SVM Full_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "<p>\n",
    "DonorsChoose.org receives hundreds of thousands of project proposals each year for classroom projects in need of funding. Right now, a large number of volunteers is needed to manually screen each submission before it's approved to be posted on the DonorsChoose.org website.\n",
    "</p>\n",
    "<p>\n",
    "    Next year, DonorsChoose.org expects to receive close to 500,000 project proposals. As a result, there are three main problems they need to solve:\n",
    "<ul>\n",
    "<li>\n",
    "    How to scale current manual processes and resources to screen 500,000 projects so that they can be posted as quickly and as efficiently as possible</li>F\n",
    "    <li>How to increase the consistency of project vetting across different volunteers to improve the experience for teachers</li>\n",
    "    <li>How to focus volunteer time on the applications that need the most assistance</li>\n",
    "    </ul>\n",
    "</p>    \n",
    "<p>\n",
    "The goal of the competition is to predict whether or not a DonorsChoose.org project proposal submitted by a teacher will be approved, using the text of project descriptions as well as additional metadata about the project, teacher, and school. DonorsChoose.org can then use this information to identify projects most likely to need further review before approval.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Notes on the Essay Data\n",
    "\n",
    "<ul>\n",
    "Prior to May 17, 2016, the prompts for the essays were as follows:\n",
    "<li>__project_essay_1:__ \"Introduce us to your classroom\"</li>\n",
    "<li>__project_essay_2:__ \"Tell us more about your students\"</li>\n",
    "<li>__project_essay_3:__ \"Describe how your students will use the materials you're requesting\"</li>\n",
    "<li>__project_essay_3:__ \"Close by sharing why your project will make a difference\"</li>\n",
    "</ul>\n",
    "\n",
    "\n",
    "<ul>\n",
    "Starting on May 17, 2016, the number of essays was reduced from 4 to 2, and the prompts for the first 2 essays were changed to the following:<br>\n",
    "<li>__project_essay_1:__ \"Describe your students: What makes your students special? Specific details about their background, your neighborhood, and your school are all helpful.\"</li>\n",
    "<li>__project_essay_2:__ \"About your project: How will these materials make a difference in your students' learning and improve their school lives?\"</li>\n",
    "<br>For all projects with project_submitted_datetime of 2016-05-17 and later, the values of project_essay_3 and project_essay_4 will be NaN.\n",
    "</ul>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Import the necessary Libraries\n",
    "we will need to import libraries that allow for data analysis and data visualization to get acclimated to the dataset. We will be using pandas, numpy, matplotlib and seaborn to conduct this. Data Exploration libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "warnings.filterwarnings(action='ignore', category=UserWarning, module='gensim')\n",
    "warnings.filterwarnings(\"ignore\",'detected Windows; aliasing chunkize to chunkize_serial')\n",
    "warnings.filterwarnings(\"ignore\", message=\"numpy.dtype size changed\")\n",
    "\n",
    "import sqlite3\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import nltk\n",
    "import string\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "\n",
    "import re\n",
    "# Tutorial about Python regular expressions: https://pymotw.com/2/re/\n",
    "import string\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import PorterStemmer\n",
    "from nltk.stem.wordnet import WordNetLemmatizer\n",
    "\n",
    "from gensim.models import Word2Vec\n",
    "from gensim.models import KeyedVectors\n",
    "import pickle\n",
    "from tqdm import tqdm\n",
    "import os\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Read in the dataset.\n",
    "We will use the pandas .read_csv() method to read in the dataset. Then we will use the. head() method to observe the first few rows of the data, to understand the information better. In our case, the feature(column) headers tell us pretty little. This is fine because we are merely trying to gain insight via classifying new data points by referencing it’s neighboring elements."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 Reading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>description</th>\n",
       "      <th>quantity</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>p233245</td>\n",
       "      <td>LC652 - Lakeshore Double-Space Mobile Drying Rack</td>\n",
       "      <td>1</td>\n",
       "      <td>149.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id                                        description  quantity  price\n",
       "0  p233245  LC652 - Lakeshore Double-Space Mobile Drying Rack         1  149.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "project_data = pd.read_csv(\"C:\\\\VipinML\\\\Assignment 2\\\\Assignments_DonorsChoose_2018\\\\train_data.csv\")\n",
    "resource_data = pd.read_csv(\"C:\\\\VipinML\\Assignment 2\\\\Assignments_DonorsChoose_2018\\\\resources.csv\")\n",
    "#Limit the data for testing purpose since processing takes few hours for full set..\n",
    "\n",
    "project_data = project_data.head(10000)\n",
    "resource_data = resource_data.head (10000)\n",
    "\n",
    "resource_data.head(1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data points in train data (10000, 17)\n",
      "--------------------------------------------------\n",
      "The attributes of data : ['Unnamed: 0' 'id' 'teacher_id' 'teacher_prefix' 'school_state'\n",
      " 'project_submitted_datetime' 'project_grade_category'\n",
      " 'project_subject_categories' 'project_subject_subcategories'\n",
      " 'project_title' 'project_essay_1' 'project_essay_2' 'project_essay_3'\n",
      " 'project_essay_4' 'project_resource_summary'\n",
      " 'teacher_number_of_previously_posted_projects' 'project_is_approved']\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of data points in train data\", project_data.shape)\n",
    "print('-'*50)\n",
    "print(\"The attributes of data :\", project_data.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>teacher_id</th>\n",
       "      <th>teacher_prefix</th>\n",
       "      <th>school_state</th>\n",
       "      <th>Date</th>\n",
       "      <th>project_grade_category</th>\n",
       "      <th>project_subject_categories</th>\n",
       "      <th>project_subject_subcategories</th>\n",
       "      <th>project_title</th>\n",
       "      <th>project_essay_1</th>\n",
       "      <th>project_essay_2</th>\n",
       "      <th>project_essay_3</th>\n",
       "      <th>project_essay_4</th>\n",
       "      <th>project_resource_summary</th>\n",
       "      <th>teacher_number_of_previously_posted_projects</th>\n",
       "      <th>project_is_approved</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>100660</td>\n",
       "      <td>p234804</td>\n",
       "      <td>cbc0e38f522143b86d372f8b43d4cff3</td>\n",
       "      <td>Mrs.</td>\n",
       "      <td>GA</td>\n",
       "      <td>2016-04-27 00:53:00</td>\n",
       "      <td>Grades PreK-2</td>\n",
       "      <td>Applied Learning</td>\n",
       "      <td>Early Development</td>\n",
       "      <td>Flexible Seating for Flexible Learning</td>\n",
       "      <td>I recently read an article about giving studen...</td>\n",
       "      <td>I teach at a low-income (Title 1) school. Ever...</td>\n",
       "      <td>We need a classroom rug that we can use as a c...</td>\n",
       "      <td>Benjamin Franklin once said, \\\"Tell me and I f...</td>\n",
       "      <td>My students need flexible seating in the class...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0       id                        teacher_id teacher_prefix  \\\n",
       "473      100660  p234804  cbc0e38f522143b86d372f8b43d4cff3           Mrs.   \n",
       "\n",
       "    school_state                Date project_grade_category  \\\n",
       "473           GA 2016-04-27 00:53:00          Grades PreK-2   \n",
       "\n",
       "    project_subject_categories project_subject_subcategories  \\\n",
       "473           Applied Learning             Early Development   \n",
       "\n",
       "                              project_title  \\\n",
       "473  Flexible Seating for Flexible Learning   \n",
       "\n",
       "                                       project_essay_1  \\\n",
       "473  I recently read an article about giving studen...   \n",
       "\n",
       "                                       project_essay_2  \\\n",
       "473  I teach at a low-income (Title 1) school. Ever...   \n",
       "\n",
       "                                       project_essay_3  \\\n",
       "473  We need a classroom rug that we can use as a c...   \n",
       "\n",
       "                                       project_essay_4  \\\n",
       "473  Benjamin Franklin once said, \\\"Tell me and I f...   \n",
       "\n",
       "                              project_resource_summary  \\\n",
       "473  My students need flexible seating in the class...   \n",
       "\n",
       "     teacher_number_of_previously_posted_projects  project_is_approved  \n",
       "473                                             2                    1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how to replace elements in list python: https://stackoverflow.com/a/2582163/4084039\n",
    "cols = ['Date' if x=='project_submitted_datetime' else x for x in list(project_data.columns)]\n",
    "#sort dataframe based on time pandas python: https://stackoverflow.com/a/49702492/4084039\n",
    "project_data['Date'] = pd.to_datetime(project_data['project_submitted_datetime'])\n",
    "project_data.drop('project_submitted_datetime', axis=1, inplace=True)\n",
    "project_data.sort_values(by=['Date'], inplace=True)\n",
    "\n",
    "# how to reorder columns pandas python: https://stackoverflow.com/a/13148611/4084039\n",
    "project_data = project_data[cols]\n",
    "project_data.head(1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 preprocessing of `project_subject_categories`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "catogories = list(project_data['project_subject_categories'].values)\n",
    "# remove special characters from list of strings python: https://stackoverflow.com/a/47301924/4084039\n",
    "\n",
    "# https://www.geeksforgeeks.org/removing-stop-words-nltk-python/\n",
    "# https://stackoverflow.com/questions/23669024/how-to-strip-a-specific-word-from-a-string\n",
    "# https://stackoverflow.com/questions/8270092/remove-all-whitespace-in-a-string-in-python\n",
    "cat_list = []\n",
    "for i in catogories:\n",
    "    temp = \"\"\n",
    "    # consider we have text like this \"Math & Science, Warmth, Care & Hunger\"\n",
    "    for j in i.split(','): # it will split it in three parts [\"Math & Science\", \"Warmth\", \"Care & Hunger\"]\n",
    "        if 'The' in j.split(): # this will split each of the catogory based on space \"Math & Science\"=> \"Math\",\"&\", \"Science\"\n",
    "            j=j.replace('The','') # if we have the words \"The\" we are going to replace it with ''(i.e removing 'The')\n",
    "        j = j.replace(' ','') # we are placeing all the ' '(space) with ''(empty) ex:\"Math & Science\"=>\"Math&Science\"\n",
    "        temp+=j.strip()+\" \" #\" abc \".strip() will return \"abc\", remove the trailing spaces\n",
    "        temp = temp.replace('&','_') # we are replacing the & value into \n",
    "    cat_list.append(temp.strip())\n",
    "    \n",
    "project_data['clean_categories'] = cat_list\n",
    "project_data.drop(['project_subject_categories'], axis=1, inplace=True)\n",
    "\n",
    "from collections import Counter\n",
    "my_counter = Counter()\n",
    "for word in project_data['clean_categories'].values:\n",
    "    my_counter.update(word.split())\n",
    "\n",
    "cat_dict = dict(my_counter)\n",
    "sorted_cat_dict = dict(sorted(cat_dict.items(), key=lambda kv: kv[1]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3 preprocessing of `project_subject_subcategories`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_catogories = list(project_data['project_subject_subcategories'].values)\n",
    "# remove special characters from list of strings python: https://stackoverflow.com/a/47301924/4084039\n",
    "\n",
    "# https://www.geeksforgeeks.org/removing-stop-words-nltk-python/\n",
    "# https://stackoverflow.com/questions/23669024/how-to-strip-a-specific-word-from-a-string\n",
    "# https://stackoverflow.com/questions/8270092/remove-all-whitespace-in-a-string-in-python\n",
    "\n",
    "sub_cat_list = []\n",
    "for i in sub_catogories:\n",
    "    temp = \"\"\n",
    "    # consider we have text like this \"Math & Science, Warmth, Care & Hunger\"\n",
    "    for j in i.split(','): # it will split it in three parts [\"Math & Science\", \"Warmth\", \"Care & Hunger\"]\n",
    "        if 'The' in j.split(): # this will split each of the catogory based on space \"Math & Science\"=> \"Math\",\"&\", \"Science\"\n",
    "            j=j.replace('The','') # if we have the words \"The\" we are going to replace it with ''(i.e removing 'The')\n",
    "        j = j.replace(' ','') # we are placeing all the ' '(space) with ''(empty) ex:\"Math & Science\"=>\"Math&Science\"\n",
    "        temp +=j.strip()+\" \"#\" abc \".strip() will return \"abc\", remove the trailing spaces\n",
    "        temp = temp.replace('&','_')\n",
    "    sub_cat_list.append(temp.strip())\n",
    "\n",
    "project_data['clean_subcategories'] = sub_cat_list\n",
    "project_data.drop(['project_subject_subcategories'], axis=1, inplace=True)\n",
    "\n",
    "# count of all the words in corpus python: https://stackoverflow.com/a/22898595/4084039\n",
    "my_counter = Counter()\n",
    "for word in project_data['clean_subcategories'].values:\n",
    "    my_counter.update(word.split())\n",
    "    \n",
    "sub_cat_dict = dict(my_counter)\n",
    "sorted_sub_cat_dict = dict(sorted(sub_cat_dict.items(), key=lambda kv: kv[1]))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "teacher_cat = list(project_data['teacher_prefix'].values)\n",
    "# remove special characters from list of strings python: https://stackoverflow.com/a/47301924/4084039\n",
    "\n",
    "# https://www.geeksforgeeks.org/removing-stop-words-nltk-python/\n",
    "# https://stackoverflow.com/questions/23669024/how-to-strip-a-specific-word-from-a-string\n",
    "# https://stackoverflow.com/questions/8270092/remove-all-whitespace-in-a-string-in-python\n",
    "cat_list = []\n",
    "for i in teacher_cat:\n",
    "    temp = \"\"\n",
    "    # consider we have text like this \"Math & Science, Warmth, Care & Hunger\"\n",
    "    j = j.replace(' ','') # we are placeing all the ' '(space) with ''(empty) ex:\"Math & Science\"=>\"Math&Science\"\n",
    "    temp+=j.strip()+\" \" #\" abc \".strip() will return \"abc\", remove the trailing spaces\n",
    "    temp = temp.replace('&','_') # we are replacing the & value into \n",
    "    cat_list.append(temp.strip())\n",
    "\n",
    "project_data.drop(['teacher_prefix'], axis=1, inplace=True)\n",
    "project_data['teacher_prefix'] = sub_cat_list\n",
    "    \n",
    "from collections import Counter\n",
    "my_counter = Counter()\n",
    "for word in project_data['teacher_prefix'].values:\n",
    "    my_counter.update(word.split())\n",
    "\n",
    "cat_dict = dict(my_counter)\n",
    "sorted_teacher_dict = dict(sorted(cat_dict.items(), key=lambda kv: kv[1]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3 Text preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge two column text dataframe: \n",
    "project_data[\"essay\"] = project_data[\"project_essay_1\"].map(str) +\\\n",
    "                        project_data[\"project_essay_2\"].map(str) + \\\n",
    "                        project_data[\"project_essay_3\"].map(str) + \\\n",
    "                        project_data[\"project_essay_4\"].map(str)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#clean project_grade_category\n",
    "project_data[\"project_grade_category\"] = \\\n",
    "project_data.apply(lambda x: (x['project_grade_category'].replace(' ', '_')), axis=1)\n",
    "project_data[\"project_grade_category\"] = \\\n",
    "project_data.apply(lambda x: (x['project_grade_category'].replace('-', '_')), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>teacher_id</th>\n",
       "      <th>school_state</th>\n",
       "      <th>Date</th>\n",
       "      <th>project_grade_category</th>\n",
       "      <th>project_title</th>\n",
       "      <th>project_essay_1</th>\n",
       "      <th>project_essay_2</th>\n",
       "      <th>project_essay_3</th>\n",
       "      <th>project_essay_4</th>\n",
       "      <th>project_resource_summary</th>\n",
       "      <th>teacher_number_of_previously_posted_projects</th>\n",
       "      <th>project_is_approved</th>\n",
       "      <th>clean_categories</th>\n",
       "      <th>clean_subcategories</th>\n",
       "      <th>teacher_prefix</th>\n",
       "      <th>essay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>100660</td>\n",
       "      <td>p234804</td>\n",
       "      <td>cbc0e38f522143b86d372f8b43d4cff3</td>\n",
       "      <td>GA</td>\n",
       "      <td>2016-04-27 00:53:00</td>\n",
       "      <td>Grades_PreK_2</td>\n",
       "      <td>Flexible Seating for Flexible Learning</td>\n",
       "      <td>I recently read an article about giving studen...</td>\n",
       "      <td>I teach at a low-income (Title 1) school. Ever...</td>\n",
       "      <td>We need a classroom rug that we can use as a c...</td>\n",
       "      <td>Benjamin Franklin once said, \\\"Tell me and I f...</td>\n",
       "      <td>My students need flexible seating in the class...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>AppliedLearning</td>\n",
       "      <td>EarlyDevelopment</td>\n",
       "      <td>EarlyDevelopment</td>\n",
       "      <td>I recently read an article about giving studen...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0       id                        teacher_id school_state  \\\n",
       "473      100660  p234804  cbc0e38f522143b86d372f8b43d4cff3           GA   \n",
       "\n",
       "                   Date project_grade_category  \\\n",
       "473 2016-04-27 00:53:00          Grades_PreK_2   \n",
       "\n",
       "                              project_title  \\\n",
       "473  Flexible Seating for Flexible Learning   \n",
       "\n",
       "                                       project_essay_1  \\\n",
       "473  I recently read an article about giving studen...   \n",
       "\n",
       "                                       project_essay_2  \\\n",
       "473  I teach at a low-income (Title 1) school. Ever...   \n",
       "\n",
       "                                       project_essay_3  \\\n",
       "473  We need a classroom rug that we can use as a c...   \n",
       "\n",
       "                                       project_essay_4  \\\n",
       "473  Benjamin Franklin once said, \\\"Tell me and I f...   \n",
       "\n",
       "                              project_resource_summary  \\\n",
       "473  My students need flexible seating in the class...   \n",
       "\n",
       "     teacher_number_of_previously_posted_projects  project_is_approved  \\\n",
       "473                                             2                    1   \n",
       "\n",
       "    clean_categories clean_subcategories    teacher_prefix  \\\n",
       "473  AppliedLearning    EarlyDevelopment  EarlyDevelopment   \n",
       "\n",
       "                                                 essay  \n",
       "473  I recently read an article about giving studen...  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "project_data.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### 1.4.2.3 Using Pretrained Models: TFIDF weighted W2V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://stackoverflow.com/a/47091490/4084039\n",
    "import re\n",
    "\n",
    "def decontracted(phrase):\n",
    "    # specific\n",
    "    phrase = re.sub(r\"won't\", \"will not\", phrase)\n",
    "    phrase = re.sub(r\"can\\'t\", \"can not\", phrase)\n",
    "\n",
    "    # general\n",
    "    phrase = re.sub(r\"n\\'t\", \" not\", phrase)\n",
    "    phrase = re.sub(r\"\\'re\", \" are\", phrase)\n",
    "    phrase = re.sub(r\"\\'s\", \" is\", phrase)\n",
    "    phrase = re.sub(r\"\\'d\", \" would\", phrase)\n",
    "    phrase = re.sub(r\"\\'ll\", \" will\", phrase)\n",
    "    phrase = re.sub(r\"\\'t\", \" not\", phrase)\n",
    "    phrase = re.sub(r\"\\'ve\", \" have\", phrase)\n",
    "    phrase = re.sub(r\"\\'m\", \" am\", phrase)\n",
    "    return phrase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y students need extensive support because they are growing and learning in a low income neighborhood.  I want to provide them a wonderful experiences of learning.\\n They are English Language Learners\n",
      "====================================================================================================\n"
     ]
    }
   ],
   "source": [
    "sent = decontracted(project_data['essay'].values[500])\n",
    "print(sent[1:200])\n",
    "print(\"=\"*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y students need extensive support because they are growing and learning in a low income neighborhood.  I want to provide them a wonderful experiences of learning.  They are English Language Learners,\n",
      "y students need extensive support because they are growing and learning in a low income neighborhood.  I want to provide them a wonderful experiences of learning.  They are English Language Learners,\n"
     ]
    }
   ],
   "source": [
    "# \\r \\n \\t remove from string python: http://texthandler.com/info/remove-line-breaks-python/\n",
    "sent = sent.replace('\\\\r', ' ')\n",
    "sent = sent.replace('\\\\\"', ' ')\n",
    "sent = sent.replace('\\\\n', ' ')\n",
    "print(sent[1:200])\n",
    "print(sent[1:200])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y students need extensive support because they are growing and learning in a low income neighborhood I want to provide them a wonderful experiences of learning They are English Language Learners and \n"
     ]
    }
   ],
   "source": [
    "#remove spacial character: https://stackoverflow.com/a/5843547/4084039\n",
    "sent = re.sub('[^A-Za-z0-9]+', ' ', sent)\n",
    "print(sent[1:200])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://gist.github.com/sebleier/554280\n",
    "# we are removing the words from the stop words list: 'no', 'nor', 'not'\n",
    "stopwords= ['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\", \"you've\",\\\n",
    "            \"you'll\", \"you'd\", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', \\\n",
    "            'she', \"she's\", 'her', 'hers', 'herself', 'it', \"it's\", 'its', 'itself', 'they', 'them', 'their',\\\n",
    "            'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', \"that'll\", 'these', 'those', \\\n",
    "            'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', \\\n",
    "            'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', \\\n",
    "            'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after',\\\n",
    "            'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further',\\\n",
    "            'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more',\\\n",
    "            'most', 'other', 'some', 'such', 'only', 'own', 'same', 'so', 'than', 'too', 'very', \\\n",
    "            's', 't', 'can', 'will', 'just', 'don', \"don't\", 'should', \"should've\", 'now', 'd', 'll', 'm', 'o', 're', \\\n",
    "            've', 'y', 'ain', 'aren', \"aren't\", 'couldn', \"couldn't\", 'didn', \"didn't\", 'doesn', \"doesn't\", 'hadn',\\\n",
    "            \"hadn't\", 'hasn', \"hasn't\", 'haven', \"haven't\", 'isn', \"isn't\", 'ma', 'mightn', \"mightn't\", 'mustn',\\\n",
    "            \"mustn't\", 'needn', \"needn't\", 'shan', \"shan't\", 'shouldn', \"shouldn't\", 'wasn', \"wasn't\", 'weren', \"weren't\", \\\n",
    "            'won', \"won't\", 'wouldn', \"wouldn't\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4.3 Merging price with project_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        id   price  quantity\n",
      "1  p000426  117.45        24\n",
      "2  p000477  443.49         6\n"
     ]
    },
    {
     "data": {
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       "      <th>school_state</th>\n",
       "      <th>Date</th>\n",
       "      <th>project_grade_category</th>\n",
       "      <th>project_title</th>\n",
       "      <th>project_essay_1</th>\n",
       "      <th>project_essay_2</th>\n",
       "      <th>project_essay_3</th>\n",
       "      <th>project_essay_4</th>\n",
       "      <th>project_resource_summary</th>\n",
       "      <th>teacher_number_of_previously_posted_projects</th>\n",
       "      <th>project_is_approved</th>\n",
       "      <th>clean_categories</th>\n",
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      ],
      "text/plain": [
       "   Unnamed: 0       id                        teacher_id school_state  \\\n",
       "0      100660  p234804  cbc0e38f522143b86d372f8b43d4cff3           GA   \n",
       "\n",
       "                 Date project_grade_category  \\\n",
       "0 2016-04-27 00:53:00          Grades_PreK_2   \n",
       "\n",
       "                            project_title  \\\n",
       "0  Flexible Seating for Flexible Learning   \n",
       "\n",
       "                                     project_essay_1  \\\n",
       "0  I recently read an article about giving studen...   \n",
       "\n",
       "                                     project_essay_2  \\\n",
       "0  I teach at a low-income (Title 1) school. Ever...   \n",
       "\n",
       "                                     project_essay_3  \\\n",
       "0  We need a classroom rug that we can use as a c...   \n",
       "\n",
       "                                     project_essay_4  \\\n",
       "0  Benjamin Franklin once said, \\\"Tell me and I f...   \n",
       "\n",
       "                            project_resource_summary  \\\n",
       "0  My students need flexible seating in the class...   \n",
       "\n",
       "   teacher_number_of_previously_posted_projects  project_is_approved  \\\n",
       "0                                             2                    1   \n",
       "\n",
       "  clean_categories clean_subcategories    teacher_prefix  \\\n",
       "0  AppliedLearning    EarlyDevelopment  EarlyDevelopment   \n",
       "\n",
       "                                               essay   price  quantity  \n",
       "0  I recently read an article about giving studen...  481.04       9.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_data = resource_data.groupby('id').agg({'price':'sum', 'quantity':'sum'}).reset_index()\n",
    "project_data = pd.merge(project_data, price_data, on='id', how='left')\n",
    "print (price_data[1:3])\n",
    "project_data.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4.3.1 Merge Project Title Count with project_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>project_essay_2</th>\n",
       "      <th>project_essay_3</th>\n",
       "      <th>...</th>\n",
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       "      <th>clean_subcategories</th>\n",
       "      <th>teacher_prefix</th>\n",
       "      <th>essay</th>\n",
       "      <th>price</th>\n",
       "      <th>quantity</th>\n",
       "      <th>project_title_count</th>\n",
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       "      <td>Grades_PreK_2</td>\n",
       "      <td>Flexible Seating for Flexible Learning</td>\n",
       "      <td>I recently read an article about giving studen...</td>\n",
       "      <td>I teach at a low-income (Title 1) school. Ever...</td>\n",
       "      <td>We need a classroom rug that we can use as a c...</td>\n",
       "      <td>...</td>\n",
       "      <td>My students need flexible seating in the class...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>AppliedLearning</td>\n",
       "      <td>EarlyDevelopment</td>\n",
       "      <td>EarlyDevelopment</td>\n",
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       "      <td>481.04</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5</td>\n",
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      ],
      "text/plain": [
       "   Unnamed: 0       id                        teacher_id school_state  \\\n",
       "0      100660  p234804  cbc0e38f522143b86d372f8b43d4cff3           GA   \n",
       "\n",
       "                 Date project_grade_category  \\\n",
       "0 2016-04-27 00:53:00          Grades_PreK_2   \n",
       "\n",
       "                            project_title  \\\n",
       "0  Flexible Seating for Flexible Learning   \n",
       "\n",
       "                                     project_essay_1  \\\n",
       "0  I recently read an article about giving studen...   \n",
       "\n",
       "                                     project_essay_2  \\\n",
       "0  I teach at a low-income (Title 1) school. Ever...   \n",
       "\n",
       "                                     project_essay_3  ...  \\\n",
       "0  We need a classroom rug that we can use as a c...  ...   \n",
       "\n",
       "                            project_resource_summary  \\\n",
       "0  My students need flexible seating in the class...   \n",
       "\n",
       "  teacher_number_of_previously_posted_projects  project_is_approved  \\\n",
       "0                                            2                    1   \n",
       "\n",
       "   clean_categories clean_subcategories    teacher_prefix  \\\n",
       "0   AppliedLearning    EarlyDevelopment  EarlyDevelopment   \n",
       "\n",
       "                                               essay   price  quantity  \\\n",
       "0  I recently read an article about giving studen...  481.04       9.0   \n",
       "\n",
       "   project_title_count  \n",
       "0                    5  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add count (total number of words) in Project Title in each row.\n",
    "\n",
    "project_title_count = project_data['project_title'].str.split().str.len()\n",
    "project_data['project_title_count'] = project_title_count\n",
    "project_data.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4.3.2 Essay count of words for each row and  merge with project_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Grades_PreK_2</td>\n",
       "      <td>Flexible Seating for Flexible Learning</td>\n",
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       "      <td>...</td>\n",
       "      <td>2</td>\n",
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       "      <td>5</td>\n",
       "      <td>225</td>\n",
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       "   Unnamed: 0       id                        teacher_id school_state  \\\n",
       "0      100660  p234804  cbc0e38f522143b86d372f8b43d4cff3           GA   \n",
       "\n",
       "                 Date project_grade_category  \\\n",
       "0 2016-04-27 00:53:00          Grades_PreK_2   \n",
       "\n",
       "                            project_title  \\\n",
       "0  Flexible Seating for Flexible Learning   \n",
       "\n",
       "                                     project_essay_1  \\\n",
       "0  I recently read an article about giving studen...   \n",
       "\n",
       "                                     project_essay_2  \\\n",
       "0  I teach at a low-income (Title 1) school. Ever...   \n",
       "\n",
       "                                     project_essay_3  ...  \\\n",
       "0  We need a classroom rug that we can use as a c...  ...   \n",
       "\n",
       "  teacher_number_of_previously_posted_projects project_is_approved  \\\n",
       "0                                            2                   1   \n",
       "\n",
       "   clean_categories  clean_subcategories    teacher_prefix  \\\n",
       "0   AppliedLearning     EarlyDevelopment  EarlyDevelopment   \n",
       "\n",
       "                                               essay   price quantity  \\\n",
       "0  I recently read an article about giving studen...  481.04      9.0   \n",
       "\n",
       "   project_title_count  essay_count  \n",
       "0                    5          225  \n",
       "\n",
       "[1 rows x 22 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add count (total number of words) in essay in each row.\n",
    "\n",
    "essay_count = project_data['essay'].str.split().str.len()\n",
    "project_data['essay_count'] = essay_count\n",
    "project_data.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>teacher_id</th>\n",
       "      <th>school_state</th>\n",
       "      <th>Date</th>\n",
       "      <th>project_grade_category</th>\n",
       "      <th>project_title</th>\n",
       "      <th>project_essay_1</th>\n",
       "      <th>project_essay_2</th>\n",
       "      <th>project_essay_3</th>\n",
       "      <th>...</th>\n",
       "      <th>teacher_number_of_previously_posted_projects</th>\n",
       "      <th>project_is_approved</th>\n",
       "      <th>clean_categories</th>\n",
       "      <th>clean_subcategories</th>\n",
       "      <th>teacher_prefix</th>\n",
       "      <th>essay</th>\n",
       "      <th>price</th>\n",
       "      <th>quantity</th>\n",
       "      <th>project_title_count</th>\n",
       "      <th>essay_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100660</td>\n",
       "      <td>p234804</td>\n",
       "      <td>cbc0e38f522143b86d372f8b43d4cff3</td>\n",
       "      <td>GA</td>\n",
       "      <td>2016-04-27 00:53:00</td>\n",
       "      <td>Grades_PreK_2</td>\n",
       "      <td>Flexible Seating for Flexible Learning</td>\n",
       "      <td>I recently read an article about giving studen...</td>\n",
       "      <td>I teach at a low-income (Title 1) school. Ever...</td>\n",
       "      <td>We need a classroom rug that we can use as a c...</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>AppliedLearning</td>\n",
       "      <td>EarlyDevelopment</td>\n",
       "      <td>EarlyDevelopment</td>\n",
       "      <td>I recently read an article about giving studen...</td>\n",
       "      <td>481.04</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0       id                        teacher_id school_state  \\\n",
       "0      100660  p234804  cbc0e38f522143b86d372f8b43d4cff3           GA   \n",
       "\n",
       "                 Date project_grade_category  \\\n",
       "0 2016-04-27 00:53:00          Grades_PreK_2   \n",
       "\n",
       "                            project_title  \\\n",
       "0  Flexible Seating for Flexible Learning   \n",
       "\n",
       "                                     project_essay_1  \\\n",
       "0  I recently read an article about giving studen...   \n",
       "\n",
       "                                     project_essay_2  \\\n",
       "0  I teach at a low-income (Title 1) school. Ever...   \n",
       "\n",
       "                                     project_essay_3  ...  \\\n",
       "0  We need a classroom rug that we can use as a c...  ...   \n",
       "\n",
       "  teacher_number_of_previously_posted_projects project_is_approved  \\\n",
       "0                                            2                   1   \n",
       "\n",
       "   clean_categories  clean_subcategories    teacher_prefix  \\\n",
       "0   AppliedLearning     EarlyDevelopment  EarlyDevelopment   \n",
       "\n",
       "                                               essay   price quantity  \\\n",
       "0  I recently read an article about giving studen...  481.04      9.0   \n",
       "\n",
       "   project_title_count  essay_count  \n",
       "0                    5          225  \n",
       "\n",
       "[1 rows x 22 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Convert NaN value to mean of the column\n",
    "project_data.fillna(project_data.mean(), inplace=True)\n",
    "project_data.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = project_data['project_is_approved'].values\n",
    "X = project_data.drop(['project_is_approved'], axis=1)\n",
    "\n",
    "# train test split\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, stratify=y)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splitting data into Train and cross validation(or test): Stratified Sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = project_data['project_is_approved'].values\n",
    "X = project_data.drop(['project_is_approved'], axis=1)\n",
    "\n",
    "# train test split\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, stratify=y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "catogories_essay = list(project_data['essay'].values)\n",
    "# remove special characters from list of strings python: https://stackoverflow.com/a/47301924/4084039\n",
    "\n",
    "# https://www.geeksforgeeks.org/removing-stop-words-nltk-python/\n",
    "# https://stackoverflow.com/questions/23669024/how-to-strip-a-specific-word-from-a-string\n",
    "# https://stackoverflow.com/questions/8270092/remove-all-whitespace-in-a-string-in-python\n",
    "cat_essay_list = []\n",
    "for i in catogories_essay:\n",
    "    temp = \"\"\n",
    "    # consider we have text like this \"Math & Science, Warmth, Care & Hunger\"\n",
    "    for j in i.split(','): # it will split it in three parts [\"Math & Science\", \"Warmth\", \"Care & Hunger\"]\n",
    "        if 'The' in j.split(): # this will split each of the catogory based on space \"Math & Science\"=> \"Math\",\"&\", \"Science\"\n",
    "            j=j.replace('The','') # if we have the words \"The\" we are going to replace it with ''(i.e removing 'The')\n",
    "        j = j.replace(' ','') # we are placeing all the ' '(space) with ''(empty) ex:\"Math & Science\"=>\"Math&Science\"\n",
    "        temp+=j.strip()+\" \" #\" abc \".strip() will return \"abc\", remove the trailing spaces\n",
    "        temp = temp.replace('&','_') # we are replacing the & value into \n",
    "    cat_essay_list.append(temp.strip())\n",
    "    \n",
    "project_data['clean_essay'] = cat_essay_list\n",
    "\n",
    "from collections import Counter\n",
    "my_counter = Counter()\n",
    "for word in project_data['clean_essay'].values:\n",
    "    my_counter.update(word.split())\n",
    "\n",
    "cat_essay_dict = dict(my_counter)\n",
    "sorted_cat_essay_dict = dict(sorted(cat_essay_dict.items(), key=lambda kv: kv[1]))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "catogories_title = list(project_data['project_title'].values)\n",
    "# remove special characters from list of strings python: https://stackoverflow.com/a/47301924/4084039\n",
    "# https://www.geeksforgeeks.org/removing-stop-words-nltk-python/\n",
    "# https://stackoverflow.com/questions/23669024/how-to-strip-a-specific-word-from-a-string\n",
    "# https://stackoverflow.com/questions/8270092/remove-all-whitespace-in-a-string-in-python\n",
    "project_title_list = []\n",
    "for i in catogories_title:\n",
    "    temp = \"\"\n",
    "    # consider we have text like this \"Math & Science, Warmth, Care & Hunger\"\n",
    "    for j in i.split(','): # it will split it in three parts [\"Math & Science\", \"Warmth\", \"Care & Hunger\"]\n",
    "        if 'The' in j.split(): # this will split each of the catogory based on space \"Math & Science\"=> \"Math\",\"&\", \"Science\"\n",
    "            j=j.replace('The','') # if we have the words \"The\" we are going to replace it with ''(i.e removing 'The')\n",
    "        j = j.replace(' ','') # we are placeing all the ' '(space) with ''(empty) ex:\"Math & Science\"=>\"Math&Science\"\n",
    "        temp+=j.strip()+\" \" #\" abc \".strip() will return \"abc\", remove the trailing spaces\n",
    "        temp = temp.replace('&','_') # we are replacing the & value into \n",
    "    project_title_list.append(temp.strip())\n",
    "\n",
    "#project_data.drop('project_title', axis=1, inplace=True)\n",
    "#project_data['project_title'] = project_title_list\n",
    "\n",
    "from collections import Counter\n",
    "my_counter = Counter()\n",
    "for word in project_data['project_title'].values:\n",
    "    my_counter.update(word.split())\n",
    "\n",
    "project_title_dict = dict(my_counter)\n",
    "sorted_project_title_dict = dict(sorted(project_title_dict.items(), key=lambda kv: kv[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 6700/6700 [00:03<00:00, 1808.70it/s]\n"
     ]
    }
   ],
   "source": [
    "# Combining all the above stundents \n",
    "from tqdm import tqdm\n",
    "X_train_preprocessed_essays = []\n",
    "# tqdm is for printing the status bar\n",
    "for sentance in tqdm(X_train['essay'].values):\n",
    "    sent = decontracted(sentance)\n",
    "    sent = sent.replace('\\\\r', ' ')\n",
    "    sent = sent.replace('\\\\\"', ' ')\n",
    "    sent = sent.replace('\\\\n', ' ')\n",
    "    sent = re.sub('[^A-Za-z0-9]+', ' ', sent)\n",
    "    # https://gist.github.com/sebleier/554280\n",
    "    sent = ' '.join(e for e in sent.split() if e not in stopwords)\n",
    "    X_train_preprocessed_essays.append(sent.lower().strip())\n",
    "   # print (X_train_preprocessed_essays)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3300/3300 [00:01<00:00, 1805.26it/s]\n"
     ]
    }
   ],
   "source": [
    "# Combining all the above stundents \n",
    "from tqdm import tqdm\n",
    "X_test_preprocessed_essays = []\n",
    "# tqdm is for printing the status bar\n",
    "for sentance in tqdm(X_test['essay'].values):\n",
    "    sent = decontracted(sentance)\n",
    "    sent = sent.replace('\\\\r', ' ')\n",
    "    sent = sent.replace('\\\\\"', ' ')\n",
    "    sent = sent.replace('\\\\n', ' ')\n",
    "    sent = re.sub('[^A-Za-z0-9]+', ' ', sent)\n",
    "    # https://gist.github.com/sebleier/554280\n",
    "    sent = ' '.join(e for e in sent.split() if e not in stopwords)\n",
    "    X_test_preprocessed_essays.append(sent.lower().strip())\n",
    "   # print (X_test_preprocessed_essays)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TruncatedSVD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split TrancatedSVd Data:\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transcatred SVD Data Using Elbow Method for Test data. Apply TruncatedSVD on TfidfVectorizer of essay text, choose the number of components (`n_components`) using elbow method : numerical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of components = 1 and explained variance = 0.0020610974468568796\n",
      "Number of components = 25 and explained variance = 0.0982640984471061\n",
      "Number of components = 100 and explained variance = 0.22490713177507624\n",
      "Number of components = 150 and explained variance = 0.2846830377442865\n",
      "Number of components = 175 and explained variance = 0.31135385440699626\n",
      "Number of components = 200 and explained variance = 0.33607688004885444\n",
      "Number of components = 400 and explained variance = 0.49183973749326027\n",
      "Number of components = 500 and explained variance = 0.5516440665768753\n",
      "Number of components = 600 and explained variance = 0.6033918023486764\n",
      "Number of components = 700 and explained variance = 0.648749116674854\n",
      "Number of components = 1000 and explained variance = 0.756918514050353\n",
      "Number of components = 1500 and explained variance = 0.874177946395998\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#https://medium.com/swlh/truncated-singular-value-decomposition-svd-using-amazon-food-reviews-891d97af5d8d\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "#n_comp = 25\n",
    "#tfidf_vec = TfidfVectorizer(analyzer=\"word\", max_features=5000, ngram_range=(1,2))   \n",
    "#svd = TruncatedSVD(n_components=n_comp, algorithm='arpack')\n",
    "#Xtest_tfidf_df = tfidf_vec.fit_transform(X_test_preprocessed_essays)\n",
    "#Xtest_svd_tfidf_essay = svd.fit_transform(Xtest_tfidf_df)\n",
    "#print (Xtest_svd_tfidf_essay)\n",
    "#Xtrain_tfidf_df = tfidf_vec.fit_transform(X_train_preprocessed_essays)\n",
    "#Xtrain_svd_tfidf_essay = svd.fit_transform(Xtrain_tfidf_df)\n",
    "#print (Xtrain_svd_tfidf_essay)\n",
    "\n",
    "#Apply TruncatedSVD on TfidfVectorizer of essay text, choose the number of components (`n_components`) using elbow method : numerical data\n",
    "\n",
    "# Program to find the optimal number of components for Truncated SVD\n",
    "n_comp = [1,25,100,150,175,200,400,500,600,700,1000,1500] # different values of components\n",
    "explained = [] # explained variance ratio for each component of Truncated SVD\n",
    "tfidf_vec = TfidfVectorizer(analyzer=\"word\", max_features=5000, ngram_range=(1,2))\n",
    "for x in n_comp:\n",
    "    svd = TruncatedSVD(n_components=x)\n",
    "    Xtest_tfidf_df = tfidf_vec.fit_transform(X_test_preprocessed_essays)\n",
    "    Xtest_svd_tfidf_essay = svd.fit_transform(Xtest_tfidf_df) \n",
    "    explained.append(svd.explained_variance_ratio_.sum())\n",
    "    print(\"Number of components = %r and explained variance = %r\"%(x,svd.explained_variance_ratio_.sum()))\n",
    "plt.plot(n_comp, explained)\n",
    "plt.xlabel('Number of components')\n",
    "plt.ylabel(\"Explained Variance\")\n",
    "plt.title(\"Plot of Number X_test_preprocessed_essays=of components v/s explained variance\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply N_comp found fron elbow method as logic givren abve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.19944929 -0.09154763  0.06831282 ... -0.0058671   0.02374907\n",
      "  -0.02577263]\n",
      " [ 0.25972251 -0.0311001  -0.02242342 ... -0.00813123 -0.03052958\n",
      "  -0.02656422]\n",
      " [ 0.24034912 -0.03372393 -0.01390729 ...  0.005973   -0.00372047\n",
      "  -0.03049773]\n",
      " ...\n",
      " [ 0.30231399 -0.17909904  0.24192332 ... -0.0145269  -0.01449715\n",
      "  -0.00468337]\n",
      " [ 0.21735995  0.00623063  0.00540521 ... -0.01462207  0.00126992\n",
      "   0.01106699]\n",
      " [ 0.21120866  0.01064331 -0.01366974 ... -0.03883509  0.01622756\n",
      "   0.02673077]]\n"
     ]
    }
   ],
   "source": [
    "# Since we found  n_comp  using elbow method using above logic, we will apply here to get \n",
    "#best value of Xtest_svd_tfidf_essay\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "tfidf_vec = TfidfVectorizer(analyzer=\"word\", max_features=5000, ngram_range=(1,2))\n",
    "n_comp = 600\n",
    "svd = TruncatedSVD(n_components=n_comp)\n",
    "Xtest_tfidf_df = tfidf_vec.fit_transform(X_test_preprocessed_essays)\n",
    "Xtest_svd_tfidf_essay = svd.fit_transform(Xtest_tfidf_df)\n",
    "print (Xtest_svd_tfidf_essay)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transcatred SVD Data Using Elbow Method for Train Data. Apply TruncatedSVD on TfidfVectorizer of essay text, choose the number of components (`n_components`) using elbow method : numerical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of components = 50 and explained variance = 0.13912735005491564\n",
      "Number of components = 60 and explained variance = 0.15460570285977568\n",
      "Number of components = 70 and explained variance = 0.16896255689827783\n",
      "Number of components = 100 and explained variance = 0.20743491648951098\n",
      "Number of components = 150 and explained variance = 0.260568228691954\n",
      "Number of components = 175 and explained variance = 0.28373352010632275\n",
      "Number of components = 200 and explained variance = 0.3054576543732288\n",
      "Number of components = 300 and explained variance = 0.38020992942764353\n",
      "Number of components = 400 and explained variance = 0.4415043212362013\n",
      "Number of components = 600 and explained variance = 0.5390769040627399\n",
      "Number of components = 700 and explained variance = 0.5792633918863819\n",
      "Number of components = 1000 and explained variance = 0.6771762279144093\n",
      "MaxExp== 0.6771762279144093\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#https://medium.com/swlh/truncated-singular-value-decomposition-svd-using-amazon-food-reviews-891d97af5d8d\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "#Apply TruncatedSVD on TfidfVectorizer of essay text, choose the number of components (`n_components`) using elbow method : numerical data\n",
    "\n",
    "# Program to find the optimal number of components for Truncated SVD\n",
    "n_comp = [50,60,70,100,150,175,200,300,400,600,700,1000] # different values of components\n",
    "explained = [] # explained variance ratio for each component of Truncated SVD\n",
    "tfidf_vec = TfidfVectorizer(analyzer=\"word\", max_features=5000, ngram_range=(1,2))\n",
    "MaxExp = -1 # Max Explained varience \n",
    "Max_svd = 0 # initially 0\n",
    "\n",
    "for x in n_comp:\n",
    "    svd = TruncatedSVD(n_components=x)\n",
    "    Xtrain_tfidf_df = tfidf_vec.fit_transform(X_train_preprocessed_essays)\n",
    "    Xtrain_svd_tfidf_essay = svd.fit_transform(Xtrain_tfidf_df) \n",
    "    explained.append(svd.explained_variance_ratio_.sum())\n",
    "    exp_sum = svd.explained_variance_ratio_.sum()\n",
    "    if exp_sum >  MaxExp :\n",
    "            Max_svd = svd\n",
    "            MaxExp  = exp_sum\n",
    "            \n",
    "    print(\"Number of components = %r and explained variance = %r\"%(x,svd.explained_variance_ratio_.sum()))\n",
    "print(\"MaxExp==\" ,MaxExp )\n",
    "percentage_var_explained =  Max_svd .explained_variance_ / np.sum( Max_svd .explained_variance_)\n",
    "cum_var_explained = np.cumsum(percentage_var_explained)\n",
    "    \n",
    "# Plotting for  MaxExp value in  list_component \n",
    "\n",
    "fig4 = plt.figure( facecolor='y', edgecolor='k')\n",
    "plt.clf()\n",
    "plt.plot( cum_var_explained , linewidth=2)\n",
    "plt.axis('tight')\n",
    "plt.grid()\n",
    "plt.xlabel('n_components')\n",
    "plt.ylabel('Cumulative_explained_variance')\n",
    "plt.title(\"Cumulative_explained_variance VS n_components\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply N_comp found fron elbow method as logic givren abve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since we found  n_comp  using elbow method using above logic, we will apply here to get \n",
    "#best value of Xrain_svd_tfidf_essay\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "tfidf_vec = TfidfVectorizer(analyzer=\"word\", max_features=5000, ngram_range=(1,2))\n",
    "n_comp = 600\n",
    "svd = TruncatedSVD(n_components=n_comp)\n",
    "#Xtest_tfidf_df = tfidf_vec.fit_transform(X_test_preprocessed_essays)\n",
    "#Xtest_svd_tfidf_essay = svd.fit_transform(Xtest_tfidf_df)\n",
    "\n",
    "Xtrain_tfidf_df = tfidf_vec.fit_transform(X_train_preprocessed_essays)\n",
    "Xtrain_svd_tfidf_essay = svd.fit_transform(Xtrain_tfidf_df)\n",
    "#print (Xtrain_svd_tfidf_essay)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Standardize (normalize) the data scale to prep for SVM.\n",
    "Because the distance between pairs of points plays a critical part on the classification, it is necessary to normalize the data This will generate an array of values. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4.1 Vectorizing Categorical data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/handling-categorical-and-numerical-features/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vectorization of clean_categories for X_train,X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['AppliedLearning', 'AppliedLearning Health_Sports', 'AppliedLearning History_Civics', 'AppliedLearning Literacy_Language', 'AppliedLearning Math_Science', 'AppliedLearning Music_Arts', 'AppliedLearning SpecialNeeds', 'Care_Hunger', 'Health_Sports', 'Health_Sports AppliedLearning', 'Health_Sports Literacy_Language', 'Health_Sports Math_Science', 'Health_Sports Music_Arts', 'Health_Sports SpecialNeeds', 'History_Civics', 'History_Civics Literacy_Language', 'History_Civics Math_Science', 'History_Civics Music_Arts', 'Literacy_Language', 'Literacy_Language AppliedLearning', 'Literacy_Language History_Civics', 'Literacy_Language Math_Science', 'Literacy_Language Music_Arts', 'Literacy_Language SpecialNeeds', 'Math_Science', 'Math_Science AppliedLearning', 'Math_Science Health_Sports', 'Math_Science History_Civics', 'Math_Science Literacy_Language', 'Math_Science Music_Arts', 'Math_Science SpecialNeeds', 'Music_Arts', 'SpecialNeeds', 'SpecialNeeds Music_Arts', 'Warmth', 'Warmth Care_Hunger']\n",
      "Shape of matrix X_train_categories_one_hot  after one hot encodig  (6700, 36)\n",
      "Shape of matrix X_test_categories_one_hot after one hot encodig  (3300, 36)\n"
     ]
    }
   ],
   "source": [
    "# we use count vectorizer to convert the values into one \n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "vectorizer = CountVectorizer(min_df=10,ngram_range=(1,4), max_features=5000,lowercase=False, binary=True)\n",
    "X_train_categories_one_hot = vectorizer.fit_transform(X_train['clean_categories'].values)\n",
    "X_test_categories_one_hot = vectorizer.transform(X_test['clean_categories'].values)\n",
    "print(vectorizer.get_feature_names())\n",
    "print(\"Shape of matrix X_train_categories_one_hot  after one hot encodig \",X_train_categories_one_hot.shape)\n",
    "print(\"Shape of matrix X_test_categories_one_hot after one hot encodig \",X_test_categories_one_hot.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Vectorization of project_grade_category for X_train,X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Grades_3_5', 'Grades_6_8', 'Grades_9_12', 'Grades_PreK_2']\n",
      "Shape of matrix X_train_project_grade_category_one_hot  after one hot encodig  (6700, 4)\n",
      "Shape of matrix X_test_project_grade_category_one_hot after one hot encodig  (3300, 4)\n"
     ]
    }
   ],
   "source": [
    "# we use count vectorizer to convert the values into one \n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "vectorizer = CountVectorizer(min_df=10,ngram_range=(1,4), max_features=5000, lowercase=False, binary=True)\n",
    "X_train_project_grade_category_one_hot = vectorizer.fit_transform(X_train['project_grade_category'].values)\n",
    "X_test_project_grade_category_one_hot = vectorizer.transform(X_test['project_grade_category'].values)\n",
    "print(vectorizer.get_feature_names())\n",
    "print(\"Shape of matrix X_train_project_grade_category_one_hot  after one hot encodig \",X_train_project_grade_category_one_hot.shape)\n",
    "print(\"Shape of matrix X_test_project_grade_category_one_hot after one hot encodig \",X_test_project_grade_category_one_hot.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['AK', 'AL', 'AR', 'AZ', 'CA', 'CO', 'CT', 'DC', 'DE', 'FL', 'GA', 'HI', 'IA', 'ID', 'IL', 'IN', 'KS', 'KY', 'LA', 'MA', 'MD', 'ME', 'MI', 'MN', 'MO', 'MS', 'MT', 'NC', 'NE', 'NH', 'NJ', 'NM', 'NV', 'NY', 'OH', 'OK', 'OR', 'PA', 'RI', 'SC', 'SD', 'TN', 'TX', 'UT', 'VA', 'WA', 'WI', 'WV']\n",
      "Shape of matrix X_train_school_state_one_hot  after one hot encodig  (6700, 48)\n",
      "Shape of matrix X_test_school_state_one_hot after one hot encodig  (3300, 48)\n"
     ]
    }
   ],
   "source": [
    "# we use count vectorizer to convert the values into one \n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "vectorizer = CountVectorizer(min_df=10,ngram_range=(1,4), max_features=5000, lowercase=False, binary=True)\n",
    "X_train_school_state_one_hot = vectorizer.fit_transform(X_train['school_state'].values)\n",
    "X_test_school_state_one_hot = vectorizer.transform(X_test['school_state'].values)\n",
    "print(vectorizer.get_feature_names())\n",
    "print(\"Shape of matrix X_train_school_state_one_hot  after one hot encodig \",X_train_school_state_one_hot.shape)\n",
    "print(\"Shape of matrix X_test_school_state_one_hot after one hot encodig \",X_test_school_state_one_hot.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vectorization of clean_subcategories for X_train,X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Economics', 'FinancialLiteracy', 'CommunityService', 'ForeignLanguages', 'Extracurricular', 'ParentInvolvement', 'Civics_Government', 'NutritionEducation', 'Warmth', 'Care_Hunger', 'SocialSciences', 'CharacterEducation', 'PerformingArts', 'TeamSports', 'Other', 'College_CareerPrep', 'Music', 'History_Geography', 'ESL', 'Health_LifeScience', 'EarlyDevelopment', 'Gym_Fitness', 'EnvironmentalScience', 'VisualArts', 'Health_Wellness', 'AppliedSciences', 'SpecialNeeds', 'Literature_Writing', 'Mathematics', 'Literacy']\n",
      "Shape of matrix X_train_sub_categories_one_hot  after one hot encodig  (6700, 30)\n",
      "Shape of matrix X_test_sub_categories_one_hot after oneX_test_sub_categories_one_hot  hot encodig  (3300, 30)\n"
     ]
    }
   ],
   "source": [
    "# we use count vectorizer to convert the values into one \n",
    "vectorizer = CountVectorizer(min_df=10,ngram_range=(1,4), max_features=5000,vocabulary=list(sorted_sub_cat_dict.keys()), lowercase=False, binary=True)\n",
    "X_train_sub_categories_one_hot = vectorizer.fit_transform(X_train['clean_subcategories'].values)\n",
    "X_test_sub_categories_one_hot = vectorizer.transform(X_test['clean_subcategories'].values)\n",
    "\n",
    "print(vectorizer.get_feature_names())\n",
    "print(\"Shape of matrix X_train_sub_categories_one_hot  after one hot encodig \",X_train_sub_categories_one_hot.shape)\n",
    "print(\"Shape of matrix X_test_sub_categories_one_hot after oneX_test_sub_categories_one_hot  hot encodig \",X_test_sub_categories_one_hot.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# you can do the similar thing with state, teacher_prefix and project_grade_category also"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TFIDF of preprocessed_essays for X_train,X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19325\n"
     ]
    }
   ],
   "source": [
    "tfidf_model = TfidfVectorizer()\n",
    "tfidf_model.fit(X_train_preprocessed_essays)\n",
    "# we are converting a dictionary with word as a key, and the idf as a value\n",
    "X_train_dictionary = dict(zip(tfidf_model.get_feature_names(), list(tfidf_model.idf_)))\n",
    "X_train_tfidf_words = set(tfidf_model.get_feature_names())\n",
    "print (len(X_train_tfidf_words))\n",
    "X_train_tfidf = tfidf_model.transform(X_train_preprocessed_essays)\n",
    "X_test_tfidf = tfidf_model.transform(X_test_preprocessed_essays)\n",
    "X_test_dictionary = dict(zip(tfidf_model.get_feature_names(), list(tfidf_model.idf_)))\n",
    "X_test_tfidf_words = set(tfidf_model.get_feature_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "## X_train TFIDF of preprocessed_essays for X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#tfidf_model = TfidfVectorizer()\n",
    "#tfidf_model.fit(X_test_preprocessed_essays)\n",
    "# we are converting a dictionary with word as a key, and the idf as a value\n",
    "#X_test_dictionary = dict(zip(tfidf_model.get_feature_names(), list(tfidf_model.idf_)))\n",
    "#X_test_dictionary = X_train_dictionary\n",
    "#X_test_tfidf_words = set(tfidf_model.get_feature_names())\n",
    "#print (len(X_test_tfidf_words))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4.2 Vectorizing Text data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# stronging variables into pickle files python: http://www.jessicayung.com/how-to-use-pickle-to-save-and-load-variables-in-python/\n",
    "# make sure you have the glove_vectors file\n",
    "with open('C:\\\\VipinML\\\\InputData\\\\glove_vectors', 'rb') as f:\n",
    "    model = pickle.load(f)\n",
    "    glove_words =  set(model.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vectorization of preprocessed_essays for X_train,X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 6700/6700 [00:01<00:00, 3677.10it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6700\n",
      "300\n"
     ]
    }
   ],
   "source": [
    "# average Word2Vec\n",
    "# compute average word2vec for each review.\n",
    "X_train_avg_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list\n",
    "for sentence in tqdm(X_train_preprocessed_essays): # for each review/sentence\n",
    "    vector = np.zeros(300) # as word vectors are of zero length\n",
    "    cnt_words =0; # num of words with a valid vector in the sentence/review\n",
    "    for word in sentence.split(): # for each word in a review/sentence\n",
    "        if word in glove_words:\n",
    "            vector += model[word]\n",
    "            cnt_words += 1\n",
    "    if cnt_words != 0:\n",
    "        vector /= cnt_words\n",
    "    X_train_avg_w2v_vectors.append(vector)\n",
    "\n",
    "print(len(X_train_avg_w2v_vectors))\n",
    "print(len(X_train_avg_w2v_vectors[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3300/3300 [00:01<00:00, 3158.10it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3300\n",
      "300\n"
     ]
    }
   ],
   "source": [
    "# average Word2Vec\n",
    "# compute average word2vec for each review.\n",
    "X_test_avg_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list\n",
    "for sentence in tqdm(X_test_preprocessed_essays): # for each review/sentence\n",
    "    vector = np.zeros(300) # as word vectors are of zero length\n",
    "    cnt_words =0; # num of words with a valid vector in the sentence/review\n",
    "    for word in sentence.split(): # for each word in a review/sentence\n",
    "        if word in glove_words:\n",
    "            vector += model[word]\n",
    "            cnt_words += 1\n",
    "    if cnt_words != 0:\n",
    "        vector /= cnt_words\n",
    "    X_test_avg_w2v_vectors.append(vector)\n",
    "\n",
    "print(len(X_test_avg_w2v_vectors))\n",
    "print(len(X_test_avg_w2v_vectors[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "## TFIDF-W2W Vecorization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 6700/6700 [00:14<00:00, 477.23it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6700\n",
      "300\n"
     ]
    }
   ],
   "source": [
    "# average Word2Vec\n",
    "# compute average word2vec for each review.\n",
    "X_train_tfidf_w2v_vectors_pessays = []; # the avg-w2v for each sentence/review is stored in this list\n",
    "for sentence in tqdm(X_train_preprocessed_essays): # for each review/sentence\n",
    "    vector = np.zeros(300) # as word vectors are of zero length\n",
    "    tf_idf_weight =0; # num of words with a valid vector in the sentence/review\n",
    "    for word in sentence.split(): # for each word in a review/sentence\n",
    "        if (word in glove_words) and (word in X_train_tfidf_words):\n",
    "            vec = model[word] # getting the vector for each word\n",
    "            # here we are multiplying idf value(dictionary[word]) and the tf value((sentence.count(word)/len(sentence.split())))\n",
    "            tf_idf = X_train_dictionary[word]*(sentence.count(word)/len(sentence.split())) # getting the tfidf value for each word\n",
    "            vector += (vec * tf_idf) # calculating tfidf weighted w2v\n",
    "            tf_idf_weight += tf_idf\n",
    "    if tf_idf_weight != 0:\n",
    "        vector /= tf_idf_weight\n",
    "    X_train_tfidf_w2v_vectors_pessays.append(vector)\n",
    "\n",
    "print(len(X_train_tfidf_w2v_vectors_pessays))\n",
    "print(len(X_train_tfidf_w2v_vectors_pessays[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3300/3300 [00:06<00:00, 526.87it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3300\n",
      "300\n"
     ]
    }
   ],
   "source": [
    "# average Word2Vec\n",
    "# compute average word2vec for each review.\n",
    "X_test_tfidf_w2v_vectors_pessays = []; # the avg-w2v for each sentence/review is stored in this list\n",
    "for sentence in tqdm(X_test_preprocessed_essays): # for each review/sentence\n",
    "    vector = np.zeros(300) # as word vectors are of zero length\n",
    "    tf_idf_weight =0; # num of words with a valid vector in the sentence/review\n",
    "    for word in sentence.split(): # for each word in a review/sentence\n",
    "        if (word in glove_words) and (word in X_test_tfidf_words):\n",
    "            vec = model[word] # getting the vector for each word\n",
    "            # here we are multiplying idf value(dictionary[word]) and the tf value((sentence.count(word)/len(sentence.split())))\n",
    "            tf_idf = X_test_dictionary[word]*(sentence.count(word)/len(sentence.split())) # getting the tfidf value for each word\n",
    "            vector += (vec * tf_idf) # calculating tfidf weighted w2v\n",
    "            tf_idf_weight += tf_idf\n",
    "    if tf_idf_weight != 0:\n",
    "        vector /= tf_idf_weight\n",
    "    X_test_tfidf_w2v_vectors_pessays.append(vector)\n",
    "\n",
    "print(len(X_test_tfidf_w2v_vectors_pessays))\n",
    "print(len(X_test_tfidf_w2v_vectors_pessays[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3300/3300 [00:00<00:00, 71089.53it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3300\n",
      "300\n"
     ]
    }
   ],
   "source": [
    "# average Word2Vec\n",
    "# compute average word2vec for each review.\n",
    "X_test_tfidf_w2v_vectors_ptitle = []; # the avg-w2v for each sentence/review is stored in this list\n",
    "for sentence in tqdm(X_test['project_title']): # for each review/sentence\n",
    "    vector = np.zeros(300) # as word vectors are of zero length\n",
    "    tf_idf_weight =0; # num of words with a valid vector in the sentence/review\n",
    "    for word in sentence.split(): # for each word in a review/sentence\n",
    "        if (word in glove_words) and (word in X_test_tfidf_words):\n",
    "            vec = model[word] # getting the vector for each word\n",
    "            # here we are multiplying idf value(dictionary[word]) and the tf value((sentence.count(word)/len(sentence.split())))\n",
    "            tf_idf = X_test_dictionary[word]*(sentence.count(word)/len(sentence.split())) # getting the tfidf value for each word\n",
    "            vector += (vec * tf_idf) # calculating tfidf weighted w2v\n",
    "            tf_idf_weight += tf_idf\n",
    "    if tf_idf_weight != 0:\n",
    "        vector /= tf_idf_weight\n",
    "    X_test_tfidf_w2v_vectors_ptitle.append(vector)\n",
    "\n",
    "print(len(X_test_tfidf_w2v_vectors_ptitle))\n",
    "print(len(X_test_tfidf_w2v_vectors_ptitle[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 6700/6700 [00:00<00:00, 86127.71it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6700\n",
      "300\n"
     ]
    }
   ],
   "source": [
    "# average Word2Vec\n",
    "# compute average word2vec for each review.\n",
    "\n",
    "X_train_tfidf_w2v_vectors_ptitle = []; # the avg-w2v for each sentence/review is stored in this list\n",
    "for sentence in tqdm(X_train['project_title']): # for each review/sentence\n",
    "    vector = np.zeros(300) # as word vectors are of zero length\n",
    "    tf_idf_weight =0; # num of words with a valid vector in the sentence/review\n",
    "    for word in sentence.split(): # for each word in a review/sentence\n",
    "        if (word in glove_words) and (word in X_train_tfidf_words):\n",
    "            vec = model[word] # getting the vector for each word\n",
    "            # here we are multiplying idf value(dictionary[word]) and the tf value((sentence.count(word)/len(sentence.split())))\n",
    "            tf_idf = X_train_dictionary[word]*(sentence.count(word)/len(sentence.split())) # getting the tfidf value for each word\n",
    "            vector += (vec * tf_idf) # calculating tfidf weighted w2v\n",
    "            tf_idf_weight += tf_idf\n",
    "    if tf_idf_weight != 0:\n",
    "        vector /= tf_idf_weight\n",
    "    X_train_tfidf_w2v_vectors_ptitle.append(vector)\n",
    "\n",
    "print(len(X_train_tfidf_w2v_vectors_ptitle))\n",
    "print(len(X_train_tfidf_w2v_vectors_ptitle[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vectorization of teacher_prefix  for X_train,X_test, X_cv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of matrix after one hot encodig  (6700, 30)\n"
     ]
    }
   ],
   "source": [
    "# we use count vectorizer to convert the values into one hot encoded features\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "vectorizer = CountVectorizer(min_df=10,ngram_range=(1,4), vocabulary=list(sorted_teacher_dict.keys()),max_features=5000, lowercase=False, binary=True)\n",
    "X_train_teacher_prefix_data = X_train['teacher_prefix']\n",
    "\n",
    "X_train_teacher_prefix_data.fillna(\"Mrs.\", inplace = True) \n",
    "\n",
    "teacher_prefix_notnull = X_train_teacher_prefix_data[pd.notnull(X_train_teacher_prefix_data)]\n",
    "\n",
    "vectorizer.fit(teacher_prefix_notnull.values)\n",
    "\n",
    "#print(vectorizer.get_feature_names())\n",
    "\n",
    "#print(teacher_prefix_notnull.values)\n",
    "\n",
    "X_train_teacher_prefix_one_hot = vectorizer.fit_transform(teacher_prefix_notnull.values)\n",
    "print(\"Shape of matrix after one hot encodig \",X_train_teacher_prefix_one_hot.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of matrix after one hot encodig  (3300, 30)\n"
     ]
    }
   ],
   "source": [
    "# we use count vectorizer to convert the values into one hot encoded features\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "vectorizer1 = CountVectorizer(min_df=10,ngram_range=(1,4), max_features=5000,vocabulary=list(sorted_teacher_dict.keys()), lowercase=False, binary=True)\n",
    "X_test_teacher_prefix_data = X_test['teacher_prefix']\n",
    "X_test_teacher_prefix_data.fillna(\"Mrs.\", inplace = True) \n",
    "teacher_prefix_notnull = X_test_teacher_prefix_data[pd.notnull(X_test_teacher_prefix_data)]\n",
    "vectorizer.fit(teacher_prefix_notnull.values)\n",
    "X_test_teacher_prefix_one_hot = vectorizer1.transform(teacher_prefix_notnull.values)\n",
    "print(\"Shape of matrix after one hot encodig \",X_test_teacher_prefix_one_hot.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vectorization of price for X_train,X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>teacher_id</th>\n",
       "      <th>school_state</th>\n",
       "      <th>Date</th>\n",
       "      <th>project_grade_category</th>\n",
       "      <th>project_title</th>\n",
       "      <th>project_essay_1</th>\n",
       "      <th>project_essay_2</th>\n",
       "      <th>project_essay_3</th>\n",
       "      <th>...</th>\n",
       "      <th>project_resource_summary</th>\n",
       "      <th>teacher_number_of_previously_posted_projects</th>\n",
       "      <th>clean_categories</th>\n",
       "      <th>clean_subcategories</th>\n",
       "      <th>teacher_prefix</th>\n",
       "      <th>essay</th>\n",
       "      <th>price</th>\n",
       "      <th>quantity</th>\n",
       "      <th>project_title_count</th>\n",
       "      <th>essay_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4645</th>\n",
       "      <td>124524</td>\n",
       "      <td>p103388</td>\n",
       "      <td>c00370dc7a454f077baca345cc3eeb11</td>\n",
       "      <td>OH</td>\n",
       "      <td>2016-09-21 08:25:22</td>\n",
       "      <td>Grades_9_12</td>\n",
       "      <td>Have a Seat! \\r\\nPottery Wheel Stools</td>\n",
       "      <td>My students live in rural Tuscarawas County, O...</td>\n",
       "      <td>I am asking for stools to go with the pottery ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>My students need stools for the pottery wheels...</td>\n",
       "      <td>1</td>\n",
       "      <td>Music_Arts</td>\n",
       "      <td>VisualArts</td>\n",
       "      <td>VisualArts</td>\n",
       "      <td>My students live in rural Tuscarawas County, O...</td>\n",
       "      <td>317.336545</td>\n",
       "      <td>16.981818</td>\n",
       "      <td>6</td>\n",
       "      <td>265</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0       id                        teacher_id school_state  \\\n",
       "4645      124524  p103388  c00370dc7a454f077baca345cc3eeb11           OH   \n",
       "\n",
       "                    Date project_grade_category  \\\n",
       "4645 2016-09-21 08:25:22            Grades_9_12   \n",
       "\n",
       "                              project_title  \\\n",
       "4645  Have a Seat! \\r\\nPottery Wheel Stools   \n",
       "\n",
       "                                        project_essay_1  \\\n",
       "4645  My students live in rural Tuscarawas County, O...   \n",
       "\n",
       "                                        project_essay_2 project_essay_3  ...  \\\n",
       "4645  I am asking for stools to go with the pottery ...             NaN  ...   \n",
       "\n",
       "                               project_resource_summary  \\\n",
       "4645  My students need stools for the pottery wheels...   \n",
       "\n",
       "     teacher_number_of_previously_posted_projects  clean_categories  \\\n",
       "4645                                            1        Music_Arts   \n",
       "\n",
       "     clean_subcategories teacher_prefix  \\\n",
       "4645          VisualArts     VisualArts   \n",
       "\n",
       "                                                  essay       price  \\\n",
       "4645  My students live in rural Tuscarawas County, O...  317.336545   \n",
       "\n",
       "       quantity  project_title_count  essay_count  \n",
       "4645  16.981818                    6          265  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head(1)\n",
    "X_test.head(1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After vectorizations\n",
      "<class 'numpy.ndarray'>\n",
      "(6700, 1) (6700,)\n",
      "(3300, 1) (3300,)\n",
      "====================================================================================================\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import Normalizer\n",
    "normalizer = Normalizer()\n",
    "# normalizer.fit(X_train['price'].values)\n",
    "# this will rise an error Expected 2D array, got 1D array instead: \n",
    "# array=[105.22 215.96  96.01 ... 368.98  80.53 709.67].\n",
    "# Reshape your data either using \n",
    "# array.reshape(-1, 1) if your data has a single feature \n",
    "# array.reshape(1, -1)  if it contains a single sample.\n",
    "\n",
    "#normalizer.fit(X_train['price'].values.reshape(-1,1))\n",
    "\n",
    "X_train_price_norm = normalizer.transform(X_train['price'].values.reshape(1,-1))\n",
    "X_test_price_norm = normalizer.transform(X_test['price'].values.reshape(1,-1))\n",
    "X_train_price_norm= X_train_price_norm.reshape(-1,1)\n",
    "X_test_price_norm=X_test_price_norm.reshape(-1,1)\n",
    "\n",
    "print(\"After vectorizations\")\n",
    "print (type(X_train_price_norm))\n",
    "print(X_train_price_norm.shape, y_train.shape)\n",
    "print(X_test_price_norm.shape, y_test.shape)\n",
    "\n",
    "print(\"=\"*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Normalization of Project Title Count."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After vectorizations\n",
      "(6700, 1) (6700,)\n",
      "(3300, 1) (3300,)\n",
      "====================================================================================================\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import Normalizer\n",
    "normalizer = Normalizer()\n",
    "\n",
    "#print (X_train['project_title_count'])\n",
    "\n",
    "X_train_project_title_count_norm = normalizer.fit_transform(X_train['project_title_count'].values.reshape(1,-1))\n",
    "X_test_project_title_count_norm = normalizer.transform(X_test['project_title_count'].values.reshape(1,-1))\n",
    "\n",
    "X_train_project_title_count_norm= X_train_project_title_count_norm.reshape(-1,1)\n",
    "X_test_project_title_count_norm=X_test_project_title_count_norm.reshape(-1,1)\n",
    "\n",
    "print(\"After vectorizations\")\n",
    "print(X_train_project_title_count_norm.shape, y_train.shape)\n",
    "print(X_test_project_title_count_norm.shape, y_test.shape)\n",
    "print(\"=\"*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Normalization of essay count words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After vectorizations\n",
      "(6700, 1) (6700,)\n",
      "(3300, 1) (3300,)\n",
      "====================================================================================================\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import Normalizer\n",
    "normalizer = Normalizer()\n",
    "\n",
    "#print (X_train['project_title_count'])\n",
    "\n",
    "X_train_essay_count_norm = normalizer.fit_transform(X_train['essay_count'].values.reshape(1,-1))\n",
    "X_test_essay_count_norm = normalizer.transform(X_test['essay_count'].values.reshape(1,-1))\n",
    "\n",
    "X_train_essay_count_norm= X_train_essay_count_norm.reshape(-1,1)\n",
    "X_test_essay_count_norm=X_test_essay_count_norm.reshape(-1,1)\n",
    "\n",
    "print(\"After vectorizations\")\n",
    "print(X_train_essay_count_norm.shape, y_train.shape)\n",
    "print(X_test_essay_count_norm.shape, y_test.shape)\n",
    "print(\"=\"*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After vectorizations\n",
      "(6700, 1) (6700,)\n",
      "(3300, 1) (3300,)\n",
      "====================================================================================================\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import Normalizer\n",
    "normalizer = Normalizer()\n",
    "\n",
    "#print (X_train['project_title_count'])\n",
    "\n",
    "X_train_quantity_norm = normalizer.fit_transform(X_train['quantity'].values.reshape(1,-1))\n",
    "X_test_quantity_norm = normalizer.transform(X_test['quantity'].values.reshape(1,-1))\n",
    "\n",
    "X_train_quantity_norm= X_train_quantity_norm.reshape(-1,1)\n",
    "X_test_quantity_norm=X_test_quantity_norm.reshape(-1,1)\n",
    "\n",
    "print(\"After vectorizations\")\n",
    "print(X_train_quantity_norm.shape, y_train.shape)\n",
    "print(X_test_quantity_norm.shape, y_test.shape)\n",
    "print(\"=\"*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After vectorizations\n",
      "(6700, 1) (6700,)\n",
      "(3300, 1) (3300,)\n",
      "====================================================================================================\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import Normalizer\n",
    "normalizer = Normalizer()\n",
    "\n",
    "X_train_teacher_number_of_previously_posted_projects_norm = normalizer.fit_transform(X_train['teacher_number_of_previously_posted_projects'].values.reshape(1,-1))\n",
    "X_test_teacher_number_of_previously_posted_projects_norm = normalizer.transform(X_test['teacher_number_of_previously_posted_projects'].values.reshape(1,-1))\n",
    "\n",
    "X_train_teacher_number_of_previously_posted_projects_norm= X_train_teacher_number_of_previously_posted_projects_norm.reshape(-1,1)\n",
    "X_test_teacher_number_of_previously_posted_projects_norm=X_test_teacher_number_of_previously_posted_projects_norm.reshape(-1,1)\n",
    "\n",
    "print(\"After vectorizations\")\n",
    "print(X_train_teacher_number_of_previously_posted_projects_norm.shape, y_train.shape)\n",
    "print(X_test_teacher_number_of_previously_posted_projects_norm.shape, y_test.shape)\n",
    "print(\"=\"*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bag of words of preprocessed_essays for X_train,X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of matrix X_train_text_bow after one hot encodig  (6700, 5000)\n",
      "Shape of matrix X_test_text_bow after one hot encodig  (3300, 5000)\n"
     ]
    }
   ],
   "source": [
    "# We are considering only the words which appeared in at least 10 documents(rows or projects).\n",
    "vectorizer = CountVectorizer(min_df=10,ngram_range=(1,4),  max_features=5000)\n",
    "X_train_text_bow = vectorizer.fit_transform(X_train_preprocessed_essays)\n",
    "X_test_text_bow = vectorizer.transform(X_test_preprocessed_essays)\n",
    "\n",
    "print(\"Shape of matrix X_train_text_bow after one hot encodig \",X_train_text_bow.shape)\n",
    "print(\"Shape of matrix X_test_text_bow after one hot encodig \",X_test_text_bow.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bag of words of project_title for X_train,X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of matrix X_train_project_title_bow after one hot encodig  (6700, 783)\n",
      "Shape of matrix X_test_project_title_bow after one hot encodig  (3300, 783)\n"
     ]
    }
   ],
   "source": [
    "# PROJECT_TITLE BOW\n",
    "# We are considering only the words which appeared in at least 10 documents(rows or projects). \n",
    "vectorizer = CountVectorizer(min_df=10,ngram_range=(1,4), max_features=5000)\n",
    "X_train_project_title_bow = vectorizer.fit_transform(X_train['project_title'])\n",
    "X_test_project_title_bow = vectorizer.transform(X_test['project_title'])\n",
    "\n",
    "print(\"Shape of matrix X_train_project_title_bow after one hot encodig \",X_train_project_title_bow .shape)\n",
    "print(\"Shape of matrix X_test_project_title_bow after one hot encodig \",X_test_project_title_bow .shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TFIDF of preprocessed_essays for X_train,X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of matrix X_train_text_tfidf after one hot encodig  (6700, 19325)\n",
      "Shape of matrix X_test_text_tfidf after one hot encodig  (3300, 19325)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "vectorizer = TfidfVectorizer()\n",
    "X_train_text_tfidf = vectorizer.fit_transform(X_train_preprocessed_essays)\n",
    "X_test_text_tfidf = vectorizer.transform(X_test_preprocessed_essays)\n",
    "\n",
    "print(\"Shape of matrix X_train_text_tfidf after one hot encodig \",X_train_text_tfidf.shape)\n",
    "print(\"Shape of matrix X_test_text_tfidf after one hot encodig \",X_test_text_tfidf.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TFIDF of Project Title for X_train,X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of matrix  X_train_project_title_tfidf after one hot encodig  (6700, 503)\n",
      "Shape of matrix  X_test_project_title_tfidf after one hot encodig  (3300, 503)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "vectorizer = TfidfVectorizer(min_df=10)\n",
    "X_train_project_title_tfidf = vectorizer.fit_transform((X_train['project_title']))\n",
    "X_test_project_title_tfidf = vectorizer.transform((X_test['project_title']))\n",
    "\n",
    "print(\"Shape of matrix  X_train_project_title_tfidf after one hot encodig \",X_train_project_title_tfidf.shape)\n",
    "print(\"Shape of matrix  X_test_project_title_tfidf after one hot encodig \",X_test_project_title_tfidf.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TFIDF AVG W2V for Project Title for X_train,X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 6700/6700 [00:00<00:00, 110132.88it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6700\n",
      "300\n"
     ]
    }
   ],
   "source": [
    "# average Word2Vec\n",
    "# compute average word2vec for each review.\n",
    "X_train_project_title_avg_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list\n",
    "for sentence in tqdm(X_train['project_title']): # for each review/sentence\n",
    "    vector = np.zeros(300) # as word vectors are of zero length\n",
    "    cnt_words =0; # num of words with a valid vector in the sentence/review\n",
    "    for word in sentence.split(): # for each word in a review/sentence\n",
    "        if word in glove_words:\n",
    "            vector += model[word]\n",
    "            cnt_words += 1\n",
    "    if cnt_words != 0:\n",
    "        vector /= cnt_words\n",
    "    X_train_project_title_avg_w2v_vectors.append(vector)\n",
    "\n",
    "print(len(X_train_project_title_avg_w2v_vectors))\n",
    "print(len(X_train_project_title_avg_w2v_vectors[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3300/3300 [00:00<00:00, 122405.12it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3300\n",
      "300\n"
     ]
    }
   ],
   "source": [
    "# average Word2Vec\n",
    "# compute average word2vec for each review.\n",
    "X_test_project_title_avg_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list\n",
    "for sentence in tqdm(X_test['project_title']): # for each review/sentence\n",
    "    vector = np.zeros(300) # as word vectors are of zero length\n",
    "    cnt_words =0; # num of words with a valid vector in the sentence/review\n",
    "    for word in sentence.split(): # for each word in a review/sentence\n",
    "        if word in glove_words:\n",
    "            vector += model[word]\n",
    "            cnt_words += 1\n",
    "    if cnt_words != 0:\n",
    "        vector /= cnt_words\n",
    "    X_test_project_title_avg_w2v_vectors.append(vector)\n",
    "\n",
    "print(len(X_test_project_title_avg_w2v_vectors))\n",
    "print(len(X_test_project_title_avg_w2v_vectors[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6700, 61) (6700,)\n",
      "(3300, 61) (3300,)\n",
      "====================================================================================================\n",
      "(6700, 564) (6700,)\n",
      "(3300, 564) (3300,)\n",
      "====================================================================================================\n",
      "(6700, 154) (6700,)\n",
      "(3300, 154) (3300,)\n",
      "====================================================================================================\n",
      "(6700, 1353) (6700,)\n",
      "(3300, 1353) (3300,)\n",
      "====================================================================================================\n"
     ]
    }
   ],
   "source": [
    "# merge two sparse matrices: https://stackoverflow.com/a/19710648/4084039\n",
    "from scipy.sparse import hstack\n",
    "\n",
    "\n",
    "X_tr = hstack((X_train_price_norm,X_train_sub_categories_one_hot,X_train_teacher_prefix_one_hot)).tocsr()\n",
    "X_te = hstack((X_test_price_norm,X_test_sub_categories_one_hot,X_test_teacher_prefix_one_hot)).tocsr()\n",
    "\n",
    "#print (X_train_price_norm)\n",
    "X_tr_bow = hstack((X_train_price_norm,X_train_sub_categories_one_hot,X_train_teacher_prefix_one_hot,\\\n",
    "                  X_train_project_title_bow )).tocsr()\n",
    "X_te_bow = hstack((X_test_price_norm,X_test_sub_categories_one_hot,X_test_teacher_prefix_one_hot,\\\n",
    "                   X_test_project_title_bow)).tocsr()\n",
    "\n",
    "#X_tr_bow = hstack((X_train_price_norm,X_train_sub_categories_one_hot,X_train_teacher_prefix_one_hot,\\\n",
    "                #   X_train_text_bow,X_train_project_title_bow)).tocsr()\n",
    "#X_te_bow = hstack((X_test_price_norm,X_test_sub_categories_one_hot,X_test_teacher_prefix_one_hot,\\\n",
    "                  # X_test_text_bow,X_test_project_title_bow)).tocsr()\n",
    "\n",
    "X_tr_tfidf = hstack((X_train_sub_categories_one_hot,X_train_teacher_prefix_one_hot,X_train_price_norm,\\\n",
    "                     X_train_project_title_tfidf)).tocsr()\n",
    "X_te_tfidf = hstack((X_test_sub_categories_one_hot,X_test_teacher_prefix_one_hot,X_test_price_norm,\\\n",
    "                     X_test_project_title_tfidf)).tocsr()\n",
    "\n",
    "\n",
    "#X_tr_tfidf = hstack((X_train_sub_categories_one_hot,X_train_teacher_prefix_one_hot,X_train_price_norm,\\\n",
    "#                     X_train_project_title_tfidf,X_train_tfidf)).tocsr()\n",
    "#X_te_tfidf = hstack((X_test_sub_categories_one_hot,X_test_teacher_prefix_one_hot,X_test_price_norm,\\\n",
    "#                     X_test_project_title_tfidf,X_test_tfidf)).tocsr()\n",
    "\n",
    "X_tr_tfidf_w2v = hstack((X_train_sub_categories_one_hot,X_train_teacher_prefix_one_hot,X_train_price_norm,\\\n",
    "                         X_train_project_title_avg_w2v_vectors,X_train_tfidf_w2v_vectors_pessays)).tocsr()\n",
    "X_te_tfidf_w2v = hstack((X_test_sub_categories_one_hot,X_test_teacher_prefix_one_hot,X_test_price_norm,\\\n",
    "                         X_test_project_title_avg_w2v_vectors,X_test_tfidf_w2v_vectors_pessays)).tocsr()\n",
    "\n",
    "\n",
    "X_tr_avg_w2v = hstack((X_train_sub_categories_one_hot,X_train_teacher_prefix_one_hot,X_train_price_norm,\\\n",
    "                       X_train_project_title_avg_w2v_vectors)).tocsr()\n",
    "X_te_avg_w2v = hstack((X_test_sub_categories_one_hot,X_test_teacher_prefix_one_hot,X_test_price_norm,\\\n",
    "                       X_test_project_title_avg_w2v_vectors)).tocsr()\n",
    "\n",
    "#set 5\n",
    "\n",
    "X_te_set5 =hstack((X_test_school_state_one_hot,X_test_categories_one_hot,X_test_sub_categories_one_hot,\\\n",
    "                  X_test_teacher_prefix_one_hot,X_test_quantity_norm,X_test_price_norm,X_test_project_grade_category_one_hot,\\\n",
    "                  X_test_teacher_number_of_previously_posted_projects_norm,X_test_price_norm,\\\n",
    "                  X_test_project_title_count_norm,X_test_essay_count_norm))\n",
    "\n",
    "X_tr_set5 =hstack((X_train_school_state_one_hot,X_train_categories_one_hot, X_train_sub_categories_one_hot,\\\n",
    "                  X_train_teacher_prefix_one_hot,X_train_quantity_norm,X_train_price_norm,X_train_project_grade_category_one_hot,\\\n",
    "                  X_train_teacher_number_of_previously_posted_projects_norm,X_train_price_norm,X_train_project_title_count_norm,\\\n",
    "                  X_train_essay_count_norm))\n",
    "\n",
    "X_te_tfidf_avg_w2v = hstack((X_test_school_state_one_hot,X_test_categories_one_hot,X_test_sub_categories_one_hot,\\\n",
    "                  X_test_tfidf_w2v_vectors_pessays,X_test_tfidf_w2v_vectors_ptitle))\n",
    "\n",
    "X_tr_tfidf_avg_w2v = hstack((X_train_school_state_one_hot,X_train_categories_one_hot, X_train_sub_categories_one_hot,\\\n",
    "                  X_train_tfidf_w2v_vectors_pessays,X_train_tfidf_w2v_vectors_ptitle))\n",
    "\n",
    "\n",
    "## Set 6 id added to cover TransactSVD logic.\n",
    "\n",
    "\n",
    "X_te_set6 =hstack((X_test_school_state_one_hot,X_test_categories_one_hot,X_test_sub_categories_one_hot,\\\n",
    "                  X_test_teacher_prefix_one_hot,X_test_quantity_norm,X_test_project_grade_category_one_hot,\\\n",
    "                  X_test_teacher_number_of_previously_posted_projects_norm,X_test_price_norm,\\\n",
    "                  X_test_project_title_count_norm,X_test_tfidf_w2v_vectors_pessays,X_test_essay_count_norm,\\\n",
    "                  X_test_tfidf_w2v_vectors_ptitle,Xtest_svd_tfidf_essay))\n",
    "\n",
    "X_tr_set6 =hstack((X_train_school_state_one_hot,X_train_categories_one_hot,X_train_sub_categories_one_hot,\\\n",
    "                  X_train_teacher_prefix_one_hot,X_train_quantity_norm,X_train_project_grade_category_one_hot,\\\n",
    "                  X_train_teacher_number_of_previously_posted_projects_norm,X_train_price_norm,\\\n",
    "                  X_train_project_title_count_norm,X_train_tfidf_w2v_vectors_pessays,X_train_essay_count_norm,\\\n",
    "                  X_train_tfidf_w2v_vectors_ptitle,Xtrain_svd_tfidf_essay))\n",
    "\n",
    "#print(\"Final Data matrix\")\n",
    "print(X_tr.shape, y_train.shape)\n",
    "print(X_te.shape, y_test.shape)\n",
    "\n",
    "print(\"=\"*100)\n",
    "\n",
    "print(X_tr_tfidf.shape, y_train.shape)\n",
    "print(X_te_tfidf.shape, y_test.shape)\n",
    "print(\"=\"*100)\n",
    "\n",
    "print(X_tr_set5.shape,y_train.shape )\n",
    "print(X_te_set5.shape,y_test.shape )\n",
    "print(\"=\"*100)\n",
    "\n",
    "print(X_tr_set6.shape,y_train.shape )\n",
    "print(X_te_set6.shape,y_test.shape )\n",
    "print(\"=\"*100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment 5: SVM\n",
    "\n",
    "<ol>\n",
    "    <li><strong>[Task-1] SVM (either SGDClassifier with log loss, or SVM) on these feature sets</strong>\n",
    "        <ul>\n",
    "            <li><font color='red'>Set 1</font>: categorical, numerical features + project_title(BOW) + preprocessed_eassay (`BOW with bi-grams` with `min_df=10` and `max_features=5000`)</li>\n",
    "            <li><font color='red'>Set 2</font>: categorical, numerical features + project_title(TFIDF)+  preprocessed_eassay (`TFIDF with bi-grams` with `min_df=10` and `max_features=5000`)</li>\n",
    "            <li><font color='red'>Set 3</font>: categorical, numerical features + project_title(AVG W2V)+  preprocessed_eassay (AVG W2V)</li>\n",
    "            <li><font color='red'>Set 4</font>: categorical, numerical features + project_title(TFIDF W2V)+  preprocessed_essay (TFIDF W2V)</li>        </ul>\n",
    "    </li>\n",
    "    <br>\n",
    "    <li><strong>Hyper paramter tuning (find best hyper parameters corresponding the algorithm that you choose)</strong>\n",
    "        <ul>\n",
    "    <li>Find the best hyper parameter which will give the maximum <a href='https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/receiver-operating-characteristic-curve-roc-curve-and-auc-1/'>AUC</a> value</li>\n",
    "    <li>Find the best hyper paramter using k-fold cross validation or simple cross validation data</li>\n",
    "    <li>Use gridsearch cv or randomsearch cv or you can also write your own for loops to do this task of hyperparameter tuning</li>          \n",
    "        </ul>\n",
    "    </li>\n",
    "    <br>\n",
    "    <li><strong>Representation of results</strong>\n",
    "        <ul>\n",
    "    <li>You need to plot the performance of model both on train data and cross validation data for each hyper parameter, like shown in the figure.\n",
    "    <img src='train_cv_auc.JPG' width=300px></li>\n",
    "    <li>Once after you found the best hyper parameter, you need to train your model with it, and find the AUC on test data and plot the ROC curve on both train and test.\n",
    "    <img src='train_test_auc.JPG' width=300px></li>\n",
    "    <li>Along with plotting ROC curve, you need to print the <a href='https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/confusion-matrix-tpr-fpr-fnr-tnr-1/'>confusion matrix</a> with predicted and original labels of test data points. Please visualize your confusion matrices using <a href='https://seaborn.pydata.org/generated/seaborn.heatmap.html'>seaborn heatmaps.\n",
    "    <img src='confusion_matrix.png' width=300px></li>\n",
    "        </ul>\n",
    "    </li>\n",
    "    <br>\n",
    "    <li><strong>[Task-2] Apply SVM on the below feature set <font color='red'> Set 5 </font> by finding the best hyper parameter as suggested in step 2 and step 3.</strong>\n",
    "    <li> Consider these set of features <font color='red'> Set 5 :</font>\n",
    "            <ul>\n",
    "                <li><strong>school_state</strong> : categorical data</li>\n",
    "                <li><strong>clean_categories</strong> : categorical data</li>\n",
    "                <li><strong>clean_subcategories</strong> : categorical data</li>\n",
    "                <li><strong>project_grade_category</strong> :categorical data</li>\n",
    "                <li><strong>teacher_prefix</strong> : categorical data</li>\n",
    "                <li><strong>quantity</strong> : numerical data</li>\n",
    "                <li><strong>teacher_number_of_previously_posted_projects</strong> : numerical data</li>\n",
    "                <li><strong>price</strong> : numerical data</li>\n",
    "                <li><strong>sentiment score's of each of the essay</strong> : numerical data</li>\n",
    "                <li><strong>number of words in the title</strong> : numerical data</li>\n",
    "                <li><strong>number of words in the combine essays</strong> : numerical data</li>\n",
    "            </ul>\n",
    "        And apply the SVM on these features by finding the best hyper paramter as suggested in step 2 and step 3 <br>\n",
    "    </li>\n",
    "    <br>\n",
    "    <li><strong>Conclusion</strong>\n",
    "        <ul>\n",
    "    <li>You need to summarize the results at the end of the notebook, summarize it in the table format. To print out a table please refer to this prettytable library<a href='http://zetcode.com/python/prettytable/'>  link</a> \n",
    "        <img src='summary.JPG' width=400px>\n",
    "    </li>\n",
    "        </ul>\n",
    "</ol>\n",
    "\n",
    "<h1>2. SVM </h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>2.4 Appling SVM on different kind of featurization as mentioned in the instructions</h2>\n",
    "\n",
    "<br>Apply SVM on different kind of featurization as mentioned in the instructions\n",
    "<br> For Every model that you work on make sure you do the step 2 and step 3 of instrucations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "def batch_predict(clf, data):\n",
    "    # roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class\n",
    "    # not the predicted outputs\n",
    "    y_data_pred = []\n",
    "    tr_loop = data.shape[0] - data.shape[0]%1000\n",
    "    # consider you X_tr shape is 49041, then your tr_loop will be 49041 - 49041%1000 = 49000\n",
    "    # in this for loop we will iterate unti the last 1000 multiplier\n",
    "    for i in range(0, tr_loop, 1000):\n",
    "        y_data_pred.extend(clf.predict_proba(data[i:i+1000])[:,1])\n",
    "    # we will be predicting for the last data points\n",
    "    if data.shape[0]%1000 !=0:\n",
    "        y_data_pred.extend(clf.predict_proba(data[tr_loop:])[:,1])\n",
    "    \n",
    "    return y_data_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we are writing our own function for predict, with defined thresould\n",
    "# we will pick a threshold that will give the least fpr\n",
    "def find_best_threshold(threshould, fpr, tpr):\n",
    "    t = threshould[np.argmax(tpr*(1-fpr))]\n",
    "    # (tpr*(1-fpr)) will be maximum if your fpr is very low and tpr is very high\n",
    "    print(\"the maximum value of tpr*(1-fpr)\", max(tpr*(1-fpr)), \"for threshold\", np.round(t,3))\n",
    "    return t\n",
    "\n",
    "def predict_with_best_t(proba, threshould):\n",
    "    predictions = []\n",
    "    for i in proba:\n",
    "        if i>=threshould:\n",
    "            predictions.append(1)\n",
    "        else:\n",
    "            predictions.append(0)\n",
    "    return predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def SVM_validation(X_train,y_train,X_test,y_test):\n",
    "    from sklearn import metrics\n",
    "    from sklearn.model_selection import GridSearchCV\n",
    "    from sklearn import svm\n",
    "    from sklearn.metrics import roc_auc_score\n",
    "    \n",
    "    \"\"\"\n",
    "    y_true : array, shape = [n_samples] or [n_samples, n_classes]\n",
    "    True binary labels or binary label indicators.\n",
    "\n",
    "    y_score : array, shape = [n_samples] or [n_samples, n_classes]\n",
    "    Target scores, can either be probability estimates of the positive class, confidence values, or non-thresholded measure of\n",
    "    decisions (as returned by “decision_function” on some classifiers). \n",
    "    For binary y_true, y_score is supposed to be the score of the class with greater label.\n",
    "\n",
    "    \"\"\"\n",
    "  \n",
    "    Cs = [0.00001,0.0001, 0.001, 0.01, 0.1, 1, 10,100,1000,10000]\n",
    "    gammas = [0.00001,0.0001, 0.001, 0.01, 0.1, 1, 10,100,1000,10000]\n",
    "   # Cs = 10. ** np.arange(-3, 8)\n",
    "  #  gammas = 10. ** np.arange(-5, 4)\n",
    "    param_grid = {'C': Cs, 'gamma' : gammas}\n",
    "    #Using GridSearchCV\n",
    "    model = GridSearchCV(svm.SVC(kernel='rbf', probability=True,class_weight = 'balanced'), param_grid, cv=3)\n",
    "        \n",
    "    model.fit(X_train, y_train)\n",
    "    \n",
    "    print(\"model.best_estimator_ = %s\" % model.best_estimator_)\n",
    "    print(\"model.score = %s\" % model.score(X_test, y_test))\n",
    "\n",
    "    y_train_pred = model.predict_proba(X_train)    \n",
    "    y_test_pred = model.predict_proba(X_test)\n",
    "\n",
    "   # calculate accuracy of class predictions\n",
    "    from sklearn import metrics\n",
    "   # print (metrics.accuracy_score(y_test, y_test_pred[:, 1]))\n",
    "\n",
    "    #the ROC curve is plotted with TPR against the FPR where TPR is on y-axis and FPR is on the x-axis.\n",
    "    train_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred[:, 1])\n",
    "    test_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred[:, 1])\n",
    "    trainAUC= float(\"{0:.2f}\".format(auc(train_fpr, train_tpr)))\n",
    "    testAUC = float(\"{0:.2f}\".format(auc(test_fpr, test_tpr)))\n",
    "\n",
    "    \n",
    "    plt.plot(train_fpr, train_tpr, label=\"train AUC =\"+str(trainAUC))\n",
    "    plt.plot(test_fpr, test_tpr, label=\"Test AUC =\"+str(testAUC))\n",
    "    plt.legend()\n",
    "    plt.xlabel(\"FPR\")\n",
    "    plt.ylabel(\"TPR\")\n",
    "    plt.title(\"AUC PLOT\")\n",
    "    plt.grid()\n",
    "    plt.show()\n",
    "     \n",
    "    print(\"=\"*100)\n",
    "    from sklearn.metrics import confusion_matrix\n",
    "    best_t = find_best_threshold(tr_thresholds, train_fpr, train_tpr)\n",
    "    print(\"Train confusion matrix\")\n",
    "    print(confusion_matrix(y_train, predict_with_best_t(y_train_pred[:, 1], best_t)))\n",
    "    print(\"Test confusion matrix\")\n",
    "    print(confusion_matrix(y_test, predict_with_best_t(y_test_pred[:, 1], best_t)))\n",
    "    \n",
    "    return [trainAUC,testAUC]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "def SVM_for_Best_Hyper_Parameter(X_train,y_train,X_test,y_test, HyperParameter):\n",
    "    from sklearn.svm import SVC\n",
    "    from sklearn.metrics import roc_auc_score\n",
    "\n",
    "    #model = SVC(gamma='auto', probability=True,class_weight = 'balanced' )  \n",
    "    model = SVC(kernel='rbf',gamma= HyperParameter, probability=True ,class_weight = 'balanced' )    \n",
    "\n",
    "    model.fit(X_train, y_train)\n",
    "    \n",
    "    y_train_pred = model.predict_proba(X_train)    \n",
    "    y_test_pred = model.predict_proba(X_test)\n",
    "   \n",
    "    #The ROC curve is plotted with TPR against the FPR where TPR is on y-axis and FPR is on the x-axis.\n",
    "    train_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred[:, 1])\n",
    "    test_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred[:, 1])\n",
    "\n",
    "    import seaborn as sns\n",
    "   \n",
    "    print(\"=\"*100)\n",
    "    from sklearn.metrics import confusion_matrix\n",
    "    best_t = find_best_threshold(tr_thresholds, train_fpr, train_tpr)\n",
    "    print(\"Train confusion matrix\")\n",
    "    train_matrix = confusion_matrix(y_train, predict_with_best_t(y_train_pred[:, 1], best_t))\n",
    "    print(train_matrix)\n",
    "                                   \n",
    "    print(\"Test confusion matrix\")\n",
    "    test_matrix = confusion_matrix(y_test, predict_with_best_t(y_test_pred[:, 1], best_t))\n",
    "    print(test_matrix)\n",
    "   \n",
    "    trainAUC= float(\"{0:.2f}\".format(auc(train_fpr, train_tpr)))\n",
    "    testAUC = float(\"{0:.2f}\".format(auc(test_fpr, test_tpr)))\n",
    "\n",
    "    \n",
    "    plt.plot(train_fpr, train_tpr, label=\"train AUC =\"+str(trainAUC))\n",
    "    plt.plot(test_fpr, test_tpr, label=\"Test AUC =\"+str(testAUC))\n",
    "    \n",
    "    # plot AUC curve. AUC curve should show best accuracy rate, since best aplha is used in the logic.\n",
    "    plt.plot(train_fpr, train_tpr, label=\"train AUC =\"+str(trainAUC))\n",
    "    plt.plot(test_fpr, test_tpr, label=\"Test AUC =\"+str(testAUC))\n",
    "    plt.legend()\n",
    "    plt.xlabel(\"FPR\")\n",
    "    plt.ylabel(\"TPR\")\n",
    "    plt.title(\"AUC PLOT\")\n",
    "    plt.grid()\n",
    "    \n",
    "     # Confusiomatrix heatmap.\n",
    "    \n",
    "    fig, (ax1, ax2) = plt.subplots(1,2,sharex=True, sharey=True)\n",
    "  #  plt.figure(figsize=(30, 60))\n",
    "    g1=sns.heatmap(test_matrix, annot=True,fmt='',cbar=True, linewidths =0.3, ax=ax1)\n",
    "    g1.set_xlabel(\"Test confusion matrix\")\n",
    "    g1.axes.get_xaxis().set_visible(True)\n",
    "    g1.axes.get_yaxis().set_visible(False)\n",
    "        \n",
    "    g2=sns.heatmap(train_matrix,annot=True,fmt='',cbar=True,linewidths =0.3,ax=ax2)\n",
    "    g2.set_xlabel(\"Train confusion matrix\")\n",
    "    g2.axes.get_xaxis().set_visible(True)\n",
    "    g2.axes.get_yaxis().set_visible(False)\n",
    "    \n",
    "    plt.show()\n",
    "    return [trainAUC,testAUC]\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the normalized data into training and test sets\n",
    "Logic below is simialr as covred in kanalysis_cross_validation(X,y), but here logic is for to  calculate confusion matrix, acuarcy ration for best K as we already foound best K after trying best accuracy for multiple K values.\n",
    "We can apply  K -fold CV to either the hyperparameter tuning, performance reporting, or both. The advantage of this approach is that the performance is less sensitive to unfortunate splits of data. In addition, it utilize data better since each example can be used for both training and validation/testing.\n",
    "\n",
    "Let's use  K -Fold CV to select the hyperparamter n_neighbors of the KNeighborsClassifier:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How to speculate the performance of the model using ROC Curve?\n",
    "#### An excellent model has AUC near to the 1 which means it has good measure of separability. A poor model has AUC near to the 0 which means it has worst measure of separability. In fact it means it is reciprocating the result. It is predicting 0s as 1s and 1s as 0s. And when AUC is 0.5, it means model has no class separation capacity whatsoever"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the normalized data into training and test sets\n",
    "This step is required to prepare us for the fitting (i.e. training) the #model later. The “X” variable is a collection of all the features. The “y” variable is the target label which specifies the #classification of 1 or 0 based. Our goal will be to identify which category the new data point should fall into. Evaluate the predictions. Evaluate the Model by reviewing the classification report or confusion matrix. By reviewing these tables, we are able to evaluate how accurate our model is with new values.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This function is used for passsing various Hyper parameter in a loop,  and get the best parameter \n",
    "# that would give best accuracy. For  each Hyperparemer, predicted value and accuracy  is calculated. \n",
    "# best Hyperparameter  is reHyper  for best accuracy. this is like gridCVSearch but shown plots for various hymer parametr.\n",
    "# after best Hyperparam is returnbed, that is used  and again best AUC plot is drawn.\n",
    "# Items 4 in your query, first three items are covered here.\n",
    "\n",
    "def SVM_HyperParam_Analysis(X_train,y_train,X_test,y_test):\n",
    "    from sklearn import model_selection\n",
    "    from mlxtend.plotting import plot_decision_regions\n",
    "    from sklearn.svm import SVC\n",
    "    from sklearn.model_selection import train_test_split\n",
    "    from sklearn.metrics import accuracy_score\n",
    "    from sklearn.metrics import roc_auc_score\n",
    "    import math\n",
    "    # Import classification report and confusion matrix to evaluate predictions\n",
    "    from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "    train_auc = []\n",
    "    test_auc = []\n",
    "    Hyperparams = [1,10,50,100,200, 300, 500, 600,800,1000 ]\n",
    "    Hyperparams = np.logspace(-2,4, 50)\n",
    "    #print (Hyperparams)\n",
    "    #Hyperparams= [10**x for x in range (-2,5)]\n",
    "    \n",
    "       \n",
    "    best_accuracy=0.0001\n",
    "    LogHyperparams =[]\n",
    "    BestHyperParam =-0.000001\n",
    "    for i in Hyperparams:\n",
    "        if (True):\n",
    "            #HyperParameter = math.log(i)\n",
    "            HyperParameter=i\n",
    "            LogHyperparams.append(i)\n",
    "            model  = SVC(kernel='linear',gamma= i,class_weight = 'balanced')\n",
    "\n",
    "            # fitting the model on crossvalidation train\n",
    "            model.fit(X_train, y_train)\n",
    "\n",
    "            # predict the response on the crossvalidation train\n",
    "            y_train_pred = model.predict(X_train)  # predicting the value using cross validation data. \n",
    "\n",
    "            # predict the response on the crossvalidation test\n",
    "            y_test_pred = model.predict(X_test)  # predicting the value using cross validation data. \n",
    "\n",
    "\n",
    "            # evaluate CV accuracy\n",
    "            acc = accuracy_score(y_test, y_test_pred, normalize=True) * float(100)  # I get the accuracy score. \n",
    "           # print (\"accuracy score = %s, Hyper Parameter  = %s \" , acc, HyperParameter)\n",
    "            if acc > best_accuracy:\n",
    "                best_accuracy =acc\n",
    "                BestHyperParam = HyperParameter\n",
    "                \n",
    "               \n",
    "           # print('\\n Test Accuracy for Hyper Parameter = %s is %s' % (HyperParameter, acc))\n",
    "           # print(\"=========================================\")\n",
    "\n",
    "            # roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class\n",
    "            # not the predicted outputs        \n",
    "            train_auc.append(roc_auc_score(y_train,y_train_pred))\n",
    "            test_auc.append(roc_auc_score(y_test, y_test_pred))\n",
    "        else:\n",
    "            continue\n",
    "    \n",
    "    #print('\\n Test Accuracy for Hyper Parameter = %s is %s' % (BestHyperParam, best_accuracy))\n",
    "    \n",
    "    plt.plot(LogHyperparams, train_auc, label='Train AUC')\n",
    "    plt.plot(LogHyperparams, test_auc, label='Test AUC')\n",
    "\n",
    "    plt.scatter(LogHyperparams, train_auc, label='Train AUC points')\n",
    "    plt.scatter(LogHyperparams, test_auc, label='Test AUC points')\n",
    "\n",
    "    plt.legend()\n",
    "    plt.xlabel(\"Hyperparams: Hyperparameter\")\n",
    "    plt.ylabel(\"AUC\")\n",
    "    plt.title(\"AUC vrs Hyperparameter\")\n",
    "    plt.grid()\n",
    "    plt.show()\n",
    "    \n",
    "    print (\"Done\")\n",
    "       \n",
    "    return BestHyperParam"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.5  Feature selection for Best Hyper Parameter /font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 2.5.1 <font color='red'> Hyper Param-Analysis  <font color='blue'> categorical, numerical features + project_title(BOW) + preprocessed_essay (BOW),<font color='red'> SET 1</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
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pbmTaKqlfuGyMpM8iy3IzGYNzzrk9ZezRsZKygCeBs4EiYIakiWZWGKmTAwwDTjWz9ZIOAzCzqUBuWKcZsAz4a6T528zs5Uz13TnnXNkyecTRFVhmZp+a2XZgLNA3oc51wJNmth7AzL5K0s6FwJtm9p8M9tU551yKZGaZaVi6EOhtZteG85cDJ5vZDZE6E4B/AacCWcBwM3sroZ13gUfN7PVwfgxwCrANeAcYambbkmx/EDAIoGXLlnljx47d55g2b95Mo0aN9rmd6qI2xVubYgWPtyZLZ6w9evSYZWb5ey0ws4xMwEXAM5H5y4HHE+q8DowHsoG2BKe0mkaWHwGsAbITygTUB54D7qmoL3l5eZYOU6dOTUs71UVtirc2xWrm8dZk6YwVmGlJPlMzeaqqCDgqMt8aWJWkzl/MbIeZfQYsAXIiy/sD481sR0mBmX0RxrQNGE1wSsw559x+ksnEMQPIkdRWUj1gADAxoc4EoAeApBbAscCnkeUDgYLoCpKOCP8K6AcszEjvnXPOJZWxu6rMbKekG4DJBNcvnjWzRZLuJzj8mRguO0dSIVBMcLfUOgBJbQiOWN5LaPoFSYcSnK6aCwzOVAzOOef2lrHEAWBmk4BJCWX3RF4bcEs4Ja67HGiVpPzMtHfUOedcyvyX484552LxxOGccy4WTxzOOedi8cThnHMuFk8czjnnYvHE4ZxzLhZPHM4552LxxOGccy4WTxzOOedi8cThnHMuFk8czjnnYsnoWFXOuZpjx44dFBUVsXXr1qruSmxNmjRh8eLFVd2N/aIysTZo0IDWrVuTnZ2dUn1PHM65lBQVFdG4cWPatGlD8FSD6mPTpk00bty4qruxX8SN1cxYt24dRUVFtG3bNqV1/FSVcy4lW7dupXnz5tUuabjySaJ58+axjiQ9cTjnUuZJo2aK+7564nDOVQvr1q0jNzeX3NxcDj/8cFq1alU6v3379pTauPrqq1myZEnsbZ933nn88Ic/3KPssssuY8KECaXzO3fupGnTpqXzH3/8MX369CEnJ4d27doxYMAAvvrqq9jbPhD5NQ7nXLXQvHlz5s6dC8Dw4cNp1KgRt9566x51zAwzo06d5N+JR48eHXu769atY8GCBTRo0IAVK1bw3e9+t8J1tmzZwvnnn8/IkSM599xzAXjnnXdYt24dhx12WOw+HGgyesQhqbekJZKWSRpaRp3+kgolLZL050h5saS54TQxUt5W0nRJSyWNC59n7pyrpZYtW0bHjh0ZPHgwXbp04YsvvmDQoEHk5+fToUMH7r///tK6p512GnPnzi09Ohg6dCgnnngip5xySplHAy+//DL9+vXj4osvZty4cSn16fnnn+f0008vTRoAZ511Fu3atdu3YA8QGTvikJQFPAmcDRQBMyRNNLPCSJ0cYBhwqpmtlxRNxVvMLDdJ0w8Dj5nZWEmjgGuApzIVh3Nub/e9tojCVRvT2mb7Iw/m3h91qNS6hYWFjB49mlGjRgEwYsQImjVrxs6dO+nRowd9+vThpJNO2mOdb775hjPOOIMRI0Zwyy238OyzzzJ06N7fbwsKCnjooYdo0qQJl112GbfddluF/Vm4cCF5eXmViqU6yOQRR1dgmZl9ambbgbFA34Q61wFPmtl6ADMr9wSggis4ZwIvh0XPAf3S2mvnXLVzzDHH7JEYCgoK6NKlC126dGHx4sV8/PHHe63TsGFD+vTpA0BeXh7Lly/fq87nn3/OihUr6NatG+3bt6e4uLi0rWQXlGvLzQOZvMbRClgZmS8CTk6ocyyApA+ALGC4mb0VLmsgaSawExhhZhOA5sAGM9sZabNVso1LGgQMAmjZsiXTpk3b54A2b96clnaqi9oUb22KFSoXb5MmTdi0aRMAt3Sv+Dx/ZZS0X5Ft27aRnZ3Npk2b2Lx5Mw0bNixdd9myZTz22GNMnTqVpk2bcu211/Kf//yHTZs2UVxczLfffsumTZuoV69e6Trbt29ny5Yte23/j3/8I+vWraNNmzYAbNy4keeee4477riDxo0b88UXX5Sus2bNGpo1a8amTZs45phj+Oijj7jiiivStGdSV1xcnPJ+jNq6dWvK/yYymTiSpV5Lsv0coDvQGvi7pI5mtgH4rpmtknQ08K6kBUCyY+PENoNCs6eBpwHy8/Ote/fulQoiatq0aaSjneqiNsVbm2KFysW7ePHiA+ZHdPXr16d+/fo0btyYRo0aUadOndK+7dq1iyZNmtCqVSu+/PJL3n33XXr27Enjxo3JysrioIMOKq1b8rdhw4ZkZ2fvFd+rr77KlClTSo9mli5dyvnnn89DDz3EOeecw1NPPcW1115LdnY2Tz31FGeddRaNGzfmuuuuY+TIkXzwwQf07t0bgEmTJtGmTRvat2+f0X1T2R87NmjQgM6dO6dUN5OJowg4KjLfGliVpM5HZrYD+EzSEoJEMsPMVgGY2aeSpgGdgVeAppLqhkcdydp0ztViXbp0oX379nTs2JGjjz6aU089tVLtfPLJJ6xevZr8/PzSspycHOrXr8+sWbPo168fs2fPJi8vjzp16pCTk1N6jeU73/kOr732GkOGDOHGG28kOzub3Nxcfvvb36YlxipXcvtauieCpPQp0BaoB8wDOiTU6Q08F75uQXBqqzlwCFA/Ur4UaB/OvwQMCF+PAq6vqC95eXmWDlOnTk1LO9VFbYq3NsVqVrl4CwsL09+R/WTjxo1V3YX9prKxJnt/gZmW5DM1YxfHLTgiuAGYDCwGXjSzRZLul3RBWG0ysE5SITAVuM3M1gHtgJmS5oXlI2z33Vi3A7dIWhYmmT9kKgbnnHN7y+gPAM1sEjApoeyeyGsDbgmnaJ0PgU5ltPkpwR1bzjnnqoAPOeKccy4WTxzOOedi8cThnHMuFk8czjnnYvHE4ZyrFtIxrDrAs88+y+rVq8tcvn37dpo1a8bdd9+9R3nr1q3ZsGFD6fyUKVPo12/3iEdvvPEGeXl5tG/fnuOPP57bb789RnTViycO51y1UDKs+ty5cxk8eDBDhgwpna9XL/VBsitKHG+99Rbt27dPeSRcgHnz5nHzzTdTUFBAYWEhCxcuLB2mpCbyxOGcq/aee+45unbtSm5uLtdffz27du1i586dXH755XTq1ImTTz6ZkSNHMm7cOObOncvFF19c5pFKQUEBt9xyCy1btmTGjBkpbf/hhx/m7rvv5thjjwWgbt26/OxnP0trjAcSf5CTcy6+N4fC6gXpbfPwTtBnROzVFi5cyPjx4/nwww+pW7cugwYNYuzYsRxzzDGsXbuWBQsWlA5w2LRpUx5//HGeeOIJcnP3fmrDt99+y3vvvcfo0aNZvXo1BQUFew3HXlYf7rzzzth9r67KPOKQ1EvShUnKL5V0dma75ZxzqZkyZQozZswgPz+f3Nxc3nvvPT755BO+//3vs2TJEn7+858zZcoUmjRpUmFbEydO5Oyzz6ZBgwZcdNFFvPLKK+zatQuo3cOoJyrviOM+4EdJyt8BxgNvZ6RHzrkDXyWODDLFzPjJT37CL3/5y72WzZ8/nzfffJNRo0bx5ptv8vTTT5fbVkFBAdOnTy+9PvHVV1/xt7/9je7du9O8eXPWr19f+lzxr7/+mhYtWgDQoUMHZs2aRYcOlXsQVXVT3jWO75jZmsRCM1sNHJS5LjnnXOp69uzJiy++yNq1a4Hg7qsVK1awZs0azIyLLrqIO+64g9mzZwPBUOrJnlexfv16pk+fTlFREcuXL2f58uWMHDmSgoICALp3787zzz8PwM6dO3nhhRfo0aMHAL/4xS944IEHWLZsGRA8E+PRRx/NeOxVpbzE0UDSXkckkrKBhpnrknPOpa5Tp07ce++99OzZkxNOOIFzzjmHL7/8kpUrV3L66aeTm5vLTTfdxIMPPgjA1VdfzbXXXrvXxfFXXnmFs88+m+zs7NKyfv36MX78eHbs2MHw4cMpLCzkxBNPpEuXLrRr146BAwcC0LlzZ37961/Tv39/2rVrR6dOnVizZq/v3TWGgnEGkyyQRgAtgRvM7Nuw7CBgJLDWzKrNTcr5+fk2c+bMfW7HH/ZTc9WmWKHyD3Jq165dZjqUYZV9uFF1VNlYk72/kmaZWX5i3fKOOO4CvgT+LWmWpNnAcmBNuMw551wtVObF8fB5GkMl3Qd8PyxeZmZb9kvPnHPOHZDKTByS/l9CkRE8tnWumcV/ErpzzrkaobzbcZPditsMOEHSNWb2bob65Jxz7gBW3qmqq5OVS/oe8CJwckWNS+oN/BbIAp4xs71u/pbUHxhOcEQzz8wukZQLPAUcDBQD/2Nm48L6Y4AzgG/CJq4ys7kV9cU551x6xB5yxMz+Hd6SWy5JWcCTwNlAETBD0sTIs8ORlAMMA041s/WSDgsX/Qe4wsyWSjoSmCVpspmVDE15m5m9HLfvzjnn9l3sQQ4lHQ9sS6FqV4KL6Z+a2XZgLNA3oc51wJNmth7AzL4K//7LzJaGr1cBXwGHxu2rc67mSMew6ldffTVLliyJve3zzjuPH/7wh3uUXXbZZUyYMKF0fufOnaW/Kgf4+OOP6dOnDzk5ObRr144BAwbw1Vdfxd52ol69eiX9AWNURSMA76vyLo6/RnD6KKoZcARwWQpttwJWRuaL2Pv01rHhtj4gOJ013MzeSuhHV6Ae8Emk+H8k3UMw/MlQM9srkUkaBAwCaNmyJdOmTUuhy+XbvHlzWtqpLmpTvLUpVqhcvE2aNKnwAyuT6tWrx9///ncAHnzwQRo1asRNN90EwLZt29i2bRtmhplRp86e34mLi4vZtGkTI0eOBIgVx7p165g/fz7169ensLCQo446CoAdO3awZcuW0rZ27txZ2vaWLVs499xz+dWvfkWvXr2A4LczK1asoGHDffv99Msvv1xuDMXFxfz+97/nuOOO46CDUh/kY+vWran/myjZ0YkTwXWE6HQ60DH8+2RZ60XWv4jgukbJ/OXA4wl1XicY9yobaEuQXJpGlh8BLAG6JZQJqA88B9xTUV/y8vIsHaZOnZqWdqqL2hRvbYrVrHLxFhYWxqo/fnaR/eChd6zN7a/bDx56x8bPLoq9zbLce++99sgjj5iZ2dKlS61Dhw7205/+1HJzc62oqMiuu+46y8vLs/bt29t9991nGzduNDOzU0891ebMmWM7duywJk2a2O23324nnHCCdevWzb788suk2xo1apTdeOONdtddd9mvfvWr0vJLL73Uxo8fXzpf0qaZ2e9+9zu7+uqrK4zj7bfftu7du1vfvn2tXbt2dv3119uuXbvMzOz555+3jh07WocOHWzYsGGl67Rq1crWr19fGvdPfvITa9++vfXu3du2bNlio0ePtoMOOsiOPfZYO/HEE23btm126623Wrt27axTp072i1/8Imlfkr2/wExL8pla5qkqM3uvZCK4EH1++EF/H7A4hZxUBBwVmW8NrEpS5y9mtsPMPguTRA6ApIOBN4C7zOyjSL++CGPaBowmOCXmnDuATJjzOcNeXcDnG7ZgwOcbtjDs1QVMmPN5RrZXWFjINddcw5w5c2jVqhUjRoxg5syZzJs3j7fffpuPP/54r3W++eYbzjjjDObNm8cpp5zCs88+m7TtgoICBg4cyMCBA0vHrarIwoULycvLS6nu9OnT+c1vfsOCBQtYvHgxf/nLXygqKuKuu+5i6tSpzJkzhw8++IDXX399r3WXLFnCzTffzKJFi2jYsCETJkzgxz/+Mbm5uaXPHlm/fj2TJk1i0aJFzJ8/n2HDhqXUr/KUN6z6sZLukbQYeILgtJPMrIeZPZFC2zOAHEltJdUDBgATE+pMAHqE22tBcOrq07D+eOCPZvZSQr+OCP8K6AcsTKEvzrn96JHJS9iyo3iPsi07inlkcvzrC6k45phj9nhuRkFBAV26dKFLly4sXrw4aeJo2LAhffr0ASAvL4/ly5fvVefzzz9nxYoVdOvWjfbt21NcXFzaVrqGWe/WrRtt2rQhKyuLAQMG8P777zN9+nTOPPNMWrRoQXZ2Npdccgl/+9vf9lr3+9//Pp06dSo3hmbNmlGnTh2uu+46xo8fH+v0VVnKuzj+MXAW8CMzO83MHie4NTYlFvzy/AZgMsERyotmtk0DE+oAAB3DSURBVEjS/ZIuCKtNBtZJKgSmEtwttQ7oT3BK7CpJc8Op5KkrL0haACwAWgAPpBytc26/WLUh+QATZZXvq+iH4dKlS/ntb3/Lu+++y/z58+nduzdbt27da53o42azsrJKr1FEjRs3jnXr1tG2bVvatGnDihUrGDt2LEDpMOslkg2znorEZCOp5LR8herXr19hDNnZ2cycOZN+/frxyiuvcN5556XUdnnKSxw/BlYDUyX9XtJZBNcWUmZmk8zsWDM7xsz+Jyy7x8wmhq/NzG4xs/Zm1snMxoblfzKzbDPLjUxzw2VnhnU7mtllZra5MoE75zLnyKbJLwCXVZ5OGzdupHHjxhx88MF88cUXTJ48udJtFRQUMGXKlNJh1v/5z3/uMcz62LFj2bFjBwBjxowpHWb98ssv57333uOtt3bf6zNp0iQKCwv32sZHH33EihUrKC4u5sUXX+S0006jW7duTJ06lXXr1rFz507Gjh3LGWeckXK/o0PHb9q0iY0bN3L++efz2GOPMWfOnErvjxLl/QBwPDA+HBG3HzAEaCnpKWC8mf11n7funKuRbut1HMNeXbDH6aqG2Vnc1uu4jG+7S5cutG/fno4dO3L00Udz6qmnVqqdTz75hNWrV5Ofv3tw2JycHOrXr8+sWbPo168fs2fPJi8vjzp16pCTk8OoUaMA+M53vsNrr73GkCFDuPHGG8nOziY3N5ff/va3e23nBz/4Af/93//NokWL6N69OxdccAGSuP/+++nevTtmxo9+9KNYRwolQ8c3bNiQiRMncuGFF7Jt2zZ27dqVnueEJLtiXtZEcDvuT4F346xX1ZPfVVU5tSne2hSrWfW/qyqukruqDjRvv/229e3bN61tVjbWOHdVxfrluJl9DfwunJxzrkz9OreiX+dWVd0NlwGxhxxxzjmXHj179qRnz55V3Y3YYg854pxzrnbzxOGccy4WTxzOOedi8cThnHMuFk8czrlqIR3DqkPFQ45v376dZs2acffdd+9R3rp1azZs2FA6P2XKFPr161c6/8Ybb5CXl0f79u05/vjjuf3222NEl9zKlSu5+OKLy62za9cuRozY6xl5GeWJwzlXLTRv3py5c+cyd+5cBg8ezJAhQ0rno8OHVKSixPHWW2/Rvn17xo0bl3Kb8+bN4+abb6agoIDCwkIWLlxImzZtUl6/LEcddVSF/fDE4ZyrOea/CI91hOFNg7/zX8zYpp577jm6du1Kbm4u119/Pbt27WLnzp1cfvnldOrUiZNPPpmRI0eWjhh78cUXl3mkUlBQwC233ELLli2ZMWNGStt/+OGHufvuuzn22GMBqFu3Lj/72c/2qnfXXXdx5ZVX0qNHD3JyckpH5N21axe33HILHTt2pFOnTqXP3Fi2bBm5ucEwfc888wwXXnghvXr1Iicnp3SU26FDh7Jp0yZyc3O54oor2LRpE3369OHEE0+kY8eOpW2lk/+OwzmXfvNfhNdugh3hoIbfrAzmAU7on9ZNLVy4kPHjx/Phhx9St25dBg0axNixYznmmGNYu3YtCxYsYNOmTRQXF9O0aVMef/xxnnjiidIP5Khvv/2W9957j9GjR7N69WoKCgr2GHW3vD7ceeedKfV3wYIFfPjhh2zcuJEuXbpw3nnnMW3aNAoLC5k3bx5r1qzhpJNO4vTTT99r3Xnz5jF79mzq1q3Lsccey4033siIESN45plnmDt3LhCMmdWmTRvefPNNIBg+Pt38iMM5l37v3L87aZTYsSUoT7MpU6YwY8YM8vPzyc3N5b333uOTTz7h+9//PkuWLOHnP/85U6ZMoUmTJhW2NXHiRM4++2waNGjARRddxCuvvMKuXbuA9A2j3q9fPxo0aMBhhx3G6aefzowZM3j//fe55JJLyMrK4vDDD+e0005j5syZe63bs2dPGjduTMOGDTn++ONZsWLFXnU6duzIW2+9xdChQ/nggw9SijsuTxzOufT7pihe+T4wM37yk5+UXu9YsmQJd999N82bN2f+/PmcdtppjBo1ip/+9KcVtlVQUMBbb71FmzZtOOmkk/jqq69Kn4NRXYZRP+6445g5cyYdOnTgtttu48EHH0yp7Tg8cTjn0q9J63jl+6Bnz568+OKLrF27FgjuvlqxYgVr1qzBzLjooou44447mD17NrDnkONR69evZ/r06RQVFZUOoz5y5Mg9hlF//vnngeD54i+88ELpMOq/+MUveOCBB1i2bBkQPPe7rFFoJ0yYwLZt21i7di1///vfyc/P5/TTT2fs2LEUFxfz5Zdf8sEHH+wxKm956tatW9ongFWrVtGoUSMuv/xybrnlltK408mvcTjn0u+se/a8xgGQ3TAoT7NOnTpx77330rNnT3bt2kV2djajRo0iKyuLa665pnRE10ceeQTYc8jxf/7zn6V3ZL3yyiucffbZZGdnl7bdr18/7rzzTp544gmGDx/O4MGDOfHEEzEzzj33XAYOHAhA586d+fWvf03//v3ZsmULkujbt2/S/p500kn06dOHlStXct9999GyZUsuvPBCPvroI0488UQk8eijj3LYYYexcePGlPbBNddcwwknnEB+fj4XXHABF110EXXq1KFevXqlQ72nk1I9RKrO8vPzLdn5wrimTZtG9+7d971D1URtirc2xQqVi3fx4sW0a9cu9RXmvxhc0/imKDjSOOuetF8YT9WmTZto3LhxlWw76q677qJFixbcfPPNGdtGZWNN9v5KmmVmex36ZPRUlaTekpZIWiZpaBl1+ksqlLRI0p8j5VdKWhpOV0bK8yQtCNscqcpcnXLOZd4J/WHIQhi+IfhbRUnDpV/GTlVJygKeBM4GioAZkiaaWWGkTg4wDDjVzNZLOiwsbwbcC+QDBswK110PPAUMAj4CJgG9gTczFYdzzqXLAw88UNVdSItMHnF0BZaZ2admth0YCySe9LsOeDJMCJjZV2F5L+BtM/s6XPY20FvSEcDBZvaP8OlUfyR4rK1zzrn9JJMXx1sBKyPzRcDJCXWOBZD0AZAFDDezt8pYt1U4FSUp34ukQQRHJrRs2ZJp06ZVNo5SmzdvTks71UVtirc2xQqVi7dJkyZs3LixUr9dqGrFxcVJ76SqiSoTq5mxdevWlP9NZDJxJPvXlXglvi6QA3QHWgN/l9SxnHVTaTMoNHsaeBqCi+PpuPDpF1BrrtoUK1Qu3s8++4zt27fTvHnzapc8DpSL4/tD3FjNjHXr1tG0aVM6d+6c0jqZTBxFwFGR+dbAqiR1PjKzHcBnkpYQJJIigmQSXXdaWN46oTyxTedcBrRu3ZqioiLWrFlT1V2JbevWrTRo0KCqu7FfVCbWBg0a0Lp16r+xyWTimAHkSGoLfA4MAC5JqDMBGAiMkdSC4NTVp8AnwIOSDgnrnQMMM7OvJW2S1A2YDlwBPJ7BGJxzoezsbNq2bVvV3aiUadOmpfxturrbH7FmLHGY2U5JNwCTCa5fPGtmiyTdD8w0s4nhsnMkFQLFwG1mtg5A0i8Jkg/A/Wb2dfj6Z8AYoCHB3VR+R5Vzzu1HGf3luJlNIrhlNlp2T+S1AbeEU+K6zwLPJimfCXRMe2edc86lxMeqcs45F4snDuecc7F44nDOOReLJw7nnHOxeOJwzjkXiycO55xzsXjicM45F4snDuecc7F44nDOOReLJw7nnHOxeOJwzjkXiycO55xzsXjicM45F4snDuecc7F44nDOOReLJw7nnHOxeOJwzjkXS0YTh6TekpZIWiZpaJLlV0laI2luOF0blveIlM2VtFVSv3DZGEmfRZblZjIG55xze8rYo2MlZQFPAmcDRcAMSRPNrDCh6jgzuyFaYGZTgdywnWbAMuCvkSq3mdnLmeq7c865smXyiKMrsMzMPjWz7cBYoG8l2rkQeNPM/pPW3jnnnKsUmVlmGpYuBHqbWcnpp8uBk6NHF5KuAh4C1gD/AoaY2cqEdt4FHjWz18P5McApwDbgHWComW1Lsv1BwCCAli1b5o0dO3afY9q8eTONGjXa53aqi9oUb22KFTzemiydsfbo0WOWmeXvtcDMMjIBFwHPROYvBx5PqNMcqB++Hgy8m7D8CIKkkp1QJqA+8BxwT0V9ycvLs3SYOnVqWtqpLmpTvLUpVjOPtyZLZ6zATEvymZrJU1VFwFGR+dbAqmgFM1tnu48Wfg/kJbTRHxhvZjsi63wRxrQNGE1wSsw559x+ksnEMQPIkdRWUj1gADAxWkHSEZHZC4DFCW0MBAqSrSNJQD9gYZr77ZxzrhwZu6vKzHZKugGYDGQBz5rZIkn3Exz+TARuknQBsBP4GriqZH1JbQiOWN5LaPoFSYcSnK6aS3CKyznn3H6SscQBYGaTgEkJZfdEXg8DhpWx7nKgVZLyM9PbS+ecc3H4L8edc87F4onDOedcLJ44nHPOxeKJwznnXCyeOJxzzsXiicM551wsnjicc87F4onDOedcLJ44nHPOxeKJwznnXCyeOJxzzsXiicM551wsnjicc87F4onDOedcLJ44nHPOxeKJwznnXCyeOJxzzsWS0cQhqbekJZKWSRqaZPlVktZImhtO10aWFUfKJ0bK20qaLmmppHHh88ydc87tJxlLHJKygCeBPkB7YKCk9kmqjjOz3HB6JlK+JVJ+QaT8YeAxM8sB1gPXZCoG55xze8vkEUdXYJmZfWpm24GxQN99aVCSgDOBl8Oi54B++9RL55xzsdTNYNutgJWR+SLg5CT1fizpdOBfwBAzK1mngaSZwE5ghJlNAJoDG8xsZ6TNVsk2LmkQMAigZcuWTJs2bR/Dgc2bN6elneqiNsVbm2IFj7cm2x+xZjJxKEmZJcy/BhSY2TZJgwmOIM4Ml33XzFZJOhp4V9ICYGMKbQaFZk8DTwPk5+db9+7dKxHCnqZNm0Y62qkualO8tSlW8Hhrsv0RayZPVRUBR0XmWwOrohXMbJ2ZbQtnfw/kRZatCv9+CkwDOgNrgaaSShLeXm0655zLrEwmjhlATngXVD1gADAxWkHSEZHZC4DFYfkhkuqHr1sApwKFZmbAVODCcJ0rgb9kMAbnnHMJMnaqysx2SroBmAxkAc+a2SJJ9wMzzWwicJOkCwiuY3wNXBWu3g74naRdBMlthJkVhstuB8ZKegCYA/whUzE455zbWyavcWBmk4BJCWX3RF4PA4YlWe9DoFMZbX5KcMeWc865KuC/HHfOOReLJw7nnHOxeOJwzjkXiycO55xzsXjicM45F0tG76qqMYY3Cf4edx8MTxxuS4CBssCKd/9t2CxYvGU9NGkNZ4U3k71zP3xTtLvshP4w/8Xk5VD+sqhU66VrvUy1U1XtH6jbjqO69LMyanJs6VKyjw6/Fh67IaP7yBNHRUqSRpnCEU+seM+/W77eXeWblTDhepCgePvustdughUfwbw/w44te5aXeO2m5Mui/yDmv5havUSVXS9T7VRV+wfqtuOoLv2sjJocW7pE99HhZHwfKfgxds2Wn59vM2fOrNzKkcSxoWEbmm5Znp5OVSSrfvC3eFvyZa1P2j1fNCO1eokqWG/Dhg00bdq04r5WdvupynT7UHas+2HbaRGznym/tweCNLwH1Sreyojso6JDutF6/UdBeZOjYMjCSjcraZaZ5SeW+xHHgSrZf5SylpVVt7w29mW9TLVTVe0fqNuOo7r0szJqcmzpUta++KYoI5vzxBHD3O9eTfcl96a30ZJrIomahONDfrMy+bKr39g9/1jH1OolqmC9uamOslnZ7acq0+1D2bHuh22nRcx+pvzeHgjS8B5Uq3grI7KPlh3WJ3LE0Tojm/O7qvaXOtmQlfCU2+yGkHdV8Dex/Kx7gqmsZVGp1ktU2fUy1U5VtX+gbjuO6tLPyqjJsaXLft5HnjgqMvybCiqEjx1R1p5/GzYL76xS8M2o3/9B3yfDI4mw7Ecj4fxHg7+J5Sf0D6aylkWlWi9RZdfLVDtV1f6Buu04qks/K6Mmx5Yue+wjMr6P/OJ4DLXpYTBQu+KtTbGCx1uTpTPWsi6O+xGHc865WDxxOOeci8UTh3POuVg8cTjnnIslo4lDUm9JSyQtkzQ0yfKrJK2RNDecrg3LcyX9Q9IiSfMlXRxZZ4ykzyLr5GYyBuecc3vK2A8AJWUBTwJnA0XADEkTI88OLzHOzG5IKPsPcIWZLZV0JDBL0mQz2xAuv83MXs5U351zzpUtk0ccXYFlZvapmW0HxgKJQ8smZWb/MrOl4etVwFfAoRnrqXPOuZRl7Hccki4EeptZyemny4GTo0cXkq4CHgLWAP8ChpjZyoR2ugLPAR3MbJekMcApwDbgHWCome01UIukQcAggJYtW+aNHTt2n2PavHkzjRo12ud2qovaFG9tihU83posnbH26NEj6e84Mpk4LgJ6JSSOrmZ2Y6ROc2CzmW2TNBjob2ZnRpYfAUwDrjSzjyJlq4F6wNPAJ2Z2fwV9WQP8Ow1htQDWpqGd6qI2xVubYgWPtyZLZ6zfM7O9zvZkcpDDIuCoyHxrYFW0gpmti8z+Hni4ZEbSwcAbwF0lSSNc54vw5TZJo4FbK+pIssArQ9LMZNm3pqpN8damWMHjrcn2R6yZvMYxA8iR1FZSPWAAMDFaITx6KHEBsDgsrweMB/5oZi8lW0eSgH5A5Qebd845F1vGjjjMbKekG4DJQBbwrJktknQ/MNPMJgI3SboA2Al8DVwVrt4fOB1oHl4HAbjKzOYCL0g6lGB0wbnA4EzF4Jxzbm+1YpDDdJE0yMyerup+7C+1Kd7aFCt4vDXZ/ojVE4dzzrlYfMgR55xzsXjicM45F4snjhRVNO5WdSDpKElTJS0OxwH7eVjeTNLbkpaGfw8JyyVpZBjzfEldIm1dGdZfKunKqoqpIpKyJM2R9Ho431bS9LDf48I7+JBUP5xfFi5vE2ljWFi+RFKvqomkYpKaSnpZ0sfhe3xKDX9vh4T/jhdKKpDUoCa9v5KelfSVpIWRsrS9n5LyJC0I1xkZ3qmaGjPzqYKJ4K6wT4CjCX54OA9oX9X9qkQcRwBdwteNCX6t3x74FcEv8AGGAg+Hr88F3iS4g60bMD0sbwZ8Gv49JHx9SFXHV0bMtwB/Bl4P518EBoSvRwE/C19fD4wKXw8gGEONcP/MA+oDbcN/B1lVHVcZsT4HXBu+rgc0ranvLdAK+AxoGHlfr6pJ7y/BnaVdgIWRsrS9n8A/CUbhULhun5T7VtU7pzpM4c6dHJkfBgyr6n6lIa6/EAxCuQQ4Iiw7AlgSvv4dMDBSf0m4fCDwu0j5HvUOlIngR6fvAGcCr4f/QdYCdRPfV4Lbxk8JX9cN6ynxvY7WO5Am4ODwg1QJ5TX1vW0FrAw/EOuG72+vmvb+Am0SEkda3s9w2ceR8j3qVTT5qarUlPwjLVEUllVb4aF6Z2A60NLCX+SHfw8Lq5UVd3XZH78BfgHsCuebAxvMbGc4H+13aUzh8m/C+tUl1qMJxnwbHZ6ae0bSQdTQ99bMPgd+DawAviB4v2ZRc9/fEul6P1uFrxPLU+KJIzXJzv1V2/uYJTUCXgFuNrON5VVNUmbllB8wJJ0PfGVms6LFSapaBcsO+FhDdQlOazxlZp2BbwlOZZSlWscbntvvS3B66UjgIKBPkqo15f2tSNz49iluTxypqXDcrepCUjZB0njBzF4Ni7/U7qFcjiAYxh7Kjrs67I9TgQskLScY0v9MgiOQppJKRkyI9rs0pnB5E4LRDKpDrBD0s8jMpofzLxMkkpr43gL0BD4zszVmtgN4FfgBNff9LZGu97MofJ1YnhJPHKmpcNyt6iC8a+IPwGIzezSyaCJQcrfFlQTXPkrKrwjv2OgGfBMeHk8GzpF0SPjN75yw7IBhZsPMrLWZtSF4v941s0uBqcCFYbXEWEv2wYVhfQvLB4R35bQFcgguKh5QzGw1sFLScWHRWUAhNfC9Da0Aukn6TvjvuiTeGvn+RqTl/QyXbZLULdx/V0TaqlhVX/ypLhPBXQv/Irjr4s6q7k8lYziN4HB0PsE4X3PDuJoTXEReGv5tFtYXwVMcPwEWAPmRtn4CLAunq6s6tgri7s7uu6qOJvhgWAa8BNQPyxuE88vC5UdH1r8z3AdLiHHnSRXEmQvMDN/fCQR30dTY9xa4D/iYYKDT5wnujKox7y9QQHD9ZgfBEcI16Xw/gfxw330CPEHCjRXlTT7kiHPOuVj8VJVzzrlYPHE455yLxROHc865WDxxOOeci8UTh3POuVg8cbi0kbQ5Yf4qSU9UVX8ORJK6KxypN1I2RtKFZa1TkykY0ff6qu6Hi8cTh6u2Ir8Q3pc2stLRlwPZvu6n8EdlmfqsaEowcu2B0h+XAt/5LuMkNZb0WTjcCZIOlrRcUrakaZJ+I+lDBc9V6BrWOSh8HsGMcNC+vmH5VZJekvQa8NfwG/zfJI2XVChpVMmHiqSnJM1U8MyG+yL9WS7pHknvAxdJui7czjxJr0j6TlhvTNjGVEmfSjoj7NNiSWPCOllhvYUKnm0wZB/201mSxkfmz5b0avh6s6T/lTRb0juSDg3Lj5H0lqRZkv4u6fhI3x+VNBV4WNJwSc9LelfBcxmuC+s1CtubHfa/ZD+3CeP8P2A2cFQF+/NBSf8Il3eRNFnSJ5IGR+rdFu7n+ZH1RwDHSJor6ZGy6iXrT2X3s0uDqv51pE81ZwKK2f2L9LkEw0I8ES4bDfQLXw8C/jd8PQ34ffj6dMIhpIEHgcvC100JfrV/EMEzF4rY/YvZ7sBWgl8MZwFvAxeGy0rqZIXbOSGcXw78ItLv5pHXDwA3hq/HEIxzJYIB9TYCnQi+cM0i+KV2HvB2ZP2m4d/BwOAk+6g7wcis0f30NcEwGCL4JfShYd0/Az8KXxtwafj6nsh+fQfICV+fTDCURknfXyd8tgQwnOC5Ew2BFgQjph5JMDjiwWGdFgS/LhbBcN67gG6Rvpe3P0uee/EYwS/XGwOHEgw0CcFQF0+HbdcJ+3Y6ew8bXl69PfrjU9VN+3yo71zEFjPLLZmRdBXBsAYAzxAMcT4BuBq4LrJeAYCZ/S08GmlK8AFygaRbwzoNgO+Gr982s68j6//TzD4Nt1lAMLTKy0B/SYMIPhyPIHhoz/xwnXGR9TtKeoAgQTViz7GZXjMzk7QA+NLMFoTbWUTwYfYecLSkx4E3gL+GsYwqZz/93czOj+ynMeE6Jul54DJJowmeJ3FFWG1XpM9/Al5VMMrxD4CXtPvhbfUj23nJzIoj838xsy3AlvBIpGvY5wclnR5uoxXQMqz/bzP7KLJ+efuzZOy2BUAjM9tEMBbS1sj7eQ4wJ6zXiGBcqBUJ+6a8eon9cVXEE4fbL8zsg/B0wxkE34IXRhcnVif4xvljM1sSXSDpZIIhwxPr7zGvYMC6W4GTzGx9+OHcIFIn2sYYgqOheWGy6x5Zti38uyvyumS+btj2iQQPEfovoD/B2ECVNRp4jeAo6iXb/WyJREbwjXxDNFknqHA/AZcSHBnkmdkOBaMJl+yn0vVT2J/l7ieC9/MhM/tdtAOKPMK1pKiceonxuCri1zjc/vRHgqOL0QnlFwNIOo1gVM9vCL7136jwq7SkzuW021XByMV1wrbeJ3gi3rfAN5JakvxZDSUaA18ouAZzaZyAJLUA6pjZK8DdBEOZV5qZrSIY3vougoRWog67R329BHjfgmepfCbporAvCpNYWfoqeC53c4LkOINgePGvwqTRA/heGevG2Z/JTAZ+Eh4lIamVpMOATQT7v6J67gDiRxxuf3qB4BpCQUL5ekkfEnw4lXxb/yXB8zPmh8ljOXA+yf2D4CJrJ+BvwHgz2yVpDrCI4DnLH5TTr7sJnoT4b4JTLY3LqZuoFcFT90q+hA0DKLkoXMEpq7K8QHCdozBS9i3QQdIsgmskF4fllwJPSboLyCa4JjOvjHb/SXBq6rvAL81slaQXgNckzSS43vJxshXDo7FU92ey9f8qqR3wj/C7wGaCa1ifSPpA0kLgTTO7LVk9gutn7gDho+O6/UbBbxX6mtnlkbJpwK1mNrOSbXYP1y8rqVQ7Cn77MsfM/hAp22xmjfahzeHAZjP7dRq66Go5P+Jw+0V48bgPwfM/XBnCI4pvgf+u6r44VxY/4nDOOReLXxx3zjkXiycO55xzsXjicM45F4snDuecc7F44nDOORfL/w8jsZMXIOvYagAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done\n"
     ]
    }
   ],
   "source": [
    "bow_hyperparam= SVM_HyperParam_Analysis(X_tr_bow,y_train,X_te_bow,y_test)\n",
    "#print (\"Hyper Param to apply is %s\" % bow_hyperparam)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5.1 <font color='red'> SVM Hyper Param -Analysis on Set 6  <font color='blue'>,<font color='red'> SET 6</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done\n"
     ]
    }
   ],
   "source": [
    "set6_hyperparam= SVM_HyperParam_Analysis(X_tr_set6,y_train,X_te_set6,y_test)\n",
    "#print (\"Hyper Param to apply is %s\" % set6_hyperparam)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5.2 <font color='red'> SVM Hyper Param -Analysis   <font color='blue'>  on TFIDF Set 2: categorical, numerical features + project_title(TFIDF)+ preprocessed_essay (TFIDF),<font color='red'> SET 2</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done\n"
     ]
    }
   ],
   "source": [
    "tfidf_hyperparam= SVM_HyperParam_Analysis(X_tr_tfidf,y_train,X_te_tfidf,y_test)\n",
    "#print (\"Hyper Param to apply is %s\" % tfidf_hyperparam)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5.3 <font color='red'> Hyper Parameter-Analysis     <font color='blue'>  on AVG W2V - categorical, numerical features + project_title(AVG W2V )+ preprocessed_essay (AVG W2V ),<font color='red'> SET 3</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done\n"
     ]
    }
   ],
   "source": [
    "avgw2v_hyperparam= SVM_HyperParam_Analysis(X_tr_avg_w2v,y_train,X_te_avg_w2v,y_test)\n",
    "#print (\"Hyper Param to apply is %s\" % avgw2v_hyperparam)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5.3 <font color='red'> Hyper Parameter-Analysis     <font color='blue'>  on TFIDF W2V - categorical, numerical features + project_title(TFIDF W2V )+ preprocessed_essay (TFIDF W2V ),<font color='red'> SET 3</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done\n"
     ]
    }
   ],
   "source": [
    "\n",
    "tfidfw2v_hyperparam= SVM_HyperParam_Analysis(X_tr_tfidf_w2v,y_train,X_te_tfidf_w2v,y_test)\n",
    "#print (\"Hyper Param to apply is %s\" % tfidfw2v_hyperparam)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyper Param Analysis on TFIDF Set 5: categorical, numerical features + SET 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM Analysis on Best Hyper Parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done\n"
     ]
    }
   ],
   "source": [
    "set5_hyperparam = SVM_HyperParam_Analysis(X_tr_set5,y_train,X_te_set5,y_test)\n",
    "#print (\"Hyper Param to apply is %s\" % set5_hyperparam)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done\n"
     ]
    }
   ],
   "source": [
    "tfidfw2v_hyperparam = SVM_HyperParam_Analysis(X_tr_tfidf_w2v,y_train,X_te_tfidf_w2v,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.01\n",
      "====================================================================================================\n",
      "the maximum value of tpr*(1-fpr) 0.5006699601203813 for threshold 0.83\n",
      "Train confusion matrix\n",
      "[[ 692  313]\n",
      " [1554 4141]]\n",
      "Test confusion matrix\n",
      "[[ 241  254]\n",
      " [ 872 1933]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "HyperParameter = set6_hyperparam\n",
    "print (HyperParameter )\n",
    "#HyperParameter=10   \n",
    "Set6_trainAUC_analysis,Set6_testAUC_analysis = SVM_for_Best_Hyper_Parameter(X_tr_set6,y_train,X_te_set6,y_test,HyperParameter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====================================================================================================\n",
      "the maximum value of tpr*(1-fpr) 0.40939656415027575 for threshold 0.845\n",
      "Train confusion matrix\n",
      "[[ 667  338]\n",
      " [2182 3513]]\n",
      "Test confusion matrix\n",
      "[[ 241  254]\n",
      " [1180 1625]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "HyperParameter =bow_hyperparam\n",
    "trainAUC_bow_analysis, testAUC_bow_analysis =SVM_for_Best_Hyper_Parameter(X_tr_bow,y_train,X_te_bow,y_test, HyperParameter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.01\n",
      "====================================================================================================\n",
      "the maximum value of tpr*(1-fpr) 0.31562328829950337 for threshold 0.834\n",
      "Train confusion matrix\n",
      "[[ 486  519]\n",
      " [1978 3717]]\n",
      "Test confusion matrix\n",
      "[[ 202  293]\n",
      " [1009 1796]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "HyperParameter = set5_hyperparam\n",
    "print (HyperParameter)\n",
    "#HyperParameter=12\n",
    "trainAUC_avg_w2v_analysis, testAUC_avg_w2v_analysis = SVM_for_Best_Hyper_Parameter(X_tr_set5,y_train,X_te_set5,y_test, HyperParameter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.01\n",
      "====================================================================================================\n",
      "the maximum value of tpr*(1-fpr) 0.3389933563088858 for threshold 0.838\n",
      "Train confusion matrix\n",
      "[[ 540  465]\n",
      " [2102 3593]]\n",
      "Test confusion matrix\n",
      "[[ 226  269]\n",
      " [1096 1709]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "HyperParameter = tfidf_hyperparam\n",
    "print (HyperParameter)\n",
    "trainAUC_tfidf_analysis, testAUC_tfidf_analysis = SVM_for_Best_Hyper_Parameter(X_tr_tfidf,y_train,X_te_tfidf,y_test,HyperParameter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.01\n",
      "====================================================================================================\n",
      "the maximum value of tpr*(1-fpr) 0.4603053215048549 for threshold 0.828\n",
      "Train confusion matrix\n",
      "[[ 633  372]\n",
      " [1533 4162]]\n",
      "Test confusion matrix\n",
      "[[ 236  259]\n",
      " [ 861 1944]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "HyperParameter = tfidfw2v_hyperparam\n",
    "print (HyperParameter)\n",
    "#HyperParameter =9999\n",
    "trainAUC_tfidf_w2v_analysis, testAUC_tfidf_w2v_analysis = SVM_for_Best_Hyper_Parameter(X_tr_tfidf_w2v,y_train,X_te_tfidf_w2v,y_test,HyperParameter)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.0 <font color='Blue'> Pretty Table  SET 3</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------------------+-----------+----------+\n",
      "|       Model Type       | Train AUC | Test AUC |\n",
      "+------------------------+-----------+----------+\n",
      "|    Regular_Analyis     |    0.77   |   0.63   |\n",
      "|      BoW_Analysis      |    0.69   |   0.55   |\n",
      "| TFIDF_Avg_W2V_Analysis |    0.59   |   0.53   |\n",
      "|   TFIDF_W2V_Analysis   |    0.73   |   0.62   |\n",
      "|     Set 6 Analysis     |    0.77   |   0.63   |\n",
      "+------------------------+-----------+----------+\n"
     ]
    }
   ],
   "source": [
    "#Draw Pretty Table using Best Analysis. Pretty table is drawn based on \n",
    "#best AUC is calcukated by passing varipus Hyperparameter in loop.\n",
    "\n",
    "from prettytable import PrettyTable\n",
    "    \n",
    "LRTable = PrettyTable()\n",
    "\n",
    "LRTable.field_names = [\"Model Type\", \"Train AUC\", \"Test AUC\"]\n",
    "LRTable.add_row([\"Regular_Analyis\", trainAUC_analysis, testAUC_analysis])\n",
    "LRTable.add_row([\"BoW_Analysis\", trainAUC_bow_analysis, testAUC_bow_analysis])\n",
    "#LRTable.add_row([\"TFIDF_Analysis\", trainAUC_tfidf, testAUC_tfidf])\n",
    "LRTable.add_row([\"TFIDF_Avg_W2V_Analysis\", trainAUC_avg_w2v_analysis, testAUC_avg_w2v_analysis])\n",
    "LRTable.add_row([\"TFIDF_W2V_Analysis\", trainAUC_tfidf_w2v_analysis, testAUC_tfidf_w2v_analysis])\n",
    "LRTable.add_row([\"Set 6 Analysis\", Set6_trainAUC_analysis, Set6_testAUC_analysis])\n",
    "\n",
    "print (LRTable)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>3. Conclusions</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SVM is a stastical method for analyzing a dataset in which there are one or more independent variables that determine the outcome. \n",
    "\n",
    "## Summary of above program as below:\n",
    "\n",
    "Lot of plots are drawn for different data set between train and test data. Test data is very near to train data.\n",
    "Please see pretty table for all comparasions.\n",
    "\n",
    "### Step 1: Import the necessary Libraries\n",
    "we will need to import libraries that allow for data analysis and data visualization to get acclimated to the dataset. We will be using pandas, numpy, matplotlib and seaborn to conduct this. Data Exploration libraries\n",
    "\n",
    "### Step 2: Read in the dataset.\n",
    "We will use the pandas .read_csv() method to read in the dataset. Then we will use the. head() method to observe the first few rows of the data, to understand the information better. In our case, the feature(column) headers tell us pretty little. This is fine because we are merely trying to gain insight via classifying new data points by referencing it’s neighboring elements.\n",
    "\n",
    "### Step 3: Standardize (normalize) the data scale to prep for SVM.\n",
    "Because the distance between pairs of points plays a critical part on the classification, it is necessary to normalize the data This will generate an array of values. \n",
    "\n",
    "### Step 4: Split the normalized data into training and test sets.\n",
    "This step is required to prepare us for the fitting (i.e. training) the model later. The “X” variable is a collection of all the features. The “y” variable is the target label which specifies the classification of 1 or 0 based. Our goal will be to identify which category the new data point should fall into.\n",
    "\n",
    "\n",
    "### Step 5: Create and Train the Model.\n",
    "Here we create a SVM Object and use the .fit() method to train the model. Upon completion of the model we should receive confirmation that the training has been complete\n",
    "\n",
    "Please see functions as covered below, used in above program: def SVM_validation(X,y): def \n",
    "\n",
    "### Step 6: Make Predictions.\n",
    "Here we review where our model was accurate and where it misclassified elements.\n",
    "\n",
    "Please see functions as covered below, used in above program: def SVM_validation(X,y):\n",
    "\n",
    "### Step 7: Evaluate the predictions.\n",
    "\n",
    "Evaluate the Model by reviewing the classification report or confusion matrix. By reviewing these tables, we are able to evaluate how accurate our model is with new values.\n",
    "\n",
    "def SVM_validation(X,y):\n",
    "\n",
    "### Setp 8:Classification Report :\n",
    "This tells us our model was around 84% accurate… Print out classification report and confusion matrix\n",
    "\n",
    "I have covered various set to show confusion matrix.\n",
    "\n",
    "Please see section 2. covered various data sets and created confusion matrix.\n",
    "\n",
    "### Step 9: Evaluate alternative Hyper Parameter for better predictions.\n",
    "To simplify the process of evaluating multiple cases of Alpha values, we create a function to derive the error using the average where our predictions were not equal to the test values.\n",
    "\n",
    "Please see section 2. covered various data sets and created error accuracy reports.\n",
    "\n",
    "### Step 10: Adjust Hyper Parameter value per error rate evaluations \n",
    "This is just fine tuning our model to increase accuracy. We will need to retrain our model with the new Alpha.\n",
    "Please see section 3 in above program. we have created confusion matrix for optimal Alpha value for various data sets. As we can see for optimal Alpha, Accuracy is much higher - so prediction is much better.\n",
    "\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "`4+"
   ]
  }
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