\n",
"Dependents 10\n",
"Education 0\n",
"Self_Employed 23\n",
"ApplicantIncome 0\n",
"CoapplicantIncome 0\n",
"LoanAmount 5\n",
"Loan_Amount_Term 6\n",
"Credit_History 29\n",
"Property_Area 0\n",
"dtype: int64"
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test.apply(lambda x: sum(x.isnull()),axis=0) #checking missing values in each column of test dataset"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [],
"source": [
"train.Gender = train.Gender.fillna('Male')"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {},
"outputs": [],
"source": [
"test.Gender = test.Gender.fillna('Male')"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [],
"source": [
"train.Married = train.Married.fillna('Yes')"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {},
"outputs": [],
"source": [
"test.Married = test.Married.fillna('Yes')"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {},
"outputs": [],
"source": [
"train.Dependents = train.Dependents.fillna('0')"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {},
"outputs": [],
"source": [
"test.Dependents = test.Dependents.fillna('0')"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {},
"outputs": [],
"source": [
"train.Self_Employed = train.Self_Employed.fillna('No')"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {},
"outputs": [],
"source": [
"test.Self_Employed = test.Self_Employed.fillna('No')"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {},
"outputs": [],
"source": [
"train.LoanAmount = train.LoanAmount.fillna(train.LoanAmount.mean())"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {},
"outputs": [],
"source": [
"test.LoanAmount = test.LoanAmount.fillna(test.LoanAmount.mean())"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"360.0 512\n",
"180.0 44\n",
"480.0 15\n",
"300.0 13\n",
"84.0 4\n",
"240.0 4\n",
"120.0 3\n",
"36.0 2\n",
"60.0 2\n",
"12.0 1\n",
"Name: Loan_Amount_Term, dtype: int64"
]
},
"execution_count": 113,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train['Loan_Amount_Term'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {},
"outputs": [],
"source": [
"train.Loan_Amount_Term = train.Loan_Amount_Term.fillna(360.0)"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"360.0 311\n",
"180.0 22\n",
"480.0 8\n",
"300.0 7\n",
"240.0 4\n",
"84.0 3\n",
"6.0 1\n",
"120.0 1\n",
"36.0 1\n",
"350.0 1\n",
"12.0 1\n",
"60.0 1\n",
"Name: Loan_Amount_Term, dtype: int64"
]
},
"execution_count": 115,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test['Loan_Amount_Term'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {},
"outputs": [],
"source": [
"test.Loan_Amount_Term = test.Loan_Amount_Term.fillna(360.0)"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.0 475\n",
"0.0 89\n",
"Name: Credit_History, dtype: int64"
]
},
"execution_count": 117,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train['Credit_History'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.0 279\n",
"0.0 59\n",
"Name: Credit_History, dtype: int64"
]
},
"execution_count": 118,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test['Credit_History'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {},
"outputs": [],
"source": [
"train.Credit_History = train.Credit_History.fillna(1.0) "
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {},
"outputs": [],
"source": [
"test.Credit_History = test.Credit_History.fillna(1.0)"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Loan_ID 0\n",
"Gender 0\n",
"Married 0\n",
"Dependents 0\n",
"Education 0\n",
"Self_Employed 0\n",
"ApplicantIncome 0\n",
"CoapplicantIncome 0\n",
"LoanAmount 0\n",
"Loan_Amount_Term 0\n",
"Credit_History 0\n",
"Property_Area 0\n",
"Loan_Status 0\n",
"dtype: int64"
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.apply(lambda x: sum(x.isnull()),axis=0) #should be zero if all values are filled"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Loan_ID 0\n",
"Gender 0\n",
"Married 0\n",
"Dependents 0\n",
"Education 0\n",
"Self_Employed 0\n",
"ApplicantIncome 0\n",
"CoapplicantIncome 0\n",
"LoanAmount 0\n",
"Loan_Amount_Term 0\n",
"Credit_History 0\n",
"Property_Area 0\n",
"dtype: int64"
]
},
"execution_count": 122,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test.apply(lambda x: sum(x.isnull()),axis=0)#should be zero if all values are filled"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {},
"outputs": [],
"source": [
"# Splitting traing data\n",
"X_train = train.iloc[:, 1: 12].values\n",
"y_train = train.iloc[:, 12].values"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {},
"outputs": [],
"source": [
"train, test = train_test_split(train, test_size=0.3, random_state=0)"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([['Male', 'No', '0', ..., 360.0, 1.0, 'Urban'],\n",
" ['Male', 'Yes', '1', ..., 360.0, 1.0, 'Rural'],\n",
" ['Male', 'Yes', '0', ..., 360.0, 1.0, 'Urban'],\n",
" ...,\n",
" ['Male', 'Yes', '1', ..., 360.0, 1.0, 'Urban'],\n",
" ['Male', 'Yes', '2', ..., 360.0, 1.0, 'Urban'],\n",
" ['Female', 'No', '0', ..., 360.0, 0.0, 'Semiurban']], dtype=object)"
]
},
"execution_count": 125,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
"labelencoder_X = LabelEncoder()\n",
"for i in range(0, 5):\n",
" X_train[:,i] = labelencoder_X.fit_transform(X_train[:,i])"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Anaconda\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:415: FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values.\n",
"If you want the future behaviour and silence this warning, you can specify \"categories='auto'\".\n",
"In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.\n",
" warnings.warn(msg, FutureWarning)\n",
"C:\\Anaconda\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:451: DeprecationWarning: The 'categorical_features' keyword is deprecated in version 0.20 and will be removed in 0.22. You can use the ColumnTransformer instead.\n",
" \"use the ColumnTransformer instead.\", DeprecationWarning)\n"
]
}
],
"source": [
"X_train[:,9] = labelencoder_X.fit_transform(X_train[:,9])\n",
"X_train[:,10] = labelencoder_X.fit_transform(X_train[:,10])\n",
"onehotencoder = OneHotEncoder(categorical_features = [7])\n",
"X_train = onehotencoder.fit_transform(X_train).toarray()"
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {},
"outputs": [],
"source": [
"\n",
"# Encoding the Dependent Variable\n",
"labelencoder_y = LabelEncoder()\n",
"y_train = labelencoder_y.fit_transform(y_train)"
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0., 0., 0., ..., 360., 1., 2.],\n",
" [ 0., 0., 0., ..., 360., 1., 0.],\n",
" [ 0., 0., 0., ..., 360., 1., 2.],\n",
" ...,\n",
" [ 0., 0., 0., ..., 360., 1., 2.],\n",
" [ 0., 0., 0., ..., 360., 1., 2.],\n",
" [ 0., 0., 0., ..., 360., 0., 1.]])"
]
},
"execution_count": 129,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"X_train"
]
},
{
"cell_type": "code",
"execution_count": 130,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1,\n",
" 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1,\n",
" 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0,\n",
" 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,\n",
" 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1,\n",
" 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0,\n",
" 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1,\n",
" 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1,\n",
" 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1,\n",
" 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0,\n",
" 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1,\n",
" 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1,\n",
" 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0,\n",
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" 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0,\n",
" 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0,\n",
" 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0,\n",
" 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1,\n",
" 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1,\n",
" 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1,\n",
" 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n",
" 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1,\n",
" 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0,\n",
" 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1,\n",
" 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0])"
]
},
"execution_count": 130,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"y_train"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {},
"outputs": [],
"source": [
"# Splitting traing data\n",
"X_test = test.iloc[:, 1: 12].values\n",
"y_test = test.iloc[:, 12].values"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 185 entries, 454 to 134\n",
"Data columns (total 13 columns):\n",
"Loan_ID 185 non-null object\n",
"Gender 185 non-null object\n",
"Married 185 non-null object\n",
"Dependents 185 non-null object\n",
"Education 185 non-null object\n",
"Self_Employed 185 non-null object\n",
"ApplicantIncome 185 non-null int64\n",
"CoapplicantIncome 185 non-null float64\n",
"LoanAmount 185 non-null float64\n",
"Loan_Amount_Term 185 non-null float64\n",
"Credit_History 185 non-null float64\n",
"Property_Area 185 non-null object\n",
"Loan_Status 185 non-null object\n",
"dtypes: float64(4), int64(1), object(8)\n",
"memory usage: 20.2+ KB\n"
]
}
],
"source": [
"test.info()"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Anaconda\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:415: FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values.\n",
"If you want the future behaviour and silence this warning, you can specify \"categories='auto'\".\n",
"In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.\n",
" warnings.warn(msg, FutureWarning)\n",
"C:\\Anaconda\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:451: DeprecationWarning: The 'categorical_features' keyword is deprecated in version 0.20 and will be removed in 0.22. You can use the ColumnTransformer instead.\n",
" \"use the ColumnTransformer instead.\", DeprecationWarning)\n"
]
}
],
"source": [
"# Encoding categorical data\n",
"# Encoding the Independent Variable\n",
"from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
"labelencoder_X = LabelEncoder()\n",
"for i in range(0, 5):\n",
" X_test[:,i] = labelencoder_X.fit_transform(X_test[:,i])\n",
"X_test[:,9] = labelencoder_X.fit_transform(X_test[:,9])\n",
"X_test[:,10] = labelencoder_X.fit_transform(X_test[:,10])\n",
"\n",
"onehotencoder = OneHotEncoder(categorical_features = [7])\n",
"X_test = onehotencoder.fit_transform(X_test).toarray()\n",
"# Encoding the Dependent Variable\n",
"labelencoder_y = LabelEncoder()\n",
"y_test = labelencoder_y.fit_transform(y_test)"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0., 0., 0., ..., 360., 1., 1.],\n",
" [ 0., 0., 0., ..., 360., 1., 1.],\n",
" [ 0., 0., 0., ..., 360., 1., 2.],\n",
" ...,\n",
" [ 0., 0., 1., ..., 360., 1., 1.],\n",
" [ 0., 0., 0., ..., 360., 1., 0.],\n",
" [ 0., 0., 0., ..., 360., 1., 1.]])"
]
},
"execution_count": 134,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_test"
]
},
{
"cell_type": "code",
"execution_count": 135,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1,\n",
" 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1,\n",
" 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1,\n",
" 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1,\n",
" 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0,\n",
" 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1,\n",
" 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0,\n",
" 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 0, 1])"
]
},
"execution_count": 135,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"y_test"
]
},
{
"cell_type": "code",
"execution_count": 136,
"metadata": {},
"outputs": [],
"source": [
"# Feature Scaling\n",
"from sklearn.preprocessing import StandardScaler\n",
"sc = StandardScaler()\n",
"X_train = sc.fit_transform(X_train)\n",
"X_test = sc.fit_transform(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(185, 112)"
]
},
"execution_count": 137,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_test.shape"
]
},
{
"cell_type": "code",
"execution_count": 138,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(614, 213)"
]
},
"execution_count": 138,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train.shape"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {},
"outputs": [],
"source": [
"# Applying PCA\n",
"from sklearn.decomposition import PCA\n",
"pca = PCA()\n",
"X_train = pca.fit_transform(X_train)\n",
"X_test = pca.fit_transform(X_test)\n",
"explained_variance = pca.explained_variance_ratio_"
]
},
{
"cell_type": "code",
"execution_count": 140,
"metadata": {},
"outputs": [],
"source": [
"# Feature Scaling\n",
"from sklearn.preprocessing import StandardScaler\n",
"sc = StandardScaler()\n",
"X_train = sc.fit_transform(X_train)\n",
"X_test = sc.fit_transform(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 141,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
" FutureWarning)\n"
]
}
],
"source": [
"# Fitting Logistic Regression to the Training set\n",
"from sklearn.linear_model import LogisticRegression\n",
"classifier = LogisticRegression(random_state = 0)\n",
"classifier.fit(X_train, y_train)\n",
"y_pred = classifier.predict(X_train)"
]
},
{
"cell_type": "code",
"execution_count": 142,
"metadata": {},
"outputs": [],
"source": [
"y_pred = classifier.predict(X_train)"
]
},
{
"cell_type": "code",
"execution_count": 143,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1,\n",
" 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0,\n",
" 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
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" 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1,\n",
" 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1,\n",
" 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0,\n",
" 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
" 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0])"
]
},
"execution_count": 143,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_pred"
]
},
{
"cell_type": "code",
"execution_count": 144,
"metadata": {},
"outputs": [],
"source": [
"# Predicting the Test set results\n",
"y_pred = classifier.predict(X_train)"
]
},
{
"cell_type": "code",
"execution_count": 145,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The accuracy of Logistic Regression is: 0.8811074918566775\n"
]
}
],
"source": [
"from sklearn import metrics\n",
"print('The accuracy of Logistic Regression is: ', metrics.accuracy_score(y_pred, y_train))"
]
},
{
"cell_type": "code",
"execution_count": 146,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
" metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
" weights='uniform')"
]
},
"execution_count": 146,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Fitting K-NN to the Training set\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)\n",
"classifier.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 147,
"metadata": {},
"outputs": [],
"source": [
"y_pred1 = classifier.predict(X_train)"
]
},
{
"cell_type": "code",
"execution_count": 148,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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]
},
"execution_count": 148,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_pred1"
]
},
{
"cell_type": "code",
"execution_count": 149,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(614,)"
]
},
"execution_count": 149,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_pred.shape"
]
},
{
"cell_type": "code",
"execution_count": 150,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(614,)"
]
},
"execution_count": 150,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_pred1.shape"
]
},
{
"cell_type": "code",
"execution_count": 151,
"metadata": {},
"outputs": [],
"source": [
"final_pred = (y_pred+y_pred1)/2"
]
},
{
"cell_type": "code",
"execution_count": 152,
"metadata": {},
"outputs": [
{
"data": {
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]
},
"execution_count": 152,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"final_pred"
]
},
{
"cell_type": "code",
"execution_count": 153,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
}
],
"source": [
"mode = print(m[0])"
]
},
{
"cell_type": "code",
"execution_count": 154,
"metadata": {},
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{
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" 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1,\n",
" 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1,\n",
" 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0,\n",
" 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
" 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1,\n",
" 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0,\n",
" 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0,\n",
" 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0,\n",
" 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1,\n",
" 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1,\n",
" 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1,\n",
" 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0,\n",
" 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
" 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0],\n",
" dtype=int64)"
]
},
"execution_count": 154,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.apply_along_axis(lambda x: np.bincount(x).argmax(), axis=0, arr=a)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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