SVM Full Analysis_orig.ipynb

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   "source": [
    "# SVM Analysis\n",
    "\n",
    "<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>\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",
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   "source": [
    "## About the DonorsChoose Data Set\n",
    "\n",
    "The `train.csv` data set provided by DonorsChoose contains the following features:\n",
    "\n",
    "Feature | Description \n",
    "----------|---------------\n",
    "**`project_id`** | A unique identifier for the proposed project. **Example:** `p036502`   \n",
    "**`project_title`**    | Title of the project. **Examples:**<br><ul><li><code>Art Will Make You Happy!</code></li><li><code>First Grade Fun</code></li></ul> \n",
    "**`project_grade_category`** | Grade level of students for which the project is targeted. One of the following enumerated values: <br/><ul><li><code>Grades PreK-2</code></li><li><code>Grades 3-5</code></li><li><code>Grades 6-8</code></li><li><code>Grades 9-12</code></li></ul>  \n",
    " **`project_subject_categories`** | One or more (comma-separated) subject categories for the project from the following enumerated list of values:  <br/><ul><li><code>Applied Learning</code></li><li><code>Care &amp; Hunger</code></li><li><code>Health &amp; Sports</code></li><li><code>History &amp; Civics</code></li><li><code>Literacy &amp; Language</code></li><li><code>Math &amp; Science</code></li><li><code>Music &amp; The Arts</code></li><li><code>Special Needs</code></li><li><code>Warmth</code></li></ul><br/> **Examples:** <br/><ul><li><code>Music &amp; The Arts</code></li><li><code>Literacy &amp; Language, Math &amp; Science</code></li>  \n",
    "  **`school_state`** | State where school is located ([Two-letter U.S. postal code](https://en.wikipedia.org/wiki/List_of_U.S._state_abbreviations#Postal_codes)). **Example:** `WY`\n",
    "**`project_subject_subcategories`** | One or more (comma-separated) subject subcategories for the project. **Examples:** <br/><ul><li><code>Literacy</code></li><li><code>Literature &amp; Writing, Social Sciences</code></li></ul> \n",
    "**`project_resource_summary`** | An explanation of the resources needed for the project. **Example:** <br/><ul><li><code>My students need hands on literacy materials to manage sensory needs!</code</li></ul> \n",
    "**`project_essay_1`**    | First application essay<sup>*</sup>  \n",
    "**`project_essay_2`**    | Second application essay<sup>*</sup> \n",
    "**`project_essay_3`**    | Third application essay<sup>*</sup> \n",
    "**`project_essay_4`**    | Fourth application essay<sup>*</sup> \n",
    "**`project_submitted_datetime`** | Datetime when project application was submitted. **Example:** `2016-04-28 12:43:56.245`   \n",
    "**`teacher_id`** | A unique identifier for the teacher of the proposed project. **Example:** `bdf8baa8fedef6bfeec7ae4ff1c15c56`  \n",
    "**`teacher_prefix`** | Teacher's title. One of the following enumerated values: <br/><ul><li><code>nan</code></li><li><code>Dr.</code></li><li><code>Mr.</code></li><li><code>Mrs.</code></li><li><code>Ms.</code></li><li><code>Teacher.</code></li></ul>  \n",
    "**`teacher_number_of_previously_posted_projects`** | Number of project applications previously submitted by the same teacher. **Example:** `2` \n",
    "\n",
    "<sup>*</sup> See the section <b>Notes on the Essay Data</b> for more details about these features.\n",
    "\n",
    "Additionally, the `resources.csv` data set provides more data about the resources required for each project. Each line in this file represents a resource required by a project:\n",
    "\n",
    "Feature | Description \n",
    "----------|---------------\n",
    "**`id`** | A `project_id` value from the `train.csv` file.  **Example:** `p036502`   \n",
    "**`description`** | Desciption of the resource. **Example:** `Tenor Saxophone Reeds, Box of 25`   \n",
    "**`quantity`** | Quantity of the resource required. **Example:** `3`   \n",
    "**`price`** | Price of the resource required. **Example:** `9.95`   \n",
    "\n",
    "**Note:** Many projects require multiple resources. The `id` value corresponds to a `project_id` in train.csv, so you use it as a key to retrieve all resources needed for a project:\n",
    "\n",
    "The data set contains the following label (the value you will attempt to predict):\n",
    "\n",
    "Label | Description\n",
    "----------|---------------\n",
    "`project_is_approved` | A binary flag indicating whether DonorsChoose approved the project. A value of `0` indicates the project was not approved, and a value of `1` indicates the project was approved."
   ]
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   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "iydYV-rsoUF4"
   },
   "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": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CB_mY9_roUF6",
    "outputId": "e9ecaa59-1666-4996-d932-79fab4aec227"
   },
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'plotly'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-ffcf479d3c69>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     33\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     34\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 35\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mplotly\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     36\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moffline\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0moffline\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     37\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgraph_objs\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mgo\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'plotly'"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\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",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\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",
    "\n",
    "from tqdm import tqdm\n",
    "import os\n",
    "\n",
    "from plotly import plotly\n",
    "import plotly.offline as offline\n",
    "import plotly.graph_objs as go\n",
    "offline.init_notebook_mode()\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XbVmhJgKoUGE"
   },
   "source": [
    "## 1.1 Reading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fv9wFU8ZoUGF"
   },
   "outputs": [],
   "source": [
    "project_data = pd.read_csv('train_data.csv')\n",
    "resource_data = pd.read_csv('resources.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GAYzkeNtoUGJ",
    "outputId": "88b611ff-da90-41fe-ae21-91a4eef86906"
   },
   "outputs": [],
   "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": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RcShjQotoUGQ",
    "outputId": "5d400178-f25a-46f6-ffd8-6657f022fd33"
   },
   "outputs": [],
   "source": [
    "print(\"Number of data points in train data\", resource_data.shape)\n",
    "print(resource_data.columns.values)\n",
    "resource_data.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WpalDcGUoUGW"
   },
   "source": [
    "## 1.2 preprocessing of `project_subject_categories`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eM4RYg1qoUGX"
   },
   "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": {
    "colab_type": "text",
    "id": "EMP3hhkvoUGb"
   },
   "source": [
    "## 1.3 preprocessing of `project_subject_subcategories`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DGM4QvrQoUGc"
   },
   "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]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9LdJ4JOloUGf"
   },
   "source": [
    "## 1.3 Text preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JRmyuIgeoUGh"
   },
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8vCZZ8sKoUGk",
    "outputId": "a3e673c8-d33b-4803-8095-6185727695fa"
   },
   "outputs": [],
   "source": [
    "project_data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "y1IMIH5ioUGo"
   },
   "outputs": [],
   "source": [
    "#### 1.4.2.3 Using Pretrained Models: TFIDF weighted W2V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ghjkQ5NJoUGq",
    "outputId": "1e5cb6dd-5776-4fe5-be4a-f541a82341d4"
   },
   "outputs": [],
   "source": [
    "# printing some random reviews\n",
    "print(project_data['essay'].values[0])\n",
    "print(\"=\"*50)\n",
    "print(project_data['essay'].values[150])\n",
    "print(\"=\"*50)\n",
    "print(project_data['essay'].values[1000])\n",
    "print(\"=\"*50)\n",
    "print(project_data['essay'].values[20000])\n",
    "print(\"=\"*50)\n",
    "print(project_data['essay'].values[99999])\n",
    "print(\"=\"*50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TGzKAL65oUGt"
   },
   "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": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xkeCI1WmoUGw",
    "outputId": "f85659af-c549-43d5-89aa-62bd78b4f708"
   },
   "outputs": [],
   "source": [
    "sent = decontracted(project_data['essay'].values[20000])\n",
    "print(sent)\n",
    "print(\"=\"*50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KAhl8dbYoUGz",
    "outputId": "cec5949b-f5f2-4899-d8fa-c645d528b231"
   },
   "outputs": [],
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "io_sbKCWoUG2",
    "outputId": "8b1e198e-c9fd-4a1f-b72d-fbfaff746b54"
   },
   "outputs": [],
   "source": [
    "#remove spacial character: https://stackoverflow.com/a/5843547/4084039\n",
    "sent = re.sub('[^A-Za-z0-9]+', ' ', sent)\n",
    "print(sent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wtuFzHzfoUG6"
   },
   "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": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ny4jT0IzoUG8",
    "outputId": "a973df34-b25b-404d-e37d-5b4377ec68b2"
   },
   "outputs": [],
   "source": [
    "# Combining all the above stundents \n",
    "from tqdm import tqdm\n",
    "preprocessed_essays = []\n",
    "# tqdm is for printing the status bar\n",
    "for sentance in tqdm(project_data['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",
    "    preprocessed_essays.append(sent.lower().strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ccPTDoKGoUG_",
    "outputId": "79740a48-c822-4071-9274-6d9625edd9ec"
   },
   "outputs": [],
   "source": [
    "# after preprocesing\n",
    "preprocessed_essays[20000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "oxBhoq--oUHC"
   },
   "source": [
    "<h2><font color='red'> 1.4 Preprocessing of `project_title`</font></h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zBxyacM6oUHD"
   },
   "outputs": [],
   "source": [
    "# similarly you can preprocess the titles also"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "P4I60VW7oUHF"
   },
   "source": [
    "## 1.5 Preparing data for models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "F_jrfczjoUHG",
    "outputId": "6e45eb13-642b-488f-b44d-e370e7430b18"
   },
   "outputs": [],
   "source": [
    "project_data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "y1YePFvOoUHI"
   },
   "source": [
    "we are going to consider\n",
    "\n",
    "       - school_state : categorical data\n",
    "       - clean_categories : categorical data\n",
    "       - clean_subcategories : categorical data\n",
    "       - project_grade_category : categorical data\n",
    "       - teacher_prefix : categorical data\n",
    "       \n",
    "       - project_title : text data\n",
    "       - text : text data\n",
    "       - project_resource_summary: text data (optinal)\n",
    "       \n",
    "       - quantity : numerical (optinal)\n",
    "       - teacher_number_of_previously_posted_projects : numerical\n",
    "       - price : numerical"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1xr5XNfxoUHJ"
   },
   "source": [
    "### 1.5.1 Vectorizing Categorical data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lxgwqyC5oUHK"
   },
   "source": [
    "- https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/handling-categorical-and-numerical-features/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_NnyJ0R1oUHK",
    "outputId": "c14e7801-3973-4b1b-b20c-31f89309114f"
   },
   "outputs": [],
   "source": [
    "# we use count vectorizer to convert the values into one \n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "vectorizer = CountVectorizer(vocabulary=list(sorted_cat_dict.keys()), lowercase=False, binary=True)\n",
    "categories_one_hot = vectorizer.fit_transform(project_data['clean_categories'].values)\n",
    "print(vectorizer.get_feature_names())\n",
    "print(\"Shape of matrix after one hot encodig \",categories_one_hot.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "miHmQ8JmoUHN",
    "outputId": "aa252bde-f653-4d4d-f0f2-15cd129b5edb"
   },
   "outputs": [],
   "source": [
    "# we use count vectorizer to convert the values into one \n",
    "vectorizer = CountVectorizer(vocabulary=list(sorted_sub_cat_dict.keys()), lowercase=False, binary=True)\n",
    "sub_categories_one_hot = vectorizer.fit_transform(project_data['clean_subcategories'].values)\n",
    "print(vectorizer.get_feature_names())\n",
    "print(\"Shape of matrix after one hot encodig \",sub_categories_one_hot.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "taK2Q5YNoUHP"
   },
   "outputs": [],
   "source": [
    "# you can do the similar thing with state, teacher_prefix and project_grade_category also"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "k_rW-gxmoUHR"
   },
   "source": [
    "### 1.5.2 Vectorizing Text data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VMvzxx_koUHR"
   },
   "source": [
    "#### 1.5.2.1 Bag of words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IB98tnXfoUHS",
    "outputId": "9f81e28b-8709-4280-ef37-db6542b3b268"
   },
   "outputs": [],
   "source": [
    "# We are considering only the words which appeared in at least 10 documents(rows or projects).\n",
    "vectorizer = CountVectorizer(min_df=10)\n",
    "text_bow = vectorizer.fit_transform(preprocessed_essays)\n",
    "print(\"Shape of matrix after one hot encodig \",text_bow.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "N0wcK6F-oUHV"
   },
   "outputs": [],
   "source": [
    "# you can vectorize the title also \n",
    "# before you vectorize the title make sure you preprocess it"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HjDdqenYoUHX"
   },
   "source": [
    "#### 1.5.2.2 TFIDF vectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rzLt7eK9oUHX",
    "outputId": "3504f45e-7c82-4c50-bd02-e1c41642ed01"
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "vectorizer = TfidfVectorizer(min_df=10)\n",
    "text_tfidf = vectorizer.fit_transform(preprocessed_essays)\n",
    "print(\"Shape of matrix after one hot encodig \",text_tfidf.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ElMda-g1oUHZ"
   },
   "source": [
    "#### 1.5.2.3 Using Pretrained Models: Avg W2V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-sKQl0jRoUHa",
    "outputId": "b466a236-5a0b-4040-dada-0e32a65d0919"
   },
   "outputs": [],
   "source": [
    "'''\n",
    "# Reading glove vectors in python: https://stackoverflow.com/a/38230349/4084039\n",
    "def loadGloveModel(gloveFile):\n",
    "    print (\"Loading Glove Model\")\n",
    "    f = open(gloveFile,'r', encoding=\"utf8\")\n",
    "    model = {}\n",
    "    for line in tqdm(f):\n",
    "        splitLine = line.split()\n",
    "        word = splitLine[0]\n",
    "        embedding = np.array([float(val) for val in splitLine[1:]])\n",
    "        model[word] = embedding\n",
    "    print (\"Done.\",len(model),\" words loaded!\")\n",
    "    return model\n",
    "model = loadGloveModel('glove.42B.300d.txt')\n",
    "\n",
    "# ============================\n",
    "Output:\n",
    "    \n",
    "Loading Glove Model\n",
    "1917495it [06:32, 4879.69it/s]\n",
    "Done. 1917495  words loaded!\n",
    "\n",
    "# ============================\n",
    "\n",
    "words = []\n",
    "for i in preproced_texts:\n",
    "    words.extend(i.split(' '))\n",
    "\n",
    "for i in preproced_titles:\n",
    "    words.extend(i.split(' '))\n",
    "print(\"all the words in the coupus\", len(words))\n",
    "words = set(words)\n",
    "print(\"the unique words in the coupus\", len(words))\n",
    "\n",
    "inter_words = set(model.keys()).intersection(words)\n",
    "print(\"The number of words that are present in both glove vectors and our coupus\", \\\n",
    "      len(inter_words),\"(\",np.round(len(inter_words)/len(words)*100,3),\"%)\")\n",
    "\n",
    "words_courpus = {}\n",
    "words_glove = set(model.keys())\n",
    "for i in words:\n",
    "    if i in words_glove:\n",
    "        words_courpus[i] = model[i]\n",
    "print(\"word 2 vec length\", len(words_courpus))\n",
    "\n",
    "\n",
    "# stronging variables into pickle files python: http://www.jessicayung.com/how-to-use-pickle-to-save-and-load-variables-in-python/\n",
    "\n",
    "import pickle\n",
    "with open('glove_vectors', 'wb') as f:\n",
    "    pickle.dump(words_courpus, f)\n",
    "\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Vb1-FcQwoUHc"
   },
   "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('glove_vectors', 'rb') as f:\n",
    "    model = pickle.load(f)\n",
    "    glove_words =  set(model.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ut5bQna3oUHd",
    "outputId": "b59444ff-f493-4d35-f3c5-31a27aa958ab",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# average Word2Vec\n",
    "# compute average word2vec for each review.\n",
    "avg_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list\n",
    "for sentence in tqdm(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",
    "    avg_w2v_vectors.append(vector)\n",
    "\n",
    "print(len(avg_w2v_vectors))\n",
    "print(len(avg_w2v_vectors[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "919fc_i1oUHf"
   },
   "source": [
    "#### 1.5.2.3 Using Pretrained Models: TFIDF weighted W2V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UnW1_qLtoUHg"
   },
   "outputs": [],
   "source": [
    "# S = [\"abc def pqr\", \"def def def abc\", \"pqr pqr def\"]\n",
    "tfidf_model = TfidfVectorizer()\n",
    "tfidf_model.fit(preprocessed_essays)\n",
    "# we are converting a dictionary with word as a key, and the idf as a value\n",
    "dictionary = dict(zip(tfidf_model.get_feature_names(), list(tfidf_model.idf_)))\n",
    "tfidf_words = set(tfidf_model.get_feature_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vWr-8LbhoUHh",
    "outputId": "543fb84e-6d7b-4098-a2a2-f677b1ecd4c0",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# average Word2Vec\n",
    "# compute average word2vec for each review.\n",
    "tfidf_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list\n",
    "for sentence in tqdm(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 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 = 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",
    "    tfidf_w2v_vectors.append(vector)\n",
    "\n",
    "print(len(tfidf_w2v_vectors))\n",
    "print(len(tfidf_w2v_vectors[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jNqYIlxZoUHj"
   },
   "outputs": [],
   "source": [
    "# Similarly you can vectorize for title also"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yHJ45fW4oUHl"
   },
   "source": [
    "### 1.5.3 Vectorizing Numerical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yHffthPioUHl"
   },
   "outputs": [],
   "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')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Og7gm3d4oUHn"
   },
   "outputs": [],
   "source": [
    "# check this one: https://www.youtube.com/watch?v=0HOqOcln3Z4&t=530s\n",
    "# standardization sklearn: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# price_standardized = standardScalar.fit(project_data['price'].values)\n",
    "# this will rise the error\n",
    "# ValueError: Expected 2D array, got 1D array instead: array=[725.05 213.03 329.   ... 399.   287.73   5.5 ].\n",
    "# Reshape your data either using array.reshape(-1, 1)\n",
    "\n",
    "price_scalar = StandardScaler()\n",
    "price_scalar.fit(project_data['price'].values.reshape(-1,1)) # finding the mean and standard deviation of this data\n",
    "print(f\"Mean : {price_scalar.mean_[0]}, Standard deviation : {np.sqrt(price_scalar.var_[0])}\")\n",
    "\n",
    "# Now standardize the data with above maen and variance.\n",
    "price_standardized = price_scalar.transform(project_data['price'].values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Az8Af4SeoUHp",
    "outputId": "6e16cd48-c9ab-43b9-f30f-57d6af2c9860"
   },
   "outputs": [],
   "source": [
    "price_standardized"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "m8vQHRmfoUHr"
   },
   "source": [
    "### 1.5.4 Merging all the above features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MeqZCqa4oUHs"
   },
   "source": [
    "- we need to merge all the numerical vectors i.e catogorical, text, numerical vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kXYx15pXoUHs",
    "outputId": "cc99465f-dbf5-483e-f86c-086b3cbd17aa"
   },
   "outputs": [],
   "source": [
    "print(categories_one_hot.shape)\n",
    "print(sub_categories_one_hot.shape)\n",
    "print(text_bow.shape)\n",
    "print(price_standardized.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_n3qDy_roUHu",
    "outputId": "0aad4b00-80f5-4603-c49f-b042eaacba96"
   },
   "outputs": [],
   "source": [
    "# merge two sparse matrices: https://stackoverflow.com/a/19710648/4084039\n",
    "from scipy.sparse import hstack\n",
    "# with the same hstack function we are concatinating a sparse matrix and a dense matirx :)\n",
    "X = hstack((categories_one_hot, sub_categories_one_hot, text_bow, price_standardized))\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "C4A4D0XpoUHw"
   },
   "outputs": [],
   "source": [
    "# please write all the code with proper documentation, and proper titles for each subsection\n",
    "# when you plot any graph make sure you use \n",
    "    # a. Title, that describes your plot, this will be very helpful to the reader\n",
    "    # b. Legends if needed\n",
    "    # c. X-axis label\n",
    "    # d. Y-axis label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "sroxCZEPoUHy"
   },
   "source": [
    "__ Computing Sentiment Scores__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "colab_type": "code",
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    "outputId": "ea7f1629-df31-4a5f-a6df-f57d8dface17"
   },
   "outputs": [],
   "source": [
    "import nltk\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "\n",
    "# import nltk\n",
    "# nltk.download('vader_lexicon')\n",
    "\n",
    "sid = SentimentIntensityAnalyzer()\n",
    "\n",
    "for_sentiment = 'a person is a person no matter how small dr seuss i teach the smallest students with the biggest enthusiasm \\\n",
    "for learning my students learn in many different ways using all of our senses and multiple intelligences i use a wide range\\\n",
    "of techniques to help all my students succeed students in my class come from a variety of different backgrounds which makes\\\n",
    "for wonderful sharing of experiences and cultures including native americans our school is a caring community of successful \\\n",
    "learners which can be seen through collaborative student project based learning in and out of the classroom kindergarteners \\\n",
    "in my class love to work with hands on materials and have many different opportunities to practice a skill before it is\\\n",
    "mastered having the social skills to work cooperatively with friends is a crucial aspect of the kindergarten curriculum\\\n",
    "montana is the perfect place to learn about agriculture and nutrition my students love to role play in our pretend kitchen\\\n",
    "in the early childhood classroom i have had several kids ask me can we try cooking with real food i will take their idea \\\n",
    "and create common core cooking lessons where we learn important math and writing concepts while cooking delicious healthy \\\n",
    "food for snack time my students will have a grounded appreciation for the work that went into making the food and knowledge \\\n",
    "of where the ingredients came from as well as how it is healthy for their bodies this project would expand our learning of \\\n",
    "nutrition and agricultural cooking recipes by having us peel our own apples to make homemade applesauce make our own bread \\\n",
    "and mix up healthy plants from our classroom garden in the spring we will also create our own cookbooks to be printed and \\\n",
    "shared with families students will gain math and literature skills as well as a life long enjoyment for healthy cooking \\\n",
    "nannan'\n",
    "ss = sid.polarity_scores(for_sentiment)\n",
    "\n",
    "for k in ss:\n",
    "    print('{0}: {1}, '.format(k, ss[k]), end='')\n",
    "\n",
    "# we can use these 4 things as features/attributes (neg, neu, pos, compound)\n",
    "# neg: 0.0, neu: 0.753, pos: 0.247, compound: 0.93"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "# Assignment 7: SVM"
   ]
  },
  {
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   "source": [
    "<ol>\n",
    "    <li><strong>[Task-1] Apply Support Vector Machines(SGDClassifier with hinge loss: Linear 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)</li>\n",
    "            <li><font color='red'>Set 2</font>: categorical, numerical features + project_title(TFIDF)+  preprocessed_eassay (TFIDF)</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_eassay (TFIDF W2V)</li>        </ul>\n",
    "    </li>\n",
    "    <br>\n",
    "    <li><strong>The hyper paramter tuning (best alpha in range [10^-4 to 10^4], and the best penalty among 'l1', 'l2')</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\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 the Support Vector Machines on these features by finding the best hyper paramter as suggested in step 2 and step 3</strong>\n",
    "        <ul>\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",
    "                <li><strong>Apply <a href='http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.TruncatedSVD.html'>TruncatedSVD</a> on <a href='https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html'>TfidfVectorizer</a> of essay text, choose the number of components (`n_components`) using <a href='https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/pca-code-example-using-non-visualization/'>elbow method</a></strong> : numerical data</li>\n",
    "            </ul>\n",
    "         <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>"
   ]
  },
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   "source": [
    "<h4><font color='red'>Note: Data Leakage</font></h4>\n",
    "\n",
    "1. There will be an issue of data-leakage if you vectorize the entire data and then split it into train/cv/test.\n",
    "2. To avoid the issue of data-leakage, make sure to split your data first and then vectorize it. \n",
    "3. While vectorizing your data, apply the method fit_transform() on you train data, and apply the method transform() on cv/test data.\n",
    "4. For more details please go through this <a href='https://soundcloud.com/applied-ai-course/leakage-bow-and-tfidf'>link.</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "__AbIboVoUH3"
   },
   "source": [
    "<h1>2. Support Vector Machines </h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vMxruDG7oUH3"
   },
   "source": [
    "<h2>2.1 Splitting data into Train and cross validation(or test): Stratified Sampling</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
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   "source": [
    "# please write all the code with proper documentation, and proper titles for each subsection\n",
    "# go through documentations and blogs before you start coding\n",
    "# first figure out what to do, and then think about how to do.\n",
    "# reading and understanding error messages will be very much helpfull in debugging your code\n",
    "# when you plot any graph make sure you use \n",
    "    # a. Title, that describes your plot, this will be very helpful to the reader\n",
    "    # b. Legends if needed\n",
    "    # c. X-axis label\n",
    "    # d. Y-axis label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RXh9o-rOoUH5"
   },
   "source": [
    "<h2>2.2 Make Data Model Ready: encoding numerical, categorical features</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "80ha3YnqoUH5"
   },
   "outputs": [],
   "source": [
    "# please write all the code with proper documentation, and proper titles for each subsection\n",
    "# go through documentations and blogs before you start coding \n",
    "# first figure out what to do, and then think about how to do.\n",
    "# reading and understanding error messages will be very much helpfull in debugging your code\n",
    "# make sure you featurize train and test data separatly\n",
    "\n",
    "# when you plot any graph make sure you use \n",
    "    # a. Title, that describes your plot, this will be very helpful to the reader\n",
    "    # b. Legends if needed\n",
    "    # c. X-axis label\n",
    "    # d. Y-axis label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Ya1V6mLSoUH7"
   },
   "source": [
    "<h2>2.3 Make Data Model Ready: encoding eassay, and project_title</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aKrh3dwvoUH7"
   },
   "outputs": [],
   "source": [
    "# please write all the code with proper documentation, and proper titles for each subsection\n",
    "# go through documentations and blogs before you start coding\n",
    "# first figure out what to do, and then think about how to do.\n",
    "# reading and understanding error messages will be very much helpfull in debugging your code\n",
    "# make sure you featurize train and test data separatly\n",
    "\n",
    "# when you plot any graph make sure you use \n",
    "    # a. Title, that describes your plot, this will be very helpful to the reader\n",
    "    # b. Legends if needed\n",
    "    # c. X-axis label\n",
    "    # d. Y-axis label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4IolabGIoUH9"
   },
   "source": [
    "<h2>2.4 Appling Support Vector Machines on different kind of featurization as mentioned in the instructions</h2>\n",
    "\n",
    "<br>Apply Support Vector Machines 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": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0IBXizg6oUH9"
   },
   "outputs": [],
   "source": [
    "# please write all the code with proper documentation, and proper titles for each subsection\n",
    "# go through documentations and blogs before you start coding\n",
    "# first figure out what to do, and then think about how to do.\n",
    "# reading and understanding error messages will be very much helpfull in debugging your code\n",
    "# when you plot any graph make sure you use \n",
    "    # a. Title, that describes your plot, this will be very helpful to the reader\n",
    "    # b. Legends if needed\n",
    "    # c. X-axis label\n",
    "    # d. Y-axis label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_T2l87JfoUH-"
   },
   "source": [
    "<h2>2.5 Support Vector Machines with added Features `Set 5` </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1JBBsjAzoUH_"
   },
   "outputs": [],
   "source": [
    "# please write all the code with proper documentation, and proper titles for each subsection\n",
    "# go through documentations and blogs before you start coding\n",
    "# first figure out what to do, and then think about how to do.\n",
    "# reading and understanding error messages will be very much helpfull in debugging your code\n",
    "# when you plot any graph make sure you use \n",
    "    # a. Title, that describes your plot, this will be very helpful to the reader\n",
    "    # b. Legends if needed\n",
    "    # c. X-axis label\n",
    "    # d. Y-axis label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "51CZPd61oUIA"
   },
   "source": [
    "<h1>3. Conclusion</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uZfzkquRoUIB"
   },
   "outputs": [],
   "source": [
    "# Please compare all your models using Prettytable library"
   ]
  }
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