SKL-Preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Preprocessing Data\n",
    "\n",
    "==============================\n",
    "\n",
    "In general, pre-processing refers to the transformations applied to your data before supplying it to the algorithm. In python, scikit-learn library has a pre-built functionality under ***sklearn.preprocessing***. There are many more options for pre-processing which we’ll explore.\n",
    "\n",
    "Learning algorithms have affinity towards certain data types on which they perform incredibly well. They are also known to give reckless predictions with unscaled or unstandardized features. \n",
    "\n",
    "Algorithm like XGBoost, specifically requires dummy encoded data while algorithm like decision tree doesn’t seem to care at all.\n",
    "\n",
    "\n",
    "\n",
    "#### Data Preprocessing in Python using Scikit Learn\n",
    "\n",
    "1. Feature Standardization\n",
    "2. Label Encoder\n",
    "3. One Hot Encoder "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scenario \n",
    "\n",
    "Dream Housing Finance company deals in all home loans. They have presence across all urban, semi urban and rural areas. Customer first apply for home loan after that company validates the customer eligibility for loan.\n",
    "\n",
    "#### Problem\n",
    "The Loan Company wants to automate the loan eligibility process (real time) based on customer detail provided while filling online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others. \n",
    "\n",
    "To automate this process, they have given a problem to identify the customers segments, those are eligible for loan amount so that they can specifically target these customers. Here they have provided a partial data set.\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, lets get started by importing important packages and the data set.\n",
    "Lets take a closer look at our data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Importing pandas\n",
    "import pandas as pd\n",
    "# Importing training data set\n",
    "X_train=pd.read_csv('https://raw.githubusercontent.com/RWorkshop/workshopdatasets/master/loanprediction/X_train.csv')\n",
    "Y_train=pd.read_csv('https://raw.githubusercontent.com/RWorkshop/workshopdatasets/master/loanprediction/Y_train.csv')\n",
    "# Importing testing data set\n",
    "X_test=pd.read_csv('https://raw.githubusercontent.com/RWorkshop/workshopdatasets/master/loanprediction/X_test.csv')\n",
    "Y_test=pd.read_csv('https://raw.githubusercontent.com/RWorkshop/workshopdatasets/master/loanprediction/Y_test.csv')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 384 entries, 0 to 383\n",
      "Data columns (total 12 columns):\n",
      "Loan_ID              384 non-null object\n",
      "Gender               384 non-null object\n",
      "Married              384 non-null object\n",
      "Dependents           384 non-null object\n",
      "Education            384 non-null object\n",
      "Self_Employed        384 non-null object\n",
      "ApplicantIncome      384 non-null int64\n",
      "CoapplicantIncome    384 non-null float64\n",
      "LoanAmount           384 non-null int64\n",
      "Loan_Amount_Term     384 non-null int64\n",
      "Credit_History       384 non-null int64\n",
      "Property_Area        384 non-null object\n",
      "dtypes: float64(1), int64(4), object(7)\n",
      "memory usage: 36.1+ KB\n"
     ]
    }
   ],
   "source": [
    "X_train.info()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(96, 1)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1. Feature Scaling\n",
    "\n",
    "Feature scaling is the method to limit the range of variables so that they can be compared on common grounds. It is performed on continuous variables. Lets plot the distribution of all the continuous variables in the data set.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAApoAAAKFCAYAAABsjEmxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f27b352f668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "X_train[X_train.dtypes[(X_train.dtypes==\"float64\")|(X_train.dtypes==\"int64\")]\n",
    "                        .index.values].hist(figsize=[11,11]);\n",
    " \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After understanding these plots, we see that **ApplicantIncome** and **CoapplicantIncome** are in similar range (0-50000 USD) \n",
    "\n",
    "**LoanAmount** is in thousands and it ranges from 0 to 600 USD. \n",
    "\n",
    "**Loan_Amount_Term** is completely different from other variables because its unit is months.\n",
    "\n",
    "If we try to apply distance based methods such as kNN on these features, feature with the largest range will dominate the outcome results and we’ll obtain less accurate predictions. We can overcome this trouble using feature scaling. Let’s do it practically.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets try out kNN on our data set to see how well it will perform."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Target\n",
       "0      Y\n",
       "1      Y\n",
       "2      Y"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(Y_train[\"Target\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initializing and Fitting a k-NN model\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn=KNeighborsClassifier(n_neighbors=5)\n",
    "knn.fit(X_train[['ApplicantIncome', 'CoapplicantIncome','LoanAmount', \n",
    "                   'Loan_Amount_Term', 'Credit_History']],Y_train[\"Target\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accuracy Score\n",
    "Checking the performance of our model on the testing data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.61458333333333337"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "accuracy_score(Y_test,knn.predict(X_test[['ApplicantIncome', 'CoapplicantIncome',\n",
    "                             'LoanAmount', 'Loan_Amount_Term', 'Credit_History']]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "We got around 61% of correct prediction which is not bad but in real world practices will this be enough ? Can we deploy this model in real world problem? To answer this question lets take a look at distribution of **Loan_Status** in train data set.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    0.705729\n",
       "N    0.294271\n",
       "Name: Target, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train.Target.value_counts()/Y_train.Target.count()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 70% of approved loans, since there are more number of approved loans we will generate a prediction where all the loans are approved and lets go ahead and check the accuracy of our prediction\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    0.635417\n",
       "N    0.364583\n",
       "Name: Target, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test.Target.value_counts()/Y_test.Target.count()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We got an accuracy of 63% just by guessing. Random guessing is getting better accuracy than our prediction model.\n",
    "\n",
    "This might be happening because of some insignificant variable with larger range will be dominating the objective function. We can remove this problem by scaling down all the features to a same range. \n",
    "\n",
    "sklearn provides a tool **MinMaxScaler** that will scale down all the features between 0 and 1. Mathematical formula for MinMaxScaler is.\n",
    "\n",
    "$${\\displaystyle x'={\\frac {x-{\\text{min}}(x)}{{\\text{max}}(x)-{\\text{min}}(x)}}} $$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets try this tool on our problem.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Importing MinMaxScaler and initializing it\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "min_max=MinMaxScaler()\n",
    "\n",
    "# Scaling down both train and test data set\n",
    "X_train_minmax=min_max.fit_transform(X_train[['ApplicantIncome', 'CoapplicantIncome',\n",
    "                'LoanAmount', 'Loan_Amount_Term', 'Credit_History']])\n",
    "\n",
    "X_test_minmax=min_max.fit_transform(X_test[['ApplicantIncome', 'CoapplicantIncome',\n",
    "                'LoanAmount', 'Loan_Amount_Term', 'Credit_History']])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, that we are done with scaling, lets apply kNN on our scaled data and check its accuracy.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "# Fitting k-NN on our scaled data set\n",
    "knn=KNeighborsClassifier(n_neighbors=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.75"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "knn.fit(X_train_minmax,Y_train[\"Target\"])\n",
    "# Checking the model's accuracy\n",
    "accuracy_score(Y_test,knn.predict(X_test_minmax))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our accuracy has increased from 61% to 75%. \n",
    "\n",
    "This shows that some of the features with larger range were dominating the prediction outcome in the domain of distance based methods(kNN).\n",
    "\n",
    "It should be kept in mind while performing distance based methods we must attempt to scale the data, so that the feature with lesser significance might not end up dominating the objective function due to its larger range. \n",
    "\n",
    "Also, features that have differents unit should also be scaled thus providing each feature equal initial weighting and at the end we will have a better prediction model.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 1\n",
    "\n",
    "Try to do the same exercise with a logistic regression model(``***parameters : penalty=\"l2\",C=0.01***``) and provide your accuracy before and after scaling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "lr = LogisticRegression(penalty=\"l2\",C=0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Without MinMaxScaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(X_train[['ApplicantIncome', 'CoapplicantIncome','LoanAmount', \n",
    "                   'Loan_Amount_Term', 'Credit_History']],Y_train[\"Target\"])\n",
    "# Checking the model's accuracy\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### With MinMaxScaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.61458333333333337"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(Y_test,lr.predict(X_test[['ApplicantIncome', 'CoapplicantIncome',\n",
    "                             'LoanAmount', 'Loan_Amount_Term', 'Credit_History']]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.63541666666666663"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(X_train_minmax,Y_train[\"Target\"])\n",
    "# Checking the model's accuracy\n",
    "accuracy_score(Y_test,lr.predict(X_test_minmax))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "\n",
    "## Standardization\n",
    "\n",
    "Standardization (or Z-score normalization) is the process where the features are rescaled so that they’ll have the \n",
    "properties of a standard normal distribution with $\\mu$=0 and $\\sigma$=1, where $\\mu$ is the mean (average) and $\\sigma$ is the standard deviation from the mean. \n",
    "\n",
    "Standard scores (also called z scores) of the samples are calculated as follows : \n",
    "\n",
    "$$z={x-\\mu  \\over \\sigma }$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Standardizing the train and test data\n",
    "from sklearn.preprocessing import scale\n",
    "\n",
    "X_train_scale=scale(X_train[['ApplicantIncome', 'CoapplicantIncome',\n",
    "                'LoanAmount', 'Loan_Amount_Term', 'Credit_History']])\n",
    "\n",
    "X_test_scale=scale(X_test[['ApplicantIncome', 'CoapplicantIncome',\n",
    "               'LoanAmount', 'Loan_Amount_Term', 'Credit_History']])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Remark : Watch the type change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X_train[['ApplicantIncome', 'CoapplicantIncome',\n",
    "                'LoanAmount', 'Loan_Amount_Term', 'Credit_History']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X_train_scale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.03769694, -0.59984568, -0.21938895,  0.27229341,  0.40886826],\n",
       "       [-0.42803219,  0.10337366, -0.2460278 ,  2.09708696,  0.40886826],\n",
       "       [-0.40575968,  0.70357117, -1.13842921, -2.46489691,  0.40886826],\n",
       "       [ 0.85943239, -0.59984568, -0.39254146,  0.27229341,  0.40886826],\n",
       "       [-0.53165601,  0.32239042, -0.07287529,  0.27229341,  0.40886826]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_scale[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting logistic regression on our standardized data set\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "log=LogisticRegression(penalty='l2',C=.01)\n",
    "log.fit(X_train_scale,Y_train[\"Target\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.75"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Checking the model's accuracy\n",
    "accuracy_score(Y_test[\"Target\"],log.predict(X_test_scale))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Exercise 2\n",
    "\n",
    "Try to do the same exercise with SVM model and provide your accuracy before and after standardization.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2 Label Encoding\n",
    "\n",
    "Previously we did the pre-processing for continuous numeric features only. However, our data set has other features too such as Gender, Married, Dependents, Self_Employed and Education. All these categorical features have string values. For example, Gender has two levels either Male or Female. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001032</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4950</td>\n",
       "      <td>0.0</td>\n",
       "      <td>125</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001824</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2882</td>\n",
       "      <td>1843.0</td>\n",
       "      <td>123</td>\n",
       "      <td>480</td>\n",
       "      <td>1</td>\n",
       "      <td>Semiurban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP002928</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3000</td>\n",
       "      <td>3416.0</td>\n",
       "      <td>56</td>\n",
       "      <td>180</td>\n",
       "      <td>1</td>\n",
       "      <td>Semiurban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001814</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>9703</td>\n",
       "      <td>0.0</td>\n",
       "      <td>112</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP002244</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2333</td>\n",
       "      <td>2417.0</td>\n",
       "      <td>136</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents Education Self_Employed  \\\n",
       "0  LP001032   Male      No          0  Graduate            No   \n",
       "1  LP001824   Male     Yes          1  Graduate            No   \n",
       "2  LP002928   Male     Yes          0  Graduate            No   \n",
       "3  LP001814   Male     Yes          2  Graduate            No   \n",
       "4  LP002244   Male     Yes          0  Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             4950                0.0         125               360   \n",
       "1             2882             1843.0         123               480   \n",
       "2             3000             3416.0          56               180   \n",
       "3             9703                0.0         112               360   \n",
       "4             2333             2417.0         136               360   \n",
       "\n",
       "   Credit_History Property_Area  \n",
       "0               1         Urban  \n",
       "1               1     Semiurban  \n",
       "2               1     Semiurban  \n",
       "3               1         Urban  \n",
       "4               1         Urban  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 384 entries, 0 to 383\n",
      "Data columns (total 12 columns):\n",
      "Loan_ID              384 non-null object\n",
      "Gender               384 non-null object\n",
      "Married              384 non-null object\n",
      "Dependents           384 non-null object\n",
      "Education            384 non-null object\n",
      "Self_Employed        384 non-null object\n",
      "ApplicantIncome      384 non-null int64\n",
      "CoapplicantIncome    384 non-null float64\n",
      "LoanAmount           384 non-null int64\n",
      "Loan_Amount_Term     384 non-null int64\n",
      "Credit_History       384 non-null int64\n",
      "Property_Area        384 non-null object\n",
      "dtypes: float64(1), int64(4), object(7)\n",
      "memory usage: 36.1+ KB\n"
     ]
    }
   ],
   "source": [
    "X_train.info()\n",
    "\n",
    "# object means \"string\" or \"text\" in this instance. (It can also mean unformatted dates too, so be careful)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Fitting a logistic regression model on whole data\n",
    "log=LogisticRegression(penalty='l2',C=.01)\n",
    "\n",
    "#N.B. This wont work. Uncomment to see the error message\n",
    "\n",
    "# log.fit(X_train,Y_train[\"Target\"])\n",
    "# Checking the model's accuracy\n",
    "# accuracy_score(Y_test,log.predict(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "We got an error saying that it cannot convert string to float.\n",
    "\n",
    "Learners like logistic regression, distance based methods such as kNN, support vector machines, tree based methods etc. in sklearn needs numeric arrays. Features having string values cannot be handled by these learners.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### LabelEncoder\n",
    "\n",
    "Sklearn provides a very efficient tool for encoding the levels of a categorical features into numeric values. \n",
    "\n",
    "LabelEncoder encode labels with value between 0 and ``n_classes-1``.\n",
    "\n",
    "Lets encode all the categorical features.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Loan_ID', 'Gender', 'Married', 'Dependents', 'Education',\n",
       "       'Self_Employed', 'ApplicantIncome', 'CoapplicantIncome',\n",
       "       'LoanAmount', 'Loan_Amount_Term', 'Credit_History', 'Property_Area'], dtype=object)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID              object\n",
       "Gender               object\n",
       "Married              object\n",
       "Dependents           object\n",
       "Education            object\n",
       "Self_Employed        object\n",
       "ApplicantIncome       int64\n",
       "CoapplicantIncome     int64\n",
       "LoanAmount            int64\n",
       "Loan_Amount_Term      int64\n",
       "Credit_History        int64\n",
       "Property_Area        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "# Importing LabelEncoder and initializing it\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "le=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50      Male\n",
       "51      Male\n",
       "52      Male\n",
       "53    Female\n",
       "54      Male\n",
       "55    Female\n",
       "56      Male\n",
       "57      Male\n",
       "58      Male\n",
       "59      Male\n",
       "Name: Gender, dtype: object"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[\"Gender\"][50:60]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 0, 1, 0, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le.transform(X_train[\"Gender\"])[50:60]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LabelEncoder()"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le.fit(X_train[\"Gender\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Iterating over all the common columns in train and test\n",
    "for col in X_test.columns.values:\n",
    "    # Encoding only categorical variables\n",
    "    if X_test[col].dtypes=='object':\n",
    "    # Using whole data to form an exhaustive list of levels\n",
    "        data=X_train[col].append(X_test[col])\n",
    "        le.fit(data.values)\n",
    "        X_train[col]=le.transform(X_train[col])\n",
    "        X_test[col]=le.transform(X_test[col])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4950</td>\n",
       "      <td>0.0</td>\n",
       "      <td>125</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2882</td>\n",
       "      <td>1843.0</td>\n",
       "      <td>123</td>\n",
       "      <td>480</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>461</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "      <td>3416.0</td>\n",
       "      <td>56</td>\n",
       "      <td>180</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>191</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9703</td>\n",
       "      <td>0.0</td>\n",
       "      <td>112</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2333</td>\n",
       "      <td>2417.0</td>\n",
       "      <td>136</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Loan_ID  Gender  Married  Dependents  Education  Self_Employed  \\\n",
       "0       13       1        0           0          0              0   \n",
       "1      193       1        1           1          0              0   \n",
       "2      461       1        1           0          0              0   \n",
       "3      191       1        1           2          0              0   \n",
       "4      300       1        1           0          0              0   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             4950                0.0         125               360   \n",
       "1             2882             1843.0         123               480   \n",
       "2             3000             3416.0          56               180   \n",
       "3             9703                0.0         112               360   \n",
       "4             2333             2417.0         136               360   \n",
       "\n",
       "   Credit_History  Property_Area  \n",
       "0               1              2  \n",
       "1               1              1  \n",
       "2               1              1  \n",
       "3               1              2  \n",
       "4               1              2  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "All our categorical features are encoded. You can look at your updated data set using X_train.head(). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Now that we are done with label encoding, lets now run a logistic regression model on the data set with both categorical and continuous features.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "# Standardizing the features\n",
    "X_train_scale=scale(X_train)\n",
    "X_test_scale=scale(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.75"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Fitting the logistic regression model\n",
    "log=LogisticRegression(penalty='l2',C=.01)\n",
    "log.fit(X_train_scale,Y_train[\"Target\"])\n",
    "# Checking the models accuracy\n",
    "accuracy_score(Y_test,log.predict(X_test_scale))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "The accuracy is still the same as we got with logistic regression after standardization from numeric features. This means categorical features that we added are not very important in the model fit.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 3\n",
    "\n",
    "Try out decision tree classifier with all the features as independent variables and comment your accuracy.\n",
    "\n",
    "Resources : Go through this article on decision trees for better understanding."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. One-Hot Encoding\n",
    "\n",
    "One-Hot Encoding transforms each categorical feature with n possible values into n binary features, with only one active.\n",
    "\n",
    "Most of the ML algorithms either learn a single weight for each feature or it computes distance between the samples. Algorithms like linear models (such as logistic regression) belongs to the first category.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['cold' 'cold' 'warm' 'cold' 'hot' 'hot' 'warm' 'cold' 'warm' 'hot']\n"
     ]
    }
   ],
   "source": [
    "from numpy import array\n",
    "from numpy import argmax\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "# define example\n",
    "data = ['cold', 'cold', 'warm', 'cold', 'hot', 'hot', 'warm', 'cold', 'warm', 'hot']\n",
    "values = array(data)\n",
    "print(values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 2 0 1 1 2 0 2 1]\n"
     ]
    }
   ],
   "source": [
    "# integer encode\n",
    "label_encoder = LabelEncoder()\n",
    "integer_encoded = label_encoder.fit_transform(values)\n",
    "print(integer_encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0]\n",
      " [0]\n",
      " [2]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [2]\n",
      " [0]\n",
      " [2]\n",
      " [1]]\n"
     ]
    }
   ],
   "source": [
    "# binary encode\n",
    "onehot_encoder = OneHotEncoder(sparse=False)\n",
    "integer_encoded = integer_encoded.reshape(len(integer_encoded), 1)\n",
    "print(integer_encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.  0.  0.]\n",
      " [ 1.  0.  0.]\n",
      " [ 0.  0.  1.]\n",
      " [ 1.  0.  0.]\n",
      " [ 0.  1.  0.]\n",
      " [ 0.  1.  0.]\n",
      " [ 0.  0.  1.]\n",
      " [ 1.  0.  0.]\n",
      " [ 0.  0.  1.]\n",
      " [ 0.  1.  0.]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "onehot_encoded = onehot_encoder.fit_transform(integer_encoded)\n",
    "print(onehot_encoded)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['cold']\n"
     ]
    }
   ],
   "source": [
    "# invert first example\n",
    "inverted = label_encoder.inverse_transform([argmax(onehot_encoded[0, :])])\n",
    "print(inverted)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, lets take look at the implementation of one-hot encoding with various algorithms.\n",
    "\n",
    "Lets create a logistic regression model for classification without one-hot encoding.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# We are using scaled variable as we saw in previous section that \n",
    "# scaling will effect the algo with l1 or l2 reguralizer\n",
    "X_train_scale=scale(X_train)\n",
    "X_test_scale=scale(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Fitting a logistic regression model\n",
    "log=LogisticRegression(penalty='l2',C=1)\n",
    "log.fit(X_train_scale,Y_train[\"Target\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.73958333333333337"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Checking the model's accuracy\n",
    "accuracy_score(Y_test,log.predict(X_test_scale))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Now we are going to encode the data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "enc=OneHotEncoder(sparse=False)\n",
    "X_train_1=X_train\n",
    "X_test_1=X_test\n",
    "columns=['Gender', 'Married', 'Dependents', 'Education','Self_Employed',\n",
    "          'Credit_History', 'Property_Area']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "for col in columns:\n",
    "    # creating an exhaustive list of all possible categorical values\n",
    "    data=X_train[[col]].append(X_test[[col]])\n",
    "    enc.fit(data)\n",
    "    # Fitting One Hot Encoding on train data\n",
    "    temp = enc.transform(X_train[[col]])\n",
    "    # Changing the encoded features into a data frame with new column names\n",
    "    temp=pd.DataFrame(temp,columns=[(col+\"_\"+str(i)) for i in data[col]\n",
    "            .value_counts().index])\n",
    "    # In side by side concatenation index values should be same\n",
    "    # Setting the index values similar to the X_train data frame\n",
    "    temp=temp.set_index(X_train.index.values)\n",
    "    # adding the new One Hot Encoded varibales to the train data frame\n",
    "    X_train_1=pd.concat([X_train_1,temp],axis=1)\n",
    "    # fitting One Hot Encoding on test data\n",
    "    temp = enc.transform(X_test[[col]])\n",
    "    # changing it into data frame and adding column names\n",
    "    temp=pd.DataFrame(temp,columns=[(col+\"_\"+str(i)) for i in data[col]\n",
    "            .value_counts().index])\n",
    "    # Setting the index for proper concatenation\n",
    "    temp=temp.set_index(X_test.index.values)\n",
    "    # adding the new One Hot Encoded varibales to test data frame\n",
    "    X_test_1=pd.concat([X_test_1,temp],axis=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Now, lets apply logistic regression model on one-hot encoded data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Standardizing the data set\n",
    "X_train_scale=scale(X_train_1)\n",
    "X_test_scale=scale(X_test_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.75"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting a logistic regression model\n",
    "log=LogisticRegression(penalty='l2',C=1)\n",
    "log.fit(X_train_scale,Y_train[\"Target\"])\n",
    "# Checking the model's accuracy\n",
    "accuracy_score(Y_test,log.predict(X_test_scale))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Here, again we got the maximum accuracy as 0.75 that we have gotten so far. \n",
    "In this case, logistic regression regularization(C) parameter 1 where as earlier we used C=0.01.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}