Feature_Engineering_Naive_Bayes_BoW_DonorChoose.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"pycharm":{"name":"#%% md\n"}},"source":["### [Link to Dataset in Kaggle](https://www.kaggle.com/manasvee1/donorschooseorg-application-screening)\n","\n","### [DonorsChoose](https://en.wikipedia.org/wiki/DonorsChoose)\n","\n","---\n","\n","## Fundamental and Intuitive note on Bayes Theorem\n","\n","![Imgur](https://imgur.com/brkFzR6.png)\n","\n","Same formulae with some notes\n","\n","![Imgur](https://imgur.com/pU0s86X.png)\n","\n","To understand why Bayes’ theorem is so important, let’s look at a general form of this problem. Our beliefs describe the world we know, so when we observe something, its conditional probability represents the likelihood of what we’ve seen given what we believe, or:\n","\n","#### P(observed | belief)\n","\n","\n","For example, suppose you believe in climate change, and therefore you expect that the area where you live will have more droughts than usual over a 10-year period. Your belief is that climate change is taking place, and our observation is the number of droughts in our area; let’s say there were 5 droughts in the last 10 years. Determining how likely it is that you’d see exactly 5 droughts in the past 10 years if there were climate change during that period may be difficult. One way to do this would be to consult an expert in climate science and ask them the probability of droughts given that their model assumes climate change.\n","\n","At this point, all we’ve done is ask, “What is the probability of what I’ve observed, given that I believe climate change is true?” But what you want is some way to quantify how strongly you believe climate change is really happening, given what you have observed. Bayes’ theorem allows you to reverse P(observed | belief), which you asked the climate scientist for, and solve for the likelihood of our beliefs given what you’ve observed, or:\n","\n","\n","#### P(belief | observed)\n","\n","\n","In this example, Bayes’ theorem allows us to transform our observation of five droughts in a decade into a statement about how strongly you believe in climate change after you have observed these droughts. The only other pieces of information you need are the general probability of 5 droughts in 10 years (which could be estimated with historical data) and our initial certainty of our belief in climate change. And while most people would have a different initial probability for climate change, Bayes’ theorem allows you to quantify exactly how much the data changes any belief.\n","\n","For example, if the expert says that 5 droughts in 10 years is very likely if we assume that climate change is happening, most people will change their previous beliefs to favor climate change a little, whether they’re skeptical of climate change or they’re Al Gore.\n","However, suppose that the expert told you that in fact, 5 droughts in 10 years was very unlikely given our assumption that climate change is happening. In that case, our prior belief in climate change would weaken slightly given the evidence. The key takeaway here is that Bayes’ theorem ultimately allows evidence to change the strength of our beliefs.\n","\n","Bayes’ theorem allows us to take our beliefs about the world, combine them with data, and then transform this combination into an estimate of the strength of our beliefs given the evidence we’ve observed. Very often our beliefs are just our initial certainty in an idea; this is the P(A) in Bayes’ theorem. We often debate topics such as whether gun control will reduce violence, whether increased testing increases student performance, or whether public health care will reduce overall health care costs. But we seldom think about how evidence should change our minds or the minds of those we’re debating. Bayes’ theorem allows us to observe evidence about these beliefs and quantify exactly how much this evidence changes our beliefs.\n","\n","## Simplest Proof of Bayes Theorem\n","\n","Let’s consider two probabilistic events A and B. We can correlate the marginal probabilities P(A) and P(B) with the conditional probabilities P(A|B) and P(B|A) using the product rule:\n","\n","![Imgur](https://imgur.com/1fGQOXC.png)\n","\n","First of all, let’s consider the marginal probability P(A): this is normally a value that determines how probable a target event is, like P(Spam) or P(Rain). As there are no other elements, this kind of probability is called Apriori, because it’s often determined by mathematical considerations or simply by a frequency count. For example, imagine we want to implement a very simple spam filter and we’ve collected 100 emails. We know that 30 are spam and 70 are regular. So we can say that P(Spam) = 0.3.\n","\n","However, we’d like to evaluate using some criteria (for simplicity, let’s consider a single one), for example, e-mail text is shorter than 50 characters. Therefore, our query becomes:\n","\n","---\n","\n","### The time complexity of Naive Bayes\n","\n","Let, n = no of data points, d = no of features in an input vector x. and c = number of classes.\n","\n","So, if we have ‘d’ features then we calculate ‘d’ likelihoods for 1 class. Then for ‘c’ class **d \\* c** likelihood probabilities\n","\n","#### T (n) = O (ndc)\n","\n","All it needs to do is computing the frequency of every feature value di for each class.\n","\n","If d is small we can neglect the presence of d and we can tell O(n).\n","\n","#### For space complexity, we store only the likelihoods for ‘c’ classes,\n","\n","S(n) = O(dc)\n","\n","For Testing phase : Since all the likelihood probabilities are already calculated in training phase then at\n","\n","the time of evaluation we just need to lookup.\n","\n","#### S(n) = O(dc)\n","\n","---\n","\n","### Difference between naive Bayes & multinomial naive Bayes\n","\n","In general, to train Naive Bayes for n-dimensional data, and k classes you need to estimate $P(x_i | c_j)$ for each $1 \\leq i \\leq n$, $1 \\leq j \\leq k$ . You can assume any probability distribution for any pair $(i,j)$ (although it's better to not assume discrete distribution for $P(x_i|c_{j_1})$ and continuous for $P(x_i | c_{j_2})$). You can have Gaussian distribution on one variable, Poisson on other and some discrete on yet another variable.\n","\n","#### Multinomial Naive Bayes simply assumes multinomial distribution for all the pairs, which seem to be a reasonable assumption in some cases, i.e. for word counts in documents.\n","\n","---\n","\n","The general term **Naive Bayes** refers the the strong independence assumptions in the model, rather than the particular distribution of each feature. A Naive Bayes model assumes that each of the features it uses are conditionally independent of one another given some class. More formally, if I want to calculate the probability of observing features $f_1$ through $f_n$, given some class c, under the Naive Bayes assumption the following holds:\n","\n","$$ p(f_1,..., f_n|c) = \\prod_{i=1}^n p(f_i|c)$$\n","\n","This means that when I want to use a Naive Bayes model to classify a new example, the posterior probability is much simpler to work with:\n","\n","$$ p(c|f_1,...,f_n) \\propto p(c)p(f_1|c)...p(f_n|c) $$\n","\n","Of course these assumptions of independence are rarely true, which may explain why some have referred to the model as the \"Idiot Bayes\" model, but in practice Naive Bayes models have performed surprisingly well, even on complex tasks where it is clear that the strong independence assumptions are false.\n","\n","Up to this point we have said nothing about the distribution of each feature. In other words, we have left $p(f_i|c)$ undefined. The term **Multinomial Naive Bayes** simply lets us know that each $p(f_i|c)$ is a multinomial distribution, rather than some other distribution. This works well for data which can easily be turned into counts, such as word counts in text.\n","\n","In summary, Naive Bayes classifier is a general term which refers to conditional independence of each of the features in the model, while Multinomial Naive Bayes classifier is a specific instance of a Naive Bayes classifier which uses a multinomial distribution for each of the features.\n","\n","References:\n","\n","Stuart J. Russell and Peter Norvig. 2003. Artificial Intelligence: A Modern Approach (2 ed.). Pearson Education. _See p. 499 for reference to \"idiot Bayes\" as well as the general definition of the Naive Bayes model and its independence assumptions_\n","\n","---\n","\n","## Note on Bag of Words\n","\n","The name “bag-of-words” comes from the algorithm simply seeking to know the number of times a given word is present within a body of text. The order or context of the words is not analyzed here. Similarly, if we have a bag filled with six pencils, eight pens, and four notebooks, the algorithm merely cares about recording the number of each of these objects, not the order in which they are found, or their orientation. You typically want to use the bag-of-words feature extraction technique for document classification. Why is this the case? We assume that documents of certain classifications contain certain\n","words. For example, we expect a document referencing political science to perhaps feature jargon such as dialectical materialism or free market capitalism; whereas a document that is referring to classical music will\n","have terms such as crescendo, diminuendo, and so forth. In these instances of document classification, the location of the word itself is not terribly important. It’s important to know what portion of the vocabulary is present in one class of document vs. another.\n","\n"]},{"cell_type":"code","execution_count":1,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:09:21.104964Z","iopub.status.busy":"2021-06-16T13:09:21.104415Z","iopub.status.idle":"2021-06-16T13:09:21.119507Z","shell.execute_reply":"2021-06-16T13:09:21.118435Z","shell.execute_reply.started":"2021-06-16T13:09:21.104921Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[],"source":["# Start of Google Colab Import related codes\n","# Keep these lines commented out in Local Drive and in Kaggle\n","\n","# from google.colab import drive\n","# drive.mount('/content/drive')\n","# import pandas as pd\n","# import os\n","# # Navigate into Drive where you want to store your Kaggle data\n","# os.chdir('/content/drive/MyDrive/Kaggle_Datasets')\n","\n","# !pip install kaggle\n","\n","# The private folder in G-Drive where my Kaggle API Token is saved - kaggle.json\n","# os.environ['KAGGLE_CONFIG_DIR']='/content/drive/MyDrive/Kaggle_Datasets'\n","\n","# this is the copied API command, the data will download to the current directory\n","# !kaggle competitions download -c donorschoose-application-screening\n","\n","# End of Google Colab Import related codes\n","\n","import warnings\n","warnings.filterwarnings(\"ignore\")\n","\n","import pandas as pd\n","import numpy as np\n","import nltk\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","from sklearn.feature_extraction.text import TfidfVectorizer\n","from sklearn.feature_extraction.text import CountVectorizer\n","from sklearn.metrics import confusion_matrix\n","from sklearn import metrics\n","from sklearn.metrics import roc_curve, auc, roc_auc_score\n","from sklearn.model_selection import GridSearchCV\n","import math\n","from sklearn.naive_bayes import MultinomialNB\n","from sklearn.model_selection import train_test_split\n","from scipy.sparse import coo_matrix, hstack\n","from sklearn.preprocessing import StandardScaler, Normalizer\n","from io import StringIO\n","import requests\n","import pickle\n","from tqdm import tqdm\n","import os\n","# from plotly import plotly\n","import plotly.graph_objs as go\n","from plotly.offline import iplot, init_notebook_mode\n","from collections import Counter\n","import re\n","import string\n","from nltk.corpus import stopwords\n","from nltk.stem import PorterStemmer\n","from nltk.stem.wordnet import WordNetLemmatizer\n","from gensim.models import Word2Vec\n","from gensim.models import KeyedVectors\n","import pickle\n","from collections import Counter\n","from wordcloud import WordCloud\n"]},{"cell_type":"code","execution_count":2,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:09:21.122032Z","iopub.status.busy":"2021-06-16T13:09:21.121419Z","iopub.status.idle":"2021-06-16T13:09:21.143033Z","shell.execute_reply":"2021-06-16T13:09:21.141820Z","shell.execute_reply.started":"2021-06-16T13:09:21.121992Z"},"trusted":true},"outputs":[],"source":["# Input data files are available in the read-only \"../input/\" directory\n","# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n","\n","# import os\n","# for dirname, _, filenames in os.walk('../input/donorschooseorg-application-screening/'):\n","#     for filename in filenames:\n","#         print(os.path.join(dirname, filename))"]},{"cell_type":"markdown","metadata":{},"source":["## 1.1 Reading Data"]},{"cell_type":"code","execution_count":3,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:09:21.146264Z","iopub.status.busy":"2021-06-16T13:09:21.145513Z","iopub.status.idle":"2021-06-16T13:09:29.757351Z","shell.execute_reply":"2021-06-16T13:09:29.756249Z","shell.execute_reply.started":"2021-06-16T13:09:21.146210Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Column names from train_project_data_original is :  Index(['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',\n","       'project_essay_3', 'project_essay_4', 'project_resource_summary',\n","       'teacher_number_of_previously_posted_projects', 'project_is_approved'],\n","      dtype='object')\n"]},{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>id</th>\n","      <th>teacher_id</th>\n","      <th>teacher_prefix</th>\n","      <th>school_state</th>\n","      <th>project_submitted_datetime</th>\n","      <th>project_grade_category</th>\n","      <th>project_subject_categories</th>\n","      <th>project_subject_subcategories</th>\n","      <th>project_title</th>\n","      <th>project_essay_1</th>\n","      <th>project_essay_2</th>\n","      <th>project_essay_3</th>\n","      <th>project_essay_4</th>\n","      <th>project_resource_summary</th>\n","      <th>teacher_number_of_previously_posted_projects</th>\n","      <th>project_is_approved</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>p036502</td>\n","      <td>484aaf11257089a66cfedc9461c6bd0a</td>\n","      <td>Ms.</td>\n","      <td>NV</td>\n","      <td>2016-11-18 14:45:59</td>\n","      <td>Grades PreK-2</td>\n","      <td>Literacy &amp; Language</td>\n","      <td>Literacy</td>\n","      <td>Super Sight Word Centers</td>\n","      <td>Most of my kindergarten students come from low...</td>\n","      <td>I currently have a differentiated sight word c...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>My students need 6 Ipod Nano's to create and d...</td>\n","      <td>26</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>p039565</td>\n","      <td>df72a3ba8089423fa8a94be88060f6ed</td>\n","      <td>Mrs.</td>\n","      <td>GA</td>\n","      <td>2017-04-26 15:57:28</td>\n","      <td>Grades 3-5</td>\n","      <td>Music &amp; The Arts, Health &amp; Sports</td>\n","      <td>Performing Arts, Team Sports</td>\n","      <td>Keep Calm and Dance On</td>\n","      <td>Our elementary school is a culturally rich sch...</td>\n","      <td>We strive to provide our diverse population of...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>My students need matching shirts to wear for d...</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>p233823</td>\n","      <td>a9b876a9252e08a55e3d894150f75ba3</td>\n","      <td>Ms.</td>\n","      <td>UT</td>\n","      <td>2017-01-01 22:57:44</td>\n","      <td>Grades 3-5</td>\n","      <td>Math &amp; Science, Literacy &amp; Language</td>\n","      <td>Applied Sciences, Literature &amp; Writing</td>\n","      <td>Lets 3Doodle to Learn</td>\n","      <td>Hello;\\r\\nMy name is Mrs. Brotherton. I teach ...</td>\n","      <td>We are looking to add some 3Doodler to our cla...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>My students need the 3doodler. We are an SEM s...</td>\n","      <td>5</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>p185307</td>\n","      <td>525fdbb6ec7f538a48beebaa0a51b24f</td>\n","      <td>Mr.</td>\n","      <td>NC</td>\n","      <td>2016-08-12 15:42:11</td>\n","      <td>Grades 3-5</td>\n","      <td>Health &amp; Sports</td>\n","      <td>Health &amp; Wellness</td>\n","      <td>\\\"Kid Inspired\\\" Equipment to Increase Activit...</td>\n","      <td>My students are the greatest students but are ...</td>\n","      <td>The student's project which is totally \\\"kid-i...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>My students need balls and other activity equi...</td>\n","      <td>16</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>p013780</td>\n","      <td>a63b5547a7239eae4c1872670848e61a</td>\n","      <td>Mr.</td>\n","      <td>CA</td>\n","      <td>2016-08-06 09:09:11</td>\n","      <td>Grades 6-8</td>\n","      <td>Health &amp; Sports</td>\n","      <td>Health &amp; Wellness</td>\n","      <td>We need clean water for our culinary arts class!</td>\n","      <td>My students are athletes and students who are ...</td>\n","      <td>For some reason in our kitchen the water comes...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>My students need a water filtration system for...</td>\n","      <td>42</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["        id                        teacher_id teacher_prefix school_state  \\\n","0  p036502  484aaf11257089a66cfedc9461c6bd0a            Ms.           NV   \n","1  p039565  df72a3ba8089423fa8a94be88060f6ed           Mrs.           GA   \n","2  p233823  a9b876a9252e08a55e3d894150f75ba3            Ms.           UT   \n","3  p185307  525fdbb6ec7f538a48beebaa0a51b24f            Mr.           NC   \n","4  p013780  a63b5547a7239eae4c1872670848e61a            Mr.           CA   \n","\n","  project_submitted_datetime project_grade_category  \\\n","0        2016-11-18 14:45:59          Grades PreK-2   \n","1        2017-04-26 15:57:28             Grades 3-5   \n","2        2017-01-01 22:57:44             Grades 3-5   \n","3        2016-08-12 15:42:11             Grades 3-5   \n","4        2016-08-06 09:09:11             Grades 6-8   \n","\n","            project_subject_categories  \\\n","0                  Literacy & Language   \n","1    Music & The Arts, Health & Sports   \n","2  Math & Science, Literacy & Language   \n","3                      Health & Sports   \n","4                      Health & Sports   \n","\n","            project_subject_subcategories  \\\n","0                                Literacy   \n","1            Performing Arts, Team Sports   \n","2  Applied Sciences, Literature & Writing   \n","3                       Health & Wellness   \n","4                       Health & Wellness   \n","\n","                                       project_title  \\\n","0                           Super Sight Word Centers   \n","1                             Keep Calm and Dance On   \n","2                              Lets 3Doodle to Learn   \n","3  \\\"Kid Inspired\\\" Equipment to Increase Activit...   \n","4   We need clean water for our culinary arts class!   \n","\n","                                     project_essay_1  \\\n","0  Most of my kindergarten students come from low...   \n","1  Our elementary school is a culturally rich sch...   \n","2  Hello;\\r\\nMy name is Mrs. Brotherton. I teach ...   \n","3  My students are the greatest students but are ...   \n","4  My students are athletes and students who are ...   \n","\n","                                     project_essay_2 project_essay_3  \\\n","0  I currently have a differentiated sight word c...             NaN   \n","1  We strive to provide our diverse population of...             NaN   \n","2  We are looking to add some 3Doodler to our cla...             NaN   \n","3  The student's project which is totally \\\"kid-i...             NaN   \n","4  For some reason in our kitchen the water comes...             NaN   \n","\n","  project_essay_4                           project_resource_summary  \\\n","0             NaN  My students need 6 Ipod Nano's to create and d...   \n","1             NaN  My students need matching shirts to wear for d...   \n","2             NaN  My students need the 3doodler. We are an SEM s...   \n","3             NaN  My students need balls and other activity equi...   \n","4             NaN  My students need a water filtration system for...   \n","\n","   teacher_number_of_previously_posted_projects  project_is_approved  \n","0                                            26                    1  \n","1                                             1                    0  \n","2                                             5                    1  \n","3                                            16                    0  \n","4                                            42                    1  "]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["# Start of Google Colab related Imports from G-Drive\n","# Keep this commented out in Local Drive and in Kaggle\n","# train_project_data_original = pd.read_csv(\"./train.zip\")\n","# test_project_data_original = pd.read_csv('./test.zip')\n","# resource_data = pd.read_csv('./resources.zip')\n","# End of Google Colab related Imports from G-Drive\n","\n","# Start of Deepnote related Imports\n","# test_project_data_original = pd.read_csv('/work/test.zip')\n","# train_project_data_original = pd.read_csv ('/work/train.zip')\n","# resource_data = pd.read_csv('/work/resources.zip')\n","# End of Deepnote related Imports\n","\n","# Imports for both Kaggle and Local Machine\n","train_project_data_original = pd.read_csv(\"../input/donorschooseorg-application-screening/train.csv\")\n","test_project_data_original = pd.read_csv('../input/donorschooseorg-application-screening/test.csv')\n","resource_data = pd.read_csv('../input/donorschooseorg-application-screening/resources.csv')\n","\n","# Reading smaller set of data for doing just experimentation\n","# train_project_data_original = pd.read_csv(\"../input/donorschoose-application-screening/train.zip\", nrows=200)\n","# test_project_data_original = pd.read_csv('../input/donorschoose-application-screening/test.zip', nrows=200)\n","# resource_data = pd.read_csv('../input/donorschoose-application-screening/resources.zip', nrows=200)\n","\n","print('Column names from train_project_data_original is : ', train_project_data_original.columns )\n","train_project_data_original.head()"]},{"cell_type":"code","execution_count":4,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:09:29.760241Z","iopub.status.busy":"2021-06-16T13:09:29.759887Z","iopub.status.idle":"2021-06-16T13:09:29.766394Z","shell.execute_reply":"2021-06-16T13:09:29.765382Z","shell.execute_reply.started":"2021-06-16T13:09:29.760207Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Shape of train_project_data_original:  (182080, 16)\n","Shape of test_project_data_original:  (78035, 15)\n"]}],"source":["print('Shape of train_project_data_original: ', train_project_data_original.shape)\n","print('Shape of test_project_data_original: ', test_project_data_original.shape)\n","# Test data will NOT have any column for 'project_is_approved'\n"]},{"cell_type":"code","execution_count":5,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:09:29.768441Z","iopub.status.busy":"2021-06-16T13:09:29.768053Z","iopub.status.idle":"2021-06-16T13:09:29.788290Z","shell.execute_reply":"2021-06-16T13:09:29.787237Z","shell.execute_reply.started":"2021-06-16T13:09:29.768396Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"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\"]\n","\n"]},{"cell_type":"markdown","metadata":{},"source":["## 2. Pre-processing Categorical Features: project_grade_category"]},{"cell_type":"code","execution_count":6,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:09:29.790138Z","iopub.status.busy":"2021-06-16T13:09:29.789627Z","iopub.status.idle":"2021-06-16T13:09:29.853700Z","shell.execute_reply":"2021-06-16T13:09:29.852619Z","shell.execute_reply.started":"2021-06-16T13:09:29.790076Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"data":{"text/plain":["Grades PreK-2    73890\n","Grades 3-5       61682\n","Grades 6-8       28197\n","Grades 9-12      18311\n","Name: project_grade_category, dtype: int64"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["train_project_data_original['project_grade_category'].value_counts()"]},{"cell_type":"code","execution_count":7,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:09:29.856067Z","iopub.status.busy":"2021-06-16T13:09:29.855389Z","iopub.status.idle":"2021-06-16T13:09:30.300681Z","shell.execute_reply":"2021-06-16T13:09:30.299617Z","shell.execute_reply.started":"2021-06-16T13:09:29.856030Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"data":{"text/plain":["grades_prek_2    73890\n","grades_3_5       61682\n","grades_6_8       28197\n","grades_9_12      18311\n","Name: project_grade_category, dtype: int64"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["\n","# Removing all the spaces, replace the '-' with '_' and convert all the letters to small\n","\n","train_project_data_original['project_grade_category'] = train_project_data_original['project_grade_category'].str.replace(' ','_').str.replace('-', '_')\n","train_project_data_original['project_grade_category'] = train_project_data_original['project_grade_category'].str.lower()\n","train_project_data_original['project_grade_category'].value_counts()"]},{"cell_type":"code","execution_count":8,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:09:30.302435Z","iopub.status.busy":"2021-06-16T13:09:30.302078Z","iopub.status.idle":"2021-06-16T13:09:30.499259Z","shell.execute_reply":"2021-06-16T13:09:30.498243Z","shell.execute_reply.started":"2021-06-16T13:09:30.302403Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"data":{"text/plain":["grades_prek_2    31471\n","grades_3_5       26645\n","grades_6_8       12069\n","grades_9_12       7850\n","Name: project_grade_category, dtype: int64"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["# Same above text-Preprocessing for test dataset -\n","test_project_data_original['project_grade_category'] = test_project_data_original['project_grade_category'].str.replace(' ','_').str.replace('-', '_')\n","test_project_data_original['project_grade_category'] = test_project_data_original['project_grade_category'].str.lower()\n","test_project_data_original['project_grade_category'].value_counts()"]},{"cell_type":"markdown","metadata":{},"source":["## Preprocessing Categorical Features: project_subject_categories"]},{"cell_type":"code","execution_count":9,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:09:30.500814Z","iopub.status.busy":"2021-06-16T13:09:30.500504Z","iopub.status.idle":"2021-06-16T13:09:30.558543Z","shell.execute_reply":"2021-06-16T13:09:30.557413Z","shell.execute_reply.started":"2021-06-16T13:09:30.500784Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"data":{"text/plain":["Literacy & Language                           39257\n","Math & Science                                28555\n","Literacy & Language, Math & Science           24499\n","Health & Sports                               16951\n","Music & The Arts                               8527\n","Special Needs                                  7065\n","Literacy & Language, Special Needs             6685\n","Applied Learning                               6310\n","Math & Science, Literacy & Language            3843\n","Applied Learning, Literacy & Language          3725\n","History & Civics                               3065\n","Math & Science, Special Needs                  3010\n","Literacy & Language, Music & The Arts          2878\n","Math & Science, Music & The Arts               2761\n","Applied Learning, Special Needs                2481\n","Health & Sports, Special Needs                 2368\n","History & Civics, Literacy & Language          2288\n","Warmth, Care & Hunger                          2191\n","Math & Science, Applied Learning               2071\n","Applied Learning, Math & Science               1711\n","Literacy & Language, History & Civics          1315\n","Health & Sports, Literacy & Language           1308\n","Applied Learning, Music & The Arts             1241\n","Math & Science, History & Civics               1087\n","Literacy & Language, Applied Learning          1038\n","Applied Learning, Health & Sports              1018\n","Math & Science, Health & Sports                 697\n","History & Civics, Music & The Arts              544\n","History & Civics, Math & Science                525\n","Special Needs, Music & The Arts                 521\n","Health & Sports, Math & Science                 470\n","History & Civics, Special Needs                 417\n","Applied Learning, History & Civics              303\n","Health & Sports, Applied Learning               297\n","Health & Sports, Music & The Arts               262\n","Music & The Arts, Special Needs                 235\n","Literacy & Language, Health & Sports            127\n","Health & Sports, History & Civics                67\n","History & Civics, Applied Learning               65\n","Special Needs, Health & Sports                   64\n","Special Needs, Warmth, Care & Hunger             35\n","Music & The Arts, Health & Sports                32\n","Health & Sports, Warmth, Care & Hunger           31\n","Music & The Arts, History & Civics               29\n","Literacy & Language, Warmth, Care & Hunger       25\n","Applied Learning, Warmth, Care & Hunger          22\n","Music & The Arts, Applied Learning               21\n","History & Civics, Health & Sports                20\n","Math & Science, Warmth, Care & Hunger            19\n","Music & The Arts, Warmth, Care & Hunger           3\n","History & Civics, Warmth, Care & Hunger           1\n","Name: project_subject_categories, dtype: int64"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["train_project_data_original['project_subject_categories'].value_counts()"]},{"cell_type":"code","execution_count":10,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:09:30.560504Z","iopub.status.busy":"2021-06-16T13:09:30.559968Z","iopub.status.idle":"2021-06-16T13:09:31.388995Z","shell.execute_reply":"2021-06-16T13:09:31.387778Z","shell.execute_reply.started":"2021-06-16T13:09:30.560448Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"data":{"text/plain":["literacy_language                       39257\n","math_science                            28555\n","literacy_language_math_science          24499\n","health_sports                           16951\n","music_arts                               8527\n","specialneeds                             7065\n","literacy_language_specialneeds           6685\n","appliedlearning                          6310\n","math_science_literacy_language           3843\n","appliedlearning_literacy_language        3725\n","history_civics                           3065\n","math_science_specialneeds                3010\n","literacy_language_music_arts             2878\n","math_science_music_arts                  2761\n","appliedlearning_specialneeds             2481\n","health_sports_specialneeds               2368\n","history_civics_literacy_language         2288\n","warmth_care_hunger                       2191\n","math_science_appliedlearning             2071\n","appliedlearning_math_science             1711\n","literacy_language_history_civics         1315\n","health_sports_literacy_language          1308\n","appliedlearning_music_arts               1241\n","math_science_history_civics              1087\n","literacy_language_appliedlearning        1038\n","appliedlearning_health_sports            1018\n","math_science_health_sports                697\n","history_civics_music_arts                 544\n","history_civics_math_science               525\n","specialneeds_music_arts                   521\n","health_sports_math_science                470\n","history_civics_specialneeds               417\n","appliedlearning_history_civics            303\n","health_sports_appliedlearning             297\n","health_sports_music_arts                  262\n","music_arts_specialneeds                   235\n","literacy_language_health_sports           127\n","health_sports_history_civics               67\n","history_civics_appliedlearning             65\n","specialneeds_health_sports                 64\n","specialneeds_warmth_care_hunger            35\n","music_arts_health_sports                   32\n","health_sports_warmth_care_hunger           31\n","music_arts_history_civics                  29\n","literacy_language_warmth_care_hunger       25\n","appliedlearning_warmth_care_hunger         22\n","music_arts_appliedlearning                 21\n","history_civics_health_sports               20\n","math_science_warmth_care_hunger            19\n","music_arts_warmth_care_hunger               3\n","history_civics_warmth_care_hunger           1\n","Name: project_subject_categories, dtype: int64"]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["# remove spaces, 'the'\n","# replace '&' with '_', and ',' with '_'\n","train_project_data_original['project_subject_categories'] = train_project_data_original['project_subject_categories'].str.replace(' The ','').str.replace(' ','').str.replace('&','_').str.replace(',','_')\n","train_project_data_original['project_subject_categories'] = train_project_data_original['project_subject_categories'].str.lower()\n","train_project_data_original['project_subject_categories'].value_counts()"]},{"cell_type":"code","execution_count":11,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:09:31.395375Z","iopub.status.busy":"2021-06-16T13:09:31.394799Z","iopub.status.idle":"2021-06-16T13:09:31.735910Z","shell.execute_reply":"2021-06-16T13:09:31.734571Z","shell.execute_reply.started":"2021-06-16T13:09:31.395319Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"data":{"text/plain":["literacy_language                       16777\n","math_science                            12031\n","literacy_language_math_science          10448\n","health_sports                            7306\n","music_arts                               3726\n","specialneeds                             3134\n","literacy_language_specialneeds           2968\n","appliedlearning                          2662\n","appliedlearning_literacy_language        1629\n","math_science_literacy_language           1594\n","math_science_specialneeds                1326\n","history_civics                           1319\n","literacy_language_music_arts             1252\n","math_science_music_arts                  1120\n","appliedlearning_specialneeds             1053\n","history_civics_literacy_language         1028\n","health_sports_specialneeds               1020\n","warmth_care_hunger                        987\n","math_science_appliedlearning              918\n","appliedlearning_math_science              758\n","health_sports_literacy_language           590\n","literacy_language_history_civics          550\n","appliedlearning_music_arts                512\n","math_science_history_civics               467\n","appliedlearning_health_sports             429\n","literacy_language_appliedlearning         411\n","math_science_health_sports                320\n","specialneeds_music_arts                   230\n","history_civics_music_arts                 211\n","history_civics_math_science               197\n","health_sports_math_science                190\n","history_civics_specialneeds               162\n","health_sports_appliedlearning             130\n","appliedlearning_history_civics            119\n","health_sports_music_arts                  115\n","music_arts_specialneeds                   112\n","literacy_language_health_sports            65\n","health_sports_history_civics               32\n","history_civics_appliedlearning             23\n","specialneeds_health_sports                 18\n","music_arts_history_civics                  16\n","literacy_language_warmth_care_hunger       12\n","specialneeds_warmth_care_hunger            12\n","history_civics_health_sports               11\n","health_sports_warmth_care_hunger           10\n","music_arts_health_sports                   10\n","music_arts_appliedlearning                  9\n","appliedlearning_warmth_care_hunger          7\n","math_science_warmth_care_hunger             4\n","music_arts_warmth_care_hunger               3\n","history_civics_warmth_care_hunger           2\n","Name: project_subject_categories, dtype: int64"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["# Same above text-Preprocessing for test dataset - project_subject_categories\n","test_project_data_original['project_subject_categories'] = test_project_data_original['project_subject_categories'].str.replace(' The ','').str.replace(' ','').str.replace('&','_').str.replace(',','_')\n","test_project_data_original['project_subject_categories'] = test_project_data_original['project_subject_categories'].str.lower()\n","test_project_data_original['project_subject_categories'].value_counts()\n"]},{"cell_type":"markdown","metadata":{},"source":["## Preprocessing Categorical Features: project_subject_subcategories"]},{"cell_type":"code","execution_count":12,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:09:31.741143Z","iopub.status.busy":"2021-06-16T13:09:31.740783Z","iopub.status.idle":"2021-06-16T13:09:31.793520Z","shell.execute_reply":"2021-06-16T13:09:31.792507Z","shell.execute_reply.started":"2021-06-16T13:09:31.741098Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"data":{"text/plain":["Literacy                                    15775\n","Literacy, Mathematics                       13863\n","Literature & Writing, Mathematics            9976\n","Literacy, Literature & Writing               9282\n","Mathematics                                  9041\n","                                            ...  \n","Music, Nutrition Education                      1\n","Extracurricular, Warmth, Care & Hunger          1\n","Civics & Government, Nutrition Education        1\n","Gym & Fitness, Social Sciences                  1\n","Economics, Nutrition Education                  1\n","Name: project_subject_subcategories, Length: 407, dtype: int64"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["train_project_data_original['project_subject_subcategories'].value_counts()"]},{"cell_type":"code","execution_count":13,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:09:31.795154Z","iopub.status.busy":"2021-06-16T13:09:31.794804Z","iopub.status.idle":"2021-06-16T13:09:32.559403Z","shell.execute_reply":"2021-06-16T13:09:32.558365Z","shell.execute_reply.started":"2021-06-16T13:09:31.795096Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"data":{"text/plain":["literacy                                15775\n","literacy_mathematics                    13863\n","literature_writing_mathematics           9976\n","literacy_literature_writing              9282\n","mathematics                              9041\n","                                        ...  \n","civics_government_parentinvolvement         1\n","economics_foreignlanguages                  1\n","history_geography_warmth_care_hunger        1\n","economics_nutritioneducation                1\n","extracurricular_warmth_care_hunger          1\n","Name: project_subject_subcategories, Length: 407, dtype: int64"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["# Same kind of cleaning as we did in 'project_subject_categories'\n","\n","train_project_data_original['project_subject_subcategories'] = train_project_data_original['project_subject_subcategories'].str.replace(' The ','').str.replace(' ','').str.replace('&','_').str.replace(',','_').str.lower()\n","\n","train_project_data_original['project_subject_subcategories'].value_counts()"]},{"cell_type":"code","execution_count":14,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:09:32.561067Z","iopub.status.busy":"2021-06-16T13:09:32.560731Z","iopub.status.idle":"2021-06-16T13:09:32.893607Z","shell.execute_reply":"2021-06-16T13:09:32.892492Z","shell.execute_reply.started":"2021-06-16T13:09:32.561034Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"data":{"text/plain":["literacy                                6859\n","literacy_mathematics                    5930\n","literature_writing_mathematics          4248\n","literacy_literature_writing             3959\n","mathematics                             3872\n","                                        ... \n","financialliteracy_foreignlanguages         1\n","music_nutritioneducation                   1\n","civics_government_foreignlanguages         1\n","civics_government_warmth_care_hunger       1\n","earlydevelopment_economics                 1\n","Name: project_subject_subcategories, Length: 390, dtype: int64"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["# Same above text-Preprocessing for test dataset: project_subject_subcategories\n","test_project_data_original['project_subject_subcategories'] = test_project_data_original['project_subject_subcategories'].str.replace(' The ','').str.replace(' ','').str.replace('&','_').str.replace(',','_').str.lower()\n","\n","test_project_data_original['project_subject_subcategories'].value_counts()"]},{"cell_type":"markdown","metadata":{},"source":["## Preprocessing Categorical Features: teacher_prefix"]},{"cell_type":"code","execution_count":15,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:09:32.895212Z","iopub.status.busy":"2021-06-16T13:09:32.894909Z","iopub.status.idle":"2021-06-16T13:09:32.946852Z","shell.execute_reply":"2021-06-16T13:09:32.945832Z","shell.execute_reply.started":"2021-06-16T13:09:32.895183Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"data":{"text/plain":["Mrs.       95405\n","Ms.        65066\n","Mr.        17667\n","Teacher     3912\n","Dr.           26\n","Name: teacher_prefix, dtype: int64"]},"execution_count":15,"metadata":{},"output_type":"execute_result"}],"source":["train_project_data_original['teacher_prefix'].value_counts()"]},{"cell_type":"code","execution_count":16,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:09:32.948609Z","iopub.status.busy":"2021-06-16T13:09:32.948281Z","iopub.status.idle":"2021-06-16T13:09:33.060283Z","shell.execute_reply":"2021-06-16T13:09:33.059394Z","shell.execute_reply.started":"2021-06-16T13:09:32.948577Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["True\n","number of nan values 4\n"]},{"data":{"text/plain":["Mrs.       95409\n","Ms.        65066\n","Mr.        17667\n","Teacher     3912\n","Dr.           26\n","Name: teacher_prefix, dtype: int64"]},"execution_count":16,"metadata":{},"output_type":"execute_result"}],"source":["# check if we have any nan values are there\n","print(train_project_data_original['teacher_prefix'].isnull().values.any())\n","print(\"number of nan values\",train_project_data_original['teacher_prefix'].isnull().values.sum())\n","\n","# If there's indeed any \"NA\" values then fill them up\n","train_project_data_original['teacher_prefix']=train_project_data_original['teacher_prefix'].fillna('Mrs.')\n","\n","train_project_data_original['teacher_prefix'].value_counts()"]},{"cell_type":"code","execution_count":17,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:09:33.061955Z","iopub.status.busy":"2021-06-16T13:09:33.061495Z","iopub.status.idle":"2021-06-16T13:09:33.366525Z","shell.execute_reply":"2021-06-16T13:09:33.365314Z","shell.execute_reply.started":"2021-06-16T13:09:33.061919Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"data":{"text/plain":["mrs        95409\n","ms         65066\n","mr         17667\n","teacher     3912\n","dr            26\n","Name: teacher_prefix, dtype: int64"]},"execution_count":17,"metadata":{},"output_type":"execute_result"}],"source":["# Remove '.'\n","# convert all the chars to small\n","\n","train_project_data_original['teacher_prefix'] = train_project_data_original['teacher_prefix'].str.replace('.','')\n","train_project_data_original['teacher_prefix'] = train_project_data_original['teacher_prefix'].str.lower()\n","train_project_data_original['teacher_prefix'].value_counts()"]},{"cell_type":"code","execution_count":18,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:09:33.368877Z","iopub.status.busy":"2021-06-16T13:09:33.368437Z","iopub.status.idle":"2021-06-16T13:09:33.510937Z","shell.execute_reply":"2021-06-16T13:09:33.509905Z","shell.execute_reply.started":"2021-06-16T13:09:33.368828Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"data":{"text/plain":["mrs        40982\n","ms         27585\n","mr          7795\n","teacher     1657\n","dr            15\n","Name: teacher_prefix, dtype: int64"]},"execution_count":18,"metadata":{},"output_type":"execute_result"}],"source":["# Same above text-Preprocessing for test dataset: 'teacher_prefix'\n","test_project_data_original['teacher_prefix'] = test_project_data_original['teacher_prefix'].str.replace('.','')\n","test_project_data_original['teacher_prefix'] = test_project_data_original['teacher_prefix'].str.lower()\n","test_project_data_original['teacher_prefix'].value_counts()"]},{"cell_type":"markdown","metadata":{},"source":["## Preprocessing Categorical Features: school_state"]},{"cell_type":"code","execution_count":19,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:09:33.512786Z","iopub.status.busy":"2021-06-16T13:09:33.512438Z","iopub.status.idle":"2021-06-16T13:09:33.564790Z","shell.execute_reply":"2021-06-16T13:09:33.563510Z","shell.execute_reply.started":"2021-06-16T13:09:33.512750Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"data":{"text/plain":["CA    25695\n","TX    12304\n","NY    12157\n","FL    10359\n","NC     8463\n","IL     7332\n","GA     6636\n","SC     6463\n","MI     5324\n","PA     5093\n","IN     4314\n","MO     4247\n","OH     4139\n","MA     4054\n","LA     3946\n","WA     3903\n","OK     3829\n","NJ     3671\n","AZ     3614\n","VA     3446\n","WI     2983\n","AL     2955\n","TN     2836\n","UT     2814\n","CT     2766\n","MD     2480\n","NV     2297\n","MS     2222\n","KY     2172\n","OR     2119\n","MN     2055\n","CO     1887\n","AR     1757\n","ID     1113\n","IA     1079\n","KS     1060\n","NM      916\n","DC      902\n","HI      869\n","WV      839\n","ME      827\n","DE      589\n","NH      587\n","AK      557\n","SD      494\n","NE      493\n","RI      475\n","MT      384\n","ND      248\n","WY      177\n","VT      139\n","Name: school_state, dtype: int64"]},"execution_count":19,"metadata":{},"output_type":"execute_result"}],"source":["train_project_data_original['school_state'].value_counts()\n"]},{"cell_type":"code","execution_count":20,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:09:33.566793Z","iopub.status.busy":"2021-06-16T13:09:33.566297Z","iopub.status.idle":"2021-06-16T13:09:33.746474Z","shell.execute_reply":"2021-06-16T13:09:33.745282Z","shell.execute_reply.started":"2021-06-16T13:09:33.566667Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"data":{"text/plain":["ca    25695\n","tx    12304\n","ny    12157\n","fl    10359\n","nc     8463\n","il     7332\n","ga     6636\n","sc     6463\n","mi     5324\n","pa     5093\n","in     4314\n","mo     4247\n","oh     4139\n","ma     4054\n","la     3946\n","wa     3903\n","ok     3829\n","nj     3671\n","az     3614\n","va     3446\n","wi     2983\n","al     2955\n","tn     2836\n","ut     2814\n","ct     2766\n","md     2480\n","nv     2297\n","ms     2222\n","ky     2172\n","or     2119\n","mn     2055\n","co     1887\n","ar     1757\n","id     1113\n","ia     1079\n","ks     1060\n","nm      916\n","dc      902\n","hi      869\n","wv      839\n","me      827\n","de      589\n","nh      587\n","ak      557\n","sd      494\n","ne      493\n","ri      475\n","mt      384\n","nd      248\n","wy      177\n","vt      139\n","Name: school_state, dtype: int64"]},"execution_count":20,"metadata":{},"output_type":"execute_result"}],"source":["# convert all of them into small letters\n","train_project_data_original['school_state'] = train_project_data_original['school_state'].str.lower()\n","train_project_data_original['school_state'].value_counts()"]},{"cell_type":"code","execution_count":21,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:09:33.748255Z","iopub.status.busy":"2021-06-16T13:09:33.747920Z","iopub.status.idle":"2021-06-16T13:09:33.827023Z","shell.execute_reply":"2021-06-16T13:09:33.825962Z","shell.execute_reply.started":"2021-06-16T13:09:33.748221Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"data":{"text/plain":["ca    10723\n","tx     5344\n","ny     5151\n","fl     4429\n","nc     3624\n","il     3177\n","ga     2922\n","sc     2619\n","mi     2317\n","pa     2293\n","in     1864\n","mo     1818\n","la     1749\n","oh     1748\n","ma     1733\n","wa     1712\n","ok     1633\n","az     1586\n","nj     1577\n","va     1447\n","al     1312\n","tn     1258\n","ut     1216\n","wi     1215\n","ct     1199\n","md     1078\n","nv      995\n","ky      963\n","ms      962\n","or      908\n","mn      864\n","co      806\n","ar      730\n","id      507\n","ks      480\n","ia      445\n","dc      373\n","hi      367\n","nm      367\n","me      344\n","wv      338\n","de      286\n","ak      239\n","nh      233\n","ne      231\n","ri      226\n","sd      207\n","mt      163\n","nd      120\n","vt       72\n","wy       65\n","Name: school_state, dtype: int64"]},"execution_count":21,"metadata":{},"output_type":"execute_result"}],"source":["# Same above text-Preprocessing for test dataset: 'school_state'\n","test_project_data_original['school_state'] = test_project_data_original['school_state'].str.lower()\n","test_project_data_original['school_state'].value_counts()"]},{"cell_type":"markdown","metadata":{},"source":["## Preprocessing Categorical Features: project_title\n","\n","#### First Expanding English language contractions in Python\n","\n","The English language has [a couple of contractions](http://en.wikipedia.org/wiki/Wikipedia%3aList_of_English_contractions). For instance:\n","\n","    you've -> you have\n","    he's -> he is"]},{"cell_type":"code","execution_count":22,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:09:33.828602Z","iopub.status.busy":"2021-06-16T13:09:33.828319Z","iopub.status.idle":"2021-06-16T13:09:33.838262Z","shell.execute_reply":"2021-06-16T13:09:33.836954Z","shell.execute_reply.started":"2021-06-16T13:09:33.828575Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[],"source":["# https://stackoverflow.com/a/47091490/4084039\n","import re\n","\n","def remove_eng_lang_contraction(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\n","\n","# For few other ways to remove contraction check below\n","# https://stackoverflow.com/questions/19790188/expanding-english-language-contractions-in-python\n"]},{"cell_type":"markdown","metadata":{},"source":["## Stopword Removal\n","\n","Stop words are a set of commonly used words in a language. Examples of stop words in English are “a”, “the”, “is”, “are” and etc. The intuition behind using stop words is that, by removing low information words from text, we can focus on the important words instead.\n","\n","For example, in the context of a search system, if your search query is “what is text preprocessing?”, you want the search system to focus on surfacing documents that talk about text preprocessing over documents that talk about what is. This can be done by preventing all words from your stop word list from being analyzed. Stop words are commonly applied in search systems, text classification applications, topic modeling, topic extraction and others.\n","In my experience, stop word removal, while effective in search and topic extraction systems, showed to be non-critical in classification systems. However, it does help reduce the number of features in consideration which helps keep your models decently sized."]},{"cell_type":"code","execution_count":23,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:09:33.839853Z","iopub.status.busy":"2021-06-16T13:09:33.839548Z","iopub.status.idle":"2021-06-16T13:09:33.854704Z","shell.execute_reply":"2021-06-16T13:09:33.853532Z","shell.execute_reply.started":"2021-06-16T13:09:33.839823Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"data":{"text/plain":["0                             Super Sight Word Centers\n","1                               Keep Calm and Dance On\n","2                                Lets 3Doodle to Learn\n","3    \\\"Kid Inspired\\\" Equipment to Increase Activit...\n","4     We need clean water for our culinary arts class!\n","Name: project_title, dtype: object"]},"execution_count":23,"metadata":{},"output_type":"execute_result"}],"source":["train_project_data_original['project_title'].head(5)"]},{"cell_type":"code","execution_count":24,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:09:33.856963Z","iopub.status.busy":"2021-06-16T13:09:33.856414Z","iopub.status.idle":"2021-06-16T13:09:33.867714Z","shell.execute_reply":"2021-06-16T13:09:33.865952Z","shell.execute_reply.started":"2021-06-16T13:09:33.856928Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["printing some random reviews before Pre-Processing \n","3 \\\"Kid Inspired\\\" Equipment to Increase Activities and Gain Better Health\n","15 A Comfy Seat!\n","10 Writing, Writing, Writing!\n"]}],"source":["print(\"printing some random reviews before Pre-Processing \")\n","print(3, train_project_data_original['project_title'].values[3])\n","print(15, train_project_data_original['project_title'].values[15])\n","print(10, train_project_data_original['project_title'].values[10])"]},{"cell_type":"code","execution_count":25,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:09:33.869652Z","iopub.status.busy":"2021-06-16T13:09:33.869216Z","iopub.status.idle":"2021-06-16T13:09:33.880995Z","shell.execute_reply":"2021-06-16T13:09:33.879168Z","shell.execute_reply.started":"2021-06-16T13:09:33.869619Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[],"source":["# Now applying all the above pre-processing steps and functions\n","# Expanding English language contractions with the function we defined above\n","# Removing all stopwords\n","\n","from tqdm import tqdm\n","def preprocess_text(text_data):\n","    preprocessed_text = []\n","    # tqdm is for printing the status bar\n","    for current_sentenceence in tqdm(text_data):\n","\n","        # Expanding English language contractions\n","        current_sentence = remove_eng_lang_contraction(current_sentenceence)\n","        current_sentence = current_sentence.replace('\\\\r', ' ')\n","        current_sentence = current_sentence.replace('\\\\n', ' ')\n","        current_sentence = current_sentence.replace('\\\\\"', ' ')\n","        current_sentence = re.sub('[^A-Za-z0-9]+', ' ', current_sentence)\n","\n","        # Removing all stopwords\n","        # https://gist.github.com/sebleier/554280\n","        current_sentence = ' '.join(e for e in current_sentence.split() if e.lower() not in stopwords)\n","        preprocessed_text.append(current_sentence.lower().strip())\n","    return preprocessed_text"]},{"cell_type":"code","execution_count":26,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:09:33.883285Z","iopub.status.busy":"2021-06-16T13:09:33.882782Z","iopub.status.idle":"2021-06-16T13:09:40.371471Z","shell.execute_reply":"2021-06-16T13:09:40.369863Z","shell.execute_reply.started":"2021-06-16T13:09:33.883249Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["100%|██████████| 182080/182080 [00:02<00:00, 61624.84it/s]"]},{"name":"stdout","output_type":"stream","text":["printing few random reviews AFTER Pre-Processing \n","3 kid inspired equipment increase activities gain better health\n","15 comfy seat\n","10 writing writing writing\n"]},{"name":"stderr","output_type":"stream","text":["\n"]}],"source":["preprocessed_titles_train = preprocess_text(train_project_data_original['project_title'].values)\n","\n","train_project_data_original['project_title'] = preprocessed_titles_train\n","\n","print(\"printing few random reviews AFTER Pre-Processing \")\n","print(3, train_project_data_original['project_title'].values[3])\n","print(15, train_project_data_original['project_title'].values[15])\n","print(10, train_project_data_original['project_title'].values[10])"]},{"cell_type":"code","execution_count":27,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:09:40.374293Z","iopub.status.busy":"2021-06-16T13:09:40.373769Z","iopub.status.idle":"2021-06-16T13:09:43.164584Z","shell.execute_reply":"2021-06-16T13:09:43.162601Z","shell.execute_reply.started":"2021-06-16T13:09:40.374239Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["100%|██████████| 78035/78035 [00:01<00:00, 62765.02it/s]"]},{"name":"stdout","output_type":"stream","text":["printing few random reviews AFTER Pre-Processing \n","3 classroom library\n","15 keeping focus movement\n","10 help reading stars shine\n"]},{"name":"stderr","output_type":"stream","text":["\n"]}],"source":["# Same above text-Preprocessing for test dataset: 'project_title'\n","\n","preprocessed_titles_test = preprocess_text(test_project_data_original['project_title'].values)\n","\n","test_project_data_original['project_title'] = preprocessed_titles_test\n","\n","print(\"printing few random reviews AFTER Pre-Processing \")\n","print(3, test_project_data_original['project_title'].values[3])\n","print(15, test_project_data_original['project_title'].values[15])\n","print(10, test_project_data_original['project_title'].values[10])"]},{"cell_type":"markdown","metadata":{},"source":["## Preprocessing Categorical Features: essay"]},{"cell_type":"code","execution_count":28,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:09:43.166456Z","iopub.status.busy":"2021-06-16T13:09:43.166062Z","iopub.status.idle":"2021-06-16T13:09:44.837792Z","shell.execute_reply":"2021-06-16T13:09:44.834953Z","shell.execute_reply.started":"2021-06-16T13:09:43.166420Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Checking out few random essay BEFORE Pre-Processing \n","9 I teach first grade in a small farming town in Illinois. We have such a diverse students population. On one side of town, we have students who come from half-million-dollar homes and on the other side of town students who come from public-housing situations. 30% of our student population receives free and reduced price lunch and school fees. My goal for all my students is to give each and every one of them the SAME opportunity to learn with every advantage possible.\\r\\n\\r\\nIf I can accomplish one thing in the short time I have my students, it would be that know that I truly love and value them as individuals.\\r\\n\\r\\nThere is nothing better than snuggling up with a good book and getting lost in it.  I strive to create a love reading by motivating students to want to read.  For that past several years I have been adding furniture such as a couch, pillows, and a tent for students to \\\"snuggle up\\\" and read a book.  I would love to add these 2 reclining chairs for students to cozy up in while reading. Currently not all students have a comfortable space for reading.  By adding these two recliners more students would be able to have a space during reading time. \\r\\nI have seen first hand students develop a love for reading because they are able to get comfortable.  Students beg for me to extend our silent reading time.nannan\n","**************************************************\n","34 ICS students are bilingual, biliterate, culturally competent, and academically successful. They have the linguistic, social emotional, and critical thinking skills to build alliances within and beyond our community and work to create an equitable and just world. \\r\\nOur mission for the 2016-2017 school-year is to foster a caring school community in order to ensure that our students have the content knowledge and social emotional skills to have complex academic conversations. Their oral language will lead to writing clear and well-organized opinion pieces citing evidence to support their opinions. We focus on academic discourse as it leads to writing with evidence; our students explain their thinking about what they are reading and math problems they attempt to solve. We provide opportunities to use vocabulary through integrating art lessons and projects with the FOSS science units. We see evidence of student learning in science in the classrooms and in the common areas.Our mission for the 2016-2017 school-year is to foster a caring school community to ensure that our students have the content knowledge and social emotional skills to have complex academic conversations. Their oral language will lead to writing clear and well-organized opinion pieces citing evidence to support their opinions. We focus on academic discourse as it leads to writing with evidence; our students explain their thinking about what they are reading and math problems they attempt to solve. We provide opportunities to use vocabulary through integrating art lessons and projects with the FOSS science units. We see evidence of student learning in science in the classrooms and in the common areas.nannan\n","**************************************************\n","147 I have the privilege of teaching the most genuine young men and women I have ever known. These are some of the best people I have ever met and I am so proud of who they are all becoming. The majority of my students are college-bound seniors who are engaged in school, but often times, are not challenged enough. That's where I come in. I push my kids with college-level material and projects. They are always up to the challenge. These students seem to come alive when literature is read, but we simply don't have enough resources at our school to expose them to life-changing readings.What if a book could change your entire view of the world? What if a book could change your entire life? All of the books provided by this project are those kinds of books. Students have worn out my old copies of the Greek tragedies and I'd love to have all of the Greek tragedies that were ever written to give to my students. They're begging for these books! I've got a waiting list in line to read them! I chose durable hardcover where possible so that these books would last a lifetime of reading from my students. These books will be used to help build our classroom independent reading library and for general research projects.\\r\\n\\r\\nI have built into my class activities that encourages students to read independently, with the ultimate goal of falling in love with reading and becoming a lifelong reader on their own.\\r\\n\\r\\nThroughout the week and the semester, students can choose from books in my classroom to do independent reading projects on. The books in this project are a few carefully selected books that I believe will benefit the lives of my students. I have chosen them to personally recommend to students. \\r\\n\\r\\nEven though these ancient plays are separated from our time period by millenniums, they still still speak to our generation which is searching for truth, for the comforts of home, for our true identity, for our family.\\r\\n\\r\\nI hope you will join my efforts to help my students understand and appreciate classic literature. I appreciate your consideration and support! Thank you!nannan\n"]}],"source":["\n","# First combine all the 4 essay columns into a single one:\n","#  'project_essay_1',  'project_essay_2',   'project_essay_3',   'project_essay_4',\n","train_project_data_original[\"essay\"] = train_project_data_original[\"project_essay_1\"].map(str) +\\\n","                        train_project_data_original[\"project_essay_2\"].map(str) + \\\n","                        train_project_data_original[\"project_essay_3\"].map(str) + \\\n","                        train_project_data_original[\"project_essay_4\"].map(str)\n","\n","test_project_data_original[\"essay\"] = test_project_data_original[\"project_essay_1\"].map(str) +\\\n","                        test_project_data_original[\"project_essay_2\"].map(str) + \\\n","                        test_project_data_original[\"project_essay_3\"].map(str) + \\\n","                        test_project_data_original[\"project_essay_4\"].map(str)\n","\n","\n","print(\"Checking out few random essay BEFORE Pre-Processing \")\n","print(9, train_project_data_original['essay'].values[9])\n","print('*'*50)\n","print(34, train_project_data_original['essay'].values[34])\n","print('*'*50)\n","print(147, train_project_data_original['essay'].values[147])"]},{"cell_type":"code","execution_count":29,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:09:44.840268Z","iopub.status.busy":"2021-06-16T13:09:44.839482Z","iopub.status.idle":"2021-06-16T13:12:01.822940Z","shell.execute_reply":"2021-06-16T13:12:01.821195Z","shell.execute_reply.started":"2021-06-16T13:09:44.840210Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["100%|██████████| 182080/182080 [01:13<00:00, 2493.37it/s]"]},{"name":"stdout","output_type":"stream","text":["Checking out few random essay AFTER Pre-Processing \n","9 teach first grade small farming town illinois diverse students population one side town students come half million dollar homes side town students come public housing situations 30 student population receives free reduced price lunch school fees goal students give every one opportunity learn every advantage possible accomplish one thing short time students would know truly love value individuals nothing better snuggling good book getting lost strive create love reading motivating students want read past several years adding furniture couch pillows tent students snuggle read book would love add 2 reclining chairs students cozy reading currently not students comfortable space reading adding two recliners students would able space reading time seen first hand students develop love reading able get comfortable students beg extend silent reading time nannan\n","**************************************************\n","34 ics students bilingual biliterate culturally competent academically successful linguistic social emotional critical thinking skills build alliances within beyond community work create equitable world mission 2016 2017 school year foster caring school community order ensure students content knowledge social emotional skills complex academic conversations oral language lead writing clear well organized opinion pieces citing evidence support opinions focus academic discourse leads writing evidence students explain thinking reading math problems attempt solve provide opportunities use vocabulary integrating art lessons projects foss science units see evidence student learning science classrooms common areas mission 2016 2017 school year foster caring school community ensure students content knowledge social emotional skills complex academic conversations oral language lead writing clear well organized opinion pieces citing evidence support opinions focus academic discourse leads writing evidence students explain thinking reading math problems attempt solve provide opportunities use vocabulary integrating art lessons projects foss science units see evidence student learning science classrooms common areas nannan\n","**************************************************\n","147 privilege teaching genuine young men women ever known best people ever met proud becoming majority students college bound seniors engaged school often times not challenged enough come push kids college level material projects always challenge students seem come alive literature read simply not enough resources school expose life changing readings book could change entire view world book could change entire life books provided project kinds books students worn old copies greek tragedies would love greek tragedies ever written give students begging books got waiting list line read chose durable hardcover possible books would last lifetime reading students books used help build classroom independent reading library general research projects built class activities encourages students read independently ultimate goal falling love reading becoming lifelong reader throughout week semester students choose books classroom independent reading projects books project carefully selected books believe benefit lives students chosen personally recommend students even though ancient plays separated time period millenniums still still speak generation searching truth comforts home true identity family hope join efforts help students understand appreciate classic literature appreciate consideration support thank nannan\n","**************************************************\n"]},{"name":"stderr","output_type":"stream","text":["\n"]}],"source":["preprocessed_essays_train = preprocess_text(train_project_data_original['essay'].values)\n","\n","train_project_data_original['essay'] = preprocessed_essays_train\n","\n","print(\"Checking out few random essay AFTER Pre-Processing \")\n","print(9, train_project_data_original['essay'].values[9])\n","print('*'*50)\n","print(34, train_project_data_original['essay'].values[34])\n","print('*'*50)\n","print(147, train_project_data_original['essay'].values[147])\n","\n","print('*'*50)"]},{"cell_type":"code","execution_count":30,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:12:01.824808Z","iopub.status.busy":"2021-06-16T13:12:01.824489Z","iopub.status.idle":"2021-06-16T13:13:00.736017Z","shell.execute_reply":"2021-06-16T13:13:00.734514Z","shell.execute_reply.started":"2021-06-16T13:12:01.824777Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["100%|██████████| 78035/78035 [00:30<00:00, 2559.57it/s]\n"]}],"source":["# Same above text-Preprocessing for test dataset: 'essay'\n","preprocessed_essays_test = preprocess_text(test_project_data_original['essay'].values)\n","test_project_data_original['essay'] = preprocessed_essays_test"]},{"cell_type":"markdown","metadata":{},"source":["## Preprocessing Numerical Values: price\n"]},{"cell_type":"code","execution_count":31,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:13:00.738462Z","iopub.status.busy":"2021-06-16T13:13:00.737955Z","iopub.status.idle":"2021-06-16T13:13:00.745509Z","shell.execute_reply":"2021-06-16T13:13:00.744542Z","shell.execute_reply.started":"2021-06-16T13:13:00.738414Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Shape of Resource datesset:  (1541272, 4)\n"]}],"source":["print('Shape of Resource datesset: ', resource_data.shape) #15.42mn rows"]},{"cell_type":"markdown","metadata":{},"source":["As we can see there's around 15.42mn rows in the original resource_data.csv. And this is because the same project will require multiple resources.\n","So, now first I want to sum all the Price and Quantity that belongs to the same Project ID.\n","\n","### How reset_index() function works.\n","\n","Say, I have a dataframe from which I remove some rows. As a result, I get a dataframe in which index is something like that: `[1,5,6,10,11]` and I would like to reset it to `[0,1,2,3,4]`. This is where I will use `reset_index()`\n","\n","`df.reset_index(drop=True)`"]},{"cell_type":"code","execution_count":32,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:13:00.747408Z","iopub.status.busy":"2021-06-16T13:13:00.747037Z","iopub.status.idle":"2021-06-16T13:13:01.788734Z","shell.execute_reply":"2021-06-16T13:13:01.787743Z","shell.execute_reply.started":"2021-06-16T13:13:00.747374Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>id</th>\n","      <th>price</th>\n","      <th>quantity</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>p000001</td>\n","      <td>459.56</td>\n","      <td>7</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>p000002</td>\n","      <td>515.89</td>\n","      <td>21</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["        id   price  quantity\n","0  p000001  459.56         7\n","1  p000002  515.89        21"]},"execution_count":32,"metadata":{},"output_type":"execute_result"}],"source":["price_data_from_resource = resource_data.groupby('id').agg({'price': 'sum', 'quantity': 'sum'}).reset_index()\n","price_data_from_resource.head(2)\n"]},{"cell_type":"code","execution_count":33,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:13:01.790678Z","iopub.status.busy":"2021-06-16T13:13:01.790356Z","iopub.status.idle":"2021-06-16T13:13:01.824132Z","shell.execute_reply":"2021-06-16T13:13:01.823241Z","shell.execute_reply.started":"2021-06-16T13:13:01.790646Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>id</th>\n","      <th>price</th>\n","      <th>quantity</th>\n","      <th>resource_cost</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>p000001</td>\n","      <td>459.56</td>\n","      <td>7</td>\n","      <td>3216.92</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>p000002</td>\n","      <td>515.89</td>\n","      <td>21</td>\n","      <td>10833.69</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["        id   price  quantity  resource_cost\n","0  p000001  459.56         7        3216.92\n","1  p000002  515.89        21       10833.69"]},"execution_count":33,"metadata":{},"output_type":"execute_result"}],"source":["# Multiply two columns and then create new column with values\n","price_data_from_resource['resource_cost'] = price_data_from_resource['price'] * price_data_from_resource['quantity']\n","price_data_from_resource.head(2)\n"]},{"cell_type":"code","execution_count":34,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:13:01.826018Z","iopub.status.busy":"2021-06-16T13:13:01.825510Z","iopub.status.idle":"2021-06-16T13:13:01.857372Z","shell.execute_reply":"2021-06-16T13:13:01.856510Z","shell.execute_reply.started":"2021-06-16T13:13:01.825966Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>id</th>\n","      <th>resource_cost</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>p000001</td>\n","      <td>3216.92</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>p000002</td>\n","      <td>10833.69</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["        id  resource_cost\n","0  p000001        3216.92\n","1  p000002       10833.69"]},"execution_count":34,"metadata":{},"output_type":"execute_result"}],"source":["price_data_from_resource = price_data_from_resource.drop(['price', 'quantity'], axis=1)\n","price_data_from_resource.head(2)"]},{"cell_type":"code","execution_count":35,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:13:01.859089Z","iopub.status.busy":"2021-06-16T13:13:01.858602Z","iopub.status.idle":"2021-06-16T13:13:02.757395Z","shell.execute_reply":"2021-06-16T13:13:02.756144Z","shell.execute_reply.started":"2021-06-16T13:13:01.859040Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Index(['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',\n","       'project_essay_3', 'project_essay_4', 'project_resource_summary',\n","       'teacher_number_of_previously_posted_projects', 'project_is_approved',\n","       'essay', 'resource_cost'],\n","      dtype='object')\n","Index(['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',\n","       'project_essay_3', 'project_essay_4', 'project_resource_summary',\n","       'teacher_number_of_previously_posted_projects', 'essay',\n","       'resource_cost'],\n","      dtype='object')\n"]}],"source":["train_project_data_original = pd.merge(train_project_data_original, price_data_from_resource, on='id', how='left')\n","test_project_data_original = pd.merge(test_project_data_original, price_data_from_resource, on='id', how='left')\n","train_project_data_original['resource_cost'].head()\n","print(train_project_data_original.columns)\n","print(test_project_data_original.columns)"]},{"cell_type":"code","execution_count":36,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:13:02.759507Z","iopub.status.busy":"2021-06-16T13:13:02.759073Z","iopub.status.idle":"2021-06-16T13:13:02.767785Z","shell.execute_reply":"2021-06-16T13:13:02.766872Z","shell.execute_reply.started":"2021-06-16T13:13:02.759471Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"data":{"text/plain":["0    1799.88\n","1     400.00\n","2     469.99\n","3    3422.35\n","4     711.00\n","Name: resource_cost, dtype: float64"]},"execution_count":36,"metadata":{},"output_type":"execute_result"}],"source":["train_project_data_original['resource_cost'].head()\n"]},{"cell_type":"code","execution_count":37,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:13:02.770046Z","iopub.status.busy":"2021-06-16T13:13:02.769680Z","iopub.status.idle":"2021-06-16T13:13:02.783725Z","shell.execute_reply":"2021-06-16T13:13:02.782804Z","shell.execute_reply.started":"2021-06-16T13:13:02.770011Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"data":{"text/plain":["0     149.00\n","1    1055.12\n","2     298.00\n","3    7521.50\n","4    1237.36\n","Name: resource_cost, dtype: float64"]},"execution_count":37,"metadata":{},"output_type":"execute_result"}],"source":["test_project_data_original['resource_cost'].head()"]},{"cell_type":"markdown","metadata":{},"source":["## Deciding which columns to drop - project_resource_summary\n","\n","Lets checkout, which words are used in summaries ('project_resource_summary') of approved and non-approved projects."]},{"cell_type":"code","execution_count":38,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:13:02.785750Z","iopub.status.busy":"2021-06-16T13:13:02.785427Z","iopub.status.idle":"2021-06-16T13:13:17.963848Z","shell.execute_reply":"2021-06-16T13:13:17.962620Z","shell.execute_reply.started":"2021-06-16T13:13:02.785717Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 864x576 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["word_cloud_for_project_resource_summary = ' '.join(train_project_data_original.loc[train_project_data_original['project_is_approved'] == 1, 'project_resource_summary'].values)\n","\n","wordcloud = WordCloud(max_font_size=None, stopwords=stopwords, background_color='white',\n","                      width=1200, height=1000).generate(word_cloud_for_project_resource_summary)\n","plt.figure(figsize=(12, 8))\n","plt.imshow(wordcloud)\n","plt.title('Top words for approved projects')\n","plt.axis(\"off\")\n","plt.show()"]},{"cell_type":"code","execution_count":39,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:13:17.966033Z","iopub.status.busy":"2021-06-16T13:13:17.965613Z","iopub.status.idle":"2021-06-16T13:13:23.586206Z","shell.execute_reply":"2021-06-16T13:13:23.584913Z","shell.execute_reply.started":"2021-06-16T13:13:17.965984Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 864x576 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["word_cloud_for_project_resource_summary = ' '.join(train_project_data_original.loc[train_project_data_original['project_is_approved'] == 0, 'project_resource_summary'].values)\n","\n","wordcloud = WordCloud(max_font_size=None, stopwords=stopwords, background_color='white',\n","                      width=1200, height=1000).generate(word_cloud_for_project_resource_summary)\n","plt.figure(figsize=(12, 8))\n","plt.imshow(wordcloud)\n","plt.title('Top words for approved projects')\n","plt.axis(\"off\")\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["Looks almost there's not real difference, hence I shall include 'project_resource_summary' within deleted-columns\n","\n","### I will drop following columns from both train and test dataset\n"]},{"cell_type":"code","execution_count":40,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:13:23.588971Z","iopub.status.busy":"2021-06-16T13:13:23.588452Z","iopub.status.idle":"2021-06-16T13:13:23.895456Z","shell.execute_reply":"2021-06-16T13:13:23.894345Z","shell.execute_reply.started":"2021-06-16T13:13:23.588914Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["after dropping  Index(['teacher_prefix', 'school_state', 'project_grade_category',\n","       'project_subject_categories', 'project_subject_subcategories',\n","       'project_title', 'project_essay_1', 'project_essay_2',\n","       'project_essay_3', 'project_essay_4',\n","       'teacher_number_of_previously_posted_projects', 'project_is_approved',\n","       'essay', 'resource_cost'],\n","      dtype='object')\n"]}],"source":["cols_to_drop_from = [\n","    'id',\n","    'teacher_id',\n","    'project_submitted_datetime',\n","    'project_resource_summary'\n","]\n","\n","train_df_pre_processed_original = train_project_data_original.drop(cols_to_drop_from, axis=1)\n","test_df_pre_processed_original = test_project_data_original.drop(cols_to_drop_from, axis=1)\n","\n","# test_project_data_original.drop(cols_to_drop)\n","print('after dropping ', train_df_pre_processed_original.columns)\n","# train_project_data_original.to_csv('preprocessed-train.csv')\n","# test_project_data_original.to_csv('preprocessed-test.csv')"]},{"cell_type":"code","execution_count":41,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:13:23.897144Z","iopub.status.busy":"2021-06-16T13:13:23.896834Z","iopub.status.idle":"2021-06-16T13:13:23.918582Z","shell.execute_reply":"2021-06-16T13:13:23.917265Z","shell.execute_reply.started":"2021-06-16T13:13:23.897112Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>teacher_prefix</th>\n","      <th>school_state</th>\n","      <th>project_grade_category</th>\n","      <th>project_subject_categories</th>\n","      <th>project_subject_subcategories</th>\n","      <th>project_title</th>\n","      <th>project_essay_1</th>\n","      <th>project_essay_2</th>\n","      <th>project_essay_3</th>\n","      <th>project_essay_4</th>\n","      <th>teacher_number_of_previously_posted_projects</th>\n","      <th>project_is_approved</th>\n","      <th>essay</th>\n","      <th>resource_cost</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>ms</td>\n","      <td>nv</td>\n","      <td>grades_prek_2</td>\n","      <td>literacy_language</td>\n","      <td>literacy</td>\n","      <td>super sight word centers</td>\n","      <td>Most of my kindergarten students come from low...</td>\n","      <td>I currently have a differentiated sight word c...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>26</td>\n","      <td>1</td>\n","      <td>kindergarten students come low income househol...</td>\n","      <td>1799.88</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  teacher_prefix school_state project_grade_category  \\\n","0             ms           nv          grades_prek_2   \n","\n","  project_subject_categories project_subject_subcategories  \\\n","0          literacy_language                      literacy   \n","\n","              project_title  \\\n","0  super sight word centers   \n","\n","                                     project_essay_1  \\\n","0  Most of my kindergarten students come from low...   \n","\n","                                     project_essay_2 project_essay_3  \\\n","0  I currently have a differentiated sight word c...             NaN   \n","\n","  project_essay_4  teacher_number_of_previously_posted_projects  \\\n","0             NaN                                            26   \n","\n","   project_is_approved                                              essay  \\\n","0                    1  kindergarten students come low income househol...   \n","\n","   resource_cost  \n","0        1799.88  "]},"execution_count":41,"metadata":{},"output_type":"execute_result"}],"source":["train_df_pre_processed_original.head(1)"]},{"cell_type":"code","execution_count":42,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:13:23.921214Z","iopub.status.busy":"2021-06-16T13:13:23.920708Z","iopub.status.idle":"2021-06-16T13:13:23.935403Z","shell.execute_reply":"2021-06-16T13:13:23.934180Z","shell.execute_reply.started":"2021-06-16T13:13:23.921158Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["All Features names in preprocessed-train.csv  Index(['teacher_prefix', 'school_state', 'project_grade_category',\n","       'project_subject_categories', 'project_subject_subcategories',\n","       'project_title', 'project_essay_1', 'project_essay_2',\n","       'project_essay_3', 'project_essay_4',\n","       'teacher_number_of_previously_posted_projects', 'project_is_approved',\n","       'essay', 'resource_cost'],\n","      dtype='object')\n","All Features names in preprocessed-test.csv  Index(['teacher_prefix', 'school_state', 'project_grade_category',\n","       'project_subject_categories', 'project_subject_subcategories',\n","       'project_title', 'project_essay_1', 'project_essay_2',\n","       'project_essay_3', 'project_essay_4',\n","       'teacher_number_of_previously_posted_projects', 'essay',\n","       'resource_cost'],\n","      dtype='object')\n"]}],"source":["print('All Features names in preprocessed-train.csv ', train_df_pre_processed_original.columns )\n","print('All Features names in preprocessed-test.csv ', test_df_pre_processed_original.columns )"]},{"cell_type":"markdown","metadata":{},"source":["## From hereon, below all work is completely on the above Pre-Processed dataset (both train and test)\n","\n","#### Split the train data into Train and Validation Set, so I can do all the experimentation below on these sets itself,\n","\n","This is specifically for applying GridSearchCV to get the best alpha to be applied finally on the original test dataset.\n"]},{"cell_type":"code","execution_count":43,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:13:23.937678Z","iopub.status.busy":"2021-06-16T13:13:23.937255Z","iopub.status.idle":"2021-06-16T13:13:24.282795Z","shell.execute_reply":"2021-06-16T13:13:24.281831Z","shell.execute_reply.started":"2021-06-16T13:13:23.937643Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["X_donor_choose_train shape  (121993, 13)\n"]},{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>teacher_prefix</th>\n","      <th>school_state</th>\n","      <th>project_grade_category</th>\n","      <th>project_subject_categories</th>\n","      <th>project_subject_subcategories</th>\n","      <th>project_title</th>\n","      <th>project_essay_1</th>\n","      <th>project_essay_2</th>\n","      <th>project_essay_3</th>\n","      <th>project_essay_4</th>\n","      <th>teacher_number_of_previously_posted_projects</th>\n","      <th>essay</th>\n","      <th>resource_cost</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>20947</th>\n","      <td>mr</td>\n","      <td>pa</td>\n","      <td>grades_6_8</td>\n","      <td>history_civics</td>\n","      <td>history_geography</td>\n","      <td>get hotter</td>\n","      <td>Students in my school are coming from a variet...</td>\n","      <td>10 million dollar debt. This is the phrase whi...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>7</td>\n","      <td>students school coming variety cultural backgr...</td>\n","      <td>108.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["      teacher_prefix school_state project_grade_category  \\\n","20947             mr           pa             grades_6_8   \n","\n","      project_subject_categories project_subject_subcategories project_title  \\\n","20947             history_civics             history_geography    get hotter   \n","\n","                                         project_essay_1  \\\n","20947  Students in my school are coming from a variet...   \n","\n","                                         project_essay_2 project_essay_3  \\\n","20947  10 million dollar debt. This is the phrase whi...             NaN   \n","\n","      project_essay_4  teacher_number_of_previously_posted_projects  \\\n","20947             NaN                                             7   \n","\n","                                                   essay  resource_cost  \n","20947  students school coming variety cultural backgr...          108.0  "]},"execution_count":43,"metadata":{},"output_type":"execute_result"}],"source":["y_train_pre_processed_original = train_df_pre_processed_original['project_is_approved'].values\n","x_train_pre_processed_original = train_df_pre_processed_original.drop(['project_is_approved'], axis=1)\n","\n","X_donor_choose_train, X_donor_choose_validation, y_donor_choose_train, y_donor_choose_validation = train_test_split(x_train_pre_processed_original, y_train_pre_processed_original, test_size=0.33, stratify=y_train_pre_processed_original)\n","print('X_donor_choose_train shape ', X_donor_choose_train.shape)\n","\n","X_donor_choose_train.head(1)"]},{"cell_type":"code","execution_count":44,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:13:24.284673Z","iopub.status.busy":"2021-06-16T13:13:24.284348Z","iopub.status.idle":"2021-06-16T13:13:24.294308Z","shell.execute_reply":"2021-06-16T13:13:24.292855Z","shell.execute_reply.started":"2021-06-16T13:13:24.284639Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["x_train_pre_processed_original name of all columns values  Index(['teacher_prefix', 'school_state', 'project_grade_category',\n","       'project_subject_categories', 'project_subject_subcategories',\n","       'project_title', 'project_essay_1', 'project_essay_2',\n","       'project_essay_3', 'project_essay_4',\n","       'teacher_number_of_previously_posted_projects', 'essay',\n","       'resource_cost'],\n","      dtype='object')\n"]}],"source":["print('x_train_pre_processed_original name of all columns values ', x_train_pre_processed_original.columns)"]},{"cell_type":"markdown","metadata":{},"source":["## Vectorization ( convert text to word count vectors with CountVectorizer ) of below Categorical features\n","\n","- teacher_prefix\n","- project_grade_category\n","- school_state\n","- project_subject_categories\n","- project_subject_subcategories\n","\n","For eac of the above categorical variable, I have to vectorize from X_donor_choose_train, X_donor_choose_validation and the original test set given in the Kaggle dataset i.e. test_df_pre_processed_original\n","\n","`CountVectorizer()` will basically create a vector from word count\n","\n","As to the workflow in the next part, after vectorizing I will merge  all these ONE HOT features with `hsstack()`\n","\n","CountVectorizer, an implementation of bag-of-words in which we code text data as a representation of features/words. The values of each of these features represent the occurrence counts of words across all documents.\n","\n","### Vectorizing Categorical data: project_subject_categories"]},{"cell_type":"code","execution_count":45,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:13:24.296330Z","iopub.status.busy":"2021-06-16T13:13:24.295903Z","iopub.status.idle":"2021-06-16T13:13:25.566607Z","shell.execute_reply":"2021-06-16T13:13:25.565418Z","shell.execute_reply.started":"2021-06-16T13:13:24.296293Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["(121993, 50)\n","(60087, 50)\n","(78035, 50)\n"]}],"source":["vectorizer_clean_categories = CountVectorizer(lowercase=False, binary=True)\n","\n","train_vectorized_ohe_clean_categories = vectorizer_clean_categories.fit_transform(X_donor_choose_train['project_subject_categories'].values)\n","print(train_vectorized_ohe_clean_categories.shape)\n","\n","validation_vectorized_ohe_clean_categories = vectorizer_clean_categories.transform(X_donor_choose_validation['project_subject_categories'].values)\n","print(validation_vectorized_ohe_clean_categories.shape)\n","\n","test_df_vectorized_ohe_clean_categories = vectorizer_clean_categories.transform(test_df_pre_processed_original['project_subject_categories'].values)\n","print(test_df_vectorized_ohe_clean_categories.shape)"]},{"cell_type":"markdown","metadata":{},"source":["### Vectorizing Categorical data: project_subject_subcategories"]},{"cell_type":"code","execution_count":46,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:13:25.568412Z","iopub.status.busy":"2021-06-16T13:13:25.568093Z","iopub.status.idle":"2021-06-16T13:13:26.846621Z","shell.execute_reply":"2021-06-16T13:13:26.844196Z","shell.execute_reply.started":"2021-06-16T13:13:25.568380Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["(121993, 400)\n","(60087, 400)\n","(78035, 400)\n"]}],"source":["vectorizer_clean_subcategories = CountVectorizer(lowercase=False, binary=True)\n","\n","train_vectorized_ohe_clean_subcategories = vectorizer_clean_subcategories.fit_transform(X_donor_choose_train['project_subject_subcategories'].values)\n","print(train_vectorized_ohe_clean_subcategories.shape)\n","\n","validation_vectorized_ohe_clean_subcategories = vectorizer_clean_subcategories.transform(X_donor_choose_validation['project_subject_subcategories'].values)\n","print(validation_vectorized_ohe_clean_subcategories.shape)\n","\n","test_df_vectorized_ohe_clean_subcategories = vectorizer_clean_subcategories.transform(test_df_pre_processed_original['project_subject_subcategories'].values)\n","print(test_df_vectorized_ohe_clean_subcategories.shape)\n"]},{"cell_type":"markdown","metadata":{},"source":["### Vectorizing Categorical data: teacher_prefix"]},{"cell_type":"code","execution_count":47,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:13:26.848557Z","iopub.status.busy":"2021-06-16T13:13:26.848241Z","iopub.status.idle":"2021-06-16T13:13:28.069497Z","shell.execute_reply":"2021-06-16T13:13:28.068222Z","shell.execute_reply.started":"2021-06-16T13:13:26.848528Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["(121993, 5)\n","(60087, 5)\n","(78035, 5)\n"]}],"source":["vectorizer_teacher_prefix = CountVectorizer(lowercase=False, binary=True)\n","\n","train_vectorized_ohe_teacher_prefix = vectorizer_teacher_prefix.fit_transform(X_donor_choose_train['teacher_prefix'].values)\n","print(train_vectorized_ohe_teacher_prefix.shape)\n","\n","validation_vectorized_ohe_teacher_prefix = vectorizer_teacher_prefix.transform(X_donor_choose_validation['teacher_prefix'].values)\n","print(validation_vectorized_ohe_teacher_prefix.shape)\n","\n","test_df_pre_processed_original['teacher_prefix'] = test_df_pre_processed_original['teacher_prefix'].fillna('None')\n","\n","test_df_vectorized_ohe_teacher_prefix = vectorizer_teacher_prefix.transform(test_df_pre_processed_original['teacher_prefix'].values)\n","print(test_df_vectorized_ohe_teacher_prefix.shape)\n"]},{"cell_type":"markdown","metadata":{},"source":["### Vectorizing Categorical data: project_grade_category"]},{"cell_type":"code","execution_count":48,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:13:28.071428Z","iopub.status.busy":"2021-06-16T13:13:28.071013Z","iopub.status.idle":"2021-06-16T13:13:29.325762Z","shell.execute_reply":"2021-06-16T13:13:29.324898Z","shell.execute_reply.started":"2021-06-16T13:13:28.071371Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["(121993, 4)\n","(60087, 4)\n","(78035, 4)\n"]}],"source":["vectorizer_project_grade_category = CountVectorizer(lowercase=False, binary=True)\n","\n","train_vectorized_ohe_project_grade_category = vectorizer_project_grade_category.fit_transform(X_donor_choose_train['project_grade_category'].values)\n","print(train_vectorized_ohe_project_grade_category.shape)\n","\n","validation_vectorized_ohe_project_grade_category = vectorizer_project_grade_category.transform(X_donor_choose_validation['project_grade_category'].values)\n","print(validation_vectorized_ohe_project_grade_category.shape)\n","\n","test_df_vectorized_ohe_project_grade_category = vectorizer_project_grade_category.transform(test_df_pre_processed_original['project_grade_category'].values)\n","print(test_df_vectorized_ohe_project_grade_category.shape)\n"]},{"cell_type":"markdown","metadata":{},"source":["### Vectorizing Categorical data: school_state"]},{"cell_type":"code","execution_count":49,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:13:29.328421Z","iopub.status.busy":"2021-06-16T13:13:29.328045Z","iopub.status.idle":"2021-06-16T13:13:30.538860Z","shell.execute_reply":"2021-06-16T13:13:30.537965Z","shell.execute_reply.started":"2021-06-16T13:13:29.328337Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["(121993, 51)\n","(60087, 51)\n","(78035, 51)\n"]}],"source":["vectorizer_school_state = CountVectorizer(lowercase=False, binary=True)\n","\n","train_vectorized_ohe_school_state = vectorizer_school_state.fit_transform(X_donor_choose_train['school_state'].values)\n","print(train_vectorized_ohe_school_state.shape)\n","\n","validation_vectorized_ohe_school_state = vectorizer_school_state.transform(X_donor_choose_validation['school_state'].values)\n","print(validation_vectorized_ohe_school_state.shape)\n","\n","test_df_vectorized_ohe_school_state = vectorizer_school_state.transform(test_df_pre_processed_original['school_state'].values)\n","print(test_df_vectorized_ohe_school_state.shape)\n"]},{"cell_type":"markdown","metadata":{},"source":["## Normalizing numerical features (resource_cost, teacher_number_of_previously_posted_projects)\n","\n","\n","Here is how normalizer works and why we should use reshape(1, -1) instead of (-1, 1)\n","\n","#### Normalizer by default normalizes on each sample(row) while StandardScaler standardises on each feature(column)\n","\n","Here if I use (-1, 1) it means any number of rows, which is the responsibility of numpy to figure out, while I am specifying that I need to have one column. Remember -1 lets numpy to calculate the unknown dimension for the resultant that will match with the original matrix.\n","\n","And vice versa, if I do reshape(1, -1) means, that I am specifying row to be 1 while leaving column numbers to be calculated by Numpy.\n","\n","So for the case, that I use (-1, 1) => i.e. Rows are unknown while columns required is 1\n","\n","### Note, for normalizing numerical data, we got to use reshape(1, -1) and NOT reshape(-1, 1).\n","\n","So now below, I shall derived the vectors for 'resource_cost' and 'teacher_number_of_previously_posted_projects' for both train and validation, and these will be later merged with the other vectorized categorical variables (that I calculated above)  to form the final matrix.\n","\n","And for the reshape, first I will reshape(1, -1) i.e. one row and unknown columns > Then apply Normalization > and then reshape again to (-1, 1) i.e. unknown rows, and 1 column\n","\n","### 2.1 Normalizing numerical feature: resource_cost\n","\n","( First I am printing some extra stuff to see the implementation of the above principle, so the below cell's code is only for experimentation and not used outside of the cell )"]},{"cell_type":"code","execution_count":50,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:13:30.540939Z","iopub.status.busy":"2021-06-16T13:13:30.540463Z","iopub.status.idle":"2021-06-16T13:13:30.562647Z","shell.execute_reply":"2021-06-16T13:13:30.561386Z","shell.execute_reply.started":"2021-06-16T13:13:30.540892Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["train_normalized_resource_cost_wrong is :  [[1.]\n"," [1.]\n"," [1.]\n"," [1.]\n"," [1.]\n"," [1.]\n"," [1.]\n"," [1.]\n"," [1.]\n"," [1.]]\n"]},{"data":{"text/plain":["'[[1.83906133e-02 3.96946770e-03 1.12896957e-03 3.67034849e-03\\n  1.48897540e-01 6.30706441e-04 1.41765263e-02 2.00495074e-03\\n  4.53619601e-03 6.88785883e-03 1.37011017e-03 1.73935829e-03\\n  5.91764982e-03 1.02206206e-02 4.50035010e-03 3.96785438e-03\\n  3.93200848e-03 8.99717107e-03 6.63265212e-03 1.50744385e-03\\n  1.91811391e-02 3.99129791e-03 6.66461598e-03 4.00891837e-02\\n  ...\\n  ]]\\n'"]},"execution_count":50,"metadata":{},"output_type":"execute_result"}],"source":["# ****** THE CODE OF THIS CELL IS ONLY FOR SHOWING HOW TO - Correct way to implement Normalization of Numerical Features. AND CODES IN THIS CELL ARE NOT USED ANYWHERE OUTSIDE OF THIS CELL *****\n","\n","normalizer = Normalizer()\n","\n","\"\"\" printing below just to inspect the shape so I know in which form I need to apply the Normalizer()\n","\n","Per the above note, we can see that shape_a produces a 2-d column vector, and given Normalizer() normalizes on each sample(row)\n","So shape_a normalized will produce column vector with each value being 1\n","\n","\"\"\"\n","\n","resource_cost_arr = X_donor_choose_train['resource_cost'].values\n","# print( 'resource_cost_arr ',  resource_cost_arr)\n","\n","shape_a = X_donor_choose_train['resource_cost'].values.reshape(-1, 1)\n","shape_b = X_donor_choose_train['resource_cost'].values.reshape(1, -1)\n","# print('shape_a ', shape_a )\n","# print('shape_b ', shape_b )\n","\n","# Below is WRONG reshape and should NOT be done\n","# Below 2 variables will produce resource_cost column vectors with each value being 1 and so is worthless\n","train_normalized_resource_cost_wrong = normalizer.transform(shape_a)\n","train_normalized_resource_cost_correct = normalizer.transform(shape_b)\n","\n","\n","print('train_normalized_resource_cost_wrong is : ', train_normalized_resource_cost_wrong[0:10])\n","# print('train_normalized_resource_cost_correct is : ', train_normalized_resource_cost_correct)\n","# Above line will print like below, i.e. a single column vector\n","'''[[1.83906133e-02 3.96946770e-03 1.12896957e-03 3.67034849e-03\n","  1.48897540e-01 6.30706441e-04 1.41765263e-02 2.00495074e-03\n","  4.53619601e-03 6.88785883e-03 1.37011017e-03 1.73935829e-03\n","  5.91764982e-03 1.02206206e-02 4.50035010e-03 3.96785438e-03\n","  3.93200848e-03 8.99717107e-03 6.63265212e-03 1.50744385e-03\n","  1.91811391e-02 3.99129791e-03 6.66461598e-03 4.00891837e-02\n","  ...\n","  ]]\n","'''"]},{"cell_type":"code","execution_count":51,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:13:30.564514Z","iopub.status.busy":"2021-06-16T13:13:30.564174Z","iopub.status.idle":"2021-06-16T13:13:30.580591Z","shell.execute_reply":"2021-06-16T13:13:30.579501Z","shell.execute_reply.started":"2021-06-16T13:13:30.564482Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["(121993, 1)\n","(60087, 1)\n","(78035, 1)\n"]}],"source":["# Our first Numerical feature - 'resource_cost'\n","normalizer = Normalizer()\n","\n","# As explainEd above first I will reshape(1, -1)\n","normalizer.fit(X_donor_choose_train['resource_cost'].values.reshape(1, -1))\n","\n","train_normalized_resource_cost = normalizer.transform(X_donor_choose_train['resource_cost'].values.reshape(1, -1))\n","\n","validation_normalized_resource_cost = normalizer.transform(X_donor_choose_validation['resource_cost'].values.reshape(1, -1))\n","\n","test_df_normalized_resource_cost = normalizer.transform(test_df_pre_processed_original['resource_cost'].values.reshape(1, -1))\n","\n","# After normalization reshape again to (-1, 1) i.e. this time unknown rows (i.e. leaving it to Numpy to decide), and specifying I need 1 column\n","train_normalized_resource_cost = train_normalized_resource_cost.reshape(-1, 1)\n","print(train_normalized_resource_cost.shape)\n","\n","validation_normalized_resource_cost = validation_normalized_resource_cost.reshape(-1, 1)\n","print(validation_normalized_resource_cost.shape)\n","\n","test_df_normalized_resource_cost = test_df_normalized_resource_cost.reshape(-1, 1)\n","print(test_df_normalized_resource_cost.shape)\n"]},{"cell_type":"markdown","metadata":{},"source":["### Normalizing next numerical feature: teacher_number_of_previously_posted_projects"]},{"cell_type":"code","execution_count":52,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:13:30.582558Z","iopub.status.busy":"2021-06-16T13:13:30.582220Z","iopub.status.idle":"2021-06-16T13:13:30.598348Z","shell.execute_reply":"2021-06-16T13:13:30.597287Z","shell.execute_reply.started":"2021-06-16T13:13:30.582525Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["(121993, 1)\n","(60087, 1)\n","(78035, 1)\n"]}],"source":["# Second Numerical feature - 'teacher_number_of_previously_posted_projects'\n","normalizer = Normalizer()\n","\n","normalizer.fit(X_donor_choose_train['teacher_number_of_previously_posted_projects'].values.reshape(1, -1))\n","\n","train_normalized_teacher_number_of_previously_posted_projects = normalizer.transform(X_donor_choose_train['teacher_number_of_previously_posted_projects'].values.reshape(1, -1))\n","\n","validation_normalized_teacher_number_of_previously_posted_projects = normalizer.transform(X_donor_choose_validation['teacher_number_of_previously_posted_projects'].values.reshape(1, -1))\n","\n","test_df_normalized_teacher_number_of_previously_posted_projects = normalizer.transform(test_df_pre_processed_original['teacher_number_of_previously_posted_projects'].values.reshape(1, -1))\n","\n","# After normalization reshape again to (-1, 1) i.e. this time unknown rows (i.e. leaving it to Numpy to decide), and specifying I need 1 column\n","train_normalized_teacher_number_of_previously_posted_projects = train_normalized_teacher_number_of_previously_posted_projects.reshape(-1, 1)\n","print(train_normalized_teacher_number_of_previously_posted_projects.shape)\n","\n","validation_normalized_teacher_number_of_previously_posted_projects = validation_normalized_teacher_number_of_previously_posted_projects.reshape(-1, 1)\n","print(validation_normalized_teacher_number_of_previously_posted_projects.shape)\n","\n","test_df_normalized_teacher_number_of_previously_posted_projects = test_df_normalized_teacher_number_of_previously_posted_projects.reshape(-1, 1)\n","print(test_df_normalized_teacher_number_of_previously_posted_projects.shape)\n"]},{"cell_type":"markdown","metadata":{},"source":["## Encoding Essay column using Bag Of Words"]},{"cell_type":"code","execution_count":53,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:13:30.600772Z","iopub.status.busy":"2021-06-16T13:13:30.600315Z","iopub.status.idle":"2021-06-16T13:14:17.122565Z","shell.execute_reply":"2021-06-16T13:14:17.121417Z","shell.execute_reply.started":"2021-06-16T13:13:30.600734Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["(121993, 17168)\n","(60087, 17168)\n","(78035, 17168)\n"]}],"source":["vectorizer_essay_bow = CountVectorizer(min_df=10)\n","\n","train_vectorized_bow_essay = vectorizer_essay_bow.fit_transform(X_donor_choose_train['essay'])\n","print(train_vectorized_bow_essay.shape)\n","\n","validation_vectorized_bow_essay = vectorizer_essay_bow.transform(X_donor_choose_validation['essay'])\n","print(validation_vectorized_bow_essay.shape)\n","\n","test_df_vectorized_bow_essay = vectorizer_essay_bow.transform(test_df_pre_processed_original['essay'])\n","print(test_df_vectorized_bow_essay.shape)\n"]},{"cell_type":"markdown","metadata":{},"source":["## 5. Merging (with hstack) all the above vectorized features that we created above\n","\n","#### You can use the scipy.sparse.hstack to concatenate sparse matrices with the same number of rows (horizontal concatenation):\n","\n","`hstack((X1, X2))`\n","\n","### We need to merge all the numerical vectors i.e categorical, text, numerical vectors, once for Naive Bayes on BOW and then for Naive Bayes on TFIDF\n","\n","## Merging all categorical, text, numerical vectors based on BOW"]},{"cell_type":"code","execution_count":54,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:14:17.124435Z","iopub.status.busy":"2021-06-16T13:14:17.124126Z","iopub.status.idle":"2021-06-16T13:14:18.085658Z","shell.execute_reply":"2021-06-16T13:14:18.084751Z","shell.execute_reply.started":"2021-06-16T13:14:17.124404Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["X_train_hstacked_all_bow_features_vectorized.shape is  (121993, 17680)\n","X_validation_hstacked_all_bow_features_vectorized.shape is  (60087, 17680)\n","test_df_hstacked_all_bow_features_vectorized.shape is  (78035, 17680)\n"]}],"source":["X_train_hstacked_all_bow_features_vectorized = hstack((train_vectorized_ohe_clean_categories, train_vectorized_ohe_clean_subcategories, train_vectorized_ohe_teacher_prefix, train_vectorized_ohe_project_grade_category, train_vectorized_ohe_school_state, train_normalized_resource_cost, train_normalized_teacher_number_of_previously_posted_projects, train_vectorized_bow_essay))\n","\n","print('X_train_hstacked_all_bow_features_vectorized.shape is ', X_train_hstacked_all_bow_features_vectorized.shape)\n","\n","X_validation_hstacked_all_bow_features_vectorized = hstack((validation_vectorized_ohe_clean_categories, validation_vectorized_ohe_clean_subcategories, validation_vectorized_ohe_teacher_prefix, validation_vectorized_ohe_project_grade_category, validation_vectorized_ohe_school_state, validation_normalized_resource_cost, validation_normalized_teacher_number_of_previously_posted_projects, validation_vectorized_bow_essay))\n","\n","print('X_validation_hstacked_all_bow_features_vectorized.shape is ', X_validation_hstacked_all_bow_features_vectorized.shape)\n","\n","test_df_hstacked_all_bow_features_vectorized = hstack((test_df_vectorized_ohe_clean_categories, test_df_vectorized_ohe_clean_subcategories, test_df_vectorized_ohe_teacher_prefix, test_df_vectorized_ohe_project_grade_category, test_df_vectorized_ohe_school_state, test_df_normalized_resource_cost, test_df_normalized_teacher_number_of_previously_posted_projects, test_df_vectorized_bow_essay))\n","\n","print('test_df_hstacked_all_bow_features_vectorized.shape is ', test_df_hstacked_all_bow_features_vectorized.shape)"]},{"cell_type":"markdown","metadata":{},"source":["## A Note on GridSearchCV() function of sklearn\n","\n","#### when using cross validation with cv=10 the data is split into 10 parts i.e. 10% /90% then each part is used for training while the rest used for validation. I recommend setting the grid search parameter\n","\n","The grid search returns a dictionary (accessible through `.cv_results_`) containing the scores for each fold train/test scores as well as the time it took to train/test each fold. Also a summary of that data is included using the mean and the standard deviation. PS. in newer version of pandas you'll need to include return_train_score=True PS.S. when using grid search, splitting the data to train/test is not necessary for model selection, because the grid search splits the data automatically (cv=10 means that the data is split to 10 folds)\n","\n","More on the above point - from this very detailed official [doc](https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation) -\n","\n","A test set should still be held out for final evaluation, but the validation set is no longer needed when doing CV. In the basic approach, called k-fold CV, the training set is split into k smaller sets (other approaches are described below, but generally follow the same principles). The following procedure is followed for each of the k “folds”:\n","\n","A model is trained using of the folds as training data;\n","\n","the resulting model is validated on the remaining part of the data (i.e., it is used as a test set to compute a performance measure such as accuracy).\n","\n","The performance measure reported by k-fold cross-validation is then the average of the values computed in the loop. This approach can be computationally expensive, but does not waste too much data (as is the case when fixing an arbitrary validation set), which is a major advantage in problems such as inverse inference where the number of samples is very small.\n","\n","## 6. Applying Multinomial Naive Bayes on BOW\n","\n","#### Using GridSearchCV with MultinomialNB on BOW based data to find best alpha that I shall apply in the next step on the test dataset to find the probabilities."]},{"cell_type":"code","execution_count":55,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:14:18.087358Z","iopub.status.busy":"2021-06-16T13:14:18.087011Z","iopub.status.idle":"2021-06-16T13:15:20.009662Z","shell.execute_reply":"2021-06-16T13:15:20.007943Z","shell.execute_reply.started":"2021-06-16T13:14:18.087326Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Fitting 10 folds for each of 20 candidates, totalling 200 fits\n"]},{"name":"stderr","output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"]},{"name":"stdout","output_type":"stream","text":["Best Alpha BOW:  0.5\n","Best Score BOW :  0.7077270577153192\n"]},{"name":"stderr","output_type":"stream","text":["[Parallel(n_jobs=1)]: Done 200 out of 200 | elapsed:   35.9s finished\n"]}],"source":["multinomial_nb_bow = MultinomialNB(class_prior=[0.5, 0.5], fit_prior=False)\n","\n","'''fit_prior bool, default=True\n","Whether to learn class prior probabilities or not. If false, a uniform prior will be used.\n","Whenever we initialize the 'class_prior' parameter to any value (other than None), then it is a good practice to initialize fit_prior = False.\n","\n","'''\n","\n","parameters = {'alpha':[0.00001, 0.00005, 0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10, 50, 100, 500, 1000, 2500, 5000, 10000]}\n","\n","clf = GridSearchCV(multinomial_nb_bow, parameters, cv=10, scoring='roc_auc', verbose=1, return_train_score=True)\n","\n","clf.fit(X_train_hstacked_all_bow_features_vectorized, y_donor_choose_train )\n","\n","# cv_results_dict\n","train_auc_bow = clf.cv_results_['mean_train_score']\n","train_auc_std_bow = clf.cv_results_['std_train_score']\n","\n","cv_auc_bow = clf.cv_results_['mean_test_score']\n","cv_auc_std_bow = clf.cv_results_['std_test_score']\n","\n","best_alpha_1_bow = clf.best_params_['alpha']\n","best_score_1_bow = clf.best_score_\n","\n","print('Best Alpha BOW: ', best_alpha_1_bow )\n","print('Best Score BOW : ', best_score_1_bow)\n"]},{"cell_type":"markdown","metadata":{},"source":["\n","### Summary of the above GridSearchCV Implementation\n","\n","We have started with hyperparameter alpha with as low as 0.0001 to 1000.Since it is difficult to plot the given range we have used log alphas on x-axis and Auc on y axis as shown in the plot.\n","\n","we observe that as log alpha approaches close to 4, both train AUc and cv AUC lines converge\n","\n","## Plotting Naive Bayes on BOW - Log of Alpha on X-axis and AUC on Y-axis"]},{"cell_type":"code","execution_count":56,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:15:20.012800Z","iopub.status.busy":"2021-06-16T13:15:20.011980Z","iopub.status.idle":"2021-06-16T13:15:20.325008Z","shell.execute_reply":"2021-06-16T13:15:20.323679Z","shell.execute_reply.started":"2021-06-16T13:15:20.012736Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["100%|██████████| 20/20 [00:00<00:00, 65433.76it/s]\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 720x360 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["alphas = [0.00001, 0.00005, 0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10, 50, 100, 500, 1000, 2500, 5000, 10000]\n","log_of_alphas = []\n","\n","for alpha in tqdm(alphas):\n","  alpha_log = np.log10(alpha)\n","  log_of_alphas.append(alpha_log)\n","\n","plt.figure(figsize=(10, 5))\n","plt.plot(log_of_alphas, train_auc_bow, label='Train AUC Curve' )\n","\n","'''\n","[mean - std, mean + std] => It creates a shaded area in between\n","Taken from - https://stackoverflow.com/a/48803361/1902852\n","\n","The Axes.fill_between() function in axes module of matplotlib library is used to fill the area between two horizontal curves\n","\n","And pyplot.gca() Get the current Axes instance on the current figure matching the given keyword args, or create one.\n","\n","\"Current\" here means that it provides a handle to the last active axes. If there is no axes yet, an axes will be created. If you create two subplots, the subplot that is created last is the current one.\n","\n","'''\n","plt.gca().fill_between(log_of_alphas, train_auc_bow - train_auc_std_bow, train_auc_bow + train_auc_std_bow, alpha=0.3, color='darkblue' )\n","\n","\n","# similarly for CV_AUC\n","plt.plot(log_of_alphas, cv_auc_bow, label='CV AUC Curve for BOW Set' )\n","plt.gca().fill_between(log_of_alphas, cv_auc_bow - cv_auc_std_bow, cv_auc_bow+cv_auc_std_bow + cv_auc_std_bow, alpha=0.3, color='darkorange' )\n","\n","\n","plt.scatter(log_of_alphas, train_auc_bow, label='Train AUC points-BOW' )\n","plt.scatter(log_of_alphas, cv_auc_bow, label='CV AUC points-BOW' )\n","\n","plt.legend()\n","plt.xlabel('Hyperparameter: Log of alpha for BOW Set')\n","plt.ylabel('AUC-BOW ')\n","plt.title('Log of Alpha-Hyperparameter with Train and CV AUC for BOW Set')\n","plt.grid()\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["### Reason of using logathims_of_alphas in the above\n","\n","One of the main reason for using log scale is log scales allow a large range to be displayed without small values being compressed down into bottom of the graph.\n","\n","Genearally, in ML/DS log transformation is done on many continuous variables very often. Mostly because of skewed distribution. When the variables span several orders of magnitude. Income is a typical example: its distribution is \"power law\", meaning that the vast majority of incomes are small and very few are big. This type of \"fat tailed\" distribution is studied in logarithmic scale because of the mathematical properties of the logarithm:\n","\n","Logarithm naturally reduces the dynamic range of a variable so the differences are preserved while the scale is not that dramatically skewed. Imagine some people got 100,000,000 loan and some got 10000 and some 0. Any feature scaling will probably put 0 and 10000 so close to each other as the biggest number anyway pushes the boundary. Logarithm solves the issue.\n","\n","If you take values 1000,000,000 and 10000 and 0 into account. In many cases, the first one is too big to let others be seen properly by your model. But if you take logarithm you will have 9, 4 and 0 respectively. As you see the dynamic range is reduced while the differences are almost preserved. It comes from any exponential nature in your feature\n","\n","$$log(x^n)= n log(x)$$\n","\n","which implies\n","\n","$$log(10^4) = 4 * log(10)$$\n","\n","and\n","\n","$$log(10^3) = 3 * log(10)$$\n","\n","### A note on the alpha parameter\n","\n","We add a small smoothing value, alpha to prevent from the probabilities going to zero\n","\n","In Multinomial Naive Bayes, the `alpha` parameter is what is known as a [_hyperparameter_](https://en.wikipedia.org/wiki/Hyperparameter_optimization); i.e. a parameter that controls the form of the model itself. In most cases, the best way to determine optimal values for hyperparameters is through a [grid search](http://scikit-learn.org/stable/modules/grid_search.html) over possible parameter values, using [cross validation](http://scikit-learn.org/stable/modules/cross_validation.html) to evaluate the performance of the model on your data at each value. Read the above links for details on how to do this with scikit-learn.\n","\n","Check out the wikipedia page http://en.wikipedia.org/wiki/Additive_smoothing.\n","\n","![Imgur](https://imgur.com/v5Vx17J.png)\n","\n","### How to choose alpha\n","\n","Basically the idea is that you want to decrease the effect of rare words: for example if you have one spam email with the word 'multinomialNB' in it, and no nonspam emails with this word, then without additive smoothing, your spam filter will classify every email with this keyword as spam.\n","\n","---\n","\n","### class_prior : array-like of shape (n_classes,), default=None\n","\n","Prior probabilities of the classes. If specified the priors are not adjusted according to the data.\n","\n","Now that GridSearchCV has completed its run with MultinomialNB, I have the best alpha that I shall now apply in the next step on the test dataset to find its class-probabilities.\n","\n","## Run MultinomialNB with the best hyperparameter value (categorical + numerical + essay features)\n","\n","From our above GridSearchCV we saw the best_alpha_1_bow was at 0.5\n","\n","Best Alpha:  0.5\n","Best Score :  0.7049241508387795"]},{"cell_type":"code","execution_count":57,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:15:20.328053Z","iopub.status.busy":"2021-06-16T13:15:20.327487Z","iopub.status.idle":"2021-06-16T13:15:22.334867Z","shell.execute_reply":"2021-06-16T13:15:22.333657Z","shell.execute_reply.started":"2021-06-16T13:15:20.327995Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["y_predicted_for_bow_with_best_alpha_train.shape is  (121993,)\n","y_predicted_for_bow_with_best_alpha_validation.shape is  (60087,)\n"]}],"source":["naive_bayes_results_for_bow_with_best_alpha = MultinomialNB(alpha = best_alpha_1_bow, class_prior=[0.5, 0.5], fit_prior=False )\n","\n","'''fit_prior bool, default=True\n","Whether to learn class prior probabilities or not. If false, a uniform prior will be used.\n","\n","Whenever we initialize the 'class_prior' parameter to any value (other than None), then it is a good practice to initialize fit_prior = False.\n","'''\n","\n","naive_bayes_results_for_bow_with_best_alpha.fit(X_train_hstacked_all_bow_features_vectorized, y_donor_choose_train)\n","\n","# Now instead of .best_param like previous implementation will be using .predict_proba\n","# Using .predict_proba directly yields the same results as using .best_param to get the best hyper-parameter through\n","y_predicted_for_bow_with_best_alpha_train = naive_bayes_results_for_bow_with_best_alpha.predict_proba(X_train_hstacked_all_bow_features_vectorized)[:, 1]\n","print('y_predicted_for_bow_with_best_alpha_train.shape is ', y_predicted_for_bow_with_best_alpha_train.shape)\n","\n","y_predicted_for_bow_with_best_alpha_validation = naive_bayes_results_for_bow_with_best_alpha.predict_proba(X_validation_hstacked_all_bow_features_vectorized)[:, 1]\n","print('y_predicted_for_bow_with_best_alpha_validation.shape is ', y_predicted_for_bow_with_best_alpha_validation.shape)\n"]},{"cell_type":"code","execution_count":58,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:15:22.337951Z","iopub.status.busy":"2021-06-16T13:15:22.337606Z","iopub.status.idle":"2021-06-16T13:15:22.689329Z","shell.execute_reply":"2021-06-16T13:15:22.687913Z","shell.execute_reply.started":"2021-06-16T13:15:22.337920Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["fpr_train_bow:  [0.00000000e+00 1.07631041e-04 1.07631041e-04 ... 9.99784738e-01\n"," 9.99784738e-01 1.00000000e+00]\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["fpr_train_bow, tpr_train_bow, thresholds_train_bow = roc_curve(y_donor_choose_train, y_predicted_for_bow_with_best_alpha_train )\n","\n","fpr_validation_bow, tpr_validation_bow, thresholds_validation_bow = roc_curve(y_donor_choose_validation, y_predicted_for_bow_with_best_alpha_validation )\n","\n","print('fpr_train_bow: ', fpr_train_bow)\n","\n","ax = plt.subplot()\n","\n","auc_bow_train = auc(fpr_train_bow, tpr_train_bow)\n","auc_bow_validation = auc(fpr_validation_bow, tpr_validation_bow)\n","\n","ax.plot(fpr_train_bow, tpr_train_bow, label='Train AUC ='+str(auc(fpr_train_bow, tpr_train_bow)))\n","ax.plot(fpr_validation_bow, tpr_validation_bow, label='Test AUC ='+str(auc(fpr_validation_bow, tpr_validation_bow)))\n","\n","plt.legend()\n","plt.xlabel('FPR')\n","plt.ylabel('TPR')\n","plt.title('AUC')\n","plt.grid(b=True, which='major', color='k', linestyle=':')\n","ax.set_facecolor('white')\n","plt.show()\n"]},{"cell_type":"markdown","metadata":{},"source":["## Bonus Calculation - Derive top 30 Important features - BOW"]},{"cell_type":"code","execution_count":59,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:15:22.691826Z","iopub.status.busy":"2021-06-16T13:15:22.691436Z","iopub.status.idle":"2021-06-16T13:15:22.749559Z","shell.execute_reply":"2021-06-16T13:15:22.748237Z","shell.execute_reply.started":"2021-06-16T13:15:22.691780Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["naive_bayes_results_for_bow_with_best_alpha.feature_log_prob_  [[ -8.05457692  -9.87613258 -10.90036114 ... -13.03506536 -15.43296064\n","  -12.72491044]\n"," [ -8.38316833 -10.18430078 -11.46111755 ... -13.05777573 -13.48733839\n","  -12.28092953]]\n","LEN of naive_bayes_results_for_bow_with_best_alpha.feature_log_prob_  17680\n","Shape of probabilities_sorted_of_all_negative_class  (17680,)\n","Shape of positive_class_sorted_proba  (17680,)\n","Length of vectorized_all_features_horizontally_stacked  ( should be  17680 ) :  17680\n","top_20_negative_class_features_labels  ['classdojo' 'used' 'skinny' 'daydream' 'abound' 'math' 'readjusting'\n"," 'loved' 'comedians' 'workbook' 'neediest' 'maple' 'nanos' 'helper'\n"," 'learner' 'notably' 'classrooms' 'learniture' 'schooler']\n","top_20_positive_class_features_labels  ['techy' 'wounded' 'classdojo' 'comedians' 'abound' 'daydream' 'loved'\n"," 'used' 'readjusting' 'workbook' 'neediest' 'nanos' 'maple' 'helper'\n"," 'learner' 'notably' 'classrooms' 'learniture' 'schooler']\n"]}],"source":["# using feature_log_prob_ to get the most important features\n","print('naive_bayes_results_for_bow_with_best_alpha.feature_log_prob_ ', naive_bayes_results_for_bow_with_best_alpha.feature_log_prob_)\n","# Its basically, just the log probability of each word, given in a 2-d array structure\n","# Note, feature_log_prob_ will give me the actual log probabilities of each features,\n","# but I need the index position of those probabilities.\n","# So, that with that same index position I can get the corresponding feature-names.\n","\n","print('LEN of naive_bayes_results_for_bow_with_best_alpha.feature_log_prob_ ', len(naive_bayes_results_for_bow_with_best_alpha.feature_log_prob_[1]))\n","\n","# So, first, I will sort the result from feature_log_prob_ , and ONLY after that\n","# get the corresponding positional-index of the top 20 features.\n","# And then I can apply the same positional-index number to get the corresponding features names (which are the labels / words )\n","# https://stackoverflow.com/a/50530697/1902852\n","\n","probabilities_sorted_of_all_negative_class = naive_bayes_results_for_bow_with_best_alpha.feature_log_prob_[0, :].argsort()\n","#class 0\n","\n","positive_class_sorted_proba = naive_bayes_results_for_bow_with_best_alpha.feature_log_prob_[1, :].argsort()\n","#class 1\n","\n","'''numpy.argsort() function is used to perform an indirect sort along the given axis. It returns an array of indices of the same shape as arr that that would sort the array. Basically it gives me the sorted array indices'''\n","\n","print(\"Shape of probabilities_sorted_of_all_negative_class \", probabilities_sorted_of_all_negative_class.shape)\n","print(\"Shape of positive_class_sorted_proba \", positive_class_sorted_proba.shape)\n","\n","# Now - Lets get all horizontally stacked features which were individually derived from CounterVectorizer earlier\n","# Using itertools.chain() method to chain multiple arrays/lists at once\n","# https://stackoverflow.com/a/34665782/1902852\n","from itertools import chain\n","\n","vectorized_all_features_horizontally_stacked = list(chain(vectorizer_clean_categories.get_feature_names(), vectorizer_clean_subcategories.get_feature_names(), vectorizer_teacher_prefix.get_feature_names(),  vectorizer_project_grade_category.get_feature_names(), vectorizer_school_state.get_feature_names(), vectorizer_essay_bow.get_feature_names()))\n","\n","# After adding all the vectorized features (which includes both categorical and text features)\n","# And now I also need to append the two left-over Numerical features which are\n","# 'resource_cost' and 'teacher_number_of_previously_posted_projects'\n","vectorized_all_features_horizontally_stacked.extend(['resource_cost', 'teacher_number_of_previously_posted_projects'])\n","\n","# The above extend() is equivalent to below append()\n","# vectorized_all_features_horizontally_stacked.append('resource_cost')\n","# vectorized_all_features_horizontally_stacked.append('teacher_number_of_previously_posted_projects' )\n","\n","print('Length of vectorized_all_features_horizontally_stacked', ' ( should be ', X_train_hstacked_all_bow_features_vectorized.shape[1], ') : ' , len(vectorized_all_features_horizontally_stacked))\n","\n","# FINALLY THE TOP 20 FEATURES\n","top_20_negative_class_features_labels = np.take(vectorized_all_features_horizontally_stacked, probabilities_sorted_of_all_negative_class[-20: -1])\n","print('top_20_negative_class_features_labels ', top_20_negative_class_features_labels)\n","\n","\n","top_20_positive_class_features_labels = np.take(vectorized_all_features_horizontally_stacked, positive_class_sorted_proba[-20: -1])\n","print('top_20_positive_class_features_labels ', top_20_positive_class_features_labels)"]},{"cell_type":"markdown","metadata":{},"source":["\n","## What is `feature_log_prob_` in the naive_bayes MultinomialNB()\n","\n","From Doc\n","\n","feature_log_prob_ is and array of shape (n_classes, n_features) => Empirical log probability of features given a class, P(x_i|y).\n","\n","#### Models like logistic regression, or Naive Bayes algorithm, predict the probabilities of observing some outcomes. In standard binary regression scenario the models give you probability of observing the \"success\" category. In multinomial case, the models return probabilities of observing each of the outcomes. Log probabilities are simply natural logarithms of the predicted probabilities.\n","\n","Let's take an example feature \"best\" for the purpose of this illustration, the `log` probability of this feature for class `1` is `-2.14006616` (as you pointed out), now if we were to convert it into actual probability score it will be `np.exp(1)**-2.14006616 = 0.11764`. Let's take one more step back to see how and why the probability of \"best\" in class `1` is `0.11764`. As per the documentation of [Multinomial Naive Bayes][2], we see that these probabilities are computed using the formula below:\n","\n","![img](https://i.stack.imgur.com/gyokC.png)\n","\n","Where, the numerator roughly corresponds to the number of times feature \"best\" appears in the class `1` (which is of our interest in this example) in the training set, and the denominator corresponds to the total count of all features for class `1`. Also, we add a small smoothing value, `alpha` to prevent from the probabilities going to zero and `n` corresponds to the total number of features i.e. size of vocabulary.\n","\n","## Predicting Probabilities for the actual test dataset of Kaggle"]},{"cell_type":"code","execution_count":60,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:15:22.752197Z","iopub.status.busy":"2021-06-16T13:15:22.751645Z","iopub.status.idle":"2021-06-16T13:15:22.957156Z","shell.execute_reply":"2021-06-16T13:15:22.955350Z","shell.execute_reply.started":"2021-06-16T13:15:22.752141Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["y_predicted_for_bow_with_best_alpha_test_df.shape is  (78035,)\n"]}],"source":["# We already have the variable \"naive_bayes_results_for_bow_with_best_alpha\" fitted as below earlier\n","# naive_bayes_results_for_bow_with_best_alpha.fit(X_train_hstacked_all_bow_features_vectorized, y_donor_choose_train)\n","y_predicted_for_bow_with_best_alpha_test_df = naive_bayes_results_for_bow_with_best_alpha.predict_proba(test_df_hstacked_all_bow_features_vectorized)[:, 1]\n","\n","print('y_predicted_for_bow_with_best_alpha_test_df.shape is ', y_predicted_for_bow_with_best_alpha_test_df.shape)"]},{"cell_type":"markdown","metadata":{},"source":["## Creating file for final submission"]},{"cell_type":"code","execution_count":61,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:19:01.010988Z","iopub.status.busy":"2021-06-16T13:19:01.010487Z","iopub.status.idle":"2021-06-16T13:19:01.096900Z","shell.execute_reply":"2021-06-16T13:19:01.095750Z","shell.execute_reply.started":"2021-06-16T13:19:01.010950Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[],"source":["sample_sub = pd.read_csv(\"../input/donorschooseorg-application-screening/sample_submission.csv\")\n","\n","# y_predicted_for_bow_with_best_alpha_test_df"]},{"cell_type":"code","execution_count":62,"metadata":{"execution":{"iopub.execute_input":"2021-06-16T13:19:13.561022Z","iopub.status.busy":"2021-06-16T13:19:13.560350Z","iopub.status.idle":"2021-06-16T13:19:13.587400Z","shell.execute_reply":"2021-06-16T13:19:13.586368Z","shell.execute_reply.started":"2021-06-16T13:19:13.560961Z"},"pycharm":{"name":"#%%\n"},"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>id</th>\n","      <th>project_is_approved</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>p233245</td>\n","      <td>0.998301</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>p096795</td>\n","      <td>0.998256</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>p236235</td>\n","      <td>0.054413</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>p233680</td>\n","      <td>0.999497</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>p171879</td>\n","      <td>0.662385</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["        id  project_is_approved\n","0  p233245             0.998301\n","1  p096795             0.998256\n","2  p236235             0.054413\n","3  p233680             0.999497\n","4  p171879             0.662385"]},"execution_count":62,"metadata":{},"output_type":"execute_result"}],"source":["submission_naive_bayes = pd.concat([sample_sub, pd.Series(y_predicted_for_bow_with_best_alpha_test_df , name='project_is_approved')] , axis=1 ).iloc[:,[0,2]]\n","submission_naive_bayes.head()\n","# submission_naive_bayes.to_csv('submission_naive_bayes.csv', index=False)\n"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.6"}},"nbformat":4,"nbformat_minor":4}