[notebook_3]_algorithm_development_extended.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab 3: Model Development and Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we are going to be focusing on building a model. In lab 2 we explored feature engineering around our research question: \"What drives the loan amount requested by Kiva borrowers?\"\n",
    "\n",
    "Now, we are ready to transform our research question into a machine learning task. We will build an initial model that we will then iterate on.\n",
    "\n",
    "In this notebook we start by getting a benchmark of performance using a linear model.\n",
    "\n",
    "We then explore the impact on the performance when we predict using more complex algorithms:\n",
    "- linear regression model\n",
    "- decision tree model\n",
    "- random forest model\n",
    "\n",
    "Comparing performance across different models will help you understand the implications of model choice and how different models perform against each other.\n",
    "\n",
    "Let's get started! We will walk though the following steps:\n",
    "\n",
    "- split our data into train and test\n",
    "- do data cleaning to fill in any null values\n",
    "- test that the assumptions needed for a linear regression model hold true\n",
    "- run a linear regression model\n",
    "- run a decision tree model\n",
    "- run a random forest model \n",
    "- compare the results of all 3 models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **If you haven't already, please run through Notebook 1 & 2!**  You need the final ouput df_abridged.csv.\n",
    "\n",
    "As a reminder, we had 3 raw data files (loans_full, loans_detail, and loans_partner_details). In Notebook 1 module_1_exploratory_analysis_with_solutions, we merged these data files and created new variables to form the data frame \"df_module_1\", we exported it as df_module_1.csv. Then in module 2, we engineered several features and removed outliers and exported this to df_abridged.csv. We will use this dataframe now. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First step is to import the libraries we want to use. Notice there are many sklearn libraries to import, this is a\n",
    "suite of libraries that contain a range of machine learning tools. Today we are using various different sklearn packages. For more information on sklearn, check out their [diagram](http://scikit-learn.org/stable/tutorial/machine_learning_map/) for when and where to use difference machine learning methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "import numpy as np\n",
    "from ggplot import *\n",
    "import time\n",
    "import re\n",
    "import seaborn as sns\n",
    "plt.style.use('ggplot')\n",
    "%matplotlib inline\n",
    "\n",
    "# packages for checking assumptions\n",
    "from scipy import stats as stats\n",
    "\n",
    "# packages for linear regression and validation \n",
    "import statsmodels.formula.api as sm\n",
    "from sklearn import linear_model\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import explained_variance_score\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "# the command below means that the output of multiple commands in a cell will be output at once.\n",
    "\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "\n",
    "# the command below tells jupyter to display up to 80 columns, this keeps everything visible\n",
    "pd.set_option('display.max_columns', 80)\n",
    "pd.set_option('expand_frame_repr', True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get more information about a new modules you have imported, run the cell below. A window containing a description of the module will appear! This provides details on the module parameters and attributes and links for further references. Try it out!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 539,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "RandomForestRegressor?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can do this for any module you import!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we import our dataset from notebook 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Read in data\n",
    "#data_path = '~/intro_course_data_science_for_good/data/'\n",
    "data_path = '~/intro_to_machine_learning/data/'\n",
    "df = pd.read_csv(data_path+'df_abridged.csv', low_memory=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code below tells us the number of rows in our dataset, it is a good sanity check to make sure the dataframe is what is what we expect as the output of notebook 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "118199"
      ]
     },
     "execution_count": 490,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing Our Data Set for Modeling and Final Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get out dataset ready for modeling! The first step is restricting our dataframe to the explanatory features we want to use in our model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Let's first take a look at what features we have after our feature engineering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Unnamed: 0', 'activity', 'borrowers', 'description.texts.en',\n",
      "       'funded_amount', 'funded_date', 'loan_amount', 'loan_amount.1', 'name',\n",
      "       'partner_id', 'planned_expiration_date', 'posted_date', 'sector',\n",
      "       'status', 'tags',\n",
      "       'terms.loss_liability.currency_exchange_coverage_rate',\n",
      "       'terms.repayment_term', 'terms.scheduled_payments', 'translator.byline',\n",
      "       'use', 'borrower_count', 'partner_delinquency_rate',\n",
      "       'partner_loans_posted', 'partner_loans_at_risk_rate',\n",
      "       'partner_delinquency_rate.1', 'partner_status', 'partner_name',\n",
      "       'posted_datetime', 'planned_expiration_datetime', 'dispursal_datetime',\n",
      "       'posted_year', 'posted_month', 'time_to_fund', 'gender', 'kids',\n",
      "       'num_tags', 'tag_#Woman Owned Biz', 'tag_#Repeat Borrower',\n",
      "       'tag_#Schooling', 'parent', 'married', 'age_int', 'children_int',\n",
      "       'age_bucket', 'county', 'province', 'capital', 'pct_female',\n",
      "       'num_partner_countries', 'more_one_partner_country', 'top_partner_id',\n",
      "       'partner_dollar_amount', 'exploratory_partner', 'days_to_fund',\n",
      "       'hours_to_fund', 'top_partners'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df.columns[0:120])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code below reduces the dataframe to the feature columns we want to include, creates a copy of this df which is the **training array**, pulls out the feature that we are trying to predict and stores this as a separate **training results** array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# list of features that we think may be interested to include\n",
    "cols = df[['loan_amount', \n",
    "           'sector', \n",
    "           'partner_delinquency_rate',\n",
    "            'posted_year',\n",
    "           'posted_month',\n",
    "           'gender',\n",
    "           'num_tags',\n",
    "           'parent',\n",
    "           'tag_#Woman Owned Biz',\n",
    "           'province',\n",
    "           'county',\n",
    "           'age_int',\n",
    "           'tag_#Repeat Borrower',\n",
    "           'children_int',\n",
    "           'borrower_count',\n",
    "            'activity',\n",
    "          'more_one_partner_country',\n",
    "          'days_to_fund',\n",
    "          'terms.repayment_term',\n",
    "           'tag_#Schooling',\n",
    "           'married',\n",
    "           'age_bucket',\n",
    "           'capital',\n",
    "           'pct_female',\n",
    "           'exploratory_partner',\n",
    "           'partner_dollar_amount',\n",
    "           'top_partner_id',\n",
    "           'num_partner_countries',\n",
    "           'days_to_fund',\n",
    "       'hours_to_fund',\n",
    "           'top_partners'\n",
    "          ]]\n",
    "# create a copy of the dataframe\n",
    "df_a = cols.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition, there are some features which have a large range of possible responses. Instead of using each possible response as a features, we will just take the top 15. So example, lets look at the top activities:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Farming', 'Dairy', 'General Store', 'Fruits & Vegetables', 'Retail',\n",
       "       'Clothing Sales', 'Agriculture', 'Poultry', 'Cereals', 'Grocery Store',\n",
       "       'Tailoring', 'Motorcycle Transport', 'Food Stall', 'Charcoal Sales',\n",
       "       'Beauty Salon', 'Fish Selling', 'Food Production/Sales', 'Food',\n",
       "       'Used Clothing', 'Home Energy', 'Food Market', 'Livestock', 'Services',\n",
       "       'Cafe', 'Primary/secondary school costs', 'Butcher Shop',\n",
       "       'Animal Sales', 'Restaurant', 'Fuel/Firewood', 'Clothing'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 492,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_a['activity'].value_counts().index[0:30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Farming', 'Dairy', 'General Store', 'Retail', 'Clothing Sales',\n",
       "       'Fruits & Vegetables', 'Poultry', 'Cereals', 'Motorcycle Transport',\n",
       "       'Tailoring', 'Grocery Store', 'Agriculture', 'Food Stall',\n",
       "       'Beauty Salon', 'Charcoal Sales', 'Fish Selling', 'Used Clothing',\n",
       "       'Food Production/Sales', 'Food', 'Services', 'Food Market', 'Livestock',\n",
       "       'Butcher Shop', 'Higher education costs', 'Cafe', 'Animal Sales',\n",
       "       'Renewable Energy Products', 'Restaurant',\n",
       "       'Primary/secondary school costs', 'Fuel/Firewood'],\n",
       "      dtype='object', name='activity')"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['borrower_count'] == 1)].groupby('activity')['loan_amount'].sum().nlargest(30).index[:30]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sklearn models can only handle boolean or numeric variables, so we will need to one-hot encode\n",
    "some of the string variables that we have decided to include in our model.The following code takes the top 15 occurences for **activity, gender, province, country and sector** and creates hot coded features for both. \n",
    "\n",
    "What does one-hot encode mean? It means we are creating a binary explanatory feature for each category in the str. So, for example, when we one-hot encode activity we create a new column for Farming that is 1 or 0 depending on whether the loan was for a farming activity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cols_to_separate = ['activity','sector','county','province','gender', 'age_bucket','capital', 'top_partners']\n",
    "for col in cols_to_separate:\n",
    "    # Find the top ten responses\n",
    "    col_vals = df.groupby(col)['loan_amount'].sum().nlargest(8).index[:8]\n",
    "    for col_val in col_vals:\n",
    "        # for each response, create a new feature column and assign binary responses: 1 for yes and 0 for no\n",
    "        if col_val not in df_a.columns:\n",
    "            df_a[col+'_'+str(col_val)] = df_a[col].apply(lambda x: 1 if x == col_val else 0)\n",
    "    df_a = df_a.drop(col, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['loan_amount', 'partner_delinquency_rate', 'posted_year',\n",
      "       'posted_month', 'num_tags', 'parent', 'tag_#Woman Owned Biz', 'age_int',\n",
      "       'tag_#Repeat Borrower', 'children_int', 'borrower_count',\n",
      "       'more_one_partner_country', 'days_to_fund', 'terms.repayment_term',\n",
      "       'tag_#Schooling', 'married', 'pct_female', 'exploratory_partner',\n",
      "       'partner_dollar_amount', 'top_partner_id', 'num_partner_countries',\n",
      "       'days_to_fund', 'hours_to_fund', 'activity_Farming', 'activity_Dairy',\n",
      "       'activity_Agriculture', 'activity_General Store', 'activity_Retail',\n",
      "       'activity_Clothing Sales', 'activity_Fruits & Vegetables',\n",
      "       'activity_Poultry', 'sector_Agriculture', 'sector_Food',\n",
      "       'sector_Retail', 'sector_Services', 'sector_Clothing',\n",
      "       'sector_Transportation', 'sector_Health', 'sector_Education',\n",
      "       'county_nairobi', 'county_mombasa', 'county_bungoma', 'county_nakuru',\n",
      "       'county_trans nzoia', 'county_kiambu', 'county_kisii',\n",
      "       'county_kakamega', 'province_rift valley', 'province_Missing',\n",
      "       'province_coast', 'province_western', 'province_nyanza',\n",
      "       'province_nairobi', 'province_eastern', 'province_central',\n",
      "       'gender_Female', 'gender_Male', 'age_bucket_(30, 40]',\n",
      "       'age_bucket_(20, 30]', 'age_bucket_(40, 50]', 'age_bucket_(50, 60]',\n",
      "       'age_bucket_(60, 70]', 'age_bucket_(70, 80]', 'capital_Missing',\n",
      "       'capital_nairobi', 'capital_mombasa', 'capital_bungoma',\n",
      "       'capital_nakuru', 'capital_kitale', 'capital_kiambu', 'capital_kisii',\n",
      "       'top_partners_VisionFund Kenya', 'top_partners_One Acre Fund',\n",
      "       'top_partners_Juhudi Kilimo', 'top_partners_Yehu Microfinance Trust',\n",
      "       'top_partners_SMEP Microfinance Bank', 'top_partners_Other',\n",
      "       'top_partners_Faulu Kenya', 'top_partners_Milango Financial Services'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df_a.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_a.columns=df_a.columns.str.replace('#','')\n",
    "df_a.columns=df_a.columns.str.replace(' ','_')\n",
    "df_a.columns=df_a.columns.str.replace('&','')\n",
    "df_a.columns=df_a.columns.str.replace(')','')\n",
    "df_a.columns=df_a.columns.str.replace('(','')\n",
    "df_a.columns=df_a.columns.str.replace(\"`\",'')\n",
    "df_a.columns=df_a.columns.str.replace(\"-\",'')\n",
    "df_a.columns=df_a.columns.str.replace(\".\",'_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "63"
      ]
     },
     "execution_count": 420,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_a.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validating assumptions are held in our dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our first model we will use to map the relationship between x (our explanatory features) and the true Y is a linear regression. As you may remember from the theory, a linear regression is a fairly rigid model with strong assumptions. \n",
    "\n",
    "Namely, the following requirements must be met:\n",
    "\n",
    "**1) Linear relationship between x and Y**\n",
    "\n",
    "**2) Normal distribution of variables**\n",
    "\n",
    "**3) No autocorrelation (Independent variables)**\n",
    "\n",
    "**4) Homoscedasticity**\n",
    "\n",
    "**5) No multicollinearity**\n",
    "\n",
    "**6) Rule of thumb: at least 20 observations per independent variable in the analysis**\n",
    "\n",
    "In the section below, we check the validity of some of these assumptions before we run our model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking features for Multicollinearity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we get run our model, let's make sure we are not including unnecessary features. A great way to do this is using a correlation plot, because for both linear regression and decision trees we do not want to include correlated features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c30c668>"
      ]
     },
     "execution_count": 402,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1L/twctSeWRpXbQIoHxQfb6Ys/hU7r3efqD2lMpJSRAGgIX7bFqomVVNSHrVX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\nrn0VASUpuUBn9UdEdgLOVNXjuqj+S4HpqnpLO8sPJa761NZ5T5ClmhR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r79Ei8nfgBizz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\nAbYL7TculL1KqFNnYCamcPSncL+DgNcx9aHsrXUtFm9xMuY69W5o+6PCt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s1oTxrVjLbH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\nhGpt8A6610ahoGfAZKvbSkGCmkLTmZQDjdr8M5rZGqg69VkSVR0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prCpDUYV1L586ngoJ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HRwGG7IV/FSqQem+kKCvjkC+CXeDLkeIDIlCEEQPO15PZALHDCzQjHvSklKjzv7D+4DVgW2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11aa302b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the correlation matrix\n",
    "corrmat  = df_a.corr()\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(12, 9))\n",
    "\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "\n",
    "# Draw the heatmap using seaborn\n",
    "sns.heatmap(corrmat, cmap=cmap, vmax=.8, square=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Woah, there is a lot of information above! However, the plot also immediately tells us some features we should cut:\n",
    "\n",
    "- gender_female and gender_male are perfectly correlated. We only need one.\n",
    "- sector_agriculture and activity_farming are correlated. In fact, this is true of many of the sectors and activities, which makes sense because we are taking the top 10 for both, so any activity that makes the top 10 in loan volume will likely dominate the associated sector.\n",
    "- country_kiamby and province_central are very correlated. In fact this is true of other counties and provinces, like Nairobi county and Nairobi province. We should probably only include the province feature.\n",
    "- number of tags is very correlated with any given tag. This makes sense because most lenders only have one tag. We should remove this feature.\n",
    "\n",
    "\n",
    "This has taught us alot!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### When we build a correlation plot we are are checking for multicollinearity!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot above is a way to check for multicollinearity. Multicollinearity is a problem when we have highly correlated explanatory features, as this makes it difficult for the model to try to differentitae between each feature's influence on the explanatory feature. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the code below, instead of plotting the correlation matrix, we filter to only the top abs correlations that are not one. This will help us isolate the interactions that are most problematic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top Absolute Correlations\n",
      "county_kisii              capital_kisii                 1.000000\n",
      "gender_Female             gender_Male                   1.000000\n",
      "county_kiambu             capital_kiambu                1.000000\n",
      "province_Missing          capital_Missing               0.999975\n",
      "county_bungoma            capital_bungoma               0.999916\n",
      "province_nairobi          capital_nairobi               0.999894\n",
      "county_trans_nzoia        capital_kitale                0.999764\n",
      "county_nakuru             capital_nakuru                0.999447\n",
      "county_nairobi            capital_nairobi               0.996751\n",
      "                          province_nairobi              0.996644\n",
      "county_mombasa            capital_mombasa               0.995928\n",
      "more_one_partner_country  num_partner_countries         0.953421\n",
      "                          top_partners_One_Acre_Fund    0.936224\n",
      "borrower_count            top_partners_One_Acre_Fund    0.915627\n",
      "pct_female                gender_Female                 0.895398\n",
      "                          gender_Male                   0.895398\n",
      "exploratory_partner       top_partners_One_Acre_Fund    0.876236\n",
      "borrower_count            more_one_partner_country      0.857248\n",
      "num_partner_countries     top_partners_One_Acre_Fund    0.845234\n",
      "borrower_count            exploratory_partner           0.842337\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Sara/anaconda/lib/python3.6/site-packages/pandas/core/generic.py:2070: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  result = self.loc[tuple(slicer)]\n",
      "/Users/Sara/anaconda/lib/python3.6/site-packages/pandas/core/indexing.py:836: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  return self._getitem_lowerdim(tup)\n"
     ]
    }
   ],
   "source": [
    "def get_redundant_pairs(df):\n",
    "    '''Get diagonal and lower triangular pairs of correlation matrix'''\n",
    "    pairs_to_drop = set()\n",
    "    cols = df.columns\n",
    "    for i in range(0, df.shape[1]):\n",
    "        for j in range(0, i+1):\n",
    "            pairs_to_drop.add((cols[i], cols[j]))\n",
    "    return pairs_to_drop\n",
    "\n",
    "def get_top_abs_correlations(df, n=5):\n",
    "    au_corr = df.corr().abs().unstack()\n",
    "    labels_to_drop = get_redundant_pairs(df)\n",
    "    au_corr = au_corr.drop(labels=labels_to_drop).sort_values(ascending=False)\n",
    "    return au_corr[0:n]\n",
    "\n",
    "print(\"Top Absolute Correlations\")\n",
    "print(get_top_abs_correlations(df_a, 20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of course, not every correlation in this list will be meaningful. This list is useful since it provides a visual shortcut to see what relationships might be problematic for multicollinearity. However, we should still use our judgement and do further validation when we see two higly correlated features whose relationship is not intuitive.\n",
    "\n",
    "The list confirms our decision to remove county, activity and gender_male. This makes intuitive sense, let's also do soem additional exploratory analysis before we remove additional features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### tag_Woman_Owned_Biz correlated with posted year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at why the tag_Woman_Owned_Biz may be correlated with posted year. We can see in the chart below that the tag seems to be used more heavily in later years. This is probably a function of how the community of loan posters in KIVA has developed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c9dd3c8>"
      ]
     },
     "execution_count": 404,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11]),\n",
       " <a list of 12 Text xticklabel objects>)"
      ]
     },
     "execution_count": 404,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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d8fwEntBq99feyp9rVqPzVgLUnUP5+P4c886hludsCGHREMLPgYVTShNSSmPwbsTunl/1\nHgZCw7m6Kt4SnBtC+Dr+INeVKaW98YvdssAGubv0eymlP6SUDsRbrC/j9xUfxh9YerVuuW/jDyQs\nF/z++viU0q0ppSNSSqvjFZDtWpRxYfxeYG0bLI5vv/dyTtRXxsbj30O7IPj3uDpzAfBF/OGkpalr\nkeX9uDN+m6Y+SYzEe1++kbv5e6KxEnkh3mX4JbpopXXDw3hLfHjdfnoB308r4g2RjwIbpJR+kvxh\nvw/VlatHun1PrcHezN/1dQewZwjhfLwJejLehH4vRoUQTsAfGhiDd1uNyuNOAH4TQngEv1G6Bd6v\nu3N3FpxS+lcI4XTg5NwcfhK/gb0C8/rPe+In+M3tB/AHJbbA79nVak3P4I/q/hl/0nM8frB2dXDX\nrIw/qPNd/IGPnfHE2OrR4Pda3r4wKS/vp/nvm/B99FRKqXZhvQh/aOP8EMIxeB/6WfjXEf7Wja6U\nRs8AXw0hbJA/j8XvIfX28fNOy5enmZTX7R8ppScBQgjT8Xu7m3QnSL4POQE4NYTwJt7NdTB+wh+R\n/EnQs/Hj9Z9419cEGlpbTYwNIRyEt4z3ApZnXoW05Tmb79t+HlgphHAo3uLbFq/B99SZ+P3B0/Ln\nj+EPdl2W/Om5JfH7TTPxhLs1XuG+B291/SSE8A/81sA6+AXvDnybPwRcEvyLyXPy8pfAz43FgcPz\ncq/Ek+UqNPlOVkrp0eD3mc+pO8fG40npkl6s83xSSm+GEHbHny48ls67Im/BH844Gd9O9U8Yb4Bv\nw91TSg/WzxRCODWXdzP8utjMMsGfGgXfRnvm5f1P3TSX4E8jzqH5w2DdllK6L4RwPd4q+x6+bU/E\nK2UJvx4uBuwQQrgF77KvPdjV3evjO3r1mqzkj3qObxj8M/yezA34I9cX4jfM34sL8YP6XnzDbl/b\niSmlK/Cb7AfiJ8IBwF65m667DsN35Hn4vYpV8Uf3H+1spmaSP9b7XfyBkQfxWu0eKaVaLWc3vIvl\nAbwbchrelbNWN0N8B6/NXwvcj1+ctsn3AXusG+XtC5Pw2m/t4Zi/4q2Dd1ov+WQdjT+5dQfeTfVn\nmtemu+M0/HHhq/Ba+Tb4tlsuhPCRni6sm+W7He9emVw3bDKeeG7pQbhD8fsi5+L7eHXgi8m/lAt+\njF+J19qvw++FtHpwo+Y0fBvchyfYrdK8r4F0dc4a3s17c17HN/Huvx5JKf0fvg3XzuU4L8eqvdBh\nAp6MfoFf5L4D7JBSmppr7QfjSWAqfn/0eymlP+Vjfwz+wMGNeJL7e17HOSml+3N598K7pH8F/Cyl\ndG6Lou6e1/NKvMWzKP4I/ostpu+xlNJf8BbKQbni1Wq6DvK9LZp/N+1R5n11p97lwAw6/87aFPyr\nOc/ix9km+LW1/n7i83hl97bUN9812xmvgFyD34YYhn8B/bXkX5U6Fk/gD+EP1+yHn1PdvT6+o02/\nfC0iIo2CP2V8aspPGQ8Wve1+FBGRAuX7mOvjvUG/7WLyAUdJTURE6u2Pd3t+Kz+qP6io+1FERIqh\n31MTEZFiKKmJiEgxSrunpr5UEZHe6e0LCgaU0pIaM2b0/CsV7e3tvZqvtxRP8QZqvJLXTfE6n68U\n6n4UEZFiKKmJiEgxlNRERKQYSmoiIlIMJTURESmGkpqIiBRDSU1ERIqhpCYiIsUo7svXIiL9ac6e\nY1qOm97JfAtNvLLvCyNqqYmISDmU1EREpBhKaiIiUgwlNRERKYaSmoiIFENJTUREiqGkJiIixVBS\nExGRYiipiYhIMZTURESkGEpqIiJSDCU1EREphpKaiIgUQ0lNRESKoaQmIiLFUFITEZFiKKmJiEgx\nlNRERKQYSmoiIlIMJTURESmGkpqIiBRDSU1ERIqhpCYiIsVQUhMRkWIoqYmISDGGV7VgMxsB/BpY\nAZgD7AnMBs4DOoAHgXExxrlmdjSwdR6/f4zxdjP7ZLNpqyqviIgMflW21LYChscY1wN+BBwHnAwc\nGWPcAGgDxprZmsBGwDrAjsCZef75pq2wrCIiUoAqk9pUYLiZDQOWBN4G1gIm5/GTgM2B9YHrY4wd\nMcan8zzLtphWRESkpcq6H4FZeNfjI8AywDbAhjHGjjz+VWApPOHNrJuvNrytybQiIiItVZnUDgCu\nizEeZmYfBf4ELFw3fgngJeCV/Llx+Nwmw7rU3t7eq8L2dr7eUjzFG6jxSl63KuJNHyDlqHq5g0WV\nSe2feJcjwIvACOAeM9s4xngTsCVwI/AYcIKZnQR8BBgWY3zBzJpN26UZM2b0uKDt7e29mq+3FE/x\nBmq8ktdtQcTrTBXl6O36lZQIq0xqpwDnmNkUvIV2OHAnMNHMFgYeBi6NMc7J09yG3+Mbl+c/qHHa\nCssqIiIFqCypxRhnAdZk1EZNph0PjG8YNrXZtCIiIq3oy9ciIlIMJTURESmGkpqIiBRDSU1ERIqh\npCYiIsVQUhMRkWIoqYmISDGU1EREpBhKaiIiUgwlNRERKYaSmoiIFENJTUREiqGkJiIixVBSExGR\nYiipiYhIMZTURESkGEpqIiJSDCU1EREphpKaiIgUQ0lNRESKoaQmIiLFUFITEZFiKKmJiEgxlNRE\nRKQYSmoiIlIMJTURESmGkpqIiBRDSU1ERIqhpCYiIsVQUhMRkWIoqYmISDGU1EREpBhKaiIiUozh\nC7oAIiJ9ac6eY1qOm97JfAtNvLLvCyP9Ti01EREphpKaiIgUQ0lNRESKUek9NTM7DBgDLAxMACYD\n5wEdwIPAuBjjXDM7GtgamA3sH2O83cw+2WzaKssrIiKDW2UtNTPbGFgP+DywEfBR4GTgyBjjBkAb\nMNbM1szj1wF2BM7Mi5hv2qrKKiIiZaiy+3E08ABwBXAVcDWwFt5aA5gEbA6sD1wfY+yIMT4NDDez\nZVtMKyIi0lKV3Y/LAB8DtgE+DlwJDIsxduTxrwJLAUsCM+vmqw1vazJtl9rb23tV2N7O11uKp3gD\nNd5gX7fOHtuvohz9HW9BLXewqDKpzQQeiTG+BSQzewPvgqxZAngJeCV/bhw+t8mwLs2YMaPHBW1v\nb+/VfL2leIo3UOOVvG5d6e9yVBGvt9uzpERYZffjzcAXzazNzNqBxYE/5nttAFsCU4BbgNFmNszM\nlsdbcy8A9zSZVkREpKXKklqM8WrgHuB2/J7aOOAg4Bgzuw1/IvLSGONdeMK6DbgsT0ezaasqq4iI\nlKHSR/pjjIc2GbxRk+nGA+Mbhk1tNq2IiEgr+vK1iIgUQ0lNRESKoaQmIiLFUFITEZFiKKmJiEgx\nlNRERKQYSmoiIlIMJTURESmGkpqIiBRDSU1ERIqhpCYiIsVQUhMRkWIoqYmISDGU1EREpBhKaiIi\nUgwlNRERKYaSmoiIFENJTUREiqGkJiIixVBSExGRYiipiYhIMZTURESkGEpqIiJSDCU1EREphpKa\niIgUQ0lNRESKoaQmIiLFUFITEZFiKKmJiEgxlNRERKQYSmoiIlIMJTURESmGkpqIiBRDSU1ERIqh\npCYiIsVQUhMRkWIMrzqAmS0H3AVsAcwGzgM6gAeBcTHGuWZ2NLB1Hr9/jPF2M/tks2mrLq+IiAxe\nlbbUzGwE8Evg9TzoZODIGOMGQBsw1szWBDYC1gF2BM5sNW2VZRURkcGv6u7Hk4BfADPy32sBk/Pn\nScDmwPrA9THGjhjj08BwM1u2xbQiIiItVdb9aGa7Ac/HGK8zs8Py4LYYY0f+/CqwFLAkMLNu1trw\nZtN2qb29vVfl7e18vaV4ijdQ4w32dZvez+Xo73gLarmDRZX31HYHOsxsc2AUcD6wXN34JYCXgFfy\n58bhc5sM69KMGTO6nqhBe3t7r+brLcVTvIEar+R160p/l6OKeL3dniUlwsq6H2OMG8YYN4oxbgzc\nC+wKTDKzjfMkWwJTgFuA0WY2zMyWB4bFGF8A7mkyrYiISEv9/Uj/QcAxZnYbsDBwaYzxLjxh3QZc\nBoxrNW0/l1VERAaZyh/pB8ittZqNmowfD4xvGDa12bQiIiKt6MvXIiJSDCU1EREphpKaiIgUQ0lN\nRESKoaQmIiLF6JenH0VEpG/M2XNMy3Gdvd1koYlX9n1hBiC11EREpBhKaiIiUgwlNRERKYaSmoiI\nFENJTUREiqGkJiIixVBSExGRYiipiYhIMZTURESkGEpqIiJSDCU1EREphpKaiIgUQ0lNRESKoaQm\nIiLFUFITEZFiKKmJiEgxlNRERKQYSmoiIlIMJTURESmGkpqIiBRDSU1ERIqhpCYiIsUYvqALICJl\nm7PnmJbjpncy30ITr+z7wkjx1FITEZFiKKmJiEgxlNRERKQYSmoiIlIMJTURESmGkpqIiBRDSU1E\nRIqhpCYiIsWo7MvXZjYCOAdYAVgE+DHwN+A8oAN4EBgXY5xrZkcDWwOzgf1jjLeb2SebTVtVeUVE\nZPCrsqW2CzAzxrgB8EXgDOBk4Mg8rA0Ya2ZrAhsB6wA7Amfm+eebtsKyiohIAapMar8Fjsqf2/BW\n2FrA5DxsErA5sD5wfYyxI8b4NDDczJZtMa2IiEhLlXU/xhhnAZjZEsClwJHASTHGjjzJq8BSwJLA\nzLpZa8Pbmkzbpfb29l6Vt7fz9ZbiKd5AjdfXsTp7v2MV5VC8vo032FT6QmMz+yhwBTAhxnixmZ1Q\nN3oJ4CXglfy5cfjcJsO6NGPGjB6Xs729vVfz9ZbiKd5Ajdff69aZ/i7HUI5XUsKrrPvRzD4IXA98\nP8Z4Th58j5ltnD9vCUwBbgFGm9kwM1seGBZjfKHFtCIiIi1V2VI7HFgaOMrMavfW9gNOM7OFgYeB\nS2OMc8xsCnAbnmTH5WkPAibWT1thWUVEpABV3lPbD09ijTZqMu14YHzDsKnNphUREWlFX74WEZFi\nKKmJiEgxlNRERKQYSmoiIlIMJTURESmGkpqIiBRDSU1ERIqhpCYiIsVQUhMRkWIoqYmISDGU1ERE\npBhKaiIiUgwlNRERKYaSmoiIFENJTUREiqGkJiIixVBSExGRYiipiYhIMZTURESkGEpqIiJSDCU1\nEREphpKaiIgUQ0lNRESKoaQmIiLFUFITEZFiKKmJiEgxlNRERKQYSmoiIlIMJTURESnG8AVdABHp\nX3P2HNNy3PRO5lto4pV9XxiRPqaWmoiIFENJTUREiqGkJiIixVBSExGRYiipiYhIMZTURESkGAP6\nkX4zGwZMAEYCbwJ7xBgfW7ClEhGRgWqgt9S+DCwaY1wX+AHwswVcHhERGcAGelJbH7gWIMb4F2Dt\nBVscEREZyNo6OjoWdBlaMrOzgctijJPy308DK8YYZ7eYZeCujIjIwNa2oAvQFwb0PTXgFWCJur+H\ndZLQoJCdIiIivTPQux9vAbYCMLPPAQ8s2OKIiMhANtBbalcAW5jZrXgr7JsLuDwiIjKADeh7aiIi\nIj0x0LsfRUREuk1JTUREiqGkJiIixVBSExGRYiipiYhIMQb6I/19zsw+DqwM3IS/T3It4CHg+Bjj\nyxXEuxjYP8b4XF8vu5OYWwNv4+t4MvB+4PAY49MVxdsZf6XZ4sALwA0xxmsrirUo8B1gM2Ap4CVg\nCnBGjPH1KmL2FzP7FPAT4HXgmBjjo3n4WTHGvSuItziwB74N/wRcAMwBvhtjTH0dr0n8i2OMO1e4\n/O/FGE83sw8BpwOjgLuA/WKM/6gg3tpAAK7D31Nbu7YcUsW5l7/qtEeM8W99vezBbMglNeB84Cjg\nVGA6cCSwIXAxsHUF8dYFrjWz04HzYoyVfociv1psUfxNLMfgF6oZwERgdAXxTgVeBq4EvpQ/b2Vm\nn48xHtXX8YBzgXuBI4BX8fXcEt9/2/ZlIDM7vtW4GOPhfRkr+2/gv4ARwO/MbJcY4z14JawKF+Lb\ncjX8nPg2MAs4A9iir4Pl19zVrjltwAfM7FmgI8bY3tfx8OPhdPxcvwLYFdgcOBs/Vvva6cBewJnA\n1cB+wEb4NWfjCuItDfzKzK4HTooxvlpBjEFnKCa1OTHGm8zsiBjjXnnYvWZmFcWbhp9cxwD355bb\nJOCJGOMrFcRbKca4oZm1AQ/FGCcAmNl+FcQCGBVj3Ch/vtbMbogxbmFmN1cUrz3GuFPDsPvNbEoF\nsZ4D9gaziQFNAAAHVElEQVSOo59ewRZjvB7AzB4DLjezL1LdO00/EGM8Jv/E0wMxxj/m2FXdlvg6\ncACwd4zxWTO7Mca4SUWx6n0wxnhx/nyVmR1QUZy3YowPmNlSMcYL8rD/NbPvVxTvWeALwL7AHWY2\nmXnXlvsrijngDcWk9pKZbQ9cY2a7Alfhr+L6V0XxOmKMLwH7mdmywPZ4rXglvIbc10aY2WhgGeCD\nZrYy3qIZUUEsgEXNbJ0Y41/NbANgtpktjXdFVuGNvN+uxVuFS+D7b1ZfB4ox/jx3Kc2IMf6hr5ff\nxGwz+xLw+xhjMrN98Bp/VfvubTP7WozxIjMbCWBmG1PRvfYY42Qzexz4bzM7iepfQL5a7kkYYWab\n4t3xX6kw3jQzOxj4vZkdjfdebIUnnyq05Xfhnpx7gjbP/75FNS3RQWEoJrU9gROAzwMr4PeAbsbv\nLVThnb77GOPzwFn5X1W+AxwN3A2MAyYDM/H1rsLewC/N7CPA48DuwG544q7CzsAP8a6dJfCXXt8C\nfKOieHvg3bn9YXfgWHx9Xowx3mhm+wOnVBRvF+D7wEV1LwrfAT+GKhFj/L9cqTwD+HBVcbKVgDWB\nZ/BK1mJ4UqvqdXt7A4cA2+GVyi/i93ururbcW/sQY3wbb6VNqijWoDFkX5NlZsuQHzSIMc7sh3j/\nXhfvxX6M93I/rV9te/6zP9ZPBj8z+3CMsapWjAxRQy6pmdln8Bu5CzHvQYNhwLgY460VxFsbmJDj\nzcrx2iqMV79+s4D34ev33RjjbRXHexVYEl+/SuKJiHRmKHY/ngJ8JcY4vTbAzJYHfgusU0G8n/dz\nvP5ev36NZ2b341079dqo4Am6/oyleIo30OMNFkMxqY2ovwBn06nuprXi9a3tgN8AG/bD99L6M5bi\nKd5AjzcoDMWkdo2Z/QG4Hn96bkn8sdjfK97AjxdjfCw/0bZJVTEWRCzFU7yBHm+wGHL31ADMbA38\nDRi1p+dujTHerXiDI56ISCtDsaUGsDz+OpulgH8C/zCzeyp824fi9SEzG4t/H6f+NVmXVhGvP2Mp\nnuIN9HiDwZBLamZ2Jv404CTe/Zql0VTwfRLFG7zxSl43xVO8YnV0dAypfzvssMPkFsNvUTzFGyrr\npniKV+q/ofjTM8Py65zeYWYb4m+1VzzFW1CxFE/xBnq8QWHIdT/ir3A6Ob9YuA2YC9wDfE/xFG8B\nxlI8xRvo8QaFodhSWwX/XaW3gINjjMvHGMfiP0+heIq3oGIpnuIN9HiDwlBMakcAI4HPAnuZWe1F\nuFX9tIjiDd54Ja+b4ilekYZi9+Nb+adgao/D/sn8xwuregRW8QZvvJLXTfEUr0hDsaU2zcxONrPF\n8y/Fboe/kLeqXxdWvMEbr+R1UzzFK9JQTGq7A/eTazP5vYWbAFHxFG8BxlI8xRvo8QaFIfmaLBER\nKdNQbKmJiEihlNRERKQYSmoiPWRmP8xPm/V0vllmtkIFRRKRTElNpOc2BUYs6EKIyPyG4vfUZAgy\ns42BE4FngBWB1/HXDM3AH4MehT9FNgk4PMY428yOAbbF39gwM0+/HbA2cKKZzQGuAX4KbAQshL+m\naN8Y4yv5vXyn5+XeQReVSDNbLJdvnRjj1DzsBuCMXK5WcbYBDgcWBpYDfh1jPCqv86nAa8DiwGdj\njG/2agOKDBJqqclQsibwsxjj6sC5wAXAaXjCWg1PViOBg83so8D+wGdijGvjv+y9TozxTOBO4JAY\n4xXAD4DZwFoxxpF4kvyJmS0M/BY4KMa4BnAj8G+dFS7G+C/g1+SfDTGzT+C/U3d1J3HagIOAb+Ry\nfg44zMyWyYv9NLBTjHGkEpoMBUpqMpTcF2Ockj+fA6wB7AycEWPsyBf9X+C/SfUMcB9wt5mdBNwb\nY/xdk2VuA4wF7jGze4Ev4+/kWw14O8b4R4AY42/w37zqygRgVzMbAewFnB1jnNMqTv4xyC8Ba5nZ\n0cDJ+GuSFs/Lmx5jfKqb20dk0FNSk6Fkdt3ntrp/9YYBI2KMc/Guvt3wltwpZtbsRbELAfvFGEfF\nGEfh7+HbHu9ybFz27MaZG+Vux/vxBPY14OzO4pjZ4nhX5JrA3cAh+E+P1GLP6iqmSEmU1GQoGWVm\nq+fPewG3AP8DjDOzNjNbJA+/wcxGAg8CD8cY/ws4Be+aBE9OtQdFrgP2MbOFzWwYMBH4L+ABoM3M\ntgIwszHA0t0s55n4/b+/xhhndBHnU8CSwJExxqvwRLwIngRFhhwlNRlK/g4cZ2YP4N13Xwf2xR+u\neCD/S8BxMcb78NcN3Wlmd+KvJDogL+cq4KT8VvRjgWl4a+lveAvpoBjj2znGsbm7cDvguW6W82rg\nfXhXaE3TOHir7mrgETO7GxiTx3+yuxtFpCR6TZYMCflJwDNijJ9e0GXpipmth7fEPp3vmYlIN+mR\nfpF+ZGYB7/JsJgFvABsDuyqhifScWmoiIlIM3VMTEZFiKKmJiEgxlNRERKQYSmoiIlIMJTURESmG\nkpqIiBTj/wGlOCAum9UdeQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x133f74d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_a.groupby('posted_year')['tag_Woman_Owned_Biz'].sum().plot(kind=\"bar\", title='Number of loans from woman owned businesses on KIVA By Year')\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In fact, we can see for the plot below of total number of tags associated with a loan by posted year that this trend is not isolated to women owned business tags. We decide to remove number of tags."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x119990438>"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11]),\n",
       " <a list of 12 Text xticklabel objects>)"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pnQC8ADjZzE4u0z4OnGVmTwF/BY5w9yVmNhuYS3R9TixlJwMXmdnawB3A5e6+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\nJiIilfH/nFushkyYwlkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11987ce80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_a.groupby('posted_year')['num_tags'].sum().plot(kind=\"bar\", title='Number of loans from woman owned businesses on KIVA By Year')\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " We will go ahead and remove activitiy, county, gender_male, number_loans_posted, more_one_partner_country, num_tags."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['loan_amount', 'partner_delinquency_rate', 'posted_year',\n",
      "       'posted_month', 'num_tags', 'parent', 'tag_Woman_Owned_Biz', 'age_int',\n",
      "       'tag_Repeat_Borrower', 'children_int', 'borrower_count',\n",
      "       'more_one_partner_country', 'days_to_fund', 'terms.repayment_term',\n",
      "       'tag_Schooling', 'married', 'pct_female', 'exploratory_partner',\n",
      "       'partner_dollar_amount', 'top_partner_id', 'num_partner_countries',\n",
      "       'days_to_fund', 'hours_to_fund', 'activity_Farming', 'activity_Dairy',\n",
      "       'activity_Agriculture', 'activity_General_Store', 'activity_Retail',\n",
      "       'activity_Clothing_Sales', 'activity_Fruits__Vegetables',\n",
      "       'activity_Poultry', 'sector_Agriculture', 'sector_Food',\n",
      "       'sector_Retail', 'sector_Services', 'sector_Clothing',\n",
      "       'sector_Transportation', 'sector_Health', 'sector_Education',\n",
      "       'county_nairobi', 'county_mombasa', 'county_bungoma', 'county_nakuru',\n",
      "       'county_trans_nzoia', 'county_kiambu', 'county_kisii',\n",
      "       'county_kakamega', 'province_rift_valley', 'province_Missing',\n",
      "       'province_coast', 'province_western', 'province_nyanza',\n",
      "       'province_nairobi', 'province_eastern', 'province_central',\n",
      "       'gender_Female', 'gender_Male', 'age_bucket_30,_40]',\n",
      "       'age_bucket_20,_30]', 'age_bucket_40,_50]', 'age_bucket_50,_60]',\n",
      "       'age_bucket_60,_70]', 'age_bucket_70,_80]', 'capital_Missing',\n",
      "       'capital_nairobi', 'capital_mombasa', 'capital_bungoma',\n",
      "       'capital_nakuru', 'capital_kitale', 'capital_kiambu', 'capital_kisii',\n",
      "       'top_partners_VisionFund_Kenya', 'top_partners_One_Acre_Fund',\n",
      "       'top_partners_Juhudi_Kilimo', 'top_partners_Yehu_Microfinance_Trust',\n",
      "       'top_partners_SMEP_Microfinance_Bank', 'top_partners_Other',\n",
      "       'top_partners_Faulu_Kenya', 'top_partners_Milango_Financial_Services'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df_a.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_a=df_a.drop(['gender_Male', 'province_Missing',  'activity_Farming', 'activity_Dairy', 'activity_General_Store',\n",
    "       'activity_Retail', 'activity_Clothing_Sales',\n",
    "       'activity_Fruits__Vegetables', 'activity_Poultry', 'activity_Cereals',\n",
    "       'activity_Grocery_Store', 'activity_Tailoring', 'county_nairobi',\n",
    "       'county_mombasa', 'county_nakuru', 'county_kisii', 'county_trans_nzoia',\n",
    "       'county_kiambu', 'county_bungoma', 'county_kisumu', 'county_uasin',\n",
    "       'county_kilifi' ], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normality\n",
    "In Module 3, we reviewed that normal distribution of variables is an assumption underlying linear regression. Let's look first at our outcome feature, loan_amount."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We already know our data is skewed by outliers, however before we begin modeling let's quantify the degree to which it is skewed.\n",
    "\n",
    "[Scipy's stats package](https://docs.scipy.org/doc/scipy-0.19.0/reference/generated/scipy.stats.skew.html) allows you to conduct a skew test. A normally distributed dataset would return about 0. A skewness value of greater than 0 means there are more loans in the left tail of the distribution. Typically, a skew score of +-5 is deemed acceptable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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59BoAAABsH4hHWyEOBAKsEAMAAMD+gZgZYgAAAIzF9oGYGWIAAACMxfaBmBliAAAAjMW2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\naGqIRURERCTQ1BCLiIiISKD9ABxwrJkXNBnGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x125b49cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (-9223372036557126442)>"
      ]
     },
     "execution_count": 407,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(df_a, aes(x='loan_amount')) + geom_density() + xlim(0,10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.352856533783493\n"
     ]
    }
   ],
   "source": [
    "print(stats.skew(df_a.loan_amount, axis=None))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This confirms our suspicion that the data is skewed to the left. However, the degree to which it is skewed is not troubling. +-3 is fairly reasonable. In the cell below we describe a way to address this if you face a dataset with more severely skewed data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Additional context on how to deal with skewed data:\n",
    "\n",
    "\n",
    "One potential solution is to **log transform** your data. For a quick review of logarithms, look [here](https://www.mathsisfun.com/algebra/logarithms.html). \n",
    "\n",
    "Note that when we log transform data, we change our interpretation of the final regression output. Previously we had a simple linear equation... \n",
    "\n",
    "    y = a + bx \n",
    "\n",
    "This could be interpreted as, with every unit increase in x, we get a m increase in outcome feature y. \n",
    "\n",
    "Our interpretation of the regression depends upon what side of the equation we log transform:\n",
    "\n",
    "**log-level (transform the outcome variable):**\n",
    "\n",
    "Now we have a log equation: \n",
    "\n",
    "    log(y) = a + bx\n",
    "    \n",
    "This is less intuitive, as we have to solve for y. When we solve for y through exponentiation, we get the following equation: \n",
    "\n",
    "    y = 10^(mx + b)\n",
    "    y = 10^(mx) * 10^(b)\n",
    "\n",
    "The default base for log is 10. The takeaway here is that the impact x would have on y is much larger than linear - it is **exponentiated**. \n",
    "\n",
    "The model log(y) = mx + b is called a log-level equation because only the left hand side of the equation (the outcome variable) is logged. The interpretation is:\n",
    "\n",
    "“if we change x by 1 (unit), we’d expect our y variable to change by 100⋅β1 percent”\n",
    "\n",
    "\n",
    "**log-log (transform the outcome and the explanatory variable):**\n",
    "\n",
    "**What about if we need to log transform both sides of the data?**\n",
    "\n",
    "Often, we need to address skewed data in both the outcome variable and the explanatory features. This results in what we call a log-log model because both sides of the equation are logged.\n",
    "\n",
    "log(y) = a + blog(x)\n",
    "\n",
    "The interpretation of a log-log model is:\n",
    "\n",
    "“if we change x by one percent, we’d expect y to change by β1 percent”\n",
    "\n",
    "[This](http://www.cazaar.com/ta/econ113/interpreting-beta) website is a very thorough coverage of how to interpret your parameter b, after transforming the data through logging."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Final feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 500,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# final feature list\n",
    "cols = df_a[['loan_amount', 'partner_delinquency_rate', 'posted_year',\n",
    "       'posted_month', 'parent',\n",
    "       'tag_Repeat_Borrower', 'children_int', 'borrower_count', 'more_one_partner_country',\n",
    "       'terms_repayment_term', 'tag_Schooling', 'married', 'pct_female','exploratory_partner',\n",
    "      'sector_Agriculture', 'sector_Food', 'tag_Woman_Owned_Biz',\n",
    "       'sector_Retail', 'sector_Services', 'sector_Clothing',\n",
    "       'sector_Transportation', 'sector_Health', 'sector_Education', 'age_int', 'capital_nairobi', 'capital_mombasa', 'capital_bungoma',\n",
    "       'capital_nakuru', 'capital_kitale', 'capital_kiambu', 'capital_kisii', 'top_partners_VisionFund_Kenya',\n",
    "      'top_partners_One_Acre_Fund',\n",
    "       'top_partners_Juhudi_Kilimo', 'top_partners_Yehu_Microfinance_Trust',\n",
    "       'top_partners_SMEP_Microfinance_Bank', 'top_partners_Other',\n",
    "       'top_partners_Faulu_Kenya', 'top_partners_Milango_Financial_Services']]\n",
    "# create a copy of the dataframe\n",
    "rf_trainArr = cols.copy()\n",
    "rf_trainArr=rf_trainArr[rf_trainArr['posted_year']>2009]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking for null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values per column:\n",
      "loan_amount                                0\n",
      "partner_delinquency_rate                   0\n",
      "posted_year                                0\n",
      "posted_month                               0\n",
      "parent                                     0\n",
      "tag_Repeat_Borrower                        0\n",
      "children_int                               0\n",
      "borrower_count                             0\n",
      "more_one_partner_country                   0\n",
      "terms_repayment_term                       0\n",
      "tag_Schooling                              0\n",
      "married                                    0\n",
      "pct_female                                 0\n",
      "exploratory_partner                        0\n",
      "sector_Agriculture                         0\n",
      "sector_Food                                0\n",
      "tag_Woman_Owned_Biz                        0\n",
      "sector_Retail                              0\n",
      "sector_Services                            0\n",
      "sector_Clothing                            0\n",
      "sector_Transportation                      0\n",
      "sector_Health                              0\n",
      "sector_Education                           0\n",
      "age_int                                    0\n",
      "capital_nairobi                            0\n",
      "capital_mombasa                            0\n",
      "capital_bungoma                            0\n",
      "capital_nakuru                             0\n",
      "capital_kitale                             0\n",
      "capital_kiambu                             0\n",
      "capital_kisii                              0\n",
      "top_partners_VisionFund_Kenya              0\n",
      "top_partners_One_Acre_Fund                 0\n",
      "top_partners_Juhudi_Kilimo                 0\n",
      "top_partners_Yehu_Microfinance_Trust       0\n",
      "top_partners_SMEP_Microfinance_Bank        0\n",
      "top_partners_Other                         0\n",
      "top_partners_Faulu_Kenya                   0\n",
      "top_partners_Milango_Financial_Services    0\n",
      "dtype: int64\n",
      "\n",
      "Missing values per row:\n",
      "0    0\n",
      "1    0\n",
      "2    0\n",
      "3    0\n",
      "4    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Create a new function:\n",
    "def num_missing(x):\n",
    "  return sum(x.isnull())\n",
    "\n",
    "#Applying per column:\n",
    "print (\"Missing values per column:\")\n",
    "print (rf_trainArr.apply(num_missing, axis=0)) #axis=0 defines that function is to be applied on each column\n",
    "\n",
    "#Applying per row:\n",
    "print (\"\\nMissing values per row:\")\n",
    "print (rf_trainArr.apply(num_missing, axis=1).head()) #axis=1 defines that function is to be applied on each row"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no nulls! We did a good job in the prior notebook filling out all most of the explanatory fields with priors. If you did have nulls, the commented out code below would drop these rows. However, be aware that if you have many nulls in one field you should rather go back and fill with a good prior than lose many rows of valuable data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "###rf_trainArr = rf_trainArr.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splitting into train and test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To understand how well our model generalizes, we need to split our data into a test and training set. In order to get a true prediction, we can't show our model the test labels. Instead we just ask the model to score based upon the test explanatory variables.\n",
    "\n",
    "In order to get to this, we split the dataframe into a test and train."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### train and test dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## X_train, y_train, X_test, y_test\n",
    "\n",
    "However, we additionally need to seperate the explanatory features from our outcome feature.\n",
    "\n",
    "To do this we need to create four subsets of data:\n",
    "- X_train: the explanatory features to train the algorithm\n",
    "- y_train: the outcome feature associated with the training features - In this case, these are the loan amounts\n",
    "- X_test: the explanatory features to test the algorithm\n",
    "- y_test: the true Y of the target features associated with the testing geatures - again, in this case, these are the loan amounts\n",
    "\n",
    "To do this we will use the sklearn model selection function **train_test_split**. If you look back to the top of this page, we imported this function at the beginning of the notebook.\n",
    "\n",
    "The names used below (X_train, X_test, y_train, y_test) are the conventions used in industry for train and test data so you will continue to see them repeatedly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['loan_amount', 'partner_delinquency_rate', 'posted_year',\n",
      "       'posted_month', 'parent', 'tag_Repeat_Borrower', 'children_int',\n",
      "       'borrower_count', 'more_one_partner_country', 'terms_repayment_term',\n",
      "       'tag_Schooling', 'married', 'pct_female', 'exploratory_partner',\n",
      "       'sector_Agriculture', 'sector_Food', 'tag_Woman_Owned_Biz',\n",
      "       'sector_Retail', 'sector_Services', 'sector_Clothing',\n",
      "       'sector_Transportation', 'sector_Health', 'sector_Education', 'age_int',\n",
      "       'capital_nairobi', 'capital_mombasa', 'capital_bungoma',\n",
      "       'capital_nakuru', 'capital_kitale', 'capital_kiambu', 'capital_kisii',\n",
      "       'top_partners_VisionFund_Kenya', 'top_partners_One_Acre_Fund',\n",
      "       'top_partners_Juhudi_Kilimo', 'top_partners_Yehu_Microfinance_Trust',\n",
      "       'top_partners_SMEP_Microfinance_Bank', 'top_partners_Other',\n",
      "       'top_partners_Faulu_Kenya', 'top_partners_Milango_Financial_Services'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(rf_trainArr.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our OLS package only requires the training data is split into train & test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90744"
      ]
     },
     "execution_count": 430,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "22687"
      ]
     },
     "execution_count": 430,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train, test = train_test_split(rf_trainArr, test_size=0.2, random_state=42)\n",
    "len(train)\n",
    "len(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our scikit learn package requires that the explanatory variables are stored seperately from the outcome variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# create a training array = all the features but not the target and a training test array = the results of the target variable for the training array\n",
    "rf_trainY = rf_trainArr['loan_amount']\n",
    "rf_trainX = rf_trainArr.drop('loan_amount', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90744"
      ]
     },
     "execution_count": 432,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "22687"
      ]
     },
     "execution_count": 432,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "90744"
      ]
     },
     "execution_count": 432,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "22687"
      ]
     },
     "execution_count": 432,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(rf_trainX, rf_trainY, test_size=0.2, random_state=42)\n",
    "\n",
    "len(X_train)\n",
    "len(X_test)\n",
    "len(y_train)\n",
    "len(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have two separate datasets: train_set and test_set. We will run the regression on train_set\n",
    "using statsmodel's \"ols\" function since the output is more intuitive. Let's try a simple example to start: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:            loan_amount   R-squared:                       0.007\n",
      "Model:                            OLS   Adj. R-squared:                  0.007\n",
      "Method:                 Least Squares   F-statistic:                     628.4\n",
      "Date:                Sun, 11 Jun 2017   Prob (F-statistic):          3.29e-138\n",
      "Time:                        08:40:51   Log-Likelihood:            -6.7563e+05\n",
      "No. Observations:               90744   AIC:                         1.351e+06\n",
      "Df Residuals:                   90742   BIC:                         1.351e+06\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept    526.6208      2.367    222.498      0.000     521.982     531.260\n",
      "pct_female    -0.7986      0.032    -25.068      0.000      -0.861      -0.736\n",
      "==============================================================================\n",
      "Omnibus:                    98930.144   Durbin-Watson:                   1.999\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):         10807347.719\n",
      "Skew:                           5.507   Prob(JB):                         0.00\n",
      "Kurtosis:                      55.317   Cond. No.                         128.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "model0 = sm.ols(formula=\"loan_amount ~ pct_female\", data=train).fit()\n",
    "print(model0.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the typical regression output. It's a lot to digest! Remember that our simple model can be represented as a linear equation, like this: \n",
    "\n",
    "    loan_amount = intercept + coef*pct_female\n",
    "\n",
    "Therefore, these results can be translated as follows: \n",
    "\n",
    "    loan_amount = 525.29 -0.79*(pct_female)\n",
    "    \n",
    "Let's state the results. Regardless of gender the predicted loan amount is 526 dollars. However, if the lender group or individual is 100% female the loan amount decreases by 79 dollars. This is consistent with our earlier explanatory analysis. How do we know however, whether this is a significant result?\n",
    "\n",
    "We have a sufficient amount of confidence in this conclusion because the **p-value** is reported to be 0.000. In technical terms, the p-value is **the probability of getting results as extreme as the ones observed. **\n",
    "\n",
    "In statistics, we want our results to fall within the 95% confidence interval, or the p-value to be <= 0.05. This means, \"[i]f repeated samples were taken and the 95% confidence interval was computed for each sample, 95% of the intervals would contain the population mean. A 95% confidence interval has a 0.95 probability of containing the population mean. 95% of the population distribution is contained in the confidence interval.\" [Read more here.](http://www.investopedia.com/terms/s/standard-error.asp) The p-value is an indicator of where we fall in the confidence interval. In English, small p-value (<= 0.05) indicates strong evidence that the coefficient is different than 0. \n",
    "\n",
    "This is a relatively simplified explanation of p-values. Don't worry if it's not immediately intuitive - [not even professional statisticians can easily explain this concept.](http://fivethirtyeight.com/features/not-even-scientists-can-easily-explain-p-values/) To get a deeper understanding, we recommend grabbing the nearest textbook on statistics to review! \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multivariate Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['loan_amount', 'partner_delinquency_rate', 'posted_year',\n",
      "       'posted_month', 'parent', 'tag_Repeat_Borrower', 'children_int',\n",
      "       'borrower_count', 'more_one_partner_country', 'terms_repayment_term',\n",
      "       'tag_Schooling', 'married', 'pct_female', 'exploratory_partner',\n",
      "       'sector_Agriculture', 'sector_Food', 'tag_Woman_Owned_Biz',\n",
      "       'sector_Retail', 'sector_Services', 'sector_Clothing',\n",
      "       'sector_Transportation', 'sector_Health', 'sector_Education', 'age_int',\n",
      "       'capital_nairobi', 'capital_mombasa', 'capital_bungoma',\n",
      "       'capital_nakuru', 'capital_kitale', 'capital_kiambu', 'capital_kisii',\n",
      "       'top_partners_VisionFund_Kenya', 'top_partners_One_Acre_Fund',\n",
      "       'top_partners_Juhudi_Kilimo', 'top_partners_Yehu_Microfinance_Trust',\n",
      "       'top_partners_SMEP_Microfinance_Bank', 'top_partners_Other',\n",
      "       'top_partners_Faulu_Kenya', 'top_partners_Milango_Financial_Services'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:            loan_amount   R-squared:                       0.295\n",
      "Model:                            OLS   Adj. R-squared:                  0.295\n",
      "Method:                 Least Squares   F-statistic:                     1086.\n",
      "Date:                Mon, 12 Jun 2017   Prob (F-statistic):               0.00\n",
      "Time:                        14:34:28   Log-Likelihood:            -6.6007e+05\n",
      "No. Observations:               90744   AIC:                         1.320e+06\n",
      "Df Residuals:                   90708   BIC:                         1.321e+06\n",
      "Df Model:                          35                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===========================================================================================================\n",
      "                                              coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------------------------\n",
      "Intercept                                7.184e+04   1618.706     44.379      0.000    6.87e+04     7.5e+04\n",
      "partner_delinquency_rate                   19.9736      0.337     59.193      0.000      19.312      20.635\n",
      "posted_year                               -35.6204      0.803    -44.352      0.000     -37.195     -34.046\n",
      "posted_month                               -4.2921      0.338    -12.682      0.000      -4.955      -3.629\n",
      "parent                                    -15.3335      3.882     -3.950      0.000     -22.941      -7.726\n",
      "tag_Repeat_Borrower                        90.6254      4.187     21.644      0.000      82.419      98.832\n",
      "children_int                               -1.1679      0.472     -2.472      0.013      -2.094      -0.242\n",
      "borrower_count                             17.8733      0.999     17.888      0.000      15.915      19.832\n",
      "more_one_partner_country                 -442.8335     12.898    -34.334      0.000    -468.113    -417.554\n",
      "terms_repayment_term                       17.9311      0.196     91.704      0.000      17.548      18.314\n",
      "tag_Schooling                              23.2736      4.216      5.521      0.000      15.011      31.536\n",
      "married                                    12.5033      2.794      4.475      0.000       7.027      17.980\n",
      "pct_female                                 -0.4597      0.030    -15.382      0.000      -0.518      -0.401\n",
      "exploratory_partner                        16.9838     10.303      1.648      0.099      -3.210      37.178\n",
      "tag_Woman_Owned_Biz                       100.5647      3.630     27.702      0.000      93.449     107.680\n",
      "sector_Agriculture                         34.1028      5.545      6.150      0.000      23.235      44.970\n",
      "sector_Food                                19.7106      5.759      3.423      0.001       8.424      30.998\n",
      "sector_Retail                              73.3402      5.936     12.355      0.000      61.706      84.975\n",
      "sector_Services                            48.4383      6.626      7.310      0.000      35.451      61.426\n",
      "sector_Clothing                            54.5584      6.983      7.813      0.000      40.873      68.244\n",
      "sector_Transportation                     118.6739      8.311     14.278      0.000     102.384     134.964\n",
      "sector_Education                           62.8040     10.930      5.746      0.000      41.382      84.226\n",
      "sector_Health                              95.5970     14.299      6.685      0.000      67.570     123.624\n",
      "capital_nairobi                             1.5502      4.246      0.365      0.715      -6.772       9.872\n",
      "capital_mombasa                            11.1865      4.299      2.602      0.009       2.760      19.613\n",
      "capital_bungoma                           -31.3006      5.420     -5.775      0.000     -41.923     -20.678\n",
      "capital_kisii                             -28.1028      5.920     -4.747      0.000     -39.705     -16.501\n",
      "capital_kitale                            -11.3717      6.096     -1.865      0.062     -23.319       0.576\n",
      "capital_kiambu                             -0.3841      6.291     -0.061      0.951     -12.713      11.945\n",
      "top_partners_VisionFund_Kenya            -100.8086      6.190    -16.285      0.000    -112.941     -88.676\n",
      "top_partners_One_Acre_Fund                907.2283     19.667     46.130      0.000     868.681     945.775\n",
      "top_partners_Juhudi_Kilimo                 39.5127      6.290      6.282      0.000      27.184      51.841\n",
      "top_partners_Yehu_Microfinance_Trust      -13.3930      6.413     -2.088      0.037     -25.963      -0.823\n",
      "top_partners_SMEP_Microfinance_Bank      -139.1631      6.590    -21.117      0.000    -152.079    -126.247\n",
      "top_partners_Faulu_Kenya                   55.5325      8.213      6.761      0.000      39.434      71.630\n",
      "top_partners_Milango_Financial_Services   108.9540      9.229     11.805      0.000      90.865     127.043\n",
      "==============================================================================\n",
      "Omnibus:                   101747.791   Durbin-Watson:                   1.989\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):         15483830.553\n",
      "Skew:                           5.649   Prob(JB):                         0.00\n",
      "Kurtosis:                      65.988   Cond. No.                     2.81e+06\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 2.81e+06. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "model1 = sm.ols(formula=\"loan_amount~ partner_delinquency_rate+ posted_year+posted_month+ \\\n",
    "parent + tag_Repeat_Borrower + children_int  + borrower_count + more_one_partner_country + terms_repayment_term + \\\n",
    "tag_Schooling + married + pct_female + exploratory_partner + tag_Woman_Owned_Biz + \\\n",
    "sector_Agriculture + sector_Food + sector_Retail + sector_Services + sector_Clothing +  +\\\n",
    "sector_Transportation + sector_Education + sector_Health + capital_nairobi +\\\n",
    "capital_mombasa + capital_bungoma + capital_kisii + capital_kitale + capital_kiambu + top_partners_VisionFund_Kenya + \\\n",
    "top_partners_One_Acre_Fund + top_partners_Juhudi_Kilimo + top_partners_Yehu_Microfinance_Trust + top_partners_SMEP_Microfinance_Bank + \\\n",
    "top_partners_Faulu_Kenya + top_partners_Milango_Financial_Services\", data=train).fit()\n",
    "print(model1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GFT + Wiki / GT  R-squared 0.295259759964\n"
     ]
    }
   ],
   "source": [
    "print (\"GFT + Wiki / GT  R-squared\", model1.rsquared)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing the output of our multivariate regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### R2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the Adjusted R Squared in our multivariate regressionis extremely low. \n",
    "\n",
    "The Adjusted R Squared is an explanation of how much of the outcome feature can be explained by the model's explanatory features. This low Adj R Squared suggests that the predictive value of these combination of variables is pretty low in  predicting loan_amount. \n",
    "\n",
    "\n",
    "**So what do we do next? A low adjusted R2 can mean a few different things:**\n",
    "\n",
    "- we need better feature engineering (let's look at our p-values do understand better what is driving the current explanatory power of the model)\n",
    "- we need more data (we do have a lot of data so this is probably not the reason)\n",
    "- do better feature selection (we are throwing too many useless features at our model)\n",
    "- we need a more sophistocated model (we can check this by comparing the performance of a more complex model on the same features... we will do this later in this notebook by trying the same model with a decision tree)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### P Values, Statistical Significance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember that we want each features importance to fall within the 95% confidence interval, or the p-value to be <= 0.05. In the multivariate regression above, most of our features are important! However, there are a few that are not:\n",
    "\n",
    "- province_eastern  \n",
    "- sector_Agriculture \n",
    "- children_int \n",
    "- sector_Retail \n",
    "- province_eastern \n",
    "- province_nairobi\n",
    "- province_coast \n",
    "\n",
    "Let's not remove them immediately, but keep this in mind for when we do additional feature selection."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now let's see how our multivariate linear model scores unseen (test) data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the cell below we use the parameters we trained using our training data to score our unseen test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 504,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "predicted_y=model0.predict(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are able to generate some exciting new metrics using our test_set that we created earlier on in the code. First, let's take a look at the predicted value of y, and the true y.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77455    3.23602\n",
       "dtype: float64"
      ]
     },
     "execution_count": 505,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's calculate the difference! \n",
    "y_diff = abs(y_test - predicted_y)\n",
    "y_diff[:1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is interesting, but the absolute value of the difference between predicted and true Y doesn't tell us too much on its own. However, it is used as an input in calculating many statistical measures of fit. Let's take a look: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Mean Absolute Error](https://www.kaggle.com/wiki/MeanAbsoluteError)\n",
    "\n",
    "The mean absolute error is calculated as the mean of the distance between predicted and true Y. We take the absolute value of (predicted Y - true Y) so we capture the concept of distance for each observation. Then we take the mean over all observations to get a sense of the entire model. The closer the final statistic is to 0, the better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 506,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 250.34\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean absolute error: %.2f\"\n",
    "     % np.mean(y_diff))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Root Mean Squared Error](https://www.kaggle.com/wiki/RootMeanSquaredError)\n",
    "\n",
    "The root mean squared error is calculated as the square root of the mean of the difference between predicted and true Y squared. This is a little more complicated to interpret than the MAE, but a useful indication of fit. Like the MAE, the closer this final statistic is to 0, the better.  \n",
    "\n",
    "We use both RMSE and MAE in practice. See [this blog post](https://medium.com/human-in-a-machine-world/mae-and-rmse-which-metric-is-better-e60ac3bde13d) for a discussion of the difference between the two metrics."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Firstly, we calculate mean squared error. The closer to 0 the better. While MSE and RMSE are equally intuitive, RMSE is more intuitive to interprete because it is in the units of our outcome variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 170857.42\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean squared error: %.2f\"\n",
    "      % (np.mean((y_diff) ** 2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root mean squared error: 413.35\n"
     ]
    }
   ],
   "source": [
    "print(\"Root mean squared error: %.2f\"\n",
    "      % np.sqrt(np.mean((y_diff) ** 2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### R2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately, STATSModel has no easy way to produce the adjusted R2 of our test set. So, we instead turn to another scikit learn package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)\n"
     ]
    }
   ],
   "source": [
    "# Linear Regression\n",
    "\n",
    "# Create linear regression object\n",
    "regr = linear_model.LinearRegression()\n",
    "\n",
    "# Train the model using the training sets\n",
    "model1 = regr.fit(X_train, y_train)\n",
    "print(model1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score: 0.295510124251\n",
      "Test score: 0.292406756155\n"
     ]
    }
   ],
   "source": [
    "# Step 3: Calculating the score of the predictive power on the training and testing dataset.\n",
    "linear_training_score = model1.score(X_train, y_train)\n",
    "linear_testing_score = model1.score(X_test, y_test)\n",
    "print(\"Train score: \" + str(linear_training_score))\n",
    "print(\"Test score: \" + str(linear_testing_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or we can use cross validation by leveraging the code below. This means we no longer have need for a test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.0172196   0.27222533  0.11498113  0.50028189  0.10783599 -0.07947569\n",
      "  0.25176915  0.09847907  0.13996535 -0.15205961]\n"
     ]
    }
   ],
   "source": [
    "# Create linear regression object\n",
    "regr = linear_model.LinearRegression()\n",
    "\n",
    "scores = cross_val_score(regr, rf_trainX, rf_trainY, cv=10, scoring='r2')\n",
    "print(scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is this comparison between train and test telling this?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both the train and test score are low. This means we are not overfitting, and confirms our suspicion that we may be underfitting. Our model is not great at explaining loan_score, we suspect this is because a linear model does not fit this data well. We can also plot out the train and test predictions against the actual."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training dataset: Plotting Predicted Against Actual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x3cf668668>"
      ]
     },
     "execution_count": 510,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1202724a8>"
      ]
     },
     "execution_count": 510,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x245ebe710>"
      ]
     },
     "execution_count": 510,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x120269be0>"
      ]
     },
     "execution_count": 510,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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480USwT3QD2OjUFsPrW1w2sYpaalWXQPW8hXosHwya2pqiMfjsq5KWjQjI9DY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\nSQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x5442167b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting a grpah of the true values vs the predicted values for the training and test datasets\n",
    "linear_y_predicted_train = model1.predict(X_train)\n",
    "fig = plt.figure(figsize=(5, 5))\n",
    "plt.scatter(y_train, linear_y_predicted_train, alpha=0.5)\n",
    "plt.xlabel('True value')\n",
    "plt.ylabel('Predicted value')\n",
    "plt.title(\"relationship between True Y and predicted Y* loan amount (training dataset)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test dataset: Plotting Predicted Against Actual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11af7bb38>"
      ]
     },
     "execution_count": 511,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x245ed15c0>"
      ]
     },
     "execution_count": 511,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x3cf67ac18>"
      ]
     },
     "execution_count": 511,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11af757b8>"
      ]
     },
     "execution_count": 511,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QV4t4JtrvVlGWL6qM64TN5RArOC19XMM8swK3YV4UcsyFJx7CDh8WazOyICsV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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12ab02198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting a grpah of the true values vs the predicted values for the training and test datasets\n",
    "linear_y_predicted_test = model1.predict(X_test)\n",
    "fig = plt.figure(figsize=(7, 7))\n",
    "plt.scatter(y_test, linear_y_predicted_test, alpha=0.5)\n",
    "plt.xlabel('True value')\n",
    "plt.ylabel('Predicted value')\n",
    "plt.title(\"relationship between True Y and predicted Y* loan amount (test dataset)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree and Random Forest\n",
    "\n",
    "\n",
    "As you learned in the class, decision trees can be used to predict and classify data features. To improve on the simple decision tree, we can use ensemble methods such as Bagging and Random Forest. In an Ensemble approach, the algorithm acts as a group of experts analysing your data and the results is the aggregation of those individual analyses. \n",
    "\n",
    "A quick refresher on Bagging\n",
    "- Grow multiple decisions of random subsets of the data\n",
    "- All features are considered at every split in the decision tree\n",
    "- The result is the average prediction of all trees\n",
    "\n",
    "A quick refresher on the Random Forest theory:\n",
    "- Random Forest is based on Decision Trees -> many trees = a forest!\n",
    "- Grows multiple trees on random subsets of the parent dataset\n",
    "- **At every split, a new random subset of features is chosen**\n",
    "- This leads to \"decorrelated\" trees which leads to a large increase in performance!\n",
    "\n",
    "\n",
    "Now we will compare linear regressions to decision trees and random forest. We will use the same features and will be predicting the same outcome feature loan_amount.\n",
    "\n",
    "** we will be looking at how we can predict the loan amount using decision trees, bagged decisions trees and the random forest algorithm.**\n",
    "\n",
    "However, it is worth mentioning that these methods can also be used to classify data, for example some interesting classification questions we could investigate are:\n",
    "- Can we classify which loans expired and which one got funded?\n",
    "- Is a loan posted by a male or female?\n",
    "\n",
    "Feel free to investigate these on your own time! Here is a very simple example of how the RandomForestClassifier works: https://bicorner.com/2015/10/26/random-forest-using-ipython/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the advantages of the RandomForest algorithms is that we can pass through a whole range of features, regardless of whether we believe there is a linear relationship between the target (loan amount) and the feature and then find out which features have the highest influence. Remember from the course notes, the RandomForest Algorithms are based on the concept of decision trees where the set of all possible values are split into non-overlapping groups. \n",
    "\n",
    "This can be really powerful tool as it requires less initial investigation and analysis than a regression analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step by Step approach to modeling:\n",
    "\n",
    "For the following methods used, there is some standard steps used to implement the algorithms. A simple overvew of these steps are:\n",
    "- **Step 1: Initiate the algorithm** This is where we define the parameters (& hyperparameters of the algorithm) of the algorithm. For example, this is where we would define the maximum depth, the minimum samples in a leaf etc. To learn more about the parameters for each algorithm, either check the module documentation on the internet or run a cell with the algorithm name followed by ? as we did at the beginning of this notebook for the RandomForestRegressor. These resources will also tell you the default values used for each parameter. \n",
    "- **Step 2: Train the algorithm** This is where we train the algorithm by fitting it to the X_train and y_train datasets. \n",
    "- **Step 3: Evaluating the algorithm** This is where we evaluate the predictive power of the algorithm by comparing the predictive loan amount values to the true values. We can do this for the training and testing dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start with **loan amount** and see if we can predict how much is requested to be funded using the decision trees and ensemble methods! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree\n",
    "Before we get into some of the more sophisticated models, let's first try an individual Decision Tree and see how it performs.  After training the model, we will be able to assess it's performance by using sklearns useful method .score, which calculates the r2 value for the data provided.  We will first print out the r2 score for the training data, and then will print out the r2 score for the test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_split=1e-07,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, presort=False, random_state=0,\n",
       "           splitter='best')"
      ]
     },
     "execution_count": 512,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score: 0.989259390205\n",
      "Test score: 0.528351544962\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "# Step 1: Initiating the DecisionTreeRegressor algorithm\n",
    "decision_regressor = DecisionTreeRegressor(random_state=0)\n",
    "# Step 2: Training the algorithm using the X_train dataset of features and y_train, the associated target features\n",
    "decision_regressor.fit(X_train, y_train)\n",
    "# Step 3: Calculating the score of the predictive power on the training and testing dataset.\n",
    "dt_training_score = decision_regressor.score(X_train, y_train)\n",
    "dt_testing_score = decision_regressor.score(X_test, y_test)\n",
    "print(\"Train score: \" + str(dt_training_score))\n",
    "print(\"Test score: \" + str(dt_testing_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the scores above (the R^2 of the regression between true and predicted loan amounts), we can see that the decision tree works very well on the training dataset, as expected. However, when applied to the testing dataset, it performs much worse. \n",
    "\n",
    "The R^2 number above are pretty telling but it is always nice to visualise how these look in a scattor plot. This allows us to visualise the variation between the predicted loan amount and the true loan amount values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 513,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11cb9df28>"
      ]
     },
     "execution_count": 513,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x245edbd68>"
      ]
     },
     "execution_count": 513,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x124f194a8>"
      ]
     },
     "execution_count": 513,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x54c843ac8>"
      ]
     },
     "execution_count": 513,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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+Vj/AfrcasD8KT0fzZO23jrprlkTvpz/70dgm3gNZP2m8BGze7zM4F/tjemm07t2iWDDG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\n5q87JgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d785518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting a grpah of the true values vs the predicted values for the training and test datasets\n",
    "dt_y_predicted_train = decision_regressor.predict(X_train)\n",
    "fig = plt.figure(figsize=(7, 7))\n",
    "plt.scatter(y_train, dt_y_predicted_train, alpha=0.5)\n",
    "plt.xlabel('True value')\n",
    "plt.ylabel('Predicted value')\n",
    "plt.title(\"relationship between True Y and predicted Y* loan amount (training dataset)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 514,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x125b93898>"
      ]
     },
     "execution_count": 514,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11c0b1550>"
      ]
     },
     "execution_count": 514,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x5441ec550>"
      ]
     },
     "execution_count": 514,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1237cb438>"
      ]
     },
     "execution_count": 514,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Gau9c1vt8leSv9FupRKU1dPcBfZ4yKnVM2WfBs0JPchznm47jvBYTUHuJMrEVlerPe448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      "text/plain": [
       "<matplotlib.figure.Figure at 0x5442051d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting a grpah of the true values vs the predicted values for the training and test datasets\n",
    "dt_y_predicted_test = decision_regressor.predict(X_test)\n",
    "fig = plt.figure(figsize=(7, 7))\n",
    "plt.scatter(y_test, dt_y_predicted_test, alpha=0.5)\n",
    "plt.xlabel('True value')\n",
    "plt.ylabel('Predicted value')\n",
    "plt.title(\"relationship between True Y and predicted Y* loan amount (test dataset)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The decison tree regressor is near perfect on the training data; however, much worse on the test data.  This is largely due to the likelihood an individual trees  of overfitting to the data. Remember overfitting can occur when the tree goes too deep, the minimum leaf sample if too small or the maximum number number of terminal nodes is too high. We can mitigate the impact of overfitting by tuning and optimising these hyperparameters. However, often it is very time consuming and difficult to find the optimal hyperparameters and therefore we look towards ensemble approaches for an easier, more general solution. \n",
    "\n",
    "## Bagging\n",
    "\n",
    "Another approach to overcome this is to build a collection of trees with sklearn's BaggingRegressor.  A bagging regressor builds some specified number of trees and trains each tree with a random subset of the data and then averages the forecast of each individual tree. \n",
    "\n",
    "This is an **Ensemble approach** where we use the results of several random analyses to find an average response. For each tree, there will be a sample of data which was not used to train the algorithm. This means that we can also measure the **out-of-bag** score ( or oob_score). This score calculates the error rate of the predictions on the sample not used while training an individual tree. (Remember a random ~2/3 of data are used to train a given tree)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 515,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Sara/anaconda/lib/python3.6/site-packages/sklearn/ensemble/bagging.py:986: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "BaggingRegressor(base_estimator=None, bootstrap=True,\n",
       "         bootstrap_features=False, max_features=1.0, max_samples=1.0,\n",
       "         n_estimators=10, n_jobs=1, oob_score=True, random_state=None,\n",
       "         verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 515,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score: 0.941757429521\n",
      "Test score: 0.728774329377\n",
      "Oob score: 0.639112286903\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import BaggingRegressor\n",
    "# Step 1: Initiating the bagging regressor algorithm\n",
    "bagging_regressor = BaggingRegressor(oob_score=True)\n",
    "# Step 2: Training the bagging regressor algorithm\n",
    "bagging_regressor.fit(X_train, y_train)\n",
    "# Step 3: Calculating the score of the predictive power on the training and testing dataset.\n",
    "br_training_score = bagging_regressor.score(X_train, y_train)\n",
    "br_testing_score = bagging_regressor.score(X_test, y_test)\n",
    "br_oob_score = bagging_regressor.oob_score_\n",
    "print(\"Train score: \" + str(br_training_score))\n",
    "print(\"Test score: \" + str(br_testing_score))\n",
    "print(\"Oob score: \" + str(br_oob_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results above show that the train R^2 score has reduced but the test R^2 score has increased. This is a good sign as it means that the algorithm is overfitting the training less and therefore improving its ability to predict the loan amounts on unseen samples. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 516,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1258282e8>"
      ]
     },
     "execution_count": 516,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1237e79e8>"
      ]
     },
     "execution_count": 516,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x122734550>"
      ]
     },
     "execution_count": 516,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x122722e48>"
      ]
     },
     "execution_count": 516,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KDV7lz533iCD3fGBiLjmWSrR6sShm85ZWyQ8/sMd1SStDS0ulcYoNZbuWpOSa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QB9/SLnmlqUWeVORAatGCOV/OmlcrhdZ3QriqIIGmbfTlbJwNpiAZtJi7G6+KYUu0UjEjl3ImKo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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x125b620f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting a grpah of the true values vs the predicted values for the training and test datasets\n",
    "br_predicted_train = bagging_regressor.predict(X_train)\n",
    "fig = plt.figure(figsize=(7, 7))\n",
    "plt.scatter(y_train, br_predicted_train, alpha=0.5)\n",
    "plt.xlabel('True value')\n",
    "plt.ylabel('Predicted value')\n",
    "plt.title(\"relationship between True Y and predicted Y* loan amount (training dataset)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 517,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x12427add8>"
      ]
     },
     "execution_count": 517,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11873c8d0>"
      ]
     },
     "execution_count": 517,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x12273c898>"
      ]
     },
     "execution_count": 517,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11d678978>"
      ]
     },
     "execution_count": 517,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WXYDjuu7ZoWWPAG8Bngdu0lq/EjgK6HJd94xAAH8ZeBNwTW5b13UfrlV/BUEQ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cb87898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting a grpah of the true values vs the predicted values for the training and test datasets\n",
    "br_predicted_test = bagging_regressor.predict(X_test)\n",
    "fig = plt.figure(figsize=(7, 7))\n",
    "plt.scatter(y_test, br_predicted_test, alpha=0.5)\n",
    "plt.xlabel('True value')\n",
    "plt.ylabel('Predicted value')\n",
    "plt.title(\"relationship between True Y and predicted Y* loan amount (test dataset)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When comparing the different regression models, we will be looking at three main outputs:\n",
    "- Train score = R^2 between the predicted and true values for the training dataset\n",
    "- Test score = R^2 between the predicted and true values for the test dataset\n",
    "- Oob_score = the error rate of predicted values for data not used whilst training an individual tree\n",
    "\n",
    "Therefore, we are defining a function below that prints out these score's us"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 518,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Since we will evaluate a few models, let's define a function that prints out the scores we care about\n",
    "def print_scores(regressor, X_train, y_train, X_test, y_test, oob_score=True):\n",
    "    print(\"Train score: \" + str(regressor.score(X_train, y_train)))\n",
    "    print(\"Test score: \" + str(regressor.score(X_test, y_test)))\n",
    "    print(\"OOB score: \" + str(regressor.oob_score_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Changing some model parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The default number of trees used in the bagging regressor is 10. This means that the algorithms splits the dataset into 10 different tress, trains each one individually, predicts the result from each one individually and then averages the result. \n",
    "\n",
    "Let's see how performance changes when we increase the number of trees from 10 to 100."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BaggingRegressor(base_estimator=None, bootstrap=True,\n",
       "         bootstrap_features=False, max_features=1.0, max_samples=1.0,\n",
       "         n_estimators=100, n_jobs=1, oob_score=True, random_state=None,\n",
       "         verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 454,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score: 0.9552670398\n",
      "Test score: 0.743851791923\n",
      "OOB score: 0.731403639759\n"
     ]
    }
   ],
   "source": [
    "# Step 1: Initiating the bagging regressor algorithm\n",
    "bagging_regressor = BaggingRegressor(n_estimators=100, oob_score=True)\n",
    "# Step 2: Training the bagging regressor algorithm\n",
    "bagging_regressor.fit(X_train, y_train)\n",
    "# Step 3: Calculating the score of the predictive power on the training and testing dataset.\n",
    "print_scores(bagging_regressor, X_train, y_train, X_test, y_test, oob_score=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we see an improvement in all score and in particular the oob_score. This is telling us that splitting the dataset into more tree leads to a more accurate average predicton of the loan_amount. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1367d4d30>"
      ]
     },
     "execution_count": 455,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11d40de10>"
      ]
     },
     "execution_count": 455,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x12dda1470>"
      ]
     },
     "execution_count": 455,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x13680f0f0>"
      ]
     },
     "execution_count": 455,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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w1gwRZibzXg8zk27gaW9fnVomUn8K2EVERKQheBtPw1u1ljA9QXZkmDA9gbdq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      "text/plain": [
       "<matplotlib.figure.Figure at 0x120e29780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting a grpah of the true values vs the predicted values for the training and test datasets\n",
    "br_y_predicted_test = bagging_regressor.predict(X_test)\n",
    "fig = plt.figure(figsize=(7, 7))\n",
    "plt.scatter(y_test, br_y_predicted_test, alpha=0.5)\n",
    "plt.xlabel('True value')\n",
    "plt.ylabel('Predicted value')\n",
    "plt.title(\"Graph showing the relationship between the true loan amount and the predicted loan amount for the testing dataset\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest\n",
    "We still observe a large discrepancy between the training data and the test data.  This is largely because the decision trees are largely correlated, meaning that Subsets of the same data are probably going to split on the same features and result in very similar predictions.\n",
    "\n",
    "To de-correlate the trees, we can use a Random Forest Regressor, which only considers a random subsample of the features at each split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 519,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           n_estimators=100, n_jobs=1, oob_score=True, random_state=None,\n",
       "           verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 519,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score: 0.95536713458\n",
      "Test score: 0.745742484071\n",
      "OOB score: 0.73013825649\n"
     ]
    }
   ],
   "source": [
    "# Step 1: Initiating the bagging regressor algorithm\n",
    "rf_regressor = RandomForestRegressor(n_estimators=100, oob_score=True)\n",
    "# Step 2: Training the bagging regressor algorithm\n",
    "rf_regressor.fit(X_train,y_train)\n",
    "# Step 3: Calculating the score of the predictive power on the training and testing dataset.\n",
    "print_scores(rf_regressor, X_train, y_train, X_test, y_test, oob_score=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the random forest algorithm has improved the generalization performance relative to the bagging regressor. This indicates that the features we are using are already highly uncorrelated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 520,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error between the predicted and true loan amounts: 7713 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x124983ef0>"
      ]
     },
     "execution_count": 520,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x122748780>"
      ]
     },
     "execution_count": 520,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11d6af668>"
      ]
     },
     "execution_count": 520,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x124964a90>"
      ]
     },
     "execution_count": 520,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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28P5pFbHiAm6Lk2JZj49KvnFnF9hYfnEhX59bBtfFyRdXdiIhot23Ut579kkY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ac3aNbKBWsVs9UGLrsH5403QqkEDhjHwV5zLQwMwZUpMCHZAvX0Y5h331cxVVG3LkVRlLXRmnIN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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d771a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf_y_predicted_train = rf_regressor.predict(X_train)\n",
    "print(\"Mean squared error between the predicted and true loan amounts: %d \" % round(mean_squared_error(y_train, rf_y_predicted_train)))\n",
    "\n",
    "fig = plt.figure(figsize=(7, 7))\n",
    "plt.scatter(y_train, rf_y_predicted_train, alpha=0.5)\n",
    "plt.xlabel('True value')\n",
    "plt.ylabel('Predicted value')\n",
    "plt.title(\"relationship between True Y and predicted Y* loan amount (train dataset)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error between the predicted and true loan amounts: 43922 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x12b22a9b0>"
      ]
     },
     "execution_count": 458,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1298d65c0>"
      ]
     },
     "execution_count": 458,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1298c8780>"
      ]
     },
     "execution_count": 458,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x12b21c438>"
      ]
     },
     "execution_count": 458,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qWbK08uIc1UKUsSAIQoviRHpgzXpMpDv/AXmymovOD96/l+QNP4Xf32GLgoQc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\nY0VRFEVpMirGiqIoitJk/j+SH3lC2PyJvgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1298e7048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf_y_predicted_test = rf_regressor.predict(X_test)\n",
    "print(\"Mean squared error between the predicted and true loan amounts: %d \" % round(mean_squared_error(y_test, rf_y_predicted_test)))\n",
    "\n",
    "fig = plt.figure(figsize=(7, 7))\n",
    "plt.scatter(y_test, rf_y_predicted_test, alpha=0.5)\n",
    "plt.xlabel('True value')\n",
    "plt.ylabel('Predicted value')\n",
    "plt.title(\"relationship between True Y and predicted Y* loan amount (test dataset)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since, we are relying of OOB score we do not in fact need to have a seperate test set. We can use our full dataset for training with a small subset held to the side for each model to comput the OOB score. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Since we will evaluate a few models, let's define a function that prints out the scores we care about\n",
    "def print_scores_full(regressor, rf_trainX,rf_trainY , oob_score=True):\n",
    "    print(\"Train score: \" + str(regressor.score(rf_trainX, rf_trainY)))\n",
    "    print(\"OOB score: \" + str(regressor.oob_score_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also increase the number of estimators in our model to 400 and set the min_samples_leaft to 20 to improve upon overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_split=1e-07, min_samples_leaf=20,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           n_estimators=400, n_jobs=1, oob_score=True, random_state=42,\n",
       "           verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 460,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score: 0.757759947568\n",
      "OOB score: 0.726812276041\n"
     ]
    }
   ],
   "source": [
    "# Step 1: Initiating the bagging regressor algorithm\n",
    "rf_regressor = RandomForestRegressor(n_estimators=400, random_state=42, min_samples_leaf=20,oob_score=True)\n",
    "# Step 2: Training the bagging regressor algorithm\n",
    "rf_regressor.fit(rf_trainX,rf_trainY)\n",
    "# Step 3: Calculating the score of the predictive power on the training and testing dataset.\n",
    "print_scores_full(rf_regressor, rf_trainX,rf_trainY , oob_score=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recall that the difference between the Bagging Regressor and the RandomForest is that in the RandomForest, at each split, a random subset of features is considered. The reducing the chance of correlation between the different trees. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's try to figure out which features are driving our model's predictions.  Although we do not have the coefficients provided by a linear regression, we can analyze what percentage of the overall variance was explained by a given feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature Ranking:\n",
      "1. feature borrower_count (0.250391)\n",
      "2. feature posted_year (0.161943)\n",
      "3. feature posted_month (0.111225)\n",
      "4. feature terms_repayment_term (0.104476)\n",
      "5. feature more_one_partner_country (0.046325)\n",
      "6. feature age_int (0.042060)\n",
      "7. feature married (0.035713)\n",
      "8. feature partner_delinquency_rate (0.035550)\n",
      "9. feature children_int (0.029197)\n",
      "10. feature top_partners_One_Acre_Fund (0.027143)\n",
      "11. feature pct_female (0.017862)\n",
      "12. feature sector_Retail (0.014358)\n",
      "13. feature sector_Agriculture (0.011253)\n",
      "14. feature parent (0.009929)\n",
      "15. feature top_partners_Yehu_Microfinance_Trust (0.007855)\n",
      "16. feature top_partners_Other (0.007043)\n",
      "17. feature tag_Repeat_Borrower (0.006609)\n",
      "18. feature capital_nairobi (0.005734)\n",
      "19. feature capital_mombasa (0.005716)\n",
      "20. feature sector_Education (0.005663)\n",
      "21. feature capital_nakuru (0.005318)\n",
      "22. feature sector_Health (0.004985)\n",
      "23. feature top_partners_VisionFund_Kenya (0.004980)\n",
      "24. feature tag_Woman_Owned_Biz (0.004556)\n",
      "25. feature tag_Schooling (0.004383)\n",
      "26. feature top_partners_SMEP_Microfinance_Bank (0.004361)\n",
      "27. feature sector_Food (0.004319)\n",
      "28. feature top_partners_Juhudi_Kilimo (0.004147)\n",
      "29. feature capital_kisii (0.003357)\n",
      "30. feature sector_Services (0.003300)\n",
      "31. feature capital_kitale (0.003278)\n",
      "32. feature capital_kiambu (0.002962)\n",
      "33. feature capital_bungoma (0.002869)\n",
      "34. feature exploratory_partner (0.002727)\n",
      "35. feature sector_Clothing (0.002645)\n",
      "36. feature top_partners_Milango_Financial_Services (0.002641)\n",
      "37. feature sector_Transportation (0.002552)\n",
      "38. feature top_partners_Faulu_Kenya (0.000577)\n"
     ]
    }
   ],
   "source": [
    "importances = rf_regressor.feature_importances_\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "print('Feature Ranking:')\n",
    "for f in range(len(importances)):\n",
    "    print(\"%d. feature %s (%f)\" % (f + 1, rf_trainX.columns[indices[f]], importances[indices[f]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 536,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13d864f98>"
      ]
     },
     "execution_count": 536,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x13f013be0>"
      ]
     },
     "execution_count": 536,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Container object of 38 artists>"
      ]
     },
     "execution_count": 536,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x125304828>,\n",
       "  <matplotlib.axis.XTick at 0x1353f2908>,\n",
       "  <matplotlib.axis.XTick at 0x1353eb5f8>,\n",
       "  <matplotlib.axis.XTick at 0x12344e898>,\n",
       "  <matplotlib.axis.XTick at 0x11d21c940>,\n",
       "  <matplotlib.axis.XTick at 0x123485a90>,\n",
       "  <matplotlib.axis.XTick at 0x1265876d8>,\n",
       "  <matplotlib.axis.XTick at 0x122778438>,\n",
       "  <matplotlib.axis.XTick at 0x1253f9780>,\n",
       "  <matplotlib.axis.XTick at 0x11b1b3358>,\n",
       "  <matplotlib.axis.XTick at 0x14792f0f0>,\n",
       "  <matplotlib.axis.XTick at 0x14cc33b70>,\n",
       "  <matplotlib.axis.XTick at 0x11ed3b828>,\n",
       "  <matplotlib.axis.XTick at 0x149510a90>,\n",
       "  <matplotlib.axis.XTick at 0x14951c588>,\n",
       "  <matplotlib.axis.XTick at 0x149522080>,\n",
       "  <matplotlib.axis.XTick at 0x149522b38>,\n",
       "  <matplotlib.axis.XTick at 0x149526630>,\n",
       "  <matplotlib.axis.XTick at 0x14952b128>,\n",
       "  <matplotlib.axis.XTick at 0x14952bbe0>,\n",
       "  <matplotlib.axis.XTick at 0x1495326d8>,\n",
       "  <matplotlib.axis.XTick at 0x1495351d0>,\n",
       "  <matplotlib.axis.XTick at 0x149535c88>,\n",
       "  <matplotlib.axis.XTick at 0x14953b780>,\n",
       "  <matplotlib.axis.XTick at 0x149f59278>,\n",
       "  <matplotlib.axis.XTick at 0x149f59d30>,\n",
       "  <matplotlib.axis.XTick at 0x149f5e828>,\n",
       "  <matplotlib.axis.XTick at 0x149f65320>,\n",
       "  <matplotlib.axis.XTick at 0x149f65dd8>,\n",
       "  <matplotlib.axis.XTick at 0x149f6a8d0>,\n",
       "  <matplotlib.axis.XTick at 0x149f6e3c8>,\n",
       "  <matplotlib.axis.XTick at 0x149f6ee80>,\n",
       "  <matplotlib.axis.XTick at 0x149f74978>,\n",
       "  <matplotlib.axis.XTick at 0x149f79470>,\n",
       "  <matplotlib.axis.XTick at 0x149f79f28>,\n",
       "  <matplotlib.axis.XTick at 0x149f7ea20>,\n",
       "  <matplotlib.axis.XTick at 0x149f85518>,\n",
       "  <matplotlib.axis.XTick at 0x149f85fd0>],\n",
       " <a list of 38 Text xticklabel objects>)"
      ]
     },
     "execution_count": 536,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(-1, 38)"
      ]
     },
     "execution_count": 536,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "        34, 35, 36, 37]), <a list of 38 Text xticklabel objects>)"
      ]
     },
     "execution_count": 536,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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RcQbwR+CA6jWXJElaYiYGq8y8Djh45Obzhn7eYZ6iT5i2UpIkScuRC4RKkiRVYrCSJEmq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\nv2/HPRn3y1iTzbQGVr/B//DHSRtx0RJKjY3bUpIlKdIAMbODJK2G/1/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R71HPPm/Db3g/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\nIOiICKyCIAiCIAg6IgKrIAiCIAiCjvj/xY6pfGYuaNwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13d864f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x149f85940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances = rf_regressor.feature_importances_\n",
    "std = np.std([tree.feature_importances_ for tree in rf_regressor.estimators_],\n",
    "             axis=0)\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "feature_names = rf_trainX.columns \n",
    "plt.figure(figsize=(10,10))\n",
    "plt.title(\"Feature importances\")\n",
    "plt.bar(range(rf_trainX.shape[1]), importances[indices],\n",
    "       color=\"r\", yerr=std[indices], align=\"center\")\n",
    "plt.xticks(range(rf_trainX.shape[1]),feature_names)\n",
    "plt.xlim([-1, rf_trainX.shape[1]])\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()\n",
    "\n",
    "plt.savefig('module_3_images/feature_importance.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is interesting here is that there is not a very clear relationship between any single feature and the loan_amount. However the aggregat these features together into either the bagged regressor or the random forest algorithm leads to very effective predictions (R^2 ~ 0.74). This is a testament to the predictive power of decisions trees and ensemble methods!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try taking the top 20 features and observe it's impact on performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 537,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "relevant_cols = [rf_trainX.columns[indices[f]] for f in range(20)]\n",
    "X_train2 = rf_trainX[relevant_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 538,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_split=1e-07, min_samples_leaf=20,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           n_estimators=400, n_jobs=1, oob_score=True, random_state=42,\n",
       "           verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 538,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score: 0.751521308997\n",
      "OOB score: 0.721850423036\n"
     ]
    }
   ],
   "source": [
    "# Step 1: Initiating the bagging regressor algorithm\n",
    "rf_regressor = RandomForestRegressor(n_estimators=400, random_state=42, min_samples_leaf=20,oob_score=True)\n",
    "# Step 2: Training the bagging regressor algorithm\n",
    "rf_regressor.fit(X_train2,rf_trainY)\n",
    "# Step 3: Calculating the score of the predictive power on the training and testing dataset.\n",
    "print_scores_full(rf_regressor, X_train2,rf_trainY, oob_score=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 543,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90744"
      ]
     },
     "execution_count": 543,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "22687"
      ]
     },
     "execution_count": 543,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "90744"
      ]
     },
     "execution_count": 543,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "22687"
      ]
     },
     "execution_count": 543,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_train2, rf_trainY, test_size=0.2, random_state=42)\n",
    "\n",
    "len(X_train)\n",
    "len(X_test)\n",
    "len(y_train)\n",
    "len(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 546,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_split=1e-07, min_samples_leaf=20,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           n_estimators=400, n_jobs=1, oob_score=True, random_state=42,\n",
       "           verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 546,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score: 0.738805595555\n",
      "Test score: 0.725433941473\n",
      "OOB score: 0.707024728515\n"
     ]
    }
   ],
   "source": [
    "# Step 1: Initiating the bagging regressor algorithm\n",
    "rf_regressor = RandomForestRegressor(n_estimators=400, random_state=42, min_samples_leaf=20,oob_score=True)\n",
    "# Step 2: Training the bagging regressor algorithm\n",
    "rf_regressor.fit(X_train,y_train)\n",
    "# Step 3: Calculating the score of the predictive power on the training and testing dataset.\n",
    "print_scores(rf_regressor, X_train, y_train, X_test, y_test, oob_score=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 549,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error between the predicted and true loan amounts: 45138 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x14af945f8>"
      ]
     },
     "execution_count": 549,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11f5792e8>"
      ]
     },
     "execution_count": 549,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1228d4a90>"
      ]
     },
     "execution_count": 549,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x14ae876d8>"
      ]
     },
     "execution_count": 549,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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jREP4K1P3DtMWlFJxamuMC80e4DVV19JVpp37kLXPAc/zfiE4QSn1x5z6JPM357m8Zu3f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\npUvjIrfML81CeqrNuE6iHRLtZkOD9bhrNwKgDx2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VwbWSgr4Bec2sFMzX4R6J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PH+qvc0SKPXqn70DhIPPSDlDLMZjFAY6+IDktfdJZGvjbBhP9sWWKtr7ZbvRtd8ti38DmuRPXeG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\n0iaoKCuKoihKm/D/A8UZOQ56ruDoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14ae7ffd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf_y_predicted_train = rf_regressor.predict(X_train)\n",
    "print(\"Mean squared error between the predicted and true loan amounts: %d \" % round(mean_squared_error(y_train, rf_y_predicted_train)))\n",
    "\n",
    "fig = plt.figure(figsize=(7, 7))\n",
    "plt.scatter(y_train, rf_y_predicted_train, alpha=0.5)\n",
    "plt.xlabel('True value')\n",
    "plt.ylabel('Predicted value')\n",
    "plt.title(\"relationship between True Y and predicted Y* loan amount (train dataset)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 548,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error between the predicted and true loan amounts: 47255 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1228cae10>"
      ]
     },
     "execution_count": 548,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11f596208>"
      ]
     },
     "execution_count": 548,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11f56b3c8>"
      ]
     },
     "execution_count": 548,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x14afee6a0>"
      ]
     },
     "execution_count": 548,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RxoIgzBvKvYawl4jjPvhTvP179KIJS5dDexTiozq0KUgFHLhmOGo1wkpVQk7E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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x245f60ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf_y_predicted_test = rf_regressor.predict(X_test)\n",
    "print(\"Mean squared error between the predicted and true loan amounts: %d \" % round(mean_squared_error(y_test, rf_y_predicted_test)))\n",
    "\n",
    "fig = plt.figure(figsize=(7, 7))\n",
    "plt.scatter(y_test, rf_y_predicted_test, alpha=0.5)\n",
    "plt.xlabel('True value')\n",
    "plt.ylabel('Predicted value')\n",
    "plt.title(\"relationship between True Y and predicted Y* loan amount (test dataset)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The scores haven't changed significantly which means that we can continue just using the top 20 features. This means the algorithms is much faster and gives us a clearer idea of which features affect the loan amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature Ranking:\n",
      "1. feature borrower_count (0.320873)\n",
      "2. feature posted_year (0.191886)\n",
      "3. feature terms_repayment_term (0.130257)\n",
      "4. feature posted_month (0.105737)\n",
      "5. feature more_one_partner_country (0.059595)\n",
      "6. feature married (0.046283)\n",
      "7. feature partner_delinquency_rate (0.031012)\n",
      "8. feature top_partners_One_Acre_Fund (0.030844)\n",
      "9. feature sector_Agriculture (0.011631)\n",
      "10. feature top_partners_Yehu_Microfinance_Trust (0.010544)\n",
      "11. feature pct_female (0.007960)\n",
      "12. feature sector_Education (0.007060)\n",
      "13. feature parent (0.007056)\n",
      "14. feature tag_Repeat_Borrower (0.006592)\n",
      "15. feature age_int (0.006199)\n",
      "16. feature children_int (0.006145)\n",
      "17. feature top_partners_VisionFund_Kenya (0.005884)\n",
      "18. feature top_partners_SMEP_Microfinance_Bank (0.005069)\n",
      "19. feature top_partners_Other (0.005030)\n",
      "20. feature top_partners_Juhudi_Kilimo (0.004345)\n"
     ]
    }
   ],
   "source": [
    "importances = rf_regressor.feature_importances_\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "print('Feature Ranking:')\n",
    "for f in range(len(importances)):\n",
    "    print(\"%d. feature %s (%f)\" % (f + 1, X_train2.columns[indices[f]], importances[indices[f]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 541,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14b11aef0>"
      ]
     },
     "execution_count": 541,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x23f418ac8>"
      ]
     },
     "execution_count": 541,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Container object of 20 artists>"
      ]
     },
     "execution_count": 541,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x14b103a20>,\n",
       "  <matplotlib.axis.XTick at 0x14b11b2b0>,\n",
       "  <matplotlib.axis.XTick at 0x23f43fdd8>,\n",
       "  <matplotlib.axis.XTick at 0x14b1183c8>,\n",
       "  <matplotlib.axis.XTick at 0x14b103278>,\n",
       "  <matplotlib.axis.XTick at 0x245f484a8>,\n",
       "  <matplotlib.axis.XTick at 0x245f48f60>,\n",
       "  <matplotlib.axis.XTick at 0x245f49a58>,\n",
       "  <matplotlib.axis.XTick at 0x245f4f550>,\n",
       "  <matplotlib.axis.XTick at 0x245f53048>,\n",
       "  <matplotlib.axis.XTick at 0x245f53b00>,\n",
       "  <matplotlib.axis.XTick at 0x245f5b5f8>,\n",
       "  <matplotlib.axis.XTick at 0x245f600f0>,\n",
       "  <matplotlib.axis.XTick at 0x245f60ba8>,\n",
       "  <matplotlib.axis.XTick at 0x245f636a0>,\n",
       "  <matplotlib.axis.XTick at 0x245f6b198>,\n",
       "  <matplotlib.axis.XTick at 0x245f6bc50>,\n",
       "  <matplotlib.axis.XTick at 0x245f71748>,\n",
       "  <matplotlib.axis.XTick at 0x245f75240>,\n",
       "  <matplotlib.axis.XTick at 0x245f75cf8>],\n",
       " <a list of 20 Text xticklabel objects>)"
      ]
     },
     "execution_count": 541,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(-1, 20)"
      ]
     },
     "execution_count": 541,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "        17, 18, 19]), <a list of 20 Text xticklabel objects>)"
      ]
     },
     "execution_count": 541,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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SJEmVGKwkSZIqMVhJkiRVYrCSJEmqxGAlSZJUicFKkiSpEoOVJElSJQYrSZKkSpZOd0BE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\namBVqVQqlUql0hA1sKpUKpVKpVJpiP8PnDj/byFWxFUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14b11aef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14b0fc828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances = rf_regressor.feature_importances_\n",
    "std = np.std([tree.feature_importances_ for tree in rf_regressor.estimators_],\n",
    "             axis=0)\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "feature_names = rf_trainX.columns \n",
    "plt.figure(figsize=(10,10))\n",
    "plt.title(\"Feature importances\")\n",
    "plt.bar(range(X_train2.shape[1]), importances[indices],\n",
    "       color=\"r\", yerr=std[indices], align=\"center\")\n",
    "plt.xticks(range(X_train2.shape[1]),feature_names)\n",
    "plt.xlim([-1, X_train2.shape[1]])\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()\n",
    "\n",
    "plt.savefig('module_3_images/feature_importance.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we move onto homework, remember to output your final dataframe so you don't have to rerun it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.to_csv(data_path+'df_module_3.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting Days to Fund"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Repeat the BaggingRegressor and the RandomForestRegressor method with a new target feature: days_to_fund. Determine the monst important features and discuss which features are intuitive or a surprise and why. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# adding the loan_amount feature back into the rf_trainArr dataset\n",
    "rf_trainArr['loan_amount'] = rf_trainRes\n",
    "# The new target feature result is the days_to_fund so pull that out and drop it from the training array\n",
    "rf_trainRes = rf_trainArr['days_to_fund']\n",
    "rf_trainArr = rf_trainArr.drop('days_to_fund', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create X_train, y_train, X_test and y_test where the y value is the days_to_fund feature. \n",
    "# Try using test_train_split function imported from sklearn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember the three basic steps:\n",
    "- step 1: Initiate the algorithm\n",
    "- step 2: Fit the algorithm to the training data\n",
    "- step 3: Evaluate the algorithm performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Use the BaggingRegressor method to predict the days_to_fund"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Use the RandomForest Regressor method to predict the days_to_fund"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Find the most important features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Advanced Homework (optional)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sklearn RandomForest library also has a classifier method, which rather than predicting a continuous numerical value such as loan amount or days to fund, it attempted to classify data on existing labels, eg: male or female. \n",
    "\n",
    "We imported this method at the beginning and you can read more about it here using the following cell or by going the sklearn [RandomForestClassifier](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html) webpage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "RandomForestClassifier?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using this module and the original feature gender, see how well we can classify loans by the gender. The procedure for training and testing data is practically the same as the RandomForestRegressor and the score is the mean accuracy of the predictions rather than the R^2 of the regression. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Advanced Material: Optimising the algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Up to this point, we have been largely been using the default values for all of the hyper-parameters.  To increase perfromance we should try adjusting these parameters.  Sklearn's GridSearchCV uses cross-validation to assess the performance of each possible permutation of the hyper-parameters that you specify."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "parameters = {'n_estimators': [100, 300, 500],\n",
    "             'max_features': ['auto', 'sqrt', 'log2'],\n",
    "              'max_depth': [None, 5, 10]\n",
    "             }\n",
    "gridrf = RandomForestRegressor()\n",
    "grid_rf = GridSearchCV(gridrf, parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-141-25471aae2115>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgrid_rf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32mC:\\Users\\PC\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, groups)\u001b[0m\n\u001b[1;32m    943\u001b[0m             \u001b[0mtrain\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mtest\u001b[0m \u001b[0mset\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    944\u001b[0m         \"\"\"\n\u001b[0;32m--> 945\u001b[0;31m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgroups\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mParameterGrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    946\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    947\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\PC\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36m_fit\u001b[0;34m(self, X, y, groups, parameter_iterable)\u001b[0m\n\u001b[1;32m    562\u001b[0m                                   \u001b[0mreturn_times\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreturn_parameters\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    563\u001b[0m                                   error_score=self.error_score)\n\u001b[0;32m--> 564\u001b[0;31m           \u001b[1;32mfor\u001b[0m \u001b[0mparameters\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mparameter_iterable\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    565\u001b[0m           for train, test in cv_iter)\n\u001b[1;32m    566\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\PC\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m    756\u001b[0m             \u001b[1;31m# was dispatched. In particular this covers the edge\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    757\u001b[0m             \u001b[1;31m# case of Parallel used with an exhausted iterator.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 758\u001b[0;31m             \u001b[1;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    759\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    760\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\PC\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    606\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    607\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 608\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    609\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    610\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\PC\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    569\u001b[0m         \u001b[0mdispatch_timestamp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    570\u001b[0m         \u001b[0mcb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBatchCompletionCallBack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdispatch_timestamp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 571\u001b[0;31m         \u001b[0mjob\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    572\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    573\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\PC\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    107\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m         \u001b[1;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 109\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    110\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    111\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\PC\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    324\u001b[0m         \u001b[1;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    325\u001b[0m         \u001b[1;31m# arguments in memory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 326\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    327\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    328\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\PC\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m         \u001b[1;32mreturn\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\PC\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m         \u001b[1;32mreturn\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\PC\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[0;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, error_score)\u001b[0m\n\u001b[1;32m    236\u001b[0m             \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 238\u001b[0;31m             \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    239\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\PC\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    324\u001b[0m                     \u001b[0mt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrees\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    325\u001b[0m                     verbose=self.verbose, class_weight=self.class_weight)\n\u001b[0;32m--> 326\u001b[0;31m                 for i, t in enumerate(trees))\n\u001b[0m\u001b[1;32m    327\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    328\u001b[0m             \u001b[1;31m# Collect newly grown trees\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\PC\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m    756\u001b[0m             \u001b[1;31m# was dispatched. In particular this covers the edge\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    757\u001b[0m             \u001b[1;31m# case of Parallel used with an exhausted iterator.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 758\u001b[0;31m             \u001b[1;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    759\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    760\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\PC\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    606\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    607\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 608\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    609\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    610\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\PC\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    569\u001b[0m         \u001b[0mdispatch_timestamp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    570\u001b[0m         \u001b[0mcb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBatchCompletionCallBack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdispatch_timestamp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 571\u001b[0;31m         \u001b[0mjob\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    572\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    573\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\PC\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    107\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m         \u001b[1;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 109\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    110\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    111\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\PC\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    324\u001b[0m         \u001b[1;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    325\u001b[0m         \u001b[1;31m# arguments in memory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 326\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    327\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    328\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\PC\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m         \u001b[1;32mreturn\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\PC\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m         \u001b[1;32mreturn\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\PC\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py\u001b[0m in \u001b[0;36m_parallel_build_trees\u001b[0;34m(tree, forest, X, y, sample_weight, tree_idx, n_trees, verbose, class_weight)\u001b[0m\n\u001b[1;32m    118\u001b[0m             \u001b[0mcurr_sample_weight\u001b[0m \u001b[1;33m*=\u001b[0m \u001b[0mcompute_sample_weight\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'balanced'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindices\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 120\u001b[0;31m         \u001b[0mtree\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcurr_sample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    121\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0mtree\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\PC\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[1;32m   1027\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1028\u001b[0m             \u001b[0mcheck_input\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcheck_input\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1029\u001b[0;31m             X_idx_sorted=X_idx_sorted)\n\u001b[0m\u001b[1;32m   1030\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1031\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\PC\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[1;32m    348\u001b[0m                                            self.min_impurity_split)\n\u001b[1;32m    349\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 350\u001b[0;31m         \u001b[0mbuilder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtree_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_idx_sorted\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    351\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    352\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_outputs_\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "grid_rf.fit(X_train2, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To find out which set of parameters performed best, we can use the .best\\_pramas\\_ method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "grid_rf.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's check out the variation in performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "means = grid_rf.cv_results_['mean_test_score']\n",
    "stds = grid_rf.cv_results_['std_test_score']\n",
    "for mean, std, params in zip(means, stds, grid_rf.cv_results_['params']):\n",
    "    print(\"%0.3f (+/-%0.03f) for %r\"\n",
    "          % (mean, std * 2, params))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
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