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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ "\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",
+ "
\n",
+ "\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",
+ "
\n",
+ "\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 \n",
+ " How to increase the consistency of project vetting across different volunteers to improve the experience for teachers \n",
+ " How to focus volunteer time on the applications that need the most assistance \n",
+ " \n",
+ " \n",
+ "\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",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "0LUPgFS9oc3A"
+ },
+ "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:**Art Will Make You Happy!First Grade FunGrades PreK-2Grades 3-5Grades 6-8Grades 9-12Applied LearningCare & HungerHealth & SportsHistory & CivicsLiteracy & LanguageMath & ScienceMusic & The ArtsSpecial NeedsWarmthMusic & The ArtsLiteracy & Language, Math & ScienceLiteracyLiterature & Writing, Social SciencesMy students need hands on literacy materials to manage sensory needs!* \n",
+ "**`project_essay_2`** | Second application essay* \n",
+ "**`project_essay_3`** | Third application essay* \n",
+ "**`project_essay_4`** | Fourth application essay* \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: * See the section Notes on the Essay Data 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."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "4nXezp54oc3B"
+ },
+ "source": [
+ "### Notes on the Essay Data\n",
+ "\n",
+ "\n",
+ "Prior to May 17, 2016, the prompts for the essays were as follows:\n",
+ "__project_essay_1:__ \"Introduce us to your classroom\" \n",
+ "__project_essay_2:__ \"Tell us more about your students\" \n",
+ "__project_essay_3:__ \"Describe how your students will use the materials you're requesting\" \n",
+ "__project_essay_3:__ \"Close by sharing why your project will make a difference\" \n",
+ " \n",
+ "\n",
+ "\n",
+ "\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:__project_essay_1:__ \"Describe your students: What makes your students special? Specific details about their background, your neighborhood, and your school are all helpful.\" \n",
+ "__project_essay_2:__ \"About your project: How will these materials make a difference in your students' learning and improve their school lives?\" \n",
+ " \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 17
+ },
+ "id": "lDzTpG88oc3D",
+ "outputId": "113ac5a6-8381-4219-f29a-382434086caa"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/html": [
+ " \n",
+ " "
+ ]
+ },
+ "metadata": {}
+ }
+ ],
+ "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 chart_studio 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": "code",
+ "source": [
+ "from google.colab import files\n",
+ "files.upload()\n"
+ ],
+ "metadata": {
+ "colab": {
+ "resources": {
+ "http://localhost:8080/nbextensions/google.colab/files.js": {
+ "data": "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",
+ "ok": true,
+ "headers": [
+ [
+ "content-type",
+ "application/javascript"
+ ]
+ ],
+ "status": 200,
+ "status_text": ""
+ }
+ },
+ "base_uri": "https://localhost:8080/",
+ "height": 73
+ },
+ "id": "rjK9wg5AU_Gn",
+ "outputId": "68ad995e-5a3b-4622-f80c-e857f2252351"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "text/html": [
+ "\n",
+ " \n",
+ " Upload widget is only available when the cell has been executed in the\n",
+ " current browser session. Please rerun this cell to enable.\n",
+ " \n",
+ " "
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Saving train_data.csv to train_data.csv\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "9GiSAGzAoc3R"
+ },
+ "source": [
+ "## 1.1 Reading Data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 346
+ },
+ "id": "pSrg84iHoc3T",
+ "outputId": "48919635-a94d-4519-e31d-65b2c5e56f27"
+ },
+ "outputs": [
+ {
+ "output_type": "error",
+ "ename": "FileNotFoundError",
+ "evalue": "ignored",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mproject_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'train_data.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mresource_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'resources.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 309\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 310\u001b[0m )\n\u001b[0;32m--> 311\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 313\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m 584\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 585\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 586\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 587\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 588\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 480\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 481\u001b[0m \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 482\u001b[0;31m \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 483\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 484\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 809\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 810\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 811\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 812\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 813\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m 1038\u001b[0m )\n\u001b[1;32m 1039\u001b[0m \u001b[0;31m# error: Too many arguments for \"ParserBase\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1040\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1041\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1042\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers/c_parser_wrapper.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0;31m# open handles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open_handles\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 52\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers/base_parser.py\u001b[0m in \u001b[0;36m_open_handles\u001b[0;34m(self, src, kwds)\u001b[0m\n\u001b[1;32m 227\u001b[0m \u001b[0mmemory_map\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"memory_map\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 228\u001b[0m \u001b[0mstorage_options\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"storage_options\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 229\u001b[0;31m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"encoding_errors\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"strict\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 230\u001b[0m )\n\u001b[1;32m 231\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m 705\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 706\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 707\u001b[0;31m \u001b[0mnewline\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 708\u001b[0m )\n\u001b[1;32m 709\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'train_data.csv'"
+ ]
+ }
+ ],
+ "source": [
+ "project_data = pd.read_csv('train_data.csv')\n",
+ "resource_data = pd.read_csv('resources.csv')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "ga6SAgwKoc3Z",
+ "outputId": "65842885-302d-4b5b-b8c7-55defe1448b2"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Number of data points in train data (109248, 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": null,
+ "metadata": {
+ "id": "JJG7pF0yoc3f",
+ "outputId": "c30e2577-12e3-46b1-eb67-0858be479499"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Number of data points in train data (1541272, 4)\n",
+ "['id' 'description' 'quantity' 'price']\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " id \n",
+ " description \n",
+ " quantity \n",
+ " price \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " p233245 \n",
+ " LC652 - Lakeshore Double-Space Mobile Drying Rack \n",
+ " 1 \n",
+ " 149.00 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " p069063 \n",
+ " Bouncy Bands for Desks (Blue support pipes) \n",
+ " 3 \n",
+ " 14.95 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# this code is taken from\n",
+ "# https://matplotlib.org/gallery/pie_and_polar_charts/pie_and_donut_labels.html#sphx-glr-gallery-pie-and-polar-charts-pie-and-donut-labels-py\n",
+ "\n",
+ "\n",
+ "y_value_counts = project_data['project_is_approved'].value_counts()\n",
+ "print(\"Number of projects thar are approved for funding \", y_value_counts[1], \", (\", (y_value_counts[1]/(y_value_counts[1]+y_value_counts[0]))*100,\"%)\")\n",
+ "print(\"Number of projects thar are not approved for funding \", y_value_counts[0], \", (\", (y_value_counts[0]/(y_value_counts[1]+y_value_counts[0]))*100,\"%)\")\n",
+ "\n",
+ "fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(aspect=\"equal\"))\n",
+ "recipe = [\"Accepted\", \"Not Accepted\"]\n",
+ "\n",
+ "data = [y_value_counts[1], y_value_counts[0]]\n",
+ "\n",
+ "wedges, texts = ax.pie(data, wedgeprops=dict(width=0.5), startangle=-40)\n",
+ "\n",
+ "bbox_props = dict(boxstyle=\"square,pad=0.3\", fc=\"w\", ec=\"k\", lw=0.72)\n",
+ "kw = dict(xycoords='data', textcoords='data', arrowprops=dict(arrowstyle=\"-\"),\n",
+ " bbox=bbox_props, zorder=0, va=\"center\")\n",
+ "\n",
+ "for i, p in enumerate(wedges):\n",
+ " ang = (p.theta2 - p.theta1)/2. + p.theta1\n",
+ " y = np.sin(np.deg2rad(ang))\n",
+ " x = np.cos(np.deg2rad(ang))\n",
+ " horizontalalignment = {-1: \"right\", 1: \"left\"}[int(np.sign(x))]\n",
+ " connectionstyle = \"angle,angleA=0,angleB={}\".format(ang)\n",
+ " kw[\"arrowprops\"].update({\"connectionstyle\": connectionstyle})\n",
+ " ax.annotate(recipe[i], xy=(x, y), xytext=(1.35*np.sign(x), 1.4*y),\n",
+ " horizontalalignment=horizontalalignment, **kw)\n",
+ "\n",
+ "ax.set_title(\"Nmber of projects that are Accepted and not accepted\")\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6-A5ffngoc3w"
+ },
+ "source": [
+ "### 1.2.1 Univariate Analysis: School State"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "978IRWUsoc3y",
+ "outputId": "036fc87d-2bcd-4194-add4-3facaed61ef6"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.plotly.v1+json": {
+ "config": {
+ "linkText": "Export to plot.ly",
+ "plotlyServerURL": "https://plot.ly",
+ "showLink": false
+ },
+ "data": [
+ {
+ "autocolorscale": false,
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+ "source": [
+ "# Pandas dataframe grouby count, mean: https://stackoverflow.com/a/19385591/4084039\n",
+ "\n",
+ "temp = pd.DataFrame(project_data.groupby(\"school_state\")[\"project_is_approved\"].apply(np.mean)).reset_index()\n",
+ "# if you have data which contain only 0 and 1, then the mean = percentage (think about it)\n",
+ "temp.columns = ['state_code', 'num_proposals']\n",
+ "\n",
+ "# How to plot US state heatmap: https://datascience.stackexchange.com/a/9620\n",
+ "\n",
+ "scl = [[0.0, 'rgb(242,240,247)'],[0.2, 'rgb(218,218,235)'],[0.4, 'rgb(188,189,220)'],\\\n",
+ " [0.6, 'rgb(158,154,200)'],[0.8, 'rgb(117,107,177)'],[1.0, 'rgb(84,39,143)']]\n",
+ "\n",
+ "data = [ dict(\n",
+ " type='choropleth',\n",
+ " colorscale = scl,\n",
+ " autocolorscale = False,\n",
+ " locations = temp['state_code'],\n",
+ " z = temp['num_proposals'].astype(float),\n",
+ " locationmode = 'USA-states',\n",
+ " text = temp['state_code'],\n",
+ " marker = dict(line = dict (color = 'rgb(255,255,255)',width = 2)),\n",
+ " colorbar = dict(title = \"% of pro\")\n",
+ " ) ]\n",
+ "\n",
+ "layout = dict(\n",
+ " title = 'Project Proposals % of Acceptance Rate by US States',\n",
+ " geo = dict(\n",
+ " scope='usa',\n",
+ " projection=dict( type='albers usa' ),\n",
+ " showlakes = True,\n",
+ " lakecolor = 'rgb(255, 255, 255)',\n",
+ " ),\n",
+ " )\n",
+ "\n",
+ "fig = go.Figure(data=data, layout=layout)\n",
+ "offline.iplot(fig, filename='us-map-heat-map')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "IjU9Eai_oc34",
+ "outputId": "0d0e0ad1-5edc-40a3-9b52-c74ae3d82a98"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "States with lowest % approvals\n",
+ " state_code num_proposals\n",
+ "46 VT 0.800000\n",
+ "7 DC 0.802326\n",
+ "43 TX 0.813142\n",
+ "26 MT 0.816327\n",
+ "18 LA 0.831245\n",
+ "==================================================\n",
+ "States with highest % approvals\n",
+ " state_code num_proposals\n",
+ "30 NH 0.873563\n",
+ "35 OH 0.875152\n",
+ "47 WA 0.876178\n",
+ "28 ND 0.888112\n",
+ "8 DE 0.897959\n"
+ ]
+ }
+ ],
+ "source": [
+ "# https://www.csi.cuny.edu/sites/default/files/pdf/administration/ops/2letterstabbrev.pdf\n",
+ "temp.sort_values(by=['num_proposals'], inplace=True)\n",
+ "print(\"States with lowest % approvals\")\n",
+ "print(temp.head(5))\n",
+ "print('='*50)\n",
+ "print(\"States with highest % approvals\")\n",
+ "print(temp.tail(5))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "-Htj_Ldkoc3-"
+ },
+ "outputs": [],
+ "source": [
+ "#stacked bar plots matplotlib: https://matplotlib.org/gallery/lines_bars_and_markers/bar_stacked.html\n",
+ "def stack_plot(data, xtick, col2='project_is_approved', col3='total'):\n",
+ " ind = np.arange(data.shape[0])\n",
+ "\n",
+ " plt.figure(figsize=(20,5))\n",
+ " p1 = plt.bar(ind, data[col3].values)\n",
+ " p2 = plt.bar(ind, data[col2].values)\n",
+ "\n",
+ " plt.ylabel('Projects')\n",
+ " plt.title('% of projects aproved state wise')\n",
+ " plt.xticks(ind, list(data[xtick].values))\n",
+ " plt.legend((p1[0], p2[0]), ('total', 'accepted'))\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "ilYj-EPyoc4F"
+ },
+ "outputs": [],
+ "source": [
+ "def univariate_barplots(data, col1, col2='project_is_approved', top=False):\n",
+ " # Count number of zeros in dataframe python: https://stackoverflow.com/a/51540521/4084039\n",
+ " temp = pd.DataFrame(project_data.groupby(col1)[col2].agg(lambda x: x.eq(1).sum())).reset_index()\n",
+ " print(temp.head(20))\n",
+ " # Pandas dataframe grouby count: https://stackoverflow.com/a/19385591/4084039\n",
+ " temp['total'] = pd.DataFrame(project_data.groupby(col1)[col2].agg({total:'count'})).reset_index()\n",
+ " temp['Avg'] = pd.DataFrame(project_data.groupby(col1)[col2].agg({Avg:'mean'})).reset_index()['Avg']\n",
+ "\n",
+ " temp.sort_values(by=['total'],inplace=True, ascending=False)\n",
+ "\n",
+ " if top:\n",
+ " temp = temp[0:top]\n",
+ "\n",
+ " stack_plot(temp, xtick=col1, col2=col2, col3='total')\n",
+ " print(temp.head(5))\n",
+ " print(\"=\"*50)\n",
+ " print(temp.tail(5))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "7YGSvPWSoc4K",
+ "outputId": "23455f42-50f9-4613-ff29-909a45ed758b"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " school_state project_is_approved\n",
+ "0 AK 290\n",
+ "1 AL 1506\n",
+ "2 AR 872\n",
+ "3 AZ 1800\n",
+ "4 CA 13205\n",
+ "5 CO 935\n",
+ "6 CT 1445\n",
+ "7 DC 414\n",
+ "8 DE 308\n",
+ "9 FL 5144\n",
+ "10 GA 3329\n",
+ "11 HI 434\n",
+ "12 IA 568\n",
+ "13 ID 579\n",
+ "14 IL 3710\n",
+ "15 IN 2214\n",
+ "16 KS 532\n",
+ "17 KY 1126\n",
+ "18 LA 1990\n",
+ "19 MA 2055\n"
+ ]
+ },
+ {
+ "ename": "NameError",
+ "evalue": "name 'total' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_3772/2430464069.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0munivariate_barplots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mproject_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'school_state'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'project_is_approved'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_3772/3603038413.py\u001b[0m in \u001b[0;36munivariate_barplots\u001b[1;34m(data, col1, col2, top)\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtemp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;31m# Pandas dataframe grouby count: https://stackoverflow.com/a/19385591/4084039\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mtemp\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'total'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mproject_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcol1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcol2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0magg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mtotal\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;34m'count'\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 7\u001b[0m \u001b[0mtemp\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Avg'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mproject_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcol1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcol2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0magg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mAvg\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;34m'mean'\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Avg'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;31mNameError\u001b[0m: name 'total' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "univariate_barplots(project_data, 'school_state', 'project_is_approved', False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ulA_DTSOoc4O"
+ },
+ "source": [
+ "__Every state is having more than 80% success rate in approval__"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "FtUHgSzdoc4Q"
+ },
+ "source": [
+ "### 1.2.2 Univariate Analysis: teacher_prefix"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "u3eGSWoPoc4R",
+ "outputId": "16d855f6-e1bf-4cda-d4a7-f2fcca03b79e"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " teacher_prefix project_is_approved total Avg\n",
+ "2 Mrs. 48997 57269 0.855559\n",
+ "3 Ms. 32860 38955 0.843537\n",
+ "1 Mr. 8960 10648 0.841473\n",
+ "4 Teacher 1877 2360 0.795339\n",
+ "0 Dr. 9 13 0.692308\n",
+ "==================================================\n",
+ " teacher_prefix project_is_approved total Avg\n",
+ "2 Mrs. 48997 57269 0.855559\n",
+ "3 Ms. 32860 38955 0.843537\n",
+ "1 Mr. 8960 10648 0.841473\n",
+ "4 Teacher 1877 2360 0.795339\n",
+ "0 Dr. 9 13 0.692308\n"
+ ]
+ }
+ ],
+ "source": [
+ "univariate_barplots(project_data, 'teacher_prefix', 'project_is_approved' , top=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7OVowKoToc4V"
+ },
+ "source": [
+ "### 1.2.3 Univariate Analysis: project_grade_category"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "dXV569pboc4X",
+ "outputId": "0b9c3108-164c-415c-c876-5d745842914c"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " project_grade_category project_is_approved total Avg\n",
+ "3 Grades PreK-2 37536 44225 0.848751\n",
+ "0 Grades 3-5 31729 37137 0.854377\n",
+ "1 Grades 6-8 14258 16923 0.842522\n",
+ "2 Grades 9-12 9183 10963 0.837636\n",
+ "==================================================\n",
+ " project_grade_category project_is_approved total Avg\n",
+ "3 Grades PreK-2 37536 44225 0.848751\n",
+ "0 Grades 3-5 31729 37137 0.854377\n",
+ "1 Grades 6-8 14258 16923 0.842522\n",
+ "2 Grades 9-12 9183 10963 0.837636\n"
+ ]
+ }
+ ],
+ "source": [
+ "univariate_barplots(project_data, 'project_grade_category', 'project_is_approved', top=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "aqKWHWBxoc4b"
+ },
+ "source": [
+ "### 1.2.4 Univariate Analysis: project_subject_categories"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "xc5btjFqoc4d"
+ },
+ "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())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "3u8K7-c8oc4h",
+ "outputId": "f7ea0700-4d27-4528-d1f5-8d32d63dac1a"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " Unnamed: 0 \n",
+ " id \n",
+ " teacher_id \n",
+ " teacher_prefix \n",
+ " school_state \n",
+ " project_submitted_datetime \n",
+ " project_grade_category \n",
+ " project_subject_subcategories \n",
+ " project_title \n",
+ " project_essay_1 \n",
+ " project_essay_2 \n",
+ " project_essay_3 \n",
+ " project_essay_4 \n",
+ " project_resource_summary \n",
+ " teacher_number_of_previously_posted_projects \n",
+ " project_is_approved \n",
+ " clean_categories \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 160221 \n",
+ " p253737 \n",
+ " c90749f5d961ff158d4b4d1e7dc665fc \n",
+ " Mrs. \n",
+ " IN \n",
+ " 2016-12-05 13:43:57 \n",
+ " Grades PreK-2 \n",
+ " ESL, Literacy \n",
+ " Educational Support for English Learners at Home \n",
+ " My students are English learners that are work... \n",
+ " \\\"The limits of your language are the limits o... \n",
+ " NaN \n",
+ " NaN \n",
+ " My students need opportunities to practice beg... \n",
+ " 0 \n",
+ " 0 \n",
+ " Literacy_Language \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 140945 \n",
+ " p258326 \n",
+ " 897464ce9ddc600bced1151f324dd63a \n",
+ " Mr. \n",
+ " FL \n",
+ " 2016-10-25 09:22:10 \n",
+ " Grades 6-8 \n",
+ " Civics & Government, Team Sports \n",
+ " Wanted: Projector for Hungry Learners \n",
+ " Our students arrive to our school eager to lea... \n",
+ " The projector we need for our school is very c... \n",
+ " NaN \n",
+ " NaN \n",
+ " My students need a projector to help with view... \n",
+ " 7 \n",
+ " 1 \n",
+ " History_Civics Health_Sports \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " clean_categories project_is_approved total Avg\n",
+ "24 Literacy_Language 20520 23655 0.867470\n",
+ "32 Math_Science 13991 17072 0.819529\n",
+ "28 Literacy_Language Math_Science 12725 14636 0.869432\n",
+ "8 Health_Sports 8640 10177 0.848973\n",
+ "40 Music_Arts 4429 5180 0.855019\n",
+ "==================================================\n",
+ " clean_categories project_is_approved total Avg\n",
+ "19 History_Civics Literacy_Language 1271 1421 0.894441\n",
+ "14 Health_Sports SpecialNeeds 1215 1391 0.873472\n",
+ "50 Warmth Care_Hunger 1212 1309 0.925898\n",
+ "33 Math_Science AppliedLearning 1019 1220 0.835246\n",
+ "4 AppliedLearning Math_Science 855 1052 0.812738\n"
+ ]
+ }
+ ],
+ "source": [
+ "univariate_barplots(project_data, 'clean_categories', 'project_is_approved', top=20)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "H0UM0Ruyoc4o"
+ },
+ "outputs": [],
+ "source": [
+ "# count of all the words in corpus python: https://stackoverflow.com/a/22898595/4084039\n",
+ "from collections import Counter\n",
+ "my_counter = Counter()\n",
+ "for word in project_data['clean_categories'].values:\n",
+ " my_counter.update(word.split())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "XzXq5ZERoc4r",
+ "outputId": "cf0e8427-0d1c-4be4-bc6e-2e55a82823cd"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# dict sort by value python: https://stackoverflow.com/a/613218/4084039\n",
+ "cat_dict = dict(my_counter)\n",
+ "sorted_cat_dict = dict(sorted(cat_dict.items(), key=lambda kv: kv[1]))\n",
+ "\n",
+ "\n",
+ "ind = np.arange(len(sorted_cat_dict))\n",
+ "plt.figure(figsize=(20,5))\n",
+ "p1 = plt.bar(ind, list(sorted_cat_dict.values()))\n",
+ "\n",
+ "plt.ylabel('Projects')\n",
+ "plt.title('% of projects aproved state wise')\n",
+ "plt.xticks(ind, list(sorted_cat_dict.keys()))\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "-atwZbVFoc4v",
+ "outputId": "49993663-51c3-4578-bd3c-73551f00a2aa",
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Warmth : 1388\n",
+ "Care_Hunger : 1388\n",
+ "History_Civics : 5914\n",
+ "Music_Arts : 10293\n",
+ "AppliedLearning : 12135\n",
+ "SpecialNeeds : 13642\n",
+ "Health_Sports : 14223\n",
+ "Math_Science : 41421\n",
+ "Literacy_Language : 52239\n"
+ ]
+ }
+ ],
+ "source": [
+ "for i, j in sorted_cat_dict.items():\n",
+ " print(\"{:20} :{:10}\".format(i,j))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "sk6rX2awoc5y"
+ },
+ "source": [
+ "### 1.2.5 Univariate Analysis: project_subject_subcategories"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "5ikIuYryoc5z"
+ },
+ "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())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "6YN38eProc52",
+ "outputId": "e28be892-53ef-482e-8cf2-6020343ef2fd"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " Unnamed: 0 \n",
+ " id \n",
+ " teacher_id \n",
+ " teacher_prefix \n",
+ " school_state \n",
+ " project_submitted_datetime \n",
+ " project_grade_category \n",
+ " project_title \n",
+ " project_essay_1 \n",
+ " project_essay_2 \n",
+ " project_essay_3 \n",
+ " project_essay_4 \n",
+ " project_resource_summary \n",
+ " teacher_number_of_previously_posted_projects \n",
+ " project_is_approved \n",
+ " clean_categories \n",
+ " clean_subcategories \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 160221 \n",
+ " p253737 \n",
+ " c90749f5d961ff158d4b4d1e7dc665fc \n",
+ " Mrs. \n",
+ " IN \n",
+ " 2016-12-05 13:43:57 \n",
+ " Grades PreK-2 \n",
+ " Educational Support for English Learners at Home \n",
+ " My students are English learners that are work... \n",
+ " \\\"The limits of your language are the limits o... \n",
+ " NaN \n",
+ " NaN \n",
+ " My students need opportunities to practice beg... \n",
+ " 0 \n",
+ " 0 \n",
+ " Literacy_Language \n",
+ " ESL Literacy \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 140945 \n",
+ " p258326 \n",
+ " 897464ce9ddc600bced1151f324dd63a \n",
+ " Mr. \n",
+ " FL \n",
+ " 2016-10-25 09:22:10 \n",
+ " Grades 6-8 \n",
+ " Wanted: Projector for Hungry Learners \n",
+ " Our students arrive to our school eager to lea... \n",
+ " The projector we need for our school is very c... \n",
+ " NaN \n",
+ " NaN \n",
+ " My students need a projector to help with view... \n",
+ " 7 \n",
+ " 1 \n",
+ " History_Civics Health_Sports \n",
+ " Civics_Government TeamSports \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " clean_subcategories project_is_approved total Avg\n",
+ "317 Literacy 8371 9486 0.882458\n",
+ "319 Literacy Mathematics 7260 8325 0.872072\n",
+ "331 Literature_Writing Mathematics 5140 5923 0.867803\n",
+ "318 Literacy Literature_Writing 4823 5571 0.865733\n",
+ "342 Mathematics 4385 5379 0.815207\n",
+ "==================================================\n",
+ " clean_subcategories project_is_approved total Avg\n",
+ "196 EnvironmentalScience Literacy 389 444 0.876126\n",
+ "127 ESL 349 421 0.828979\n",
+ "79 College_CareerPrep 343 421 0.814727\n",
+ "17 AppliedSciences Literature_Writing 361 420 0.859524\n",
+ "3 AppliedSciences College_CareerPrep 330 405 0.814815\n"
+ ]
+ }
+ ],
+ "source": [
+ "univariate_barplots(project_data, 'clean_subcategories', 'project_is_approved', top=50)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "l0hscXcToc57"
+ },
+ "outputs": [],
+ "source": [
+ "# count of all the words in corpus python: https://stackoverflow.com/a/22898595/4084039\n",
+ "from collections import Counter\n",
+ "my_counter = Counter()\n",
+ "for word in project_data['clean_subcategories'].values:\n",
+ " my_counter.update(word.split())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "f8dg8oiroc5-",
+ "outputId": "2bddce12-5786-464b-cf10-a6b0c15b0d07"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# dict sort by value python: https://stackoverflow.com/a/613218/4084039\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",
+ "\n",
+ "ind = np.arange(len(sorted_sub_cat_dict))\n",
+ "plt.figure(figsize=(20,5))\n",
+ "p1 = plt.bar(ind, list(sorted_sub_cat_dict.values()))\n",
+ "\n",
+ "plt.ylabel('Projects')\n",
+ "plt.title('% of projects aproved state wise')\n",
+ "plt.xticks(ind, list(sorted_sub_cat_dict.keys()))\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "fgTozC6woc6B",
+ "outputId": "57592fd7-0b73-4fe3-c220-485e61100e36"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Economics : 269\n",
+ "CommunityService : 441\n",
+ "FinancialLiteracy : 568\n",
+ "ParentInvolvement : 677\n",
+ "Extracurricular : 810\n",
+ "Civics_Government : 815\n",
+ "ForeignLanguages : 890\n",
+ "NutritionEducation : 1355\n",
+ "Warmth : 1388\n",
+ "Care_Hunger : 1388\n",
+ "SocialSciences : 1920\n",
+ "PerformingArts : 1961\n",
+ "CharacterEducation : 2065\n",
+ "TeamSports : 2192\n",
+ "Other : 2372\n",
+ "College_CareerPrep : 2568\n",
+ "Music : 3145\n",
+ "History_Geography : 3171\n",
+ "Health_LifeScience : 4235\n",
+ "EarlyDevelopment : 4254\n",
+ "ESL : 4367\n",
+ "Gym_Fitness : 4509\n",
+ "EnvironmentalScience : 5591\n",
+ "VisualArts : 6278\n",
+ "Health_Wellness : 10234\n",
+ "AppliedSciences : 10816\n",
+ "SpecialNeeds : 13642\n",
+ "Literature_Writing : 22179\n",
+ "Mathematics : 28074\n",
+ "Literacy : 33700\n"
+ ]
+ }
+ ],
+ "source": [
+ "for i, j in sorted_sub_cat_dict.items():\n",
+ " print(\"{:20} :{:10}\".format(i,j))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "YqtXGwRUoc6E"
+ },
+ "source": [
+ "### 1.2.6 Univariate Analysis: Text features (Title)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "UQHyF0ASoc6F",
+ "outputId": "ecd2350b-7399-4456-ddf8-34389eee54fa"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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SJmhohagkpwPfBfZIsirJ69qhw3jgIuXPBq5IcjnwOeCoqhpb6PzPgY8DK+mNlPpKi58AvCDJdcAL2r4kSZIkSZI2UUNbI6qqDp8g/mfjxM4Czpqg/Qpgr3HitwP7zyxLSZIkSZIkdWVUb82TJEmSJEnSZsZClCRJkiRJkjphIUqSJEmSJEmdsBAlSZIkSZKkTliIkiRJkiRJUicsREmSJEmSJKkTFqIkSZIkSZLUCQtRkiRJkiRJ6oSFKEmSJEmSJHXCQpQkSZIkSZI6YSFKkiRJkiRJnbAQJUmSJEmSpE5YiJIkSZIkSVInLERJkiRJkiSpExaiJEmSJEmS1AkLUZIkSZIkSeqEhShJkiRJkiR1wkKUJEmSJEmSOmEhSpIkSZIkSZ2wECVJkiRJkqROWIiSJEmSJElSJyxESZIkSZIkqRMWoiRJkiRJktQJC1GSJEmSJEnqxNAKUUmWJbk1yVV9sXcn+WmSy9rnRX3Hjk2yMsm1SQ7oix/YYiuTLO2L757ke0muS/KZJFsN61kkSZIkSZI0c8McEXUKcOA48b+rqn3a51yAJHsChwFPaOf87yRbJNkC+DBwELAncHhrC/DX7VoLgTuB1w3xWSRJkiRJkjRDQytEVdWFwB0DNj8UOKOq7q6qHwMrgX3bZ2VV3VBVvwHOAA5NEuB5wOfa+acCL92oDyBJkiRJkqSNahRrRB2T5Io2dW/bFtsZuLmvzaoWmyj+KODnVXXPevFxJVmSZEWSFbfddtvGeg5JkiRJkiRtgK4LUR8BHgfsA6wBPtDiGadtTSM+rqo6qaoWVdWi+fPnb1jGkiRJkiRJ2ijmdXmzqrplbDvJx4Avt91VwK59TXcBVrft8eI/A7ZJMq+NiupvL0mSJEmSpE1Qp4WoJDtW1Zq2+zJg7I16ZwOfTvJBYCdgIfB9eiOfFibZHfgpvQXNX1VVleTrwJ/QWzdqMfCl7p5EkiRJ0qZowdJzRp3CyNx4wsGjTkGSpjS0QlSS04HnANsnWQUcBzwnyT70ptHdCLwBoKquTnIm8EPgHuDoqrq3XecY4DxgC2BZVV3dbvE24IwkfwX8ADh5WM8iSZIkSZKkmRtaIaqqDh8nPGGxqKqOB44fJ34ucO448RvovVVPkiRJkiRJs8Ao3ponSZIkSZKkzZCFKEmSJEmSJHXCQpQkSZIkSZI6MWUhKsnjkjy4bT8nyRuTbDP81CRJkiRJkjSXDDIi6izg3iS/T2+x8d2BTw81K0mSJEmSJM05gxSi7quqe4CXAX9fVf8N2HG4aUmSJEmSJGmuGaQQ9dskhwOLgS+32JbDS0mSJEmSJElz0SCFqCOBPwKOr6ofJ9kd+ORw05IkSZIkSdJcM2+ANi+oqjeO7bRi1K+GmJMkSZIkSZLmoEFGRC0eJ/ZnGzkPSZIkSZIkzXETjohq60K9Ctg9ydl9h7YGbh92YpIkSZIkSZpbJpua9x1gDbA98IG++DrgimEmJUmSJEmSpLlnwkJUVd0E3JTk1cDqqvo1QJKHArsAN3aSoSRJkiRJkuaEQdaIOhO4r2//XuCzw0lHkiRJkiRJc9Ughah5VfWbsZ22vdXwUpIkSZIkSdJcNEgh6rYkh4ztJDkU+NnwUpIkSZIkSdJcNNli5WOOAj6V5MNAAauAI4aalSRJkiRJkuacKQtRVXU9sF+SRwCpqnXDT0uSJEmSJElzzZRT85LskORk4LNVtS7Jnkle10FukiRJkiRJmkMGWSPqFOA8YKe2/2/Am4eVkCRJkiRJkuamQQpR21fVmcB9AFV1D3DvULOSJEmSJEnSnDNIIeqXSR5Fb6FykuwH/GKoWUmSJEmSJGnOGeSteW8BzgYel+TbwHzgT4aalSRJkiRJkuacKUdEVdWlwB8DTwfeADyhqq6Y6rwky5LcmuSqvtjfJvlRkiuSfCHJNi2+IMmvklzWPh/tO+cpSa5MsjLJiUnS4tslWZ7kuva97YY/viRJkiRJkroyYSEqyfPa98uBQ4A9gMcDL0nysiR/nGSLSa59CnDgerHlwF5V9UR6i54f23fs+qrap32O6ot/BFgCLGyfsWsuBc6vqoXA+W1fkiRJkiRJm6jJpub9MfCvwEsmOP4o4J3AC8Y7WFUXJlmwXuxrfbsXMcUUvyQ7Ao+squ+2/dOAlwJfAQ4FntOangp8A3jbZNeTJEmSJEnS6ExYiKqq49r3kRO1SXLyDO79WuAzffu7J/kBsBZ4Z1V9E9gZWNXXZlWLAexQVWtajmuSPHoGuUiSJEmSJGnIplysPMnvAccBz26hC4D3VtUvqup107lpkncA9wCfaqE1wG5VdXuSpwBfTPIEIOOcXtO43xJ60/vYbbfdppOyJEmSJEmSZmjKxcqBZcA64E/bZy3wT9O9YZLFwIuBV1dVAVTV3VV1e9u+BLie3npUq4Bd+k7fBVjdtm9pU/fGpvDdOtE9q+qkqlpUVYvmz58/3dQlSZIkSZI0A4MUoh5XVcdV1Q3t8x7gsdO5WZID6a3jdEhV3dUXnz+28HmSx9JblPyGNvVuXZL92tvyjgC+1E47G1jcthf3xSVJkiRJkrQJGqQQ9askzxzbSfIM4FdTnZTkdOC7wB5JViV5HfCPwNbA8iSXJfloa/5s4IoklwOfA46qqjvasT8HPg6spDdS6istfgLwgiTX0Vsw/YQBnkWSJEmSJEkjMuUaUcBRwGltrSiAO/ndSKQJVdXh44THXdy8qs4Czprg2Apgr3HitwP7T5WHJEmSJEmSNg2TFqKSPAjYo6r2TvJIgKpa20lmkiRJkiRJmlMmnZpXVfcBx7TttRahJEmSJEmSNF2DrBG1PMl/T7Jrku3GPkPPTJIkSZIkSXPKIGtEvbZ9H90XK6b55jxJkiRJkiRtnqYsRFXV7l0kIkmSJEmSpLltykJUkocA/xV4Jr2RUN8EPlpVvx5ybpIkSZIkSZpDBpmadxqwDviHtn848AngFcNKSpIkSZIkSXPPIIWoPapq7779rye5fFgJSZIkSZIkaW4a5K15P0iy39hOkqcB3x5eSpIkSZIkSZqLBhkR9TTgiCQ/afu7AdckuRKoqnri0LKTJEmSJEnSnDFIIerAoWchSZIkSZKkOW/KQlRV3dRFIpIkSZIkSZrbBlkjSpIkSZIkSZqxCQtRSR7cZSKSJEmSJEma2yYbEfVdgCSf6CgXSZIkSZIkzWGTrRG1VZLFwNOTvHz9g1X1+eGlJUmSJEmSpLlmskLUUcCrgW2Al6x3rAALUZIkSZIkSRrYhIWoqvoW8K0kK6rq5A5zkiRJkiRJ0hw02YioMZ9I8kbg2W3/AuCjVfXb4aUlSZIkSZKkuWaQQtT/BrZs3wCvAT4CvH5YSUmSJEmbswVLzxl1CiNz4wkHjzoFSdIQDVKIempV7d23/69JLh9WQpIkSZIkSZqbHjRAm3uTPG5sJ8ljgXuHl5IkSZIkSZLmokFGRP0P4OtJbgACPAY4cqhZSZIkSZIkac6ZckRUVZ0PLATe2D57VNXXB7l4kmVJbk1yVV9suyTLk1zXvrdt8SQ5McnKJFckeXLfOYtb++uSLO6LPyXJle2cE5Nk8EeXJEmSJElSlwaZmkdV3V1VV1TV5VV19wZc/xTgwPViS4Hzq2ohcH7bBziIXsFrIbCE3oLoJNkOOA54GrAvcNxY8aq1WdJ33vr3kiRJkiRJ0iZioELUdFXVhcAd64UPBU5t26cCL+2Ln1Y9FwHbJNkROABYXlV3VNWdwHLgwHbskVX13aoq4LS+a0mSJEmSJGkTM2khqk2X23Uj33OHqloD0L4f3eI7Azf3tVvVYpPFV40TlyRJkiRJ0iZo0kJUG2n0xY5yGW99p5pG/IEXTpYkWZFkxW233TaDFCVJkiRJkjRdg0zNuyjJUzfiPW9p0+po37e2+Cqgf/TVLsDqKeK7jBN/gKo6qaoWVdWi+fPnb5SHkCRJkiRJ0oYZpBD1XHrFqOvb2+yuTHLFDO55NjD25rvFwJf64ke06YD7Ab9oU/fOA16YZNu2SPkLgfPasXVJ9mtvyzui71qSJEmSJEnaxMwboM1B0714ktOB5wDbJ1lF7+13JwBnJnkd8BPgFa35ucCLgJXAXcCRAFV1R5L3ARe3du+tqrEF0P+c3pv5Hgp8pX0kSZIkSZK0CZqyEFVVNyV5JrCwqv4pyXzgEYNcvKoOn+DQ/uO0LeDoCa6zDFg2TnwFsNcguUiSJEmSJGm0ppyal+Q44G3AsS20JfDJYSYlSZIkSZKkuWeQNaJeBhwC/BKgqlYDWw8zKUmSJEmSJM09gxSiftOmzRVAkocPNyVJkiRJkiTNRYMUos5M8v8D2yT5L8C/AB8bblqSJEmSJEmaawZZrPz9SV4ArAUeD7yrqpYPPTNJkiRJkiTNKVMWoporgYfSm5535fDSkSRJkiRJ0lw1yFvzXg98H3g58CfARUleO+zEJEmSJEmSNLcMMiLqfwBPqqrbAZI8CvgOsGyYiUmSJEmSJGluGWSx8lXAur79dcDNw0lHkiRJkiRJc9WEI6KSvKVt/hT4XpIv0Vsj6lB6U/UkSZIkSZKkgU02NW/r9n19+4z50vDSkSRJkiRJ0lw1YSGqqt7Tv5/k4VX1y+GnJEmSJEmSpLloysXKk/wRcDLwCGC3JHsDb6iq/zrs5CRJkjQ7LVh6zqhTGKkbTzh41ClIkrRJGmSx8r8HDgBuB6iqy4FnDzMpSZIkSZIkzT2DFKKoqvXfknfvEHKRJEmSJEnSHDbl1Dzg5iRPByrJVsAbgWuGm5YkSZIkSZLmmkFGRB0FHA3sDKwC9mn7kiRJkiRJ0sCmHBFVVT8DXt1BLpIkSZIkSZrDBnlr3u7AXwAL+ttX1SHDS0uSJEmSJElzzSBrRH0ROBn4Z+C+4aYjSZIkSZKkuWqQQtSvq+rEoWciSZIkSZKkOW2QQtSHkhwHfA24eyxYVZcOLStJkiRJkiTNOYMUov4QeA3wPH43Na/aviRJkiRJkjSQQQpRLwMeW1W/GXYykiRJkiRJmrseNECby4FtNtYNk+yR5LK+z9okb07y7iQ/7Yu/qO+cY5OsTHJtkgP64ge22MokSzdWjpIkSZIkSdr4BhkRtQPwoyQXc/81og6Zzg2r6lpgH4AkWwA/Bb4AHAn8XVW9v799kj2Bw4AnADsB/5Lk8e3wh4EXAKuAi5OcXVU/nE5ekiRJkiRJGq5BClHHDfH++wPXV9VNSSZqcyhwRlXdDfw4yUpg33ZsZVXdAJDkjNbWQpQkSZIkSdImaMpCVFVdMMT7Hwac3rd/TJIjgBXAW6vqTmBn4KK+NqtaDODm9eJPG2KukiRJkiRJmoEp14hKsq6t47Q2ya+T3Jtk7UxvnGQr4BDgsy30EeBx9KbtrQE+MNZ0nNNrkvh491qSZEWSFbfddtuM8pYkSZIkSdL0DDIiauv+/SQv5XdT42biIODSqrql3eeWvnt8DPhy210F7Np33i7A6rY9Ufx+quok4CSARYsWjVuskiRJkiRJ0nANskbU/VTVFzfSG+oOp29aXpIdq2pN230ZcFXbPhv4dJIP0lusfCHwfXojohYm2Z3egueHAa/aCHlJkiQBsGDpOaNOYaRuPOHgUacgSZLmmCkLUUle3rf7IGARE0yBG1SSh9F7290b+sJ/k2Sfdu0bx45V1dVJzqS3CPk9wNFVdW+7zjHAecAWwLKqunomeUmSJEmSJGl4BhkR9ZK+7XvoFYkOnclNq+ou4FHrxV4zSfvjgePHiZ8LnDuTXCRJkiRJktSNQdaIOrKLRCRJkiRJkjS3TViISvKuSc6rqnrfEPKRJEmSJEnSHDXZiKhfjhN7OPA6etPqLERJkiRJkiRpYBMWoqrqA2PbSbYG3gQcCZwBfGCi8yRJkiRJkqTxTLpGVJLtgLcArwZOBZ5cVXd2kZgkSZIkSZLmlsnWiPpb4OXAScAfVtV/dJaVJEmSJEmS5pwHTXLsrcBOwDuB1UnWts+6JGu7SU+SJEmSJElzxWRrRE1WpJIkSZIkSZI2iMUmSZIkSZIkdcJClCRJkiRJkjphIUqSJEmSJEmdsBAlSZIkSZKkTliIkiRJkiRJUicsREmSJEmSJKkTFqIkSZIkSZLUCQtRkiRJkiRJ6oTMASziAAANG0lEQVSFKEmSJEmSJHXCQpQkSZIkSZI6YSFKkiRJkiRJnbAQJUmSJEmSpE5YiJIkSZIkSVInLERJkiRJkiSpE/NGnYAkSRquBUvPGXUKI3PjCQePOgVJkiT1GdmIqCQ3JrkyyWVJVrTYdkmWJ7mufW/b4klyYpKVSa5I8uS+6yxu7a9LsnhUzyNJkiRJkqTJjXpq3nOrap+qWtT2lwLnV9VC4Py2D3AQsLB9lgAfgV7hCjgOeBqwL3DcWPFKkiRJkiRJm5ZRF6LWdyhwats+FXhpX/y06rkI2CbJjsABwPKquqOq7gSWAwd2nbQkSZIkSZKmNspCVAFfS3JJkiUttkNVrQFo349u8Z2Bm/vOXdViE8UlSZIkSZK0iRnlYuXPqKrVSR4NLE/yo0naZpxYTRK//8m9QtcSgN122206uUqSJEmSJGmGRjYiqqpWt+9bgS/QW+PpljbljvZ9a2u+Cti17/RdgNWTxNe/10lVtaiqFs2fP39jP4okSZIkSZIGMJJCVJKHJ9l6bBt4IXAVcDYw9ua7xcCX2vbZwBHt7Xn7Ab9oU/fOA16YZNu2SPkLW0ySJEmSJEmbmFFNzdsB+EKSsRw+XVVfTXIxcGaS1wE/AV7R2p8LvAhYCdwFHAlQVXckeR9wcWv33qq6o7vHkCRJkiRJ0qBGUoiqqhuAvceJ3w7sP068gKMnuNYyYNnGzlGSJEmSJEkb1yjfmidJkiRJkqTNiIUoSZIkSZIkdcJClCRJkiRJkjphIUqSJEmSJEmdsBAlSZIkSZKkTliIkiRJkiRJUicsREmSJEmSJKkTFqIkSZIkSZLUiXmjTkCSpEEsWHrOqFMYmRtPOHjUKUiSJEkbhSOiJEmSJEmS1AkLUZIkSZIkSeqEhShJkiRJkiR1wkKUJEmSJEmSOmEhSpIkSZIkSZ2wECVJkiRJkqROWIiSJEmSJElSJyxESZIkSZIkqRMWoiRJkiRJktQJC1GSJEmSJEnqhIUoSZIkSZIkdcJClCRJkiRJkjphIUqSJEmSJEmdsBAlSZIkSZKkTliIkiRJkiRJUic6L0Ql2TXJ15Nck+TqJG9q8Xcn+WmSy9rnRX3nHJtkZZJrkxzQFz+wxVYmWdr1s0iSJEmSJGlw80Zwz3uAt1bVpUm2Bi5Jsrwd+7uqen9/4yR7AocBTwB2Av4lyePb4Q8DLwBWARcnObuqftjJU0iSJEmSJGmDdF6Iqqo1wJq2vS7JNcDOk5xyKHBGVd0N/DjJSmDfdmxlVd0AkOSM1tZClCRJkiRJ0iZopGtEJVkAPAn4Xgsdk+SKJMuSbNtiOwM39522qsUmio93nyVJViRZcdttt23EJ5AkSZIkSdKgRlaISvII4CzgzVW1FvgI8DhgH3ojpj4w1nSc02uS+AODVSdV1aKqWjR//vwZ5y5JkiRJkqQNN4o1okiyJb0i1Keq6vMAVXVL3/GPAV9uu6uAXftO3wVY3bYnikuSJEmSJGkTM4q35gU4Gbimqj7YF9+xr9nLgKva9tnAYUkenGR3YCHwfeBiYGGS3ZNsRW9B87O7eAZJkiRJkiRtuFGMiHoG8BrgyiSXtdjbgcOT7ENvet2NwBsAqurqJGfSW4T8HuDoqroXIMkxwHnAFsCyqrq6yweRJEmSJEnS4Ebx1rxvMf76TudOcs7xwPHjxM+d7DxJkiRJkiRtOkb61jxJkiRJkiRtPixESZIkSZIkqRMWoiRJkiRJktQJC1GSJEmSJEnqhIUoSZIkSZIkdcJClCRJkiRJkjphIUqSJEmSJEmdsBAlSZIkSZKkTliIkiRJkiRJUicsREmSJEmSJKkTFqIkSZIkSZLUCQtRkiRJkiRJ6oSFKEmSJEmSJHXCQpQkSZIkSZI6YSFKkiRJkiRJnbAQJUmSJEmSpE5YiJIkSZIkSVInLERJkiRJkiSpExaiJEmSJEmS1AkLUZIkSZIkSeqEhShJkiRJkiR1wkKUJEmSJEmSOmEhSpIkSZIkSZ2Y9YWoJAcmuTbJyiRLR52PJEmSJEmSxjerC1FJtgA+DBwE7AkcnmTP0WYlSZIkSZKk8czqQhSwL7Cyqm6oqt8AZwCHjjgnSZIkSZIkjWPeqBOYoZ2Bm/v2VwFPG1Eu0mZhwdJzRp3CyNx4wsEzOn9z7juYef9JkiRJmv1SVaPOYdqSvAI4oKpe3/ZfA+xbVX+xXrslwJK2uwdwbaeJzk3bAz8bdRKzmP03ffbd9Nl302ffzYz9N3323fTZd9Nn382M/Td99t302XfTZ99tPI+pqvlTNZrtI6JWAbv27e8CrF6/UVWdBJzUVVKbgyQrqmrRqPOYrey/6bPvps++mz77bmbsv+mz76bPvps++25m7L/ps++mz76bPvuue7N9jaiLgYVJdk+yFXAYcPaIc5IkSZIkSdI4ZvWIqKq6J8kxwHnAFsCyqrp6xGlJkiRJkiRpHLO6EAVQVecC5446j82QUx1nxv6bPvtu+uy76bPvZsb+mz77bvrsu+mz72bG/ps++2767Lvps+86NqsXK5ckSZIkSdLsMdvXiJIkSZIkSdIsYSFKU0qyLMmtSa7qi70vyRVJLkvytSQ7jTLHTdUEffe3SX7U+u8LSbYZZY6bsgn67xVJrk5yXxLfbjGA8fpRE5vg9267JMuTXNe+tx1ljrNFkjcluar9mX3zqPOZTZL8t9ZvVyU5PclDRp3TbJFk1yRfT3JN68M3jTqn2SLJHu3vdmOftf7ZHVySbZJ8rv0975okfzTqnGaDJA9J8v0kl7c/s+8ZdU6zTZIbk1zZ/tyuGHU+s02SLZL8IMmXR53L5sJClAZxCnDgerG/raonVtU+wJeBd3We1exwCg/su+XAXlX1RODfgGO7TmoWOYUH9t9VwMuBCzvPZvY6hQf2oyZ2Cg/sr6XA+VW1EDi/7WsSSfYC/guwL7A38OIkC0eb1eyQZGfgjcCiqtqL3gtZDhttVrPKPcBbq+oPgP2Ao5PsOeKcZoWquraq9ml/v3sKcBfwhRGnNZt8CPhqVf0/9P67d82I85kt7gaeV1V7A/sABybZb8Q5zUbPbX9+/T9qN9yb8M9rpyxEaUpVdSFwx3qxtX27DwdcbGwcE/Td16rqnrZ7EbBL54nNEhP03zVVde2IUpqVxutHTWyC/joUOLVtnwq8tNOkZqc/AC6qqrvaf/MuAF424pxmk3nAQ5PMAx4GrB5xPrNGVa2pqkvb9jp6/7jYebRZzUr7A9dX1U2jTmQ2SPJI4NnAyQBV9Zuq+vlos5odquc/2u6W7eO/LdSJJLsABwMfH3UumxMLUZq2JMcnuRl4NY6Imq7XAl8ZdRKSprRDVa2B3j9ygUePOJ/Z4Crg2UkeleRhwIuAXUec06xQVT8F3g/8BFgD/KKqvjbarGanJAuAJwHfG20ms9JhwOmjTmIWeSxwG/BPbYrPx5M8fNRJzRZtatRlwK3A8qryz+yGKeBrSS5JsmTUycwyfw/8JXDfqBPZnFiI0rRV1TuqalfgU8Axo85ntknyDnrTBz416lwkaWOrqmuAv6Y3HfmrwOX0/punKbQ1yA4Fdgd2Ah6e5D+PNqvZJ8kjgLOAN683kltTSLIVcAjw2VHnMovMA54MfKSqngT8EqdxD6yq7m1TQncB9m3TuzW4Z1TVk4GD6E1HfvaoE5oNkrwYuLWqLhl1LpsbC1HaGD4N/L+jTmI2SbIYeDHw6qpy6LG06bslyY4A7fvWEeczK1TVyVX15Kp6Nr3pjteNOqdZ4vnAj6vqtqr6LfB54OkjzmlWSbIlvSLUp6rq86POZxY6CLi0qm4ZdSKzyCpgVd9Ins/RK0xpA7TpjN/AtS03SFWtbt+30lvXbd/RZjRrPAM4JMmNwBnA85J8crQpbR4sRGla1ltw9hDgR6PKZbZJciDwNuCQqrpr1PlIGsjZwOK2vRj40ghzmTWSPLp970bvJQNO8xnMT4D9kjwsSeit1eMiqgNqfXYycE1VfXDU+cxSh+Of1w1SVf8O3JxkjxbaH/jhCFOaNZLMH3uLdJKH0ivG+2+LASV5eJKtx7aBF9KbHq8pVNWxVbVLVS2gNx35X6vKEcgdmDfqBLTpS3I68Bxg+ySrgOOAF7X/ob0PuAk4anQZbrom6LtjgQcDy3t/V+aiqrL/xjFB/90B/AMwHzgnyWVVdcDostz0jdePVXXyaLPadE3we3cCcGaS19ErErxidBnOKmcleRTwW+Doqrpz1AnNBlX1vSSfAy6lN53xB8BJo81qVnkG8BrgyrbmDMDbq+rcEeY0a7Q13V4AvGHUucxCfwF8qk1tvAE4csT5zBY7Aqcm2YLeQIkzq+rLI85pNtkB+EL7d8U84NNV9dXRpiRNLs4KkiRJkiRJUhecmidJkiRJkqROWIiSJEmSJElSJyxESZIkSZIkqRMWoiRJkiRJktQJC1GSJEmSJEnqhIUoSZIkSZIkdcJClCRJkiRJkjphIUqSJEmSJEmd+D8sV1hDGyQ3zwAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#How to calculate number of words in a string in DataFrame: https://stackoverflow.com/a/37483537/4084039\n",
+ "word_count = project_data['project_title'].str.split().apply(len).value_counts()\n",
+ "word_dict = dict(word_count)\n",
+ "word_dict = dict(sorted(word_dict.items(), key=lambda kv: kv[1]))\n",
+ "\n",
+ "\n",
+ "ind = np.arange(len(word_dict))\n",
+ "plt.figure(figsize=(20,5))\n",
+ "p1 = plt.bar(ind, list(word_dict.values()))\n",
+ "\n",
+ "plt.ylabel('Numeber of projects')\n",
+ "plt.title('Words for each title of the project')\n",
+ "plt.xticks(ind, list(word_dict.keys()))\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "51BD8dOioc6I"
+ },
+ "outputs": [],
+ "source": [
+ "approved_word_count = project_data[project_data['project_is_approved']==1]['project_title'].str.split().apply(len)\n",
+ "approved_word_count = approved_word_count.values\n",
+ "\n",
+ "rejected_word_count = project_data[project_data['project_is_approved']==0]['project_title'].str.split().apply(len)\n",
+ "rejected_word_count = rejected_word_count.values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "PhMvROIvoc6L",
+ "outputId": "d9d2855a-b5f9-4fe7-8879-6eab5b3cde28"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# https://glowingpython.blogspot.com/2012/09/boxplot-with-matplotlib.html\n",
+ "plt.boxplot([approved_word_count, rejected_word_count])\n",
+ "plt.xticks([1,2],('Approved Projects','Rejected Projects'))\n",
+ "plt.ylabel('Words in project title')\n",
+ "plt.grid()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "ifNxNmA0oc6O",
+ "outputId": "682ce3fd-d1fb-4523-c356-584f444aea61"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(10,3))\n",
+ "sns.distplot(approved_word_count, hist=False, label=\"Approved Projects\")\n",
+ "sns.distplot(rejected_word_count, hist=False, label=\"Not Approved Projects\")\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "-rcU9zgXoc6Q"
+ },
+ "source": [
+ "### 1.2.7 Univariate Analysis: Text features (Project Essay's)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "Aa4a8iSioc6R"
+ },
+ "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": {
+ "id": "hrGwRq-Hoc6U",
+ "outputId": "e941226f-9b5c-413e-c8a4-e6223108bd54"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#How to calculate number of words in a string in DataFrame: https://stackoverflow.com/a/37483537/4084039\n",
+ "word_count = project_data['essay'].str.split().apply(len).value_counts()\n",
+ "word_dict = dict(word_count)\n",
+ "word_dict = dict(sorted(word_dict.items(), key=lambda kv: kv[1]))\n",
+ "\n",
+ "\n",
+ "ind = np.arange(len(word_dict))\n",
+ "plt.figure(figsize=(20,5))\n",
+ "p1 = plt.bar(ind, list(word_dict.values()))\n",
+ "\n",
+ "plt.ylabel('Number of projects')\n",
+ "plt.xlabel('Number of words in each eassay')\n",
+ "plt.title('Words for each essay of the project')\n",
+ "plt.xticks(ind, list(word_dict.keys()))\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "rhK9C3-Foc6W",
+ "outputId": "8562b5fc-14a8-48f3-9c59-924949aaa938"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "D:\\installed\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6571: UserWarning:\n",
+ "\n",
+ "The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.distplot(word_count.values)\n",
+ "plt.title('Words for each essay of the project')\n",
+ "plt.xlabel('Number of words in each eassay')\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "yxJn8uEooc6Y"
+ },
+ "outputs": [],
+ "source": [
+ "approved_word_count = project_data[project_data['project_is_approved']==1]['essay'].str.split().apply(len)\n",
+ "approved_word_count = approved_word_count.values\n",
+ "\n",
+ "rejected_word_count = project_data[project_data['project_is_approved']==0]['essay'].str.split().apply(len)\n",
+ "rejected_word_count = rejected_word_count.values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "aWXaw61joc6a",
+ "outputId": "eb06fdff-4c51-41c6-c268-94a40a2b12f5"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# https://glowingpython.blogspot.com/2012/09/boxplot-with-matplotlib.html\n",
+ "plt.boxplot([approved_word_count, rejected_word_count])\n",
+ "plt.title('Words for each essay of the project')\n",
+ "plt.xticks([1,2],('Approved Projects','Rejected Projects'))\n",
+ "plt.ylabel('Words in project title')\n",
+ "plt.grid()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "gC2O5Xhqoc6d",
+ "outputId": "6a594b11-9ef3-4499-de8b-0936e6bba97a"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(10,3))\n",
+ "sns.distplot(approved_word_count, hist=False, label=\"Approved Projects\")\n",
+ "sns.distplot(rejected_word_count, hist=False, label=\"Not Approved Projects\")\n",
+ "plt.title('Words for each essay of the project')\n",
+ "plt.xlabel('Number of words in each eassay')\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "SBVSAdFRoc6f"
+ },
+ "source": [
+ "### 1.2.8 Univariate Analysis: Cost per project"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "xk4fJ4rKoc6g",
+ "outputId": "6ab35ab8-9bff-4637-8bcc-96f02a90b4ee"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " id \n",
+ " description \n",
+ " quantity \n",
+ " price \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " p233245 \n",
+ " LC652 - Lakeshore Double-Space Mobile Drying Rack \n",
+ " 1 \n",
+ " 149.00 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " p069063 \n",
+ " Bouncy Bands for Desks (Blue support pipes) \n",
+ " 3 \n",
+ " 14.95 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " id \n",
+ " price \n",
+ " quantity \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " p000001 \n",
+ " 459.56 \n",
+ " 7 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " p000002 \n",
+ " 515.89 \n",
+ " 21 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# https://glowingpython.blogspot.com/2012/09/boxplot-with-matplotlib.html\n",
+ "plt.boxplot([approved_price, rejected_price])\n",
+ "plt.title('Box Plots of Cost per approved and not approved Projects')\n",
+ "plt.xticks([1,2],('Approved Projects','Rejected Projects'))\n",
+ "plt.ylabel('Words in project title')\n",
+ "plt.grid()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "WjeL5bs5oc6s",
+ "outputId": "d7f9b31c-6f88-494d-979e-5c3ff95eb5f3"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(10,3))\n",
+ "sns.distplot(approved_price, hist=False, label=\"Approved Projects\")\n",
+ "sns.distplot(rejected_price, hist=False, label=\"Not Approved Projects\")\n",
+ "plt.title('Cost per approved and not approved Projects')\n",
+ "plt.xlabel('Cost of a project')\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "m1YvWrg5oc6v",
+ "outputId": "55ff1776-ae3d-45fa-ae25-1eae0176070c"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+------------+-------------------+-----------------------+\n",
+ "| Percentile | Approved Projects | Not Approved Projects |\n",
+ "+------------+-------------------+-----------------------+\n",
+ "| 0 | 0.66 | 1.97 |\n",
+ "| 5 | 13.59 | 41.9 |\n",
+ "| 10 | 33.88 | 73.67 |\n",
+ "| 15 | 58.0 | 99.109 |\n",
+ "| 20 | 77.38 | 118.56 |\n",
+ "| 25 | 99.95 | 140.892 |\n",
+ "| 30 | 116.68 | 162.23 |\n",
+ "| 35 | 137.232 | 184.014 |\n",
+ "| 40 | 157.0 | 208.632 |\n",
+ "| 45 | 178.265 | 235.106 |\n",
+ "| 50 | 198.99 | 263.145 |\n",
+ "| 55 | 223.99 | 292.61 |\n",
+ "| 60 | 255.63 | 325.144 |\n",
+ "| 65 | 285.412 | 362.39 |\n",
+ "| 70 | 321.225 | 399.99 |\n",
+ "| 75 | 366.075 | 449.945 |\n",
+ "| 80 | 411.67 | 519.282 |\n",
+ "| 85 | 479.0 | 618.276 |\n",
+ "| 90 | 593.11 | 739.356 |\n",
+ "| 95 | 801.598 | 992.486 |\n",
+ "| 100 | 9999.0 | 9999.0 |\n",
+ "+------------+-------------------+-----------------------+\n"
+ ]
+ }
+ ],
+ "source": [
+ "# http://zetcode.com/python/prettytable/\n",
+ "from prettytable import PrettyTable\n",
+ "\n",
+ "x = PrettyTable()\n",
+ "x.field_names = [\"Percentile\", \"Approved Projects\", \"Not Approved Projects\"]\n",
+ "\n",
+ "for i in range(0,101,5):\n",
+ " x.add_row([i,np.round(np.percentile(approved_price,i), 3), np.round(np.percentile(rejected_price,i), 3)])\n",
+ "print(x)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "BKYdquMWoc6y"
+ },
+ "source": [
+ "1.2.9 Univariate Analysis: teacher_number_of_previously_posted_projects 1.2.10 Univariate Analysis: project_resource_summary ![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}