DAY 1-Working with Pandas.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python Libraries\n",
    "Lets take one step ahead in our journey to learn Python by getting acquainted with some useful libraries. The first step is obviously to learn to import them into our environment. There are several ways of doing so in Python:\n",
    "\n",
    "### Following are a list of libraries, you will need for any scientific computations and data analysis:\n",
    "\n",
    "* **NumPy** stands for Numerical Python. The most powerful feature of NumPy is n-dimensional array. This library also contains basic linear algebra functions, Fourier transforms,  advanced random number capabilities and tools for integration with other low level languages like Fortran, C and C++ <br><br>\n",
    "* **SciPy** stands for Scientific Python. SciPy is built on NumPy. It is one of the most useful library for variety of high level science and engineering modules like discrete Fourier transform, Linear Algebra, Optimization and Sparse matrices.<br><br>\n",
    "* **Matplotlib** for plotting vast variety of graphs, starting from histograms to line plots to heat plots.. You can use Pylab feature in ipython notebook (ipython notebook –pylab = inline) to use these plotting features inline. If you ignore the inline option, then pylab converts ipython environment to an environment, very similar to Matlab. You can also use Latex commands to add math to your plot.<br><br>\n",
    "* **Pandas** for structured data operations and manipulations. It is extensively used for data munging and preparation. Pandas were added relatively recently to Python and have been instrumental in boosting Python’s usage in data scientist community.<br><br>\n",
    "* **Scikit Learn** for machine learning. Built on NumPy, SciPy and matplotlib, this library contains a lot of effiecient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction.<br><br>\n",
    "* **Statsmodels** for statistical modeling. Statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. An extensive list of descriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator.<br><br>\n",
    "* **Seaborn** for statistical data visualization. Seaborn is a library for making attractive and informative statistical graphics in Python. It is based on matplotlib. Seaborn aims to make visualization a central part of exploring and understanding data.<br><br>\n",
    "* **Bokeh** for creating interactive plots, dashboards and data applications on modern web-browsers. It empowers the user to generate elegant and concise graphics in the style of D3.js. Moreover, it has the capability of high-performance interactivity over very large or streaming datasets.<br><br>\n",
    "* **Blaze** for extending the capability of Numpy and Pandas to distributed and streaming datasets. It can be used to access data from a multitude of sources including Bcolz, MongoDB, SQLAlchemy, Apache Spark, PyTables, etc. Together with Bokeh, Blaze can act as a very powerful tool for creating effective visualizations and dashboards on huge chunks of data.<br><br>\n",
    "* **Scrapy** for web crawling. It is a very useful framework for getting specific patterns of data. It has the capability to start at a website home url and then dig through web-pages within the website to gather information.<br><br>\n",
    "* **SymPy** for symbolic computation. It has wide-ranging capabilities from basic symbolic arithmetic to calculus, algebra, discrete mathematics and quantum physics. Another useful feature is the capability of formatting the result of the computations as LaTeX code.<br><br>\n",
    "* **Requests** for accessing the web. It works similar to the the standard python library urllib2 but is much easier to code. You will find subtle differences with urllib2 but for beginners, Requests might be more convenient.\n",
    "<br><br>\n",
    "### Additional libraries, you might need:\n",
    "<br><br>\n",
    "* **os** for Operating system and file operations<br><br>\n",
    "* **networkx** and **igraph** for graph based data manipulations<br><br>\n",
    "* **regular expressions** for finding patterns in text data<br><br>\n",
    "* **BeautifulSoup** for scrapping web. It is inferior to Scrapy as it will extract information from just a single webpage in a run.<br><br>\n",
    "Now that we are familiar with Python fundamentals and additional libraries, lets take a deep dive into problem solving through Python. Yes I mean making a predictive model! In the process, we use some powerful libraries and also come across the next level of data structures. We will take you through the 3 key phases:\n",
    "\n",
    "**Data Exploration** – finding out more about the data we have<br>\n",
    "**Data Munging** – cleaning the data and playing with it to make it better suit statistical modeling<br>\n",
    "**Predictive Modeling** – running the actual algorithms and having fun<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Understanding the problem statement is the first and foremost step. This would help you give an intuition of what you will face ahead of time. Let us see the problem statement -\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. 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. 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.\n",
    "\n",
    " It is a classification problem where we have to predict whether a loan would be approved or not. In a classification problem, we have to predict discrete values based on a given set of independent variable(s). Classification can be of two types:\n",
    "\n",
    "## Binary Classification :\n",
    " In this classification we have to predict either of the two given classes. For example: classifying the gender as male or female, predicting the result as win or loss, etc.\n",
    "## Multiclass Classification : \n",
    " Here we have to classify the data into three or more classes. For example: classifying a movie's genre as comedy, action or romantic, classify fruits as oranges, apples, or pears, etc.\n",
    "\n",
    "Loan prediction is a very common real-life problem that each retail bank faces atleast once in its lifetime. If done correctly, it can save a lot of man hours at the end of a retail bank.\n",
    "\n",
    "Although this course is specifically built to give you a walkthrough of Loan Prediction problem, you can always refer the content to get a comprehensive overview to solve a classification problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np                     # For mathematical calculations\n",
    "import seaborn as sns                  # For data visualization\n",
    "import matplotlib.pyplot as plt        # For plotting graphs\n",
    "%matplotlib inline\n",
    "import warnings                        # To ignore any warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data\n",
    "For this practice problem, we have been given three CSV files: train, test and sample submission.\n",
    "\n",
    "Train file will be used for training the model, i.e. our model will learn from this file. It contains all the independent variables and the target variable.\n",
    "Test file contains all the independent variables, but not the target variable. We will apply the model to predict the target variable for the test data.\n",
    "Sample submission file contains the format in which we have to submit our predictions.\n",
    "Reading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('train_u6lujuX_CVtuZ9i.csv')\n",
    "test  = pd.read_csv('test_Y3wMUE5_7gLdaTN.csv') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s make a copy of train and test data so that even if we have to make any changes in these datasets we would not lose the original datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_original = train.copy()\n",
    "test_original = test.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we will look at the structure of the train and test datasets. Firstly, we will check the features present in our data and then we will look at their data types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Loan_ID', 'Gender', 'Married', 'Dependents', 'Education',\n",
       "       'Self_Employed', 'ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',\n",
       "       'Loan_Amount_Term', 'Credit_History', 'Property_Area', 'Loan_Status'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Loan_ID', 'Gender', 'Married', 'Dependents', 'Education',\n",
       "       'Self_Employed', 'ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',\n",
       "       'Loan_Amount_Term', 'Credit_History', 'Property_Area', 'Loan_Status'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Loan_ID', 'Gender', 'Married', 'Dependents', 'Education',\n",
       "       'Self_Employed', 'ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',\n",
       "       'Loan_Amount_Term', 'Credit_History', 'Property_Area'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "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    float64\n",
       "LoanAmount           float64\n",
       "Loan_Amount_Term     float64\n",
       "Credit_History       float64\n",
       "Property_Area         object\n",
       "Loan_Status           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print datatypes for each variables\n",
    "train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((614, 13), (367, 12))"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape,test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have 614 rows and 13 columns in the train dataset and 367 rows and 12 columns in test dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Univariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will first look at the target variable, i.e., Loan_Status. As it is a categorical variable, let us look at its frequency table, percentage distribution and bar plot.\n",
    "\n",
    "Frequency table of a variable will give us the count of each category in that variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    422\n",
       "N    192\n",
       "Name: Loan_Status, dtype: int64"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Loan_Status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    0.687296\n",
       "N    0.312704\n",
       "Name: Loan_Status, dtype: float64"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalise can be set to true to print the proportions instead of Numbers.\n",
    "train['Loan_Status'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2cd96c2b1d0>"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Loan_Status'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The loan of 422(around 69%) people out of 614 was approved.\n",
    "\n",
    "Now lets visualize each variable separately. Different types of variables are Categorical, ordinal and numerical.\n",
    "\n",
    "* Categorical features: These features have categories (Gender, Married, Self_Employed, Credit_History, Loan_Status)\n",
    "* Ordinal features: Variables in categorical features having some order involved (Dependents, Education, Property_Area)\n",
    "* Numerical features: These features have numerical values (ApplicantIncome, CoapplicantIncome, LoanAmount, Loan_Amount_Term)\n",
    "\n",
    "Let’s visualize the categorical and ordinal features first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2cd964287f0>"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.subplot(221)\n",
    "train['Gender'].value_counts(normalize=True).plot.bar(figsize=(20,10),title='Gender')\n",
    "\n",
    "plt.subplot(222)\n",
    "train['Married'].value_counts(normalize=True).plot.bar(figsize=(20,10),title='Married')\n",
    "\n",
    "plt.subplot(223)\n",
    "train['Self_Employed'].value_counts(normalize=True).plot.bar(figsize=(20,10),title='Self_Employed')\n",
    "\n",
    "plt.subplot(224)\n",
    "train['Credit_History'].value_counts(normalize=True).plot.bar(figsize=(20,10),title='Credit_History')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be inferred from the above bar plots that:\n",
    "\n",
    "* 80% applicants in the dataset are male.\n",
    "* Around 65% of the applicants in the dataset are married.\n",
    "* Around 15% applicants in the dataset are self employed.\n",
    "* Around 85% applicants have repaid their debts.\n",
    "\n",
    "Now let’s visualize the ordinal variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2cd96f41b38>"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.subplot(131)\n",
    "train['Dependents'].value_counts(normalize=True).plot.bar(figsize=(24,6),title='Dependents')\n",
    "\n",
    "plt.subplot(132)\n",
    "train['Education'].value_counts(normalize=True).plot.bar(figsize=(24,6),title='Education')\n",
    "\n",
    "plt.subplot(133)\n",
    "train['Property_Area'].value_counts(normalize=True).plot.bar(figsize=(24,6),title='Property_Area')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following inferences can be made from the above bar plots:\n",
    "\n",
    "* Most of the applicants don’t have any dependents.\n",
    "* Around 80% of the applicants are Graduate.\n",
    "* Most of the applicants are from Semiurban area."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets visualise Numerical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.subplot(121)\n",
    "sns.distplot(train['ApplicantIncome']);\n",
    "\n",
    "plt.subplot(122)\n",
    "train['ApplicantIncome'].plot.box(figsize=(16,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be inferred that most of the data in the distribution of applicant income is towards left which means it is not normally distributed. We will try to make it normal in later sections as algorithms works better if the data is normally distributed.\n",
    "\n",
    "The boxplot confirms the presence of a lot of outliers/extreme values. This can be attributed to the income disparity in the society. Part of this can be driven by the fact that we are looking at people with different education levels. Let us segregate them by Education:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0.98,'')"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.boxplot(column='ApplicantIncome', by ='Education')\n",
    "plt.suptitle(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that there are a higher number of graduates with very high incomes, which are appearing to be the outliers.\n",
    "\n",
    "Let’s look at the Coapplicant income distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JkiRJ1WLAHaW2rl4yGfAMbuk2dvQ4TFmSJEmSqsWAO0oHOnoAmNnywjO4LQ31AD6HK0mSJElVZMAdpQOd3QCHrYML0NntTMqSJEmSVC0VBdyIOD8iHo6IbRFxxSD7myPim8X+OyNiYdm+TxTlD0fEeSOdMyLWRcR9EfHziLgtImYc2SWOr+cP9eC+EHCb7cGVJEmSpKobMeBGRD3wJeAdwFJgZUQsHVBtFbAnM08HPg9cXRy7FLgQOBM4H/hyRNSPcM7/kJmvyszfBp4EPnKE1ziuDg1RHrAOLuBauJIkSZJURZX04J4DbMvMRzOzC7gFuGBAnQuAm4rt24C3RkQU5bdkZmdmPgZsK8435Dkz83mA4vhpwKSamvhAZyngDjZE2R5cSZIkSaqeSgLufGB72esdRdmgdTKzB9gHzB3m2GHPGRH/A/gN8Erg2sEaFRGXRsSWiNiya9euCi5jfOzvKD2D+6JJphodoixJkiRJ1VZJwI1Bygb2qg5VZ7TlpY3MDwInAVuB9w3WqMy8PjPPzsyz582bN1iVl8T+Yohy+TJBTQ11BNDpEGVJkiRJqppKAu4O4OSy1wuAp4aqExENwDHA7mGOHfGcmdkLfBP4wwraOGEODVEuC7h1ETQ11NmDK0mSJElVVEnAvRtYHBGLIqKJ0qRRGwbU2QBcXGy/B/h+ZmZRfmExy/IiYDFw11DnjJLT4dAzuP8S+MWRXeL42t/RQ2tTPfV1L+6Ebmmsp8NlgiRJkiSpakYMuMUztR8B7qA0ZPjWzHwwIq6KiD8oqq0D5kbENuDjwBXFsQ8CtwIPAX8PfDgze4c6J6WhyzdFxP3A/cCJwFXjdrXj4EBHz4smmOrX0mgPriRp4kXEyRGxKSK2RsSDEfHRovzYiPhuRDxSfJ9TlEdEfKFYpu/nEfE7Zee6uKj/SERcXFb+moi4vzjmC8WH0JKOwOrVq2lpaSEiaGlpYfXq1dVuklQTDk9qg8jMjcDGAWWfLNvuAN47xLFrgbUVnrMPeGMlbaqWA509Lxqe3K+loZ6OHgOuJGnC9QD/MTN/GhEzgXsi4rvAvwG+l5mfK9abvwK4nNISfYuLr3OBrwDnRsSxwKeAsynNi3FPRGzIzD1FnUuBn1D63X0+8J0JvEappqxevZrrrruOq6++mssuu4zrrruOyy+/HIBrrx10flVJFapkiLLKPN/R/aIZlPu1NNbT6RBlSdIEy8ydmfnTYns/pZFR83nxEn43Ae8uti8Avp4lPwFmR8SJwHnAdzNzdxFqvwucX+yblZk/Lh4/+nrZuSSNwQ033MDVV1/Nxz/+caZPn87HP/5xrr76am644YZqN02a8gy4o3Sgs4eZgwxRbnaIsiSpyiJiIXAWcCdwQmbuhFIIBo4vqo12Cb/5xfbA8sHevypL+ElTTWdnJ5dddtmLyi677DI6Ozur1CKpdhhwR+lAx+ABt6Wh3oArSaqaiJgB/A3wscx8friqg5SNeWm/FxVWaQk/aappbm7muuuue1HZddddR3Nzc5VaJNUOA+4o7e8Y4hncxjo6XAdXklQFEdFIKdx+IzP/d1H8dDG8mOL7M0X5aJfw21FsDyyXNEYf+tCHuPzyy7nmmmtob2/nmmuu4fLLL+dDH/pQtZsmTXkG3FEqTTI1+DO4vX1Jd68hV5I0cYoZjdcBWzPzmrJd5Uv4XQx8u6z8A8Vsyq8D9hVDmO8A3h4Rc4oZl98O3FHs2x8Rryve6wNl55I0Btdeey2XXXYZV155Ja2trVx55ZVcdtllTjAljYOKZlFWSV9fDvMMbj0AHd29NNb7uYEkacK8EXg/cH9E3FuUXQl8Drg1IlYBT/LCagcbgXcC24B24IMAmbk7Iv6C0lr1AFdl5u5i+98BXwOmUZo92RmUpSN07bXXGmill4ABdxQOdPUADPEMbinUdnb3MbNlQpslSTqKZeZmBn9OFuCtg9RP4MNDnOtG4MZByrcAy46gmZIkTQi7GkfhQEcp4A7+DG7Rg+tauJIkSZJUFQbcUTjQ2d+DO/gzuAAdroUrSZIkSVVhwB2F/R3dAMwYbIhyY+lWulSQJEmSJFWHAXcU9ncM9wxuqQe30yHKkiRJklQVBtxROBRwh3sG1yHKkiRJklQVBtxR6H8Gd7Ahys0OUZYkSZKkqjLgjsKBjqEnmaqLoKmhzoArSZIkSVViwB2F/R3dRMD0YjjyQC0NdXT0OERZkiRJkqrBgDsK+zt7mNHUQF1dDLq/ubHeHlxJkiRJqhID7igc6OgZdAblfi0NdXQ6yZQkSZIkVYUBdxT2d/QMOsFUv5bGejpcJkiSJEmSqsKAOwoHOnuYMcgSQf1aHKIsSZIkSVVjwB2FA509tA4bcOtcB1eSJEmSqsSAOwrtXT20Ng33DG49nQ5RliRJkqSqMOCOQntXL9ObB18iCEqzKHf3Jr19OYGtkiRJkiSBAXdUDnb1Mr1p6IDb0li6nT6HK0mSJEkTb+jxtjpMW1cP04cbotxYCr8d3b3DPqt7851PDrnvonNPGXsDJUmSJOkoZg9uhXr7ko7uvuF7cBuKgNvjRFOSJEmSNNEMuBU6WAw7Hi7gNjtEWZIkSZKqxoBbofauHoCKhih3GnAlSZIkacIZcCt0sGvkHtyWhv4eXIcoS5IkSdJEM+BWqK2zgoDbP8mUa+FKkiRJ0oQz4FboYPfIQ5SnNdUTQFtnzwS1SpIkSZLUz4BboUp6cOsimNHSwP4OA64kSZIkTTQDboXai2dwpw0TcAFmGnAlSZIkqSoMuBXqH6LcOswQZYCZzY0839E9EU2SJEmSJJUx4FaokiHKYA+uJEmSJFWLAbdCh5YJah6hB7elkbbOHnr7ciKaJUmSJEkqGHAr1NZV6pWd1jhyD27iTMqSJEmSNNEMuBU62NVLc0Md9XUxbL1ZLaUeXocpS5IkSdLEqijgRsT5EfFwRGyLiCsG2d8cEd8s9t8ZEQvL9n2iKH84Is4b6ZwR8Y2i/IGIuDEiGo/sEsdHe1cvrSMMT4bSEGWA/RVONOVQZkmSJEkaHyMG3IioB74EvANYCqyMiKUDqq0C9mTm6cDngauLY5cCFwJnAucDX46I+hHO+Q3glcBvAdOAPzmiKxwnbV09Iw5PhtIQZRi5B7erp4/1dz3JZ7+z9dDzvZIkSZKksaukB/ccYFtmPpqZXcAtwAUD6lwA3FRs3wa8NSKiKL8lMzsz8zFgW3G+Ic+ZmRuzANwFLDiySxwfB7t6aW0eOeDOKALu851D9+Dubuviuh/+ivt/vY/2rl6272kft3ZKkiRJ0tGqkoA7H9he9npHUTZonczsAfYBc4c5dsRzFkOT3w/8/WCNiohLI2JLRGzZtWtXBZdxZNq6epk2whq4AA11dUxvqh+yB/eJ59r48g+2sfdgFxedcwp1AU88Z8CVJEmSpCNVScAdbFalgQ+ODlVntOXlvgz8U2b+aLBGZeb1mXl2Zp49b968waqMq4NdPUyvYIgywKyWxiED7t8/8Bvau3r5t797GsvmH8PLjmnhyd1t49lUSZIkSToqVRJwdwAnl71eADw1VJ2IaACOAXYPc+yw54yITwHzgI9XchETob3CIcpQeg53qEmm7tuxl2NbmzhhVgsApxzbyvY9B51sSpIkSZKOUCUB925gcUQsiogmSpNGbRhQZwNwcbH9HuD7xTO0G4ALi1mWFwGLKT1XO+Q5I+JPgPOAlZnZd2SXN37aKxyiDP0Bd/Ae3Pu272PBnGmHXp967HS6evp4+vmOcWmnJEmSJB2tRgy4xTO1HwHuALYCt2bmgxFxVUT8QVFtHTA3IrZR6nW9ojj2QeBW4CFKz9J+ODN7hzpnca7rgBOAH0fEvRHxyXG61iPS3tVDa1OlPbiNHOjooW9Ar+wz+zv49d6DLJgz/VDZKXNL20/u9jlcSZIkSToSFXVJZuZGYOOAsk+WbXcA7x3i2LXA2krOWZRX1k06wdo7e5lWccBtoDeTPe1dzJ3RfKj859v3AXByWQ/u7GmNzGpp4Mnd7bzu5XPHt9GSJEmSdBSpZIjyUS8zae/uZfooenABntnf+aLy+3bspb4uOPGYFwJuRHDKsdN54jknmpIkSZKkI2HArUBXbx+9fcn0Sp/BbS7VGxhw792+lzNOmElTw4tv+ylzW9nT3s3zQ0xMJUmSJEkamQG3Au2dvQCj6MEtAm7ZxFGZyc937OPVJx9zWP1Tjy2ew3U9XEmSJEkaMwNuBdq7SwG3teJZlA8fovzEc+3sO9jNqxbMPqz+ibNbaKgLJ5qSJEk6Sqxfv55ly5ZRX1/PsmXLWL9+fbWbJNUEA24F2jtLS/5UOslUU0MdzQ117CoLuPft2AvAq04+POA21NUxf840A64kSdJRYP369axZs4Zrr72Wjo4Orr32WtasWWPIlcaBAbcC7V2jG6IMpV7cZ/a/MET53u17mdZYz+LjZwxa/5Q50/n13oP09E6apX8lSZL0Eli7di0XXXQRq1evpqWlhdWrV3PRRRexdu1hC49IGqVJuSTPZPNCwK38ds1saeCZ58t6cLfvZdn8WTTUD/6ZwvGzWujtS7bvOcii41qPrMGSJEmatB566CHa2tq48cYbWb58OZs3b+aSSy7hiSeeqHbTpCnPHtwKtHeVhiiPpgd3VkvDoWdwu3v7eOCp5wd9/rbfvJml9XIf3XXgCFoqSZKkya6pqYnVq1ezYsUKGhsbWbFiBatXr6apqanaTZOmPANuBfp7cFubRz9EOTP58a+eo6unj98e5PnbfvNmlALurwy4kiRJNa2rq4svfvGLbNq0ie7ubjZt2sQXv/hFurq6qt00acpziHIF+ntwp41yiHJHdx+PP9fOf77tPk6dO523vPL4IetPa6qntbmBXz3TdsTtlSRJ0uS1dOlS3v3ud7N69Wq2bt3KkiVLuOiii/jWt75V7aZJU549uBU49Axu4+h6cAFW3XQ3+w52c92/fg0zmocPyPNmNPPos/bgSpIk1bI1a9Zw8803v2gW5Ztvvpk1a9ZUu2nSlGcPbgUOBdxRDVEu3dpHd7XxX9/7KpacOGvEY+bNbOJXu+zBlSSNTkTcCLwLeCYzlxVlxwLfBBYCjwN/lJl7IiKA/w68E2gH/k1m/rQ45mLg/y1O+5eZeVNR/hrga8A0YCPw0czMCbk4qQatXLkS4EU9uGvXrj1ULmns7MGtQHtXD/V1QdMQMyAPZs700iQBF517Cn/4mgUVHTNvRjO727rY3ebzF5KkUfkacP6AsiuA72XmYuB7xWuAdwCLi69Lga/AoUD8KeBc4BzgUxExpzjmK0Xd/uMGvpekUVq5ciUPPPAAvb29PPDAA4ZbaZwYcCvQ3tXL9KZ6Sh96V+bY1iZuX72cq/7gzIqPcSZlSdJYZOY/AbsHFF8A3FRs3wS8u6z861nyE2B2RJwInAd8NzN3Z+Ye4LvA+cW+WZn546LX9utl55IkaVIx4FagvbN3VEsE9Vs2/5gh170dzHEz+gOuw5QlSUfshMzcCVB875/pcD6wvazejqJsuPIdg5QfJiIujYgtEbFl165d43IRkiSNhgG3Au3dvUwfxQzKYzWntYmm+jqXCpIkvZQGG46UYyg/vDDz+sw8OzPPnjdv3hE0UZKksTHgVuBgV8+YenBHqy6CRce1GnAlSePh6WJ4McX3Z4ryHcDJZfUWAE+NUL5gkHJJkiYdA24F2sY4RHksXj6v1SHKkqTxsAG4uNi+GPh2WfkHouR1wL5iCPMdwNsjYk4xudTbgTuKffsj4nXFDMwfKDuXpDFav349y5Yto76+nmXLlrF+/fpqN0mqCS4TVIH27l5mT2sc9XE33/nkqI85bd4M/uGhp+nq6aOpwc8fJEkji4j1wJuB4yJiB6XZkD8H3BoRq4AngfcW1TdSWiJoG6Vlgj4IkJm7I+IvgLuLeldlZv/EVf+OF5YJ+k7xJWmM1q9fz5o1a1i3bh3Lly9n8+bNrFq1CsDZlKUjZMCtQHtnDycd0zIh7/Xyea309iVP7m7j9ONnTsh7SpKmtswc6i/itw5SN4EPD3GeG4EbBynfAiw7kjZKesHatWtZt24dK1asAGDFihWsW7eO1atXG3ClI2QXYQXau3qZNkFDlE+bNwMmAFHrAAAey0lEQVSAXzlMWZIkqSZt3bqV5cuXv6hs+fLlbN26tUotkmqHAbcCB7t7aZ2AWZSh1IMLONGUJElSjVqyZAmf/vSnX/QM7qc//WmWLFlS7aZJU54BtwJtnRMzizLAzJZGjp/ZzK+esQdXkiSpFq1YsYLPfvazPPvss/T19fHss8/y2c9+9tCQZUljZ8AdQW9f0tnTN2FDlKE0TPnRZ+3BlSRJqkXf+ta3mDlzJtOmTaOuro5p06Yxc+ZMvvWtb1W7adKUZ8AdQXtXD8CEDVEGOP34GTzy9AH6+nLC3lOSJEkTY8eOHbzpTW9i586d9PX1sXPnTt70pjexY8eOajdNmvIMuCM42NULMKE9uEtOnMWBzh527Dk4Ye8pSZKkiXP77bfzmc98hra2Nj7zmc9w++23V7tJUk0w4I6gvQi4rc0TF3CXnjQLgId2Pj9h7ylJkqSJ09rayllnnUVjYyNnnXUWra2t1W6SVBNcB3cEbcUQ5WmNE3erXnHCTOqiFHDPX/ayCXtfSZIkTYzM5LzzzqO7u5vGxkaam5ur3SSpJhhwR9A/RHmiZlGG0nDoRce1stUeXEmSpJrT0NBAe3s7fX19AHR3d9Pb20tDg3+aS0fKIcojaKvCEGUoPYdrwJUkSapNfX19vOENb+Cpp57iDW94w6GwK+nIGHBHcLAKQ5Sh9Bzujj0H2Xewe0LfV5IkSS+tnp4eli5dyj333MNJJ53EPffcw9KlS+np6al206Qpz4A7gmpMMgWlHlyAX9iLK0mSVHMWLlw47GtJY2PAHUFbFZYJAjjzRGdSliRJqlUbN27kkksuYe/evVxyySVs3Lix2k2SaoJPso+gf4jy9KaJvVXzZjYzt7XJ53AlSZJqTESQmfzVX/0VX/nKV6irqztULunI2IM7grbOoge3cWJ7cCOCpSfNsgdXkiSpxmQmzc3NhyaW6uvro7m5mcyscsukqc+AO4KD3b20NNZRXzfxn6gtOXEWv3z6AD29zqonSZJUKyKCSy65hMw89HXJJZfYgyuNAwPuCNq7emid4OHJ/ZaeOIuunj4efbatKu8vSZKk8ZeZ3HDDDVxzzTW0t7dzzTXXcMMNN9iDK42DigJuRJwfEQ9HxLaIuGKQ/c0R8c1i/50RsbBs3yeK8ocj4ryRzhkRHynKMiKOO7LLO3Ltnb0TPsFUv/6ZlB96ymHKkiRJteLMM8/kXe96F1deeSWtra1ceeWVvOtd7+LMM8+sdtOkKW/EgBsR9cCXgHcAS4GVEbF0QLVVwJ7MPB34PHB1cexS4ELgTOB84MsRUT/COf8v8DbgiSO8tnFxoLN6Pbgvn9dKU0OdE01JkiTVkDVr1nDffffxne98h66uLr7zne9w3333sWbNmmo3TZryKklu5wDbMvNRgIi4BbgAeKiszgXAnxfbtwFfjNJDBBcAt2RmJ/BYRGwrzsdQ58zMnxVlR3Jd42Z3WxfHtjZV5b0b6+s444QZPGgPriRJUs1YuXIlAKtXr2br1q0sWbKEtWvXHiqXNHaVBNz5wPay1zuAc4eqk5k9EbEPmFuU/2TAsfOL7ZHOOayIuBS4FOCUU04ZzaGjsrutiyUnzXrJzj+S15wyh1u37KCzp5fmhuoMlZYkSdLgxqNT5sEHH+Siiy7ioosuGvWxPrcrvVglz+AO9n/twP+Thqoz2vKKZeb1mXl2Zp49b9680Rw6Ks+1dTG3Sj24AMsXz+Ngdy8/fWJv1dogSZKkwZXPhDyWr1Mvv/2Ijpf0YpUE3B3AyWWvFwBPDVUnIhqAY4DdwxxbyTmrrru3j30Hu6s2RBngdS8/lvq6YPO2XVVrgyRJkiRNBZUMUb4bWBwRi4BfU5o0auD4iQ3AxcCPgfcA38/MjIgNwM0RcQ1wErAYuItSD+5I56y6Pe1dABPag3vznU8eVrZgzjS+9bOn+M/nvXLC2iFJkiRJU82IPbiZ2QN8BLgD2ArcmpkPRsRVEfEHRbV1wNxiEqmPA1cUxz4I3EppQqq/Bz6cmb1DnRMgIv40InZQ6tX9eUR8dfwud3R2t5UC7rGtzdVqAgCnHz+Dp/YeZE/RHkmSJEnS4Spa/yYzNwIbB5R9smy7A3jvEMeuBdZWcs6i/AvAFypp10tt94H+gFu9IcoAi+fN4Htbn+Gff/Ucv//bJ1a1LZIkSZI0WVXyDO5R67mix3TujOoG3PlzptPSWMePHvE5XEmSJEkaigF3GC8MUa5uwK2vC15+3Ax+9MizzpYnSZIkSUMw4A7juQOdRMCc6dUNuFB6DvfXew/y2LNt1W6KJEmSJE1KBtxhPNfWxZzpTdTXHfkC3kdq8fEzANi87dkqt0SSJEmSJicD7jB2t3VVfXhyv2Nbmzj52Gl8b+sz1W6KJEmSJE1KBtxhPDeJAm5E8O5Xz+dHj+zi13sPVrs5kiRJkjTpGHCHsbuti7mTJOAC/NHZJ5PArXdvr3ZTJEmSJGnSMeAOYzINUQY4+djp/IvF87h1y3Z6+5xNWZIkSZLKGXCH0NuX7GmfXD24ACtfezI793Xww1/6LK4kSZIklTPgDmFvexeZ1V8Dd6C3LT2B42Y0s/4uhylLkiRJUjkD7hB2t3UBMHdGc5Vb8mKN9XW85zUL+P4vnuHp5zuq3RxJkiRJmjQMuEN49kARcCdZDy7Aha89md6+ZP1dT1a7KZIkSZI0aRhwh9Dfg3vsjMkXcBce18rblpzAV3/0GLv2d1a7OZIkSZI0KRhwh7C7rRQcJ9szuP0+8c5X0tHdyzXf/WW1myJJkiRJk0JDtRswWT1X9ODOmT45A+5p82bw/tefyk3//DgfeP2pLDlxVrWbJEmSNCW96tP/wL6D3VV7/4VX/N2Ev+cx0xq571Nvn/D3lV5qBtwh7G7r4phpjTTWT95O7o++dTF/+7Nf85d/9xD/c9W5RES1myRJkjTl7DvYzeOf+/1qN2NCVSNUSxNh8qa3KnuubfKtgTvQ7OlNfOyti/m/257jHx56utrNkSRJkqSqsgd3CLsPdE26529vvvPwWZPr6+p42awW1vzt/bzm1DkcN8mWNZIkSZKkiWIP7hB2t02+gDuY+rrgj84+mec7erj8tp+TmdVukiRJkiRVhT24Q3iurZPfOXVOtZtRkZcd08LvLTmBv7t/Jx/75r2cu2jui/ZfdO4pVWqZJEmSJE0cA+4g+vqSPe3dk/4Z3HKvP20uv3x6Pxvv38miua0cP6ul2k2SJEmaEmYuuYLfuumKajdjQs1cAnB0Taylo4MBdxD7DnbT25dTYohyv7oI/vA1C/jC9x7h5rue5N+/+XSaGhyBLkmSNJL9Wz/nLMpSjTABDaJ/Ddy5M6ZOwAWY1dLI+157Mrv2d/Kte3/t87iSJEmSjioG3EHsLgLuVOrB7bf4+Jm8dcnx3Lt9L3c+trvazZEkSZKkCWPAHcTutk5gagZcgDe/4njOOGEGf3f/Tp54rq3azZEk1YCIOD8iHo6IbRFxdD2sKEmaMgy4gzg0RLl1aq4pWxfBH73mZGZPa+SmHz/O1p3PV7tJkqQpLCLqgS8B7wCWAisjYml1WyVJ0uEMuIPYfaAUcOe0Nla5JWM3vbmBS5Yvoqm+jvevu8ueXEnSkTgH2JaZj2ZmF3ALcEGV2yRJ0mGcRXkQv3m+g5ktDTQ31Fe7KUdkzvQmPvjGRdz048f51+vu5JZLX8/82dOq3SxJ0tQzH9he9noHcO7AShFxKXApwCmnuAa7ppaxzir8xNXvGueWjM6pl98+puOOmTZ1O3Kk4RhwB/HjXz3HWafMqXYzxsUJs1r42gfP4f1fvZP3fuWf+Z9/ci4vnzej2s2SJE0tMUjZYVP1Z+b1wPUAZ599tlP5a8o4oiWCPuc/dWkycYjyAE8+186jz7bx5jPmVbsp4+ahp57n4jcsZN/Bbv7ltZv5/+54mJvvfJKb73yy2k2TJE0NO4CTy14vAJ6qUlskSRqSAXeAH/7yGQDe/IraCbgAJ82exr/93dNoqK/jhh89ymPP+kyuJKlidwOLI2JRRDQBFwIbqtwmSZIOY8Ad4Ie/3MXJx05j0XGt1W7KuDtuZjP/9ndfzsyWRr72z4/x8G/2V7tJkqQpIDN7gI8AdwBbgVsz88HqtkqSpMMZcMt09vTyz796jjefcTwRgz1uNPXNnt7Epb/7cubNaOavf/I4/+c+R5hJkkaWmRsz84zMPC0z11a7PZIkDcaAW+bux/bQ3tXLm2ro+dvBzGhu4E/+xcs55djprF7/M9b87f20dfZUu1mSJEmSdEScRbnMD3/5DE31dbz+tLnVbspLrqWxng++cRG/3nuQG370KP/0yC7+/F+eyRtOO45pTVN7eSRJkiRJRycDbpkfPLyL1y6aQ2vz0XFbGuvruPKdS/i9pSfwn/7Xfay6aQsNdcGZJ83ilLmtPLrrAD29SW9f0tPXR09f0tJYz+xpjbzpjHm84mUzefUpszl+Zku1L0WSJEmSKgu4EXE+8N+BeuCrmfm5Afubga8DrwGeA96XmY8X+z4BrAJ6gT/NzDuGO2dELAJuAY4Ffgq8PzO7juwyR/brvQd55JkD/NHZJ49cuca8duGx3PGx32XzI8/y0yf3sOWJPdy/Yy/tXb001AcNdXXU1wXTGus42N3L1n0dbHliz6HjZ09v5OQ50zl5zjQuWb6IZfOPoaXRXmBJkiRJE2vEgBsR9cCXgN+jtA7e3RGxITMfKqu2CtiTmadHxIXA1cD7ImIppaUEzgROAv4xIs4ojhnqnFcDn8/MWyLiuuLcXxmPix3ODx/eBdTe8kCVamms521LT+BtS084VDbcOrldPX3s3HeQ7bvb2b7nINv3tHP/r/ex8YHf0NRQx6sWHMNrFx7Lby+YzW8vOIYTj2k5oom7MpPOnj4a60thW4dr6+zhuQNdtDbXM3t6k/dJkiRJR51KenDPAbZl5qMAEXELcAFQHnAvAP682L4N+GKU0swFwC2Z2Qk8FhHbivMx2DkjYivwFuCios5NxXlf8oA7d0YTv/9bJ3L68TNe6reaVIYLscNpaqjj1LmtnDr3heWU9nd0s+i4Vu5+fDd3Pb6H6//pUXr6EoBjW5s4fd4MTju+lYVzWzlhVgvzZjYze3ojQSmIHezuZdf+Dp5+vpOn9h7k8efaeOK5drbvbqejp4/eviSAaU31tDY3MGd6I2847TgWzp3OibOnHTrntMZ6GuuDpoY6GuvqqKuBoNfT20dHTx+7D3Sx60AHt93za57Z38Ezz3eya38new920dHd96Jj+u/5GS+bwenzZjB/znROPKZ0j5ob6misr6OpoY6GuqjZWcMnm96+pLu3j67ePnp6k7bOHva2d7OnvYu9B7vZ197F3vbuQ//f1EUwvamemS0NzGxpLL6XtmcV31sa66r23y8zySy2+19DWdkL+ynK+8sS6Oju5UBHD/s7etjf2c2Bjh4OdJZeH+jsoa2zh+lN9cyaVrr2WS2NpWuf9sI9aGmsp72rl/auHjq6+2pyiTdJklS5SgLufGB72esdwLlD1cnMnojYB8wtyn8y4Nj5xfZg55wL7C3W2xtY/yV13pkv47wzXzYRb1WzZrY08uyBLhYdN4NFx82g+6z5/GZfBzv2HmTn3oMkyR0PPs3utpFHnDfV13HK3OksnNvKrJZGWhrraWmso6cvD/3hu6eti/+1ZTttXb3DnquxGGY9VqU/2ccuj+DwpBRu+4Y4x7GtTRw/s5lFx7VyzLRGWpsb+K35s9jd3s2u/R088vQBvn3vU+zvGHqW7IjS89j1htyXTG/msP8dj0RdlIIwlP5bAqUPjQ5tc/i+stfloRNK/+YYpGxgeJ0I9XVB7yhu2szmBu7/9HkvYYskSdJkV0nAHeyv3oF/cQxVZ6jywdLGcPUPb1TEpcClxcsDEfHwYPUmwHHAs1V671py2H18pEoNmUqeGLzYf5Pjw/s4fibsXsZV43aqU8ftTEepe+6559mIGOLHlKQy/r6RKlPR7+ZKAu4OoHzmpQXAU0PU2RERDcAxwO4Rjh2s/FlgdkQ0FL24g70XAJl5PXB9Be1/SUXElsw8u9rtmOq8j+PHezk+vI/jx3t5dMrMo3NSC2mU/Bkpja9Kxm3eDSyOiEUR0URp0qgNA+psAC4utt8DfD8zsyi/MCKai9mRFwN3DXXO4phNxTkozvntsV+eJEmSJOloMWIPbvFM7UeAOygt6XNjZj4YEVcBWzJzA7AO+OtiEqndlAIrRb1bKU1I1QN8ODN7AQY7Z/GWlwO3RMRfAj8rzi1JkiRJ0rAiJ3LGkBoUEZcWw6V1BLyP48d7OT68j+PHeylJQ/NnpDS+DLiSJEmSpJow9rVTJEmSJEmaRAy4kiRJkqSaYMAdo4g4PyIejohtEXFFtdszWUTEjRHxTEQ8UFZ2bER8NyIeKb7PKcojIr5Q3MOfR8TvlB1zcVH/kYi4uKz8NRFxf3HMFyJisLWTp7yIODkiNkXE1oh4MCI+WpR7L0cpIloi4q6IuK+4l58uyhdFxJ3FfflmMaM7xazv3yzuy50RsbDsXJ8oyh+OiPPKyo+anwcRUR8RP4uI24vX3kdJVRcRL4uIWyLiVxHxUERsjIgzJuB9/zwi/lOxfVVEvG2cz/+xiJhe9vrxiDhuPN9DqjUG3DGIiHrgS8A7gKXAyohYWt1WTRpfA84fUHYF8L3MXAx8r3gNpfu3uPi6FPgKlEIc8CngXOAc4FP9Qa6oc2nZcQPfq1b0AP8xM5cArwM+XPwb816OXifwlsx8FfBq4PyIeB1wNfD54l7uAVYV9VcBezLzdODzRT2K+38hcCale/XlIuwdbT8PPgpsLXvtfZRUVcUHtH8L/CAzT8vMpcCVwAkT2Y7M/GRm/uM4n/ZjwPQRa0k6xIA7NucA2zLz0czsAm4BLqhymyaFzPwnSktFlbsAuKnYvgl4d1n517PkJ8DsiDgROA/4bmbuzsw9wHcphZITgVmZ+eNizeSvl52rpmTmzsz8abG9n1KgmI/3ctSKe3KgeNlYfCXwFuC2onzgvey/x7cBby3+eLoAuCUzOzPzMWAbpZ8FR83Pg4hYAPw+8NXideB9lFR9K4DuzLyuvyAz7wU2R8R/iYgHihFL7wOIiBkR8b2I+GlRfkFRvjAifhERNxWjoW7r7z0tek6vLkYE3RURpw9sRER8LSLeU2y/NiL+uRg9dFdEzCzO/6PifX8aEW8o6r45In5QvN8vIuIbUfKnwEnApojYNOC9FkZplNcNURqd9A8RMa3Yd3pE/GPx3j+NiNOK8w12L94cET+MiFsj4pcR8bmI+OOizfdHxGlFvXkR8TcRcXfx9cbx/o8ojRcD7tjMB7aXvd5RlGlwJ2TmTigFN+D4onyo+zhc+Y5BymtalIZ2ngXcifdyTIoewnuBZyiF/F8BezOzp6hSfv2H7lmxfx8wl9Hf41r034A/A/qK13PxPkqqvmXAPYOU/z+URu68Cngb8F+KD3g7gH+Vmb9DKRz/1+IDOIBXANdn5m8DzwP/vux8z2fmOcAXKf08HFSUHtX4JvDRYvTQ24CDlH4H/V7xvu8DvlB22FmUemuXAi8H3piZXwCeAlZk5opB3mox8KXMPBPYC/xhUf6NovxVwBuAncPcC4qyjwK/BbwfOKO4zq8Cq4s6/53SaJ3XFu/z1aGuX6o2A+7YDPasoustjd5Q93G05TUrImYAfwN8LDOfH67qIGXey0Jm9mbmq4EFlHoKlwxWrfjuvRxERLwLeCYzy/+IHO7avY+Sqm05sL74HfA08EPgtZR+3nwmIn4O/COlD9P6hzNvz8z/W2z/z+Ic/daXfX/9MO/7CmBnZt4NkJnPFx/0NQI3RMT9wP+iFGb73ZWZOzKzD7gXWFjB9T1W9FRDKeAvjIiZwPzM/NvivTsys32YewFwdzFyrJPSB8D/UJTfX9aOtwFfLD4s3gDMKt5LmnQaqt2AKWoHcHLZ6wWUPmHT4J6OiBMzc2fxaeEzRflQ93EH8OYB5T8oyhcMUr8mRUQjpXD7jcz830Wx9/IIZObeiPgBpeeaZ0dEQ/FHR/n199/LHRHRABxDadj9cP/fHw0/D94I/EFEvBNoAWZR6sHwPkqqtgeB9wxSPtTkiX8MzANek5ndEfE4pZ9rcPgHa1nB9mDvO9j+/wA8TanHtI5ST3K/zrLtXir7G33gMdMY+pqHm0iy/Dx9Za/7ytpRB7w+Mw9W0C6pquzBHZu7gcVRmj20idKEKRuq3KbJbAPQP3vvxcC3y8o/UDwX8jpgXzHs9g7g7RExJ0oTIr0duKPYtz8iXlcMJfpA2blqSnF964CtmXlN2S7v5SgVzw3NLranUfoUeiuwiRf+IBp4L/vv8XuA7xfPKW8ALozS7MCLKA0Nu4uj5OdBZn4iMxdk5kJK1/j9zPxjvI+Squ/7QHNEfKi/ICJeS2niu/cVj6nMA36X0s+bYyiNSOmOiBXAqWXnOiUi+ntnVwKby/a9r+z7j4dpzy+Ak4o2UDx/2/9B386il/b9QH0F17YfqLintBjttSMi3l28d3OUniP+Jwa/F5X6B+Aj/S8i4tWjOFaaUPbgjkFm9kTERyiFh3rgxsx8sMrNmhQiYj2lHsPjImIHpRl8PwfcGhGrgCeB9xbVNwLvpDTJTDvwQYDM3B0Rf0HpD16AqzKzf+Kqf0dppuZpwHeKr1r0Rkq//O4vhgNBaUZI7+XonQjcFKVZeuuAWzPz9oh4CLglIv4S+BmlDxQovv91RGyj1ON4IUBmPhgRtwIPUZrl+sOZ2QtwlP88uBzvo6QqysyMiH8F/LcoLTHWATxO6ZnWGcB9lHpU/ywzfxMR3wD+T0RsoTQc+Bdlp9sKXBwRfwU8QrEqQaE5Iu6k9Ltk5TDt6Somcbq2+GD1IKUPV78M/E1EvJfSh4NtFVze9cB3ImLnEM/hDub9wF9FxFVAN6W/Ff6W0rDqgffilRWe80+BLxXDuhsoBebLKjxWmlBR+kBdkiRJOnoVkzrenpnLBtn3OHB2Zj47wc2SNEoOUZYkSZIk1QR7cCVJkiT9/+3dWYgcVRSH8e/vFhUXEIMb6GA0AfctggtiID67QpAQfBJENDEgPor4IIqI+4L6oOC+IIgoxLjHLdGQxWgQxIDBGBGUiLvm+FC3tRk6Yw+MUdvvB01X3aq6594aaOZwuvpKI8EKriRJkiRpJJjgSpIkSZJGggmuJEmSJGkkmOBKk5Rk/ySPJfkkyYdJnk8yczvEvSbJlW372iRzp7j/K9paeb39DUn2ncoYkiRJ0t/JBFeahCShW0vu1aqaUVVH0K1Pu9/2HEdVXV1VS6e42yuA3f/yLEmSJOlfygRXmpw5wC9VdU+voapWAcuS3JjkgyRr2wLvJNkjyUtJVrb2s1v7WJL1SR5MsibJU73qaauc3pBkeXsdNn4QSR5IckHbnp3krSSr2/l7tv7faHFXJjm1nXtmkldbvPVJHk5nIXAg8EqSV8bFGkvyUZL7kqxLsqQtXE+Sw5IsbbFXJpnR+ht0L85M8lqSJ5J8nOT6JPPbmNcmmdHOm57k6SQr2uu0qf4jSpIkaTSZ4EqTcxTw/oD284DjgGOBucCNSQ4AfgTOraoT6JLjm1oVGGAWcG9VHQNsAS7t629LVZ0M3AHcsq3BJNkFeBxYVFW92D8AXwJntbjzgNv6Ljuerlp7BHAocFpV3QZ8DsypqjkDQh0O3FlVRwLfAOe39odb+7HAqcCmCe4FrW0RcDSwAJjZ5nk/cHk751bg5qqa3eLcv635S5IkSf1McKWpcTrwaFX9VlWbgdeA2UCA65KsAZYCB/Hn15k/q6o32/ZDrY+eR/veT5kg7ixgU1WtAKiqLVX1K7AzcF+StcCTdMlsz/Kq2lhVW4FVwNgQ8/u0VaqhS/DHkuwJHFRVz7TYP1bV9xPcC4AVVbWpqn4CPgGWtPa1feOYC9yRZBXwLLBXiyVJkiRNaKd/egDSf8w64IIB7RnQBjAfmA6cWFW/JNkA7NqO1bhza4jtQXEHHV8MbKarmO5AV0nu+alv+zeG+xwYf81ubHvO22of38/Wvv2tfePYATilqn4YYlySJEnSH6zgSpPzMjAtycW9hiSzga+BeUl2TDIdOANYDuwNfNmS2znAIX19HZykV529EFjWd2xe3/vbE4xnPXBgGwPt+dudWtxNrUq7ANhxiLl9CwxdKa2qLcDGJOe02NPac8SvM/heDGsJcFlvJ8lxk7hWkiRJ/2MmuNIkVFUB5wJntWWC1gHXAI8Aa4DVdEnwVVX1Bd0zqicleY+umru+r7uPgIva15f3Ae7uOzYtybt0z6sunmA8P9MlwbcnWQ28SFchvqv1/Q4wE/huiOndC7ww/kem/sICYGGbw1vA/nS/Mj3oXgxrId09W5PkQ+CSSVwrSZKk/7F0/69L2p6SjAHPVdVRA45tAE6qqq+287AkSZKk/zQruJIkSZKkkWAFV5IkSZI0EqzgSpIkSZJGggmuJEmSJGkkmOBKkiRJkkaCCa4kSZIkaSSY4EqSJEmSRsLvickEE/rM3FEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.subplot(121)\n",
    "sns.distplot(train['CoapplicantIncome'])\n",
    "\n",
    "plt.subplot(122)\n",
    "train['CoapplicantIncome'].plot.box(figsize=(16,5))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see a similar distribution as that of the applicant income. Majority of coapplicant’s income ranges from 0 to 5000. We also see a lot of outliers in the coapplicant income and it is not normally distributed.\n",
    "\n",
    "Let’s look at the distribution of LoanAmount variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.subplot(121)\n",
    "df = train.dropna()\n",
    "sns.distplot(df['LoanAmount']);\n",
    "\n",
    "plt.subplot(122)\n",
    "train['LoanAmount'].plot.box(figsize=(16,5))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see a lot of outliers in this variable and the distribution is fairly normal. We will treat the outliers in later sections."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we would like to know how well each feature correlate with Loan Status."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Missing Values and Outliers Treatements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After exploring all the variables in our data, we can now impute the missing values and treat the outliers because missing data and outliers can have adverse effect on the model performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s list out feature-wise count of missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 614 entries, 0 to 613\n",
      "Data columns (total 13 columns):\n",
      "Loan_ID              614 non-null object\n",
      "Gender               601 non-null object\n",
      "Married              611 non-null object\n",
      "Dependents           599 non-null object\n",
      "Education            614 non-null object\n",
      "Self_Employed        582 non-null object\n",
      "ApplicantIncome      614 non-null int64\n",
      "CoapplicantIncome    614 non-null float64\n",
      "LoanAmount           592 non-null float64\n",
      "Loan_Amount_Term     600 non-null float64\n",
      "Credit_History       564 non-null float64\n",
      "Property_Area        614 non-null object\n",
      "Loan_Status          614 non-null object\n",
      "dtypes: float64(4), int64(1), object(8)\n",
      "memory usage: 62.4+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "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": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are missing values in Gender, Married, Dependents, Self_Employed, LoanAmount, Loan_Amount_Term and Credit_History features.\n",
    "\n",
    "* We will treat the missing values in all the features one by one.\n",
    "\n",
    "* We can consider these methods to fill the missing values:\n",
    "\n",
    "##### For numerical variables: imputation using mean or median\n",
    "##### For categorical variables: imputation using mode\n",
    "There are very less missing values in Gender, Married, Dependents, Credit_History and Self_Employed features so we can fill them using the mode of the features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Gender'].fillna(train['Gender'].mode()[0],inplace=True)\n",
    "train['Married'].fillna(train['Married'].mode()[0],inplace=True)\n",
    "train['Dependents'].fillna(train['Dependents'].mode()[0],inplace=True)\n",
    "train['Self_Employed'].fillna(train['Self_Employed'].mode()[0], inplace=True)\n",
    "train['Credit_History'].fillna(train['Credit_History'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "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           22\n",
       "Loan_Amount_Term     14\n",
       "Credit_History        0\n",
       "Property_Area         0\n",
       "Loan_Status           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let’s try to find a way to fill the missing values in Loan_Amount_Term. We will look at the value count of the Loan amount term variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "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": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Loan_Amount_Term'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be seen that in loan amount term variable, the value of 360 is repeating the most. So we will replace the missing values in this variable using the mode of this variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Loan_Amount_Term'].fillna(train['Loan_Amount_Term'].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['LoanAmount'].fillna(train['LoanAmount'].median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "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": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see that all the missing values have been filled in the test dataset. Let’s fill all the missing values in the *test* dataset too with the same approach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['Gender'].fillna(train['Gender'].mode()[0], inplace=True)\n",
    "test['Dependents'].fillna(train['Dependents'].mode()[0], inplace=True)\n",
    "test['Self_Employed'].fillna(train['Self_Employed'].mode()[0], inplace=True)\n",
    "test['Credit_History'].fillna(train['Credit_History'].mode()[0], inplace=True)\n",
    "test['Loan_Amount_Term'].fillna(train['Loan_Amount_Term'].mode()[0], inplace=True)\n",
    "test['LoanAmount'].fillna(train['LoanAmount'].median(), inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outlier Treatement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we saw earlier in univariate analysis, LoanAmount contains outliers so we have to treat them as the presence of outliers affects the distribution of the data. Let's examine what can happen to a data set with outliers. For the sample data set:\n",
    "\n",
    "1, 1, 2, 2, 2, 2, 3, 3, 3, 4, 4\n",
    "\n",
    "We find the following: mean, median, mode, and standard deviation\n",
    "\n",
    "Mean = 2.58\n",
    "\n",
    "Median = 2.5\n",
    "\n",
    "Mode = 2\n",
    "\n",
    "Standard Deviation = 1.08\n",
    "\n",
    "If we add an outlier to the data set:\n",
    "\n",
    "1, 1, 2, 2, 2, 2, 3, 3, 3, 4, 4, 400\n",
    "\n",
    "The new values of our statistics are:\n",
    "\n",
    "Mean = 35.38\n",
    "\n",
    "Median = 2.5\n",
    "\n",
    "Mode = 2\n",
    "\n",
    "Standard Deviation = 114.74\n",
    "\n",
    "It can be seen that having outliers often has a significant effect on the mean and standard deviation and hence affecting the distribution. We must take steps to remove outliers from our data sets.\n",
    "\n",
    "Due to these outliers bulk of the data in the loan amount is at the left and the right tail is longer. This is called right skewness. One way to remove the skewness is by doing the log transformation. As we take the log transformation, it does not affect the smaller values much, but reduces the larger values. So, we get a distribution similar to normal distribution.\n",
    "\n",
    "Let’s visualize the effect of log transformation. We will do the similar changes to the test file simultaneously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAEblJREFUeJzt3X+MXeV95/H3pzhpHLyJk5LMIhut6dZK2423WzJCadFG16E/SIkCfyQqEU1NlpVVlU3TrVeJ0/6B+kckql2aptJuJC+kcVU2DksSgQqbFtHcRpUWWpv8MMTpYlEXDASnSqAdGrWa9rt/zEEaMRPGc8+9c+88835J1tzznOec89Wjez8+88w556aqkCS16/umXYAkabIMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1Ljtk27AICLLrqo9uzZwwsvvMCFF1447XJmjuOykmOyOsdlda2Oy4kTJ/6mqt6wVr+ZCPo9e/Zw/PhxhsMhg8Fg2uXMHMdlJcdkdY7L6lodlyR/fT79nLqRpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGzcSdsZJW2nP43nVvc2jfIjccvpczt1w9gYq0WXlGL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxawZ9kk8mOZfkkVXW/ZckleSibjlJfjfJ6SRfS3LZJIqWJJ2/8zmj/xRw1Usbk1wC/DTwxLLmdwB7u38HgU/0L1GS1MeaQV9VXwK+vcqqjwEfAmpZ2zXA79eSB4GdSS4eS6WSpJGMNEef5F3AU1X11Zes2gU8uWz5bNcmSZqSdT+mOMmrgd8Afma11au01SptJDnI0vQOc3NzDIdDFhYWGA6H6y2peY7LSlthTA7tW1z3NnPbl7ZrfWzWayu8X17OKM+j/9fApcBXkwDsBh5OcjlLZ/CXLOu7G3h6tZ1U1RHgCMD8/HwNBgOGwyGDwWCEktrmuKy0FcbkhhGfR3/ryW2cuX4w/oI2sa3wfnk56566qaqTVfXGqtpTVXtYCvfLquqbwD3AL3ZX37wVeL6qnhlvyZKk9Tifyys/Dfxf4E1Jzia58WW63wc8DpwG/ifwy2OpUpI0sjWnbqrqvWus37PsdQE39S9LasMoXwcojZt3xkpS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNO58vB/9kknNJHlnW9l+TfCPJ15J8PsnOZes+kuR0kr9M8rOTKlySdH7O54z+U8BVL2m7H3hzVf1b4P8BHwFI8qPAdcC/6bb5H0kuGFu1kqR1WzPoq+pLwLdf0vbHVbXYLT4I7O5eXwMcq6p/qKq/Ak4Dl4+xXknSOm0bwz7+A/CZ7vUuloL/RWe7thWSHAQOAszNzTEcDllYWGA4HI6hpLY4LittljE5tG9x7U5jNLd96ZibYWw20mZ5v0xKr6BP8hvAInDHi02rdKvVtq2qI8ARgPn5+RoMBgyHQwaDQZ+SmuS4rLRZxuSGw/du6PEO7Vvk1pPbOHP9YEOPO+s2y/tlUkYO+iQHgHcCV1bVi2F+FrhkWbfdwNOjlydJ6mukyyuTXAV8GHhXVf39slX3ANcl+f4klwJ7gT/vX6YkaVRrntEn+TQwAC5Kcha4maWrbL4fuD8JwINV9UtV9WiSO4GvszSlc1NV/dOkipckrW3NoK+q967SfPvL9P8o8NE+RUmSxsc7YyWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNW7NoE/yySTnkjyyrO31Se5P8lj383Vde5L8bpLTSb6W5LJJFi9JWtv5nNF/CrjqJW2HgQeqai/wQLcM8A5gb/fvIPCJ8ZQpSRrVmkFfVV8Cvv2S5muAo93ro8C1y9p/v5Y8COxMcvG4ipUkrd+oc/RzVfUMQPfzjV37LuDJZf3Odm2SpCnZNub9ZZW2WrVjcpCl6R3m5uYYDocsLCwwHA7HXNLm57istFnG5NC+xQ093tz2pWNuhrHZSJvl/TIpowb9s0kurqpnuqmZc137WeCSZf12A0+vtoOqOgIcAZifn6/BYMBwOGQwGIxYUrscl5U2y5jccPjeDT3eoX2L3HpyG5x8YeR9nLnl6jFWNBs2y/tlUkadurkHONC9PgDcvaz9F7urb94KPP/iFI8kaTrWPKNP8mlgAFyU5CxwM3ALcGeSG4EngPd03e8Dfg44Dfw98P4J1CxJWoc1g76q3vs9Vl25St8CbupblCRpfLwzVpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS43oFfZL/nOTRJI8k+XSSVyW5NMlDSR5L8pkkrxxXsZKk9Rs56JPsAn4FmK+qNwMXANcBvwV8rKr2At8BbhxHoZKk0fSdutkGbE+yDXg18AzwduCubv1R4Nqex5Ak9TBy0FfVU8B/A55gKeCfB04Az1XVYtftLLCrb5GSpNGlqkbbMHkd8Fng54HngP/dLd9cVT/U9bkEuK+q9q2y/UHgIMDc3Nxbjh07xsLCAjt27BipnpY5LittljE5+dTzG3q8ue3w7Hf77WPfrteOp5gZslneL+u1f//+E1U1v1a/bT2O8VPAX1XVtwCSfA74SWBnkm3dWf1u4OnVNq6qI8ARgPn5+RoMBgyHQwaDQY+S2uS4rLRZxuSGw/du6PEO7Vvk1pN9PtZw5vrBeIqZIZvl/TIpfebonwDemuTVSQJcCXwd+CLw7q7PAeDufiVKkvroM0f/EEt/dH0YONnt6wjwYeDXkpwGfgC4fQx1SpJG1Ot3vKq6Gbj5Jc2PA5f32a8kaXy8M1aSGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuP6fV28tAXsOXzvtEuQeul1Rp9kZ5K7knwjyakkP5Hk9UnuT/JY9/N14ypWkrR+faduPg58oap+GPgx4BRwGHigqvYCD3TLkqQpGTnok7wGeBtwO0BV/WNVPQdcAxztuh0Fru1bpCRpdH3O6H8Q+Bbwe0m+nOS2JBcCc1X1DED3841jqFOSNKJU1WgbJvPAg8AVVfVQko8Dfwt8oKp2Luv3napaMU+f5CBwEGBubu4tx44dY2FhgR07doxUT8scl5U2ckxOPvX8hhxnHOa2w7Pf7bePfbteO55iZkirn6H9+/efqKr5tfr1Cfp/CTxYVXu65X/P0nz8DwGDqnomycXAsKre9HL7mp+fr+PHjzMcDhkMBiPV0zLHZaWNHJPNdNXNoX2L3Hqy38V0Z265ekzVzI5WP0NJzivoR566qapvAk8meTHErwS+DtwDHOjaDgB3j3oMSVJ/fa+j/wBwR5JXAo8D72fpP487k9wIPAG8p+cxJEk99Ar6qvoKsNqvDVf22a8kaXx8BIIkNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMb1DvokFyT5cpI/7JYvTfJQkseSfKb7PllJ0pSM44z+g8CpZcu/BXysqvYC3wFuHMMxJEkj6hX0SXYDVwO3dcsB3g7c1XU5Clzb5xiSpH76ntH/DvAh4J+75R8AnquqxW75LLCr5zEkST1sG3XDJO8EzlXViSSDF5tX6VrfY/uDwEGAubk5hsMhCwsLDIfDUUtqluOy0kaOyaF9i2t3mhFz2/vX2+J7bat/hkYOeuAK4F1Jfg54FfAals7wdybZ1p3V7waeXm3jqjoCHAGYn5+vwWDAcDhkMBj0KKlNjstKGzkmNxy+d0OOMw6H9i1y68k+H2s4c/1gPMXMkK3+GRp56qaqPlJVu6tqD3Ad8CdVdT3wReDdXbcDwN29q5QkjWwS19F/GPi1JKdZmrO/fQLHkCSdp36/43WqaggMu9ePA5ePY7+SpP68M1aSGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1biyXV0qzbM8murN1FvQdrzO3XD2mSjQuntFLUuMMeklqnEEvSY1zjl7SWPWZ43d+fzI8o5ekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1buSgT3JJki8mOZXk0SQf7Npfn+T+JI91P183vnIlSevV54x+EThUVT8CvBW4KcmPAoeBB6pqL/BAtyxJmpKRg76qnqmqh7vXfwecAnYB1wBHu25HgWv7FilJGl2qqv9Okj3Al4A3A09U1c5l675TVSumb5IcBA4CzM3NveXYsWMsLCywY8eO3vW0xnFZaT1jcvKp5ydczeyY2w7PfnfaVYxu367XTmS/rX6G9u/ff6Kq5tfq1zvok+wA/hT4aFV9Lslz5xP0y83Pz9fx48cZDocMBoNe9bTIcVlpPWOylb545NC+RW49uXmfVTiph5q1+hlKcl5B3+uqmySvAD4L3FFVn+uan01ycbf+YuBcn2NIkvrpc9VNgNuBU1X128tW3QMc6F4fAO4evTxJUl99fse7AngfcDLJV7q2XwduAe5MciPwBPCefiVKK6dfDu1b5IYtNCUj9TFy0FfVnwH5HquvHHW/kqTx8s5YSWqcQS9JjTPoJalxBr0kNW7z3lkhqTl9bm6b1M1WLTDotWG20h2q0ixx6kaSGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zssrJW15fS/9nfVr+D2jl6TGeUYvqQkvd1a+1b+/wKCXpJ5m/dENBr3WxccYSJuPc/SS1LiJndEnuQr4OHABcFtV3TKpY201rV8hIGm8JhL0SS4A/jvw08BZ4C+S3FNVX5/E8bQ+Tr9IW8ukzugvB05X1eMASY4B1wBjD/ppnt3O+h9gJAkmN0e/C3hy2fLZrk2StMFSVePfafIe4Ger6j92y+8DLq+qDyzrcxA42C2+CfhL4CLgb8Ze0ObnuKzkmKzOcVldq+Pyr6rqDWt1mtTUzVngkmXLu4Gnl3eoqiPAkeVtSY5X1fyEatq0HJeVHJPVOS6r2+rjMqmpm78A9ia5NMkrgeuAeyZ0LEnSy5jIGX1VLSb5T8AfsXR55Ser6tFJHEuS9PImdh19Vd0H3LfOzY6s3WVLclxWckxW57isbkuPy0T+GCtJmh0+AkGSGjf1oE9ySZIvJjmV5NEkH5x2TbMgyauS/HmSr3bj8pvTrmmWJLkgyZeT/OG0a5kVSc4kOZnkK0mOT7ueWZFkZ5K7knyjy5mfmHZNG20Wnl65CByqqoeT/AvgRJL7fVwC/wC8vaoWkrwC+LMk/6eqHpx2YTPig8Ap4DXTLmTG7K+qFq8X7+PjwBeq6t3dVYCvnnZBG23qZ/RV9UxVPdy9/juWPrxb/i7aWrLQLb6i++cfVIAku4GrgdumXYtmW5LXAG8Dbgeoqn+squemW9XGm3rQL5dkD/DjwEPTrWQ2dNMTXwHOAfdXleOy5HeADwH/PO1CZkwBf5zkRHfnueAHgW8Bv9dN9d2W5MJpF7XRZibok+wAPgv8alX97bTrmQVV9U9V9e9YurP48iRvnnZN05bkncC5qjox7Vpm0BVVdRnwDuCmJG+bdkEzYBtwGfCJqvpx4AXg8HRL2ngzEfTdHPRngTuq6nPTrmfWdL9qDoGrplzKLLgCeFeSM8Ax4O1J/mC6Jc2Gqnq6+3kO+DxLT5Hd6s4CZ5f9NnwXS8G/pUw96JOEpfmzU1X129OuZ1YkeUOSnd3r7cBPAd+YblXTV1UfqardVbWHpUdr/ElV/cKUy5q6JBd2FzPQTU38DPDIdKuavqr6JvBkkjd1TVcygcelz7pZuOrmCuB9wMluPhrg17s7a7eyi4Gj3Ze4fB9wZ1V5KaG+lzng80vnTWwD/ldVfWG6Jc2MDwB3dFfcPA68f8r1bDjvjJWkxk196kaSNFkGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9Jjfv/ylYTKyh9oSQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['LoanAmount_log'] = np.log(train['LoanAmount'])\n",
    "train['LoanAmount_log'].hist(bins=20)\n",
    "test['LoanAmount_log'] = np.log(test['LoanAmount'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the distribution looks much closer to normal and effect of extreme values has been significantly subsided. Let’s build a logistic regression model and make predictions for the test dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bivariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Categorical Independent Variable vs Target Variable\n",
    "First of all we will find the relation between target variable and categorical independent variables. Let us look at the stacked bar plot now which will give us the proportion of approved and unapproved loans."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(614, 14)"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(614, 14)"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.dropna()\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2cd96dcc8d0>"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Gender=pd.crosstab(train['Gender'],train['Loan_Status'])\n",
    "Gender.div(Gender.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True, figsize=(4,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be inferred that the proportion of male and female applicants is more or less same for both approved and unapproved loans."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let us visualize the remaining categorical variables vs target variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Married = pd.crosstab(train['Married'],train['Loan_Status'])\n",
    "Dependents = pd.crosstab(train['Dependents'],train['Loan_Status'])\n",
    "Education = pd.crosstab(train['Education'],train['Loan_Status'])\n",
    "Self_Employed = pd.crosstab(train['Self_Employed'],train['Loan_Status'])\n",
    "\n",
    "Married.div(Married.sum(1).astype(float),axis=0).plot(kind ='bar',stacked ='True', figsize =(4,4))\n",
    "plt.show()\n",
    "\n",
    "Dependents.div(Dependents.sum(1).astype(float),axis=0).plot(kind='bar',stacked='True',figsize=(4,4))\n",
    "plt.show()\n",
    "\n",
    "Education.div(Education.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True, figsize=(4,4))\n",
    "plt.show()\n",
    "\n",
    "Self_Employed.div(Self_Employed.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True, figsize=(4,4))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Proportion of married applicants is higher for the approved loans.\n",
    "* Distribution of applicants with 1 or 3+ dependents is similar across both the categories of Loan_Status.\n",
    "* There is nothing significant we can infer from Self_Employed vs Loan_Status plot.\n",
    "\n",
    "Now we will look at the relationship between remaining categorical independent variables and Loan_Status."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Credit_History=pd.crosstab(train['Credit_History'],train['Loan_Status'])\n",
    "Property_Area=pd.crosstab(train['Property_Area'],train['Loan_Status'])\n",
    "\n",
    "Credit_History.div(Credit_History.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True, figsize=(4,4))\n",
    "plt.show()\n",
    "\n",
    "Property_Area.div(Property_Area.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* It seems people with credit history as 1 are more likely to get their loans approved.\n",
    "* Proportion of loans getting approved in semiurban area is higher as compared to that in rural or urban areas.\n",
    "\n",
    "Now let’s visualize numerical independent variables with respect to target variable.\n",
    "\n",
    "### Numerical Independent Variable vs Target Variable\n",
    "We will try to find the mean income of people for which the loan has been approved vs the mean income of people for which the loan has not been approved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2cd96eae588>"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.groupby('Loan_Status')['ApplicantIncome'].mean().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here the y-axis represents the mean applicant income. We don’t see any change in the mean income. So, let’s make bins for the applicant income variable based on the values in it and analyze the corresponding loan status for each bin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins=[0,2500,4000,6000,81000]\n",
    "group=['HIgh','Average','Low', 'Very high']\n",
    "train['Income_bin']=pd.cut(train['ApplicantIncome'],bins,right=True,labels=group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Income_bin=pd.crosstab(train['Income_bin'],train['Loan_Status'])\n",
    "Income_bin.div(Income_bin.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True)\n",
    "plt.xlabel('ApplicantIncome')\n",
    "P = plt.ylabel('Percentage')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Percentage')"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Income_bin=pd.crosstab(train['Income_bin'],train['Loan_Status'])\n",
    "Income_bin.div(Income_bin.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True)\n",
    "plt.xlabel('ApplicantIncome')\n",
    "plt.ylabel('Percentage')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be inferred that Applicant income does not affect the chances of loan approval which contradicts our hypothesis in which we assumed that if the applicant income is high the chances of loan approval will also be high.\n",
    "\n",
    "We will analyze the coapplicant income and loan amount variable in similar manner."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins=[0,1000,3000,42000]\n",
    "group=['Low','Average','High']\n",
    "train['Coapplicant_Income_bin']=pd.cut(train['CoapplicantIncome'],bins,include_lowest=True,labels=group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Coapplicant_Income_bin=pd.crosstab(train['Coapplicant_Income_bin'],train['Loan_Status'])\n",
    "Coapplicant_Income_bin.div(Coapplicant_Income_bin.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True)\n",
    "plt.xlabel('CoapplicantIncome')\n",
    "P = plt.ylabel('Percentage')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It shows that if coapplicant’s income is less the chances of loan approval are high. But this does not look right. The possible reason behind this may be that most of the applicants don’t have any coapplicant so the coapplicant income for such applicants is 0 and hence the loan approval is not dependent on it. So we can make a new variable in which we will combine the applicant’s and coapplicant’s income to visualize the combined effect of income on loan approval.\n",
    "\n",
    "Let us combine the Applicant Income and Coapplicant Income and see the combined effect of Total Income on the Loan_Status."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Total_Income']=train['ApplicantIncome']+train['CoapplicantIncome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins=[0,2500,4000,6000,81000]\n",
    "group=['Very High','High','Low', 'Average']\n",
    "train['Total_Income_bin']=pd.cut(train['Total_Income'],bins,labels=group)\n",
    "Total_Income_bin=pd.crosstab(train['Total_Income_bin'],train['Loan_Status'])\n",
    "Total_Income_bin.div(Total_Income_bin.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True)\n",
    "plt.xlabel('Total_Income')\n",
    "P = plt.ylabel('Percentage')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that Proportion of loans getting approved for applicants having low Total_Income is very less as compared to that of applicants with Average, High and Very High Income.\n",
    "\n",
    "Let’s visualize the Loan amount variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins=[0,100,200,700]\n",
    "group=['Average','Low','High']\n",
    "train['LoanAmount_bin']=pd.cut(train['LoanAmount'],bins,labels=group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "LoanAmount_bin=pd.crosstab(train['LoanAmount_bin'],train['Loan_Status'])\n",
    "LoanAmount_bin.div(LoanAmount_bin.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True)\n",
    "plt.xlabel('LoanAmount')\n",
    "P = plt.ylabel('Percentage')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be seen that the proportion of approved loans is higher for Low and Average Loan Amount as compared to that of High Loan Amount which supports our hypothesis in which we considered that the chances of loan approval will be high when the loan amount is less.\n",
    "\n",
    "Let’s drop the bins which we created for the exploration part. We will change the 3+ in dependents variable to 3 to make it a numerical variable.We will also convert the target variable’s categories into 0 and 1 so that we can find its correlation with numerical variables. One more reason to do so is few models like logistic regression takes only numeric values as input. We will replace N with 0 and Y with 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=train.drop(['Income_bin', 'Coapplicant_Income_bin', 'LoanAmount_bin', 'Total_Income_bin', 'Total_Income'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Dependents'].replace('3+', 3,inplace=True)\n",
    "test['Dependents'].replace('3+', 3,inplace=True)\n",
    "train['Loan_Status'].replace('N', 0,inplace=True)\n",
    "train['Loan_Status'].replace('Y', 1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets look at the correlation between all the numerical variables. We will use the heat map to visualize the correlation. Heatmaps visualize data through variations in coloring. The variables with darker color means their correlation is more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matrix = train.corr()\n",
    "f, ax = plt.subplots(figsize=(9, 6))\n",
    "sns.heatmap(matrix, vmax=.8, square=True, cmap=\"BuPu\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the most correlated variables are (ApplicantIncome - LoanAmount) and (Credit_History - Loan_Status). LoanAmount is also correlated with CoapplicantIncome."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Building : Part I"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us make our first model to predict the target variable. We will start with Logistic Regression which is used for predicting binary outcome.\n",
    "\n",
    "Logistic Regression is a classification algorithm. It is used to predict a binary outcome (1 / 0, Yes / No, True / False) given a set of independent variables.\n",
    "Logistic regression is an estimation of Logit function. Logit function is simply a log of odds in favor of the event.\n",
    "This function creates a s-shaped curve with the probability estimate, which is very similar to the required step wise function\n",
    "\n",
    "Lets drop the Loan_ID variable as it do not have any effect on the loan status. We will do the same changes to the test dataset which we did for the training dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=train.drop('Loan_ID',axis=1)\n",
    "test=test.drop('Loan_ID',axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We will use scikit-learn (sklearn) for making different models which is an open source library for Python. It is one of the most efficient tool which contains many inbuilt functions that can be used for modeling in Python.\n",
    "\n",
    "* Sklearn requires the target variable in a separate dataset. So, we will drop our target variable from the train dataset and save it in another dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use scikit-learn (sklearn) for making different models which is an open source library for Python. It is one of the most efficient tool which contains many inbuilt functions that can be used for modeling in Python.\n",
    "\n",
    "To learn further about sklearn, refer here: http://scikit-learn.org/stable/tutorial/index.html\n",
    "\n",
    "Sklearn requires the target variable in a separate dataset. So, we will drop our target variable from the train dataset and save it in another dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train.drop('Loan_Status',1)\n",
    "y = train.Loan_Status"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will make dummy variables for the categorical variables. Dummy variable turns categorical variables into a series of 0 and 1, making them lot easier to quantify and compare. Let us understand the process of dummies first:\n",
    "\n",
    "* Consider the “Gender” variable. It has two classes, Male and Female.\n",
    "\n",
    "* As logistic regression takes only the numerical values as input, we have to change male and female into numerical value.\n",
    "\n",
    "* Once we apply dummies to this variable, it will convert the “Gender” variable into two variables(Gender_Male and Gender_Female), one for each class, i.e. Male and Female.\n",
    "\n",
    "* Gender_Male will have a value of 0 if the gender is Female and a value of 1 if the gender is Male."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=pd.get_dummies(X)\n",
    "train=pd.get_dummies(train)\n",
    "test=pd.get_dummies(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will train the model on training dataset and make predictions for the test dataset. But can we validate these predictions? One way of doing this is we can divide our train dataset into two parts: train and validation. We can train the model on this train part and using that make predictions for the validation part. In this way we can validate our predictions as we have the true predictions for the validation part (which we do not have for the test dataset).\n",
    "\n",
    "We will use the train_test_split function from sklearn to divide our train dataset. So, first let us import train_test_split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_cv, y_train, y_cv = train_test_split(X,y, test_size =0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset has been divided into training and validation part. Let us import LogisticRegression and accuracy_score from sklearn and fit the logistic regression model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, 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": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here the C parameter represents inverse of regularization strength. Regularization is applying a penalty to increasing the magnitude of parameter values in order to reduce overfitting. Smaller values of C specify stronger regularization. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s predict the Loan_Status for validation set and calculate its accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_cv = model.predict(x_cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us calculate how accurate our predictions are by calculating the accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8216216216216217"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_cv,pred_cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So our predictions are almost 75% accurate, i.e. we have identified 75% of the loan status correctly.\n",
    "\n",
    "Let’s make predictions for the test dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_test = model.predict(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets import the submission file which we have to submit on the solution checker."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission=pd.read_csv(\"Sample_Submission_ZAuTl8O_FK3zQHh.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We only need the Loan_ID and the corresponding Loan_Status for the final submission. we will fill these columns with the Loan_ID of test dataset and the predictions that we made, i.e., pred_test respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission['Loan_Status']=pred_test\n",
    "submission['Loan_ID']=test_original['Loan_ID']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression using stratified k-folds cross validation\n",
    "\n",
    "\n",
    "To check how robust our model is to unseen data, we can use Validation. It is a technique which involves reserving a particular sample of a dataset on which you do not train the model. Later, you test your model on this sample before finalizing it. Some of the common methods for validation are listed below:\n",
    "\n",
    "* The validation set approach\n",
    "* k-fold cross validation\n",
    "* Leave one out cross validation (LOOCV)\n",
    "* Stratified k-fold cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we will learn about stratified k-fold cross validation. Let us understand how it works:\n",
    "\n",
    "* Stratification is the process of rearranging the data so as to ensure that each fold is a good representative of the whole.\n",
    "* For example, in a binary classification problem where each class comprises of 50% of the data, it is best to arrange the data such that in every fold, each class comprises of about half the instances.\n",
    "* It is generally a better approach when dealing with both bias and variance.\n",
    "* A randomly selected fold might not adequately represent the minor class, particularly in cases where there is a huge class imbalance.\n",
    "\n",
    "#### Now let’s make a cross validation logistic model with stratified 5 folds and make predictions for test dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 of kfold 5\n",
      "accuracy_score 0.7983870967741935\n",
      "\n",
      "2 of kfold 5\n",
      "accuracy_score 0.8306451612903226\n",
      "\n",
      "3 of kfold 5\n",
      "accuracy_score 0.8114754098360656\n",
      "\n",
      "4 of kfold 5\n",
      "accuracy_score 0.7950819672131147\n",
      "\n",
      "5 of kfold 5\n",
      "accuracy_score 0.8278688524590164\n"
     ]
    }
   ],
   "source": [
    "i=1\n",
    "kf = StratifiedKFold(n_splits=5,random_state=1,shuffle=True)\n",
    "for train_index,test_index in kf.split(X,y):\n",
    "     print('\\n{} of kfold {}'.format(i,kf.n_splits))\n",
    "     xtr,xvl = X.loc[train_index],X.loc[test_index]\n",
    "     ytr,yvl = y[train_index],y[test_index]\n",
    "    \n",
    "     model = LogisticRegression(random_state=1)\n",
    "     model.fit(xtr, ytr)\n",
    "     pred_test = model.predict(xvl)\n",
    "     score = accuracy_score(yvl,pred_test)\n",
    "     print('accuracy_score',score)\n",
    "     i+=1\n",
    "pred_test = model.predict(test)\n",
    "pred=model.predict_proba(xvl)[:,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### The mean validation accuracy for this model turns out to be 0.81. Let us visualize the roc curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "fpr, tpr, _ = metrics.roc_curve(yvl,  pred)\n",
    "auc = metrics.roc_auc_score(yvl, pred)\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.plot(fpr,tpr,label=\"validation, auc=\"+str(auc))\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.legend(loc=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We got an auc value of 0.77."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission['Loan_Status']=pred_test\n",
    "submission['Loan_ID']=test_original['Loan_ID']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember we need predictions in Y and N. So let’s convert 1 and 0 to Y and N."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission['Loan_Status'].replace(0, 'N',inplace=True)\n",
    "submission['Loan_Status'].replace(1, 'Y',inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets convert the submission to .csv format and make submission to check the accuracy on the leaderboard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(submission, columns=['Loan_ID','Loan_Status']).to_csv('Logistic.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this submission we got an accuracy of 0.78472 on the leaderboard. Now we will try to improve this accuracy using different approaches."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the domain knowledge, we can come up with new features that might affect the target variable. We will create the following three new features:\n",
    "\n",
    "* Total Income - As discussed during bivariate analysis we will combine the Applicant Income and Coapplicant Income. If the total income is high, chances of loan approval might also be high.\n",
    "* EMI - EMI is the monthly amount to be paid by the applicant to repay the loan. Idea behind making this variable is that people who have high EMI’s might find it difficult to pay back the loan. We can calculate the EMI by taking the ratio of loan amount with respect to loan amount term.\n",
    "* Balance Income - This is the income left after the EMI has been paid. Idea behind creating this variable is that if this value is high, the chances are high that a person will repay the loan and hence increasing the chances of loan approval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Total_Income']=train['ApplicantIncome']+train['CoapplicantIncome']\n",
    "test['Total_Income']=test['ApplicantIncome']+test['CoapplicantIncome']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s check the distribution of Total Income."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train['Total_Income']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see it is shifted towards left, i.e., the distribution is right skewed. So, let’s take the log transformation to make the distribution normal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Total_Income_log'] = np.log(train['Total_Income'])\n",
    "sns.distplot(train['Total_Income_log']);\n",
    "test['Total_Income_log'] = np.log(test['Total_Income'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the distribution looks much closer to normal and effect of extreme values has been significantly subsided. Let’s create the EMI feature now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['EMI']=train['LoanAmount']/train['Loan_Amount_Term']\n",
    "test['EMI']=test['LoanAmount']/test['Loan_Amount_Term']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s check the distribution of EMI variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train['EMI']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us create Balance Income feature now and check its distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Balance Income']=train['Total_Income']-(train['EMI']*1000) # Multiply with 1000 to make the units equal \n",
    "test['Balance Income']=test['Total_Income']-(test['EMI']*1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train['Balance Income']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us now drop the variables which we used to create these new features. Reason for doing this is, the correlation between those old features and these new features will be very high and logistic regression assumes that the variables are not highly correlated. We also wants to remove the noise from the dataset, so removing correlated features will help in reducing the noise too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=train.drop(['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', 'Loan_Amount_Term'], axis=1)\n",
    "test=test.drop(['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', 'Loan_Amount_Term'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After creating new features, we can continue the model building process. So we will start with logistic regression model and then move over to more complex models like RandomForest and XGBoost.\n",
    "\n",
    "We will build the following models in this section.\n",
    "\n",
    "* Logistic Regression\n",
    "* Decision Tree\n",
    "* Random Forest\n",
    "* XGBoost\n",
    "Let’s prepare the data for feeding into the models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train.drop('Loan_Status',1)\n",
    "y = train.Loan_Status        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 of kfold 5\n",
      "accuracy_score 0.7983870967741935\n",
      "\n",
      "2 of kfold 5\n",
      "accuracy_score 0.8225806451612904\n",
      "\n",
      "3 of kfold 5\n",
      "accuracy_score 0.7786885245901639\n",
      "\n",
      "4 of kfold 5\n",
      "accuracy_score 0.7868852459016393\n",
      "\n",
      "5 of kfold 5\n",
      "accuracy_score 0.8278688524590164\n"
     ]
    }
   ],
   "source": [
    "i=1\n",
    "kf = StratifiedKFold(n_splits=5,random_state=1,shuffle=True)\n",
    "for train_index,test_index in kf.split(X,y):\n",
    "     print('\\n{} of kfold {}'.format(i,kf.n_splits))\n",
    "     xtr,xvl = X.loc[train_index],X.loc[test_index]\n",
    "     ytr,yvl = y[train_index],y[test_index]\n",
    "    \n",
    "     model = LogisticRegression(random_state=1)\n",
    "     model.fit(xtr, ytr)\n",
    "     pred_test = model.predict(xvl)\n",
    "     score = accuracy_score(yvl,pred_test)\n",
    "     print('accuracy_score',score)\n",
    "     i+=1\n",
    "pred_test = model.predict(test)\n",
    "pred=model.predict_proba(xvl)[:,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mean validation accuracy for this model is 0.812"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission['Loan_Status']=pred_test            # filling Loan_Status with predictions\n",
    "submission['Loan_ID']=test_original['Loan_ID'] # filling Loan_ID with test Loan_ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replacing 0 and 1 with N and Y\n",
    "submission['Loan_Status'].replace(0, 'N',inplace=True)\n",
    "submission['Loan_Status'].replace(1, 'Y',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting submission file to .csv format\n",
    "pd.DataFrame(submission, columns=['Loan_ID','Loan_Status']).to_csv('Log2.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this submission we got an accuracy of 0.7847 on the leaderboard. So we can infer feature engineering has not improved the model. Let us look at some other algorithms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Decision tree is a type of supervised learning algorithm(having a pre-defined target variable) that is mostly used in classification problems. In this technique, we split the population or sample into two or more homogeneous sets(or sub-populations) based on most significant splitter / differentiator in input variables.\n",
    "\n",
    "Decision trees use multiple algorithms to decide to split a node in two or more sub-nodes. The creation of sub-nodes increases the homogeneity of resultant sub-nodes. In other words, we can say that purity of the node increases with respect to the target variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 of kfold 5\n",
      "accuracy_score 0.7258064516129032\n",
      "\n",
      "2 of kfold 5\n",
      "accuracy_score 0.7419354838709677\n",
      "\n",
      "3 of kfold 5\n",
      "accuracy_score 0.7049180327868853\n",
      "\n",
      "4 of kfold 5\n",
      "accuracy_score 0.680327868852459\n",
      "\n",
      "5 of kfold 5\n",
      "accuracy_score 0.7049180327868853\n"
     ]
    }
   ],
   "source": [
    "i=1\n",
    "kf = StratifiedKFold(n_splits=5,random_state=1,shuffle=True)\n",
    "for train_index,test_index in kf.split(X,y):\n",
    "     print('\\n{} of kfold {}'.format(i,kf.n_splits))\n",
    "     xtr,xvl = X.loc[train_index],X.loc[test_index]\n",
    "     ytr,yvl = y[train_index],y[test_index]\n",
    "    \n",
    "     model = tree.DecisionTreeClassifier(random_state=1)\n",
    "     model.fit(xtr, ytr)\n",
    "     pred_test = model.predict(xvl)\n",
    "     score = accuracy_score(yvl,pred_test)\n",
    "     print('accuracy_score',score)\n",
    "     i+=1\n",
    "pred_test = model.predict(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mean validation accuracy for this model is 0.69"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission['Loan_Status']=pred_test            # filling Loan_Status with predictions\n",
    "submission['Loan_ID']=test_original['Loan_ID'] # filling Loan_ID with test Loan_ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replacing 0 and 1 with N and Y\n",
    "submission['Loan_Status'].replace(0, 'N',inplace=True)\n",
    "submission['Loan_Status'].replace(1, 'Y',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting submission file to .csv format\n",
    "pd.DataFrame(submission, columns=['Loan_ID','Loan_Status']).to_csv('Decision Tree.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We got an accuracy of 0.63 which is much lesser than the accuracy from logistic regression model. So let’s build another model, i.e. Random Forest, a tree based ensemble algorithm and try to improve our model by improving the accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* RandomForest is a tree based bootstrapping algorithm wherein a certain no. of weak learners (decision trees) are combined to make a powerful prediction model.\n",
    "* For every individual learner, a random sample of rows and a few randomly chosen variables are used to build a decision tree model.\n",
    "* Final prediction can be a function of all the predictions made by the individual learners.\n",
    "* In case of regression problem, the final prediction can be mean of all the predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 of kfold 5\n",
      "accuracy_score 0.8225806451612904\n",
      "\n",
      "2 of kfold 5\n",
      "accuracy_score 0.8145161290322581\n",
      "\n",
      "3 of kfold 5\n",
      "accuracy_score 0.7377049180327869\n",
      "\n",
      "4 of kfold 5\n",
      "accuracy_score 0.7295081967213115\n",
      "\n",
      "5 of kfold 5\n",
      "accuracy_score 0.8114754098360656\n"
     ]
    }
   ],
   "source": [
    "i=1\n",
    "kf = StratifiedKFold(n_splits=5,random_state=1,shuffle=True)\n",
    "for train_index,test_index in kf.split(X,y):\n",
    "     print('\\n{} of kfold {}'.format(i,kf.n_splits))\n",
    "     xtr,xvl = X.loc[train_index],X.loc[test_index]\n",
    "     ytr,yvl = y[train_index],y[test_index]\n",
    "    \n",
    "     model = RandomForestClassifier(random_state=1, max_depth=10)\n",
    "     model.fit(xtr, ytr)\n",
    "     pred_test = model.predict(xvl)\n",
    "     score = accuracy_score(yvl,pred_test)\n",
    "     print('accuracy_score',score)\n",
    "     i+=1\n",
    "pred_test = model.predict(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mean validation accuracy for this model is 0.766\n",
    "\n",
    "We will try to improve the accuracy by tuning the hyperparameters for this model. We will use grid search to get the optimized values of hyper parameters. Grid-search is a way to select the best of a family of hyper parameters, parametrized by a grid of parameters.\n",
    "\n",
    "We will tune the max_depth and n_estimators parameters. max_depth decides the maximum depth of the tree and n_estimators decides the number of trees that will be used in random forest model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Provide range for max_depth from 1 to 20 with an interval of 2 and from 1 to 200 with an interval of 20 for n_estimators\n",
    "paramgrid = {'max_depth': list(range(1, 20, 2)), 'n_estimators': list(range(1, 200, 20))}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search=GridSearchCV(RandomForestClassifier(random_state=1),paramgrid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_cv, y_train, y_cv = train_test_split(X,y, test_size =0.3, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score='raise',\n",
       "       estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=1, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'max_depth': [1, 3, 5, 7, 9, 11, 13, 15, 17, 19], 'n_estimators': [1, 21, 41, 61, 81, 101, 121, 141, 161, 181]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the grid search model\n",
    "grid_search.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score='raise',\n",
       "       estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=1, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'max_depth': [1, 3, 5, 7, 9, 11, 13, 15, 17, 19], 'n_estimators': [1, 21, 41, 61, 81, 101, 121, 141, 161, 181]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GridSearchCV(cv=None, error_score='raise',\n",
    "       estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
    "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
    "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
    "            min_samples_leaf=1, min_samples_split=2,\n",
    "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
    "            oob_score=False, random_state=1, verbose=0, warm_start=False),\n",
    "       fit_params=None, iid=True, n_jobs=1,\n",
    "       param_grid={'max_depth': [1, 3, 5, 7, 9, 11, 13, 15, 17, 19], 'n_estimators': [1, 21, 41, 61, 81, 101, 121, 141, 161, 181]},\n",
    "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
    "       scoring=None, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=3, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=141, n_jobs=1,\n",
       "            oob_score=False, random_state=1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Estimating the optimized value\n",
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=3, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=41, n_jobs=1,\n",
       "            oob_score=False, random_state=1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
    "            max_depth=3, max_features='auto', max_leaf_nodes=None,\n",
    "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
    "            min_samples_leaf=1, min_samples_split=2,\n",
    "            min_weight_fraction_leaf=0.0, n_estimators=41, n_jobs=1,\n",
    "            oob_score=False, random_state=1, verbose=0, warm_start=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, the optimized value for the max_depth variable is 3 and for n_estimator is 41. Now let’s build the model using these optimized values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 of kfold 5\n",
      "accuracy_score 0.7983870967741935\n",
      "\n",
      "2 of kfold 5\n",
      "accuracy_score 0.8225806451612904\n",
      "\n",
      "3 of kfold 5\n",
      "accuracy_score 0.8032786885245902\n",
      "\n",
      "4 of kfold 5\n",
      "accuracy_score 0.7786885245901639\n",
      "\n",
      "5 of kfold 5\n",
      "accuracy_score 0.819672131147541\n"
     ]
    }
   ],
   "source": [
    "i=1\n",
    "kf = StratifiedKFold(n_splits=5,random_state=1,shuffle=True)\n",
    "for train_index,test_index in kf.split(X,y):\n",
    "     print('\\n{} of kfold {}'.format(i,kf.n_splits))\n",
    "     xtr,xvl = X.loc[train_index],X.loc[test_index]\n",
    "     ytr,yvl = y[train_index],y[test_index]\n",
    "    \n",
    "     model = RandomForestClassifier(random_state=1, max_depth=3, n_estimators=41)\n",
    "     model.fit(xtr, ytr)\n",
    "     pred_test = model.predict(xvl)\n",
    "     score = accuracy_score(yvl,pred_test)\n",
    "     print('accuracy_score',score)\n",
    "     i+=1\n",
    "pred_test = model.predict(test)\n",
    "pred2=model.predict_proba(test)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission['Loan_Status']=pred_test            # filling Loan_Status with predictions\n",
    "submission['Loan_ID']=test_original['Loan_ID'] # filling Loan_ID with test Loan_ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replacing 0 and 1 with N and Y\n",
    "submission['Loan_Status'].replace(0, 'N',inplace=True)\n",
    "submission['Loan_Status'].replace(1, 'Y',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting submission file to .csv format\n",
    "pd.DataFrame(submission, columns=['Loan_ID','Loan_Status']).to_csv('Random Forest.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We got an accuracy of 0.7638 from the random forest model on leaderboard.\n",
    "\n",
    "Let us find the feature importance now, i.e. which features are most important for this problem. We will use feature_importances_ attribute of sklearn to do so."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2cd96e73550>"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances=pd.Series(model.feature_importances_, index=X.columns)\n",
    "importances.plot(kind='barh', figsize=(12,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that Credit_History is the most important feature followed by Balance Income, Total Income, EMI. So, feature engineering helped us in predicting our target variable."
   ]
  },
  {
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}