Untitled2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b684fa41-f771-42bb-b71a-45d74f803e6b",
   "metadata": {},
   "source": [
    "# PROBLEM STATEMENT\n",
    "\n",
    "The task involves predicting whether a project proposal on the DonorsChoose platform will be approved based on various features provided in the dataset. DonorsChoose is a platform where teachers can post project proposals seeking funding for classroom resources. The dataset contains a range of features, including teacher and school information, project details, and textual descriptions, which need to be analyzed to make accurate predictions.\n",
    "\n",
    "The dataset used for this analysis is sourced from Kaggle.\n",
    "* Source: Kaggle - https://www.kaggle.com/code/saketranjan/group142-donorschoosedataset/notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "69019d57-c32a-4c6d-95ef-ceb11418d75b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing all the necessary libraries\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import nltk\n",
    "from tqdm import tqdm\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import WordNetLemmatizer, PorterStemmer\n",
    "import string\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from scipy.sparse import hstack\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import randint\n",
    "import xgboost as xgb\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import accuracy_score, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f4859055-e63d-4356-96bd-a0b932b7f3c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the dataset\n",
    "df = pd.read_csv('Preprocessed_DonorsChoose_dataset.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a172a032-de68-40e3-b93b-4b0d1c7bd04e",
   "metadata": {},
   "source": [
    "# DATA EXPLORATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8f16f28f-d19c-469b-a763-664cf8e8f4ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows: 109248\n",
      "Number of columns: 14\n"
     ]
    }
   ],
   "source": [
    "# Explore size and shape\n",
    "num_rows, num_cols = df.shape\n",
    "print(\"Number of rows:\", num_rows)\n",
    "print(\"Number of columns:\", num_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "14bcd979-9dea-409f-8792-4ea4e4bc3d4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'teacher_prefix', 'school_state', 'project_grade_category',\n",
       "       'project_subject_categories', 'project_subject_subcategories',\n",
       "       'teacher_number_of_previously_posted_projects', 'project_is_approved',\n",
       "       'price', 'quantity', 'cleaned_titles', 'cleaned_essays',\n",
       "       'cleaned_summary', 'isdigit_summary'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "05a2c373-6038-40cf-ac86-53300024de9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</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>teacher_number_of_previously_posted_projects</th>\n",
       "      <th>project_is_approved</th>\n",
       "      <th>price</th>\n",
       "      <th>quantity</th>\n",
       "      <th>cleaned_titles</th>\n",
       "      <th>cleaned_essays</th>\n",
       "      <th>cleaned_summary</th>\n",
       "      <th>isdigit_summary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>p253737</td>\n",
       "      <td>mrs</td>\n",
       "      <td>in</td>\n",
       "      <td>grades_prek_2</td>\n",
       "      <td>literacy_language</td>\n",
       "      <td>esl_literacy</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>154.60</td>\n",
       "      <td>23</td>\n",
       "      <td>educational support english learners home</td>\n",
       "      <td>students english learners working english seco...</td>\n",
       "      <td>students_need_opportunities_practice_beginning...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>p258326</td>\n",
       "      <td>mr</td>\n",
       "      <td>fl</td>\n",
       "      <td>grades_6_8</td>\n",
       "      <td>history_civics_health_sports</td>\n",
       "      <td>civics_government_teamsports</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>299.00</td>\n",
       "      <td>1</td>\n",
       "      <td>wanted projector hungry learners</td>\n",
       "      <td>students arrive school eager learn polite gene...</td>\n",
       "      <td>students_need_projector_help_viewing_education...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>p182444</td>\n",
       "      <td>ms</td>\n",
       "      <td>az</td>\n",
       "      <td>grades_6_8</td>\n",
       "      <td>health_sports</td>\n",
       "      <td>health_wellness_teamsports</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>516.85</td>\n",
       "      <td>22</td>\n",
       "      <td>soccer equipment awesome middle school students</td>\n",
       "      <td>true champions not always ones win guts mia ha...</td>\n",
       "      <td>students_need_shine_guards_athletic_socks_socc...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>p246581</td>\n",
       "      <td>mrs</td>\n",
       "      <td>ky</td>\n",
       "      <td>grades_prek_2</td>\n",
       "      <td>literacy_language_math_science</td>\n",
       "      <td>literacy_mathematics</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>232.90</td>\n",
       "      <td>4</td>\n",
       "      <td>techie kindergarteners</td>\n",
       "      <td>work unique school filled esl english second l...</td>\n",
       "      <td>students_need_engage_reading_math_way_inspire_...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>p104768</td>\n",
       "      <td>mrs</td>\n",
       "      <td>tx</td>\n",
       "      <td>grades_prek_2</td>\n",
       "      <td>math_science</td>\n",
       "      <td>mathematics</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>67.98</td>\n",
       "      <td>4</td>\n",
       "      <td>interactive math tools</td>\n",
       "      <td>second grade classroom next year made around 2...</td>\n",
       "      <td>students_need_hands_practice_mathematics_fun_p...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id teacher_prefix school_state project_grade_category  \\\n",
       "0  p253737            mrs           in          grades_prek_2   \n",
       "1  p258326             mr           fl             grades_6_8   \n",
       "2  p182444             ms           az             grades_6_8   \n",
       "3  p246581            mrs           ky          grades_prek_2   \n",
       "4  p104768            mrs           tx          grades_prek_2   \n",
       "\n",
       "       project_subject_categories project_subject_subcategories  \\\n",
       "0               literacy_language                  esl_literacy   \n",
       "1    history_civics_health_sports  civics_government_teamsports   \n",
       "2                   health_sports    health_wellness_teamsports   \n",
       "3  literacy_language_math_science          literacy_mathematics   \n",
       "4                    math_science                   mathematics   \n",
       "\n",
       "   teacher_number_of_previously_posted_projects  project_is_approved   price  \\\n",
       "0                                             0                    0  154.60   \n",
       "1                                             7                    1  299.00   \n",
       "2                                             1                    0  516.85   \n",
       "3                                             4                    1  232.90   \n",
       "4                                             1                    1   67.98   \n",
       "\n",
       "   quantity                                   cleaned_titles  \\\n",
       "0        23        educational support english learners home   \n",
       "1         1                 wanted projector hungry learners   \n",
       "2        22  soccer equipment awesome middle school students   \n",
       "3         4                           techie kindergarteners   \n",
       "4         4                           interactive math tools   \n",
       "\n",
       "                                      cleaned_essays  \\\n",
       "0  students english learners working english seco...   \n",
       "1  students arrive school eager learn polite gene...   \n",
       "2  true champions not always ones win guts mia ha...   \n",
       "3  work unique school filled esl english second l...   \n",
       "4  second grade classroom next year made around 2...   \n",
       "\n",
       "                                     cleaned_summary  isdigit_summary  \n",
       "0  students_need_opportunities_practice_beginning...                0  \n",
       "1  students_need_projector_help_viewing_education...                0  \n",
       "2  students_need_shine_guards_athletic_socks_socc...                0  \n",
       "3  students_need_engage_reading_math_way_inspire_...                0  \n",
       "4  students_need_hands_practice_mathematics_fun_p...                0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a899506-6ac1-4a41-b25e-073ef8552801",
   "metadata": {},
   "source": [
    "OBSERVATIONS\n",
    "* id: Unique identifier for each project.\n",
    "* teacher_prefix: Prefix of the teacher (e.g., Mr., Mrs., Ms.).\n",
    "* school_state: State where the school is located.\n",
    "* project_grade_category: Grade category for the project (e.g., grades_prek_2, grades_6_8).\n",
    "* project_subject_categories: Broad subject category for the project (e.g., literacy_language, health_sports).\n",
    "* project_subject_subcategories: More specific subject subcategory (e.g., esl_literacy, civics_government_teamsports).\n",
    "* teacher_number_of_previously_posted_projects: Number of projects the teacher has previously posted.\n",
    "* project_is_approved: Whether the project is approved (1 for approved, 0 for not approved).\n",
    "* price: Cost of the project.\n",
    "* quantity: Number of items requested in the project.\n",
    "* cleaned_titles: Cleaned version of project titles.\n",
    "* cleaned_essays: Cleaned version of project essays.\n",
    "* cleaned_summary: Cleaned version of project summaries.\n",
    "* isdigit_summary: Binary indicator if the summary contains only digits (0 for no, 1 for yes)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "401f0580-ad6a-48db-ba5c-2a65104570ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "project_is_approved\n",
       "1    92706\n",
       "0    16542\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['project_is_approved'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "69ced244-57c7-42e9-813d-46dd7a81c6e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 109248 entries, 0 to 109247\n",
      "Data columns (total 14 columns):\n",
      " #   Column                                        Non-Null Count   Dtype  \n",
      "---  ------                                        --------------   -----  \n",
      " 0   id                                            109248 non-null  object \n",
      " 1   teacher_prefix                                109248 non-null  object \n",
      " 2   school_state                                  109248 non-null  object \n",
      " 3   project_grade_category                        109248 non-null  object \n",
      " 4   project_subject_categories                    109248 non-null  object \n",
      " 5   project_subject_subcategories                 109248 non-null  object \n",
      " 6   teacher_number_of_previously_posted_projects  109248 non-null  int64  \n",
      " 7   project_is_approved                           109248 non-null  int64  \n",
      " 8   price                                         109248 non-null  float64\n",
      " 9   quantity                                      109248 non-null  int64  \n",
      " 10  cleaned_titles                                109205 non-null  object \n",
      " 11  cleaned_essays                                109248 non-null  object \n",
      " 12  cleaned_summary                               109248 non-null  object \n",
      " 13  isdigit_summary                               109248 non-null  int64  \n",
      "dtypes: float64(1), int64(4), object(9)\n",
      "memory usage: 11.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ad4aec54-adc5-4875-988e-19f934df5b4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>teacher_number_of_previously_posted_projects</th>\n",
       "      <th>project_is_approved</th>\n",
       "      <th>price</th>\n",
       "      <th>quantity</th>\n",
       "      <th>isdigit_summary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>109248.000000</td>\n",
       "      <td>109248.000000</td>\n",
       "      <td>109248.000000</td>\n",
       "      <td>109248.000000</td>\n",
       "      <td>109248.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11.153165</td>\n",
       "      <td>0.848583</td>\n",
       "      <td>298.119343</td>\n",
       "      <td>16.965610</td>\n",
       "      <td>0.144222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>27.777154</td>\n",
       "      <td>0.358456</td>\n",
       "      <td>367.498030</td>\n",
       "      <td>26.182942</td>\n",
       "      <td>0.351317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>104.310000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>206.220000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>379.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>451.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9999.000000</td>\n",
       "      <td>930.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       teacher_number_of_previously_posted_projects  project_is_approved  \\\n",
       "count                                 109248.000000        109248.000000   \n",
       "mean                                      11.153165             0.848583   \n",
       "std                                       27.777154             0.358456   \n",
       "min                                        0.000000             0.000000   \n",
       "25%                                        0.000000             1.000000   \n",
       "50%                                        2.000000             1.000000   \n",
       "75%                                        9.000000             1.000000   \n",
       "max                                      451.000000             1.000000   \n",
       "\n",
       "               price       quantity  isdigit_summary  \n",
       "count  109248.000000  109248.000000    109248.000000  \n",
       "mean      298.119343      16.965610         0.144222  \n",
       "std       367.498030      26.182942         0.351317  \n",
       "min         0.660000       1.000000         0.000000  \n",
       "25%       104.310000       4.000000         0.000000  \n",
       "50%       206.220000       9.000000         0.000000  \n",
       "75%       379.000000      21.000000         0.000000  \n",
       "max      9999.000000     930.000000         1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81a7aeaf-a333-4ce9-9319-5adf7db6dd4d",
   "metadata": {},
   "source": [
    "OBSERVATIONS\n",
    "* teacher_number_of_previously_posted_projects - The number of previously posted projects by teachers has a wide range, from 0 to 451. The median value is 2, meaning that half of the teachers have posted 2 or fewer projects. The mean is higher than the median, indicating a right-skewed distribution with some teachers having an exceptionally high number of previously posted projects.\n",
    "* project_is_approved - The dataset is likely to have class imbalance, with many more approved projects compared to non-approved ones. \n",
    "* price - The price varies significantly, with some projects being very expensive (up to 9999.00) while others are much cheaper. The mean price is higher than the median, which suggests a right-skewed distribution with a few very high values.\n",
    "* quantity - Quantity also shows high variability, with a few projects having very large quantities. The mean is higher than the median, suggesting a right-skewed distribution.\n",
    "* isdigit_summary - This is a binary feature indicating the presence of digits in the summary. The mean of 0.14 indicates that this feature is present in 14% of the summaries. The feature is sparse and not widely used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f37421ff-fca6-4173-be8d-e33ecd33fe40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "project_is_approved\n",
       "1    92706\n",
       "0    16542\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['project_is_approved'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3da2e0f2-bfe1-4c93-b7e4-726804f7eee9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "project_subject_subcategories\n",
       "literacy                          9486\n",
       "literacy_mathematics              8325\n",
       "literature_writing_mathematics    5923\n",
       "literacy_literature_writing       5571\n",
       "mathematics                       5379\n",
       "                                  ... \n",
       "communityservice_gym_fitness         1\n",
       "parentinvolvement_teamsports         1\n",
       "gym_fitness_socialsciences           1\n",
       "communityservice_music               1\n",
       "economics_foreignlanguages           1\n",
       "Name: count, Length: 401, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['project_subject_subcategories'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "956f90b4-9c2f-4617-831b-0ed7f099766d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "project_subject_categories\n",
       "literacy_language                       23655\n",
       "math_science                            17072\n",
       "literacy_language_math_science          14636\n",
       "health_sports                           10177\n",
       "music_arts                               5180\n",
       "specialneeds                             4226\n",
       "literacy_language_specialneeds           3961\n",
       "appliedlearning                          3771\n",
       "math_science_literacy_language           2289\n",
       "appliedlearning_literacy_language        2191\n",
       "history_civics                           1851\n",
       "math_science_specialneeds                1840\n",
       "literacy_language_music_arts             1757\n",
       "math_science_music_arts                  1642\n",
       "appliedlearning_specialneeds             1467\n",
       "history_civics_literacy_language         1421\n",
       "health_sports_specialneeds               1391\n",
       "warmth_care_hunger                       1309\n",
       "math_science_appliedlearning             1220\n",
       "appliedlearning_math_science             1052\n",
       "literacy_language_history_civics          809\n",
       "health_sports_literacy_language           803\n",
       "appliedlearning_music_arts                758\n",
       "math_science_history_civics               652\n",
       "literacy_language_appliedlearning         636\n",
       "appliedlearning_health_sports             608\n",
       "math_science_health_sports                414\n",
       "history_civics_math_science               322\n",
       "history_civics_music_arts                 312\n",
       "specialneeds_music_arts                   302\n",
       "health_sports_math_science                271\n",
       "history_civics_specialneeds               252\n",
       "health_sports_appliedlearning             192\n",
       "appliedlearning_history_civics            178\n",
       "health_sports_music_arts                  155\n",
       "music_arts_specialneeds                   138\n",
       "literacy_language_health_sports            72\n",
       "health_sports_history_civics               43\n",
       "history_civics_appliedlearning             42\n",
       "specialneeds_health_sports                 42\n",
       "specialneeds_warmth_care_hunger            23\n",
       "health_sports_warmth_care_hunger           23\n",
       "music_arts_health_sports                   19\n",
       "music_arts_history_civics                  18\n",
       "history_civics_health_sports               13\n",
       "math_science_warmth_care_hunger            11\n",
       "music_arts_appliedlearning                 10\n",
       "appliedlearning_warmth_care_hunger         10\n",
       "literacy_language_warmth_care_hunger        9\n",
       "music_arts_warmth_care_hunger               2\n",
       "history_civics_warmth_care_hunger           1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['project_subject_categories'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d83622c2-a53c-4298-9f16-3ea4bc500b0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['project_subject_categories'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "038e6bd6-8968-4238-92ff-1566b3727c28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "teacher_prefix\n",
       "mrs        57272\n",
       "ms         38955\n",
       "mr         10648\n",
       "teacher     2360\n",
       "dr            13\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['teacher_prefix'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b9607d62-3800-433e-9bcf-f8d236e2f7d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "school_state\n",
       "ca    15388\n",
       "tx     7396\n",
       "ny     7318\n",
       "fl     6185\n",
       "nc     5091\n",
       "il     4350\n",
       "ga     3963\n",
       "sc     3936\n",
       "mi     3161\n",
       "pa     3109\n",
       "in     2620\n",
       "mo     2576\n",
       "oh     2467\n",
       "la     2394\n",
       "ma     2389\n",
       "wa     2334\n",
       "ok     2276\n",
       "nj     2237\n",
       "az     2147\n",
       "va     2045\n",
       "wi     1827\n",
       "al     1762\n",
       "ut     1731\n",
       "tn     1688\n",
       "ct     1663\n",
       "md     1514\n",
       "nv     1367\n",
       "ms     1323\n",
       "ky     1304\n",
       "or     1242\n",
       "mn     1208\n",
       "co     1111\n",
       "ar     1049\n",
       "id      693\n",
       "ia      666\n",
       "ks      634\n",
       "nm      557\n",
       "dc      516\n",
       "hi      507\n",
       "me      505\n",
       "wv      503\n",
       "nh      348\n",
       "ak      345\n",
       "de      343\n",
       "ne      309\n",
       "sd      300\n",
       "ri      285\n",
       "mt      245\n",
       "nd      143\n",
       "wy       98\n",
       "vt       80\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['school_state'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8e2c319d-301b-4a7a-ad29-b5f4e2e9a83e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['school_state'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a482dfaa-dcdc-4efc-a42f-5b308e6b2715",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "project_grade_category\n",
       "grades_prek_2    44225\n",
       "grades_3_5       37137\n",
       "grades_6_8       16923\n",
       "grades_9_12      10963\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['project_grade_category'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6599aed9-20df-4795-b2d9-9287a966e019",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "teacher_number_of_previously_posted_projects\n",
       "0      30014\n",
       "1      16058\n",
       "2      10350\n",
       "3       7110\n",
       "4       5266\n",
       "       ...  \n",
       "270        1\n",
       "294        1\n",
       "425        1\n",
       "325        1\n",
       "306        1\n",
       "Name: count, Length: 374, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['teacher_number_of_previously_posted_projects'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "234c83a8-f664-4893-b69c-0875faff8024",
   "metadata": {},
   "source": [
    "# Handling Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "067995c5-ef46-4dbe-951f-2b7186abd223",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values in each column:\n",
      " id                                               0\n",
      "teacher_prefix                                   0\n",
      "school_state                                     0\n",
      "project_grade_category                           0\n",
      "project_subject_categories                       0\n",
      "project_subject_subcategories                    0\n",
      "teacher_number_of_previously_posted_projects     0\n",
      "project_is_approved                              0\n",
      "price                                            0\n",
      "quantity                                         0\n",
      "cleaned_titles                                  43\n",
      "cleaned_essays                                   0\n",
      "cleaned_summary                                  0\n",
      "isdigit_summary                                  0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Check for missing values\n",
    "missing_values = df.isnull().sum()\n",
    "print(\"Missing values in each column:\\n\", missing_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb4e358e-ddb0-4f67-9f0f-b835936b8dfc",
   "metadata": {},
   "source": [
    "Using the mode method here ensures that the missing values are replaced with the most frequent title, which might help maintain the distribution and relationships within the dataset. If we want to keep the dataset as clean as possible and avoid introducing a new category we opt for mode imputation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0089029d-bbe1-4d84-8fc1-0413aa01c85a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "# Handling missing values by fill my mode method\n",
    "mode_title = df['cleaned_titles'].mode()[0]\n",
    "df['cleaned_titles'] = df['cleaned_titles'].fillna(mode_title)\n",
    "print(df['cleaned_titles'].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "773d13c7-f016-4627-ac20-179e6d900c80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</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>teacher_number_of_previously_posted_projects</th>\n",
       "      <th>project_is_approved</th>\n",
       "      <th>price</th>\n",
       "      <th>quantity</th>\n",
       "      <th>cleaned_titles</th>\n",
       "      <th>cleaned_essays</th>\n",
       "      <th>cleaned_summary</th>\n",
       "      <th>isdigit_summary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>p253737</td>\n",
       "      <td>mrs</td>\n",
       "      <td>in</td>\n",
       "      <td>grades_prek_2</td>\n",
       "      <td>literacy_language</td>\n",
       "      <td>esl_literacy</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>154.60</td>\n",
       "      <td>23</td>\n",
       "      <td>educational support english learners home</td>\n",
       "      <td>students english learners working english seco...</td>\n",
       "      <td>students_need_opportunities_practice_beginning...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>p258326</td>\n",
       "      <td>mr</td>\n",
       "      <td>fl</td>\n",
       "      <td>grades_6_8</td>\n",
       "      <td>history_civics_health_sports</td>\n",
       "      <td>civics_government_teamsports</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>299.00</td>\n",
       "      <td>1</td>\n",
       "      <td>wanted projector hungry learners</td>\n",
       "      <td>students arrive school eager learn polite gene...</td>\n",
       "      <td>students_need_projector_help_viewing_education...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>p182444</td>\n",
       "      <td>ms</td>\n",
       "      <td>az</td>\n",
       "      <td>grades_6_8</td>\n",
       "      <td>health_sports</td>\n",
       "      <td>health_wellness_teamsports</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>516.85</td>\n",
       "      <td>22</td>\n",
       "      <td>soccer equipment awesome middle school students</td>\n",
       "      <td>true champions not always ones win guts mia ha...</td>\n",
       "      <td>students_need_shine_guards_athletic_socks_socc...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>p246581</td>\n",
       "      <td>mrs</td>\n",
       "      <td>ky</td>\n",
       "      <td>grades_prek_2</td>\n",
       "      <td>literacy_language_math_science</td>\n",
       "      <td>literacy_mathematics</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>232.90</td>\n",
       "      <td>4</td>\n",
       "      <td>techie kindergarteners</td>\n",
       "      <td>work unique school filled esl english second l...</td>\n",
       "      <td>students_need_engage_reading_math_way_inspire_...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>p104768</td>\n",
       "      <td>mrs</td>\n",
       "      <td>tx</td>\n",
       "      <td>grades_prek_2</td>\n",
       "      <td>math_science</td>\n",
       "      <td>mathematics</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>67.98</td>\n",
       "      <td>4</td>\n",
       "      <td>interactive math tools</td>\n",
       "      <td>second grade classroom next year made around 2...</td>\n",
       "      <td>students_need_hands_practice_mathematics_fun_p...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id teacher_prefix school_state project_grade_category  \\\n",
       "0  p253737            mrs           in          grades_prek_2   \n",
       "1  p258326             mr           fl             grades_6_8   \n",
       "2  p182444             ms           az             grades_6_8   \n",
       "3  p246581            mrs           ky          grades_prek_2   \n",
       "4  p104768            mrs           tx          grades_prek_2   \n",
       "\n",
       "       project_subject_categories project_subject_subcategories  \\\n",
       "0               literacy_language                  esl_literacy   \n",
       "1    history_civics_health_sports  civics_government_teamsports   \n",
       "2                   health_sports    health_wellness_teamsports   \n",
       "3  literacy_language_math_science          literacy_mathematics   \n",
       "4                    math_science                   mathematics   \n",
       "\n",
       "   teacher_number_of_previously_posted_projects  project_is_approved   price  \\\n",
       "0                                             0                    0  154.60   \n",
       "1                                             7                    1  299.00   \n",
       "2                                             1                    0  516.85   \n",
       "3                                             4                    1  232.90   \n",
       "4                                             1                    1   67.98   \n",
       "\n",
       "   quantity                                   cleaned_titles  \\\n",
       "0        23        educational support english learners home   \n",
       "1         1                 wanted projector hungry learners   \n",
       "2        22  soccer equipment awesome middle school students   \n",
       "3         4                           techie kindergarteners   \n",
       "4         4                           interactive math tools   \n",
       "\n",
       "                                      cleaned_essays  \\\n",
       "0  students english learners working english seco...   \n",
       "1  students arrive school eager learn polite gene...   \n",
       "2  true champions not always ones win guts mia ha...   \n",
       "3  work unique school filled esl english second l...   \n",
       "4  second grade classroom next year made around 2...   \n",
       "\n",
       "                                     cleaned_summary  isdigit_summary  \n",
       "0  students_need_opportunities_practice_beginning...                0  \n",
       "1  students_need_projector_help_viewing_education...                0  \n",
       "2  students_need_shine_guards_athletic_socks_socc...                0  \n",
       "3  students_need_engage_reading_math_way_inspire_...                0  \n",
       "4  students_need_hands_practice_mathematics_fun_p...                0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe11d1ab-7e49-481f-9691-92a7066f8fcf",
   "metadata": {},
   "source": [
    "# Visualizing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5a0c20a2-3ba9-4963-9032-47ec9e09914b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of approved projects:85%\n",
      "Percentage of NOT approved projects:15%\n"
     ]
    }
   ],
   "source": [
    "# Count the number of approved and not approved projects\n",
    "approval_counts = df['project_is_approved'].value_counts()\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.bar(approval_counts.index, approval_counts.values, color=['red', 'green'])\n",
    "\n",
    "plt.xlabel('Project Approval Status')\n",
    "plt.ylabel('Number of Projects')\n",
    "plt.title('Number of Projects by Approval Status')\n",
    "plt.xticks(ticks=[0, 1], labels=['Not Approved', 'Approved'])\n",
    "\n",
    "plt.show()\n",
    "\n",
    "print('Percentage of approved projects:{}%'.format(round((approval_counts[1]*100)/len(df)),2))\n",
    "print('Percentage of NOT approved projects:{}%'.format(round((approval_counts[0]*100)/len(df)),2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5e7b30e-25d7-4ffe-b444-c8c745b7708a",
   "metadata": {},
   "source": [
    "This shows the data imbalance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0ef52906-1d7b-4cbd-8cb3-701d4ecc2a84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Group by school_state and sum project_is_approved to get the number of approved projects per state\n",
    "state_approval_counts = df.groupby('school_state')['project_is_approved'].sum().sort_values()\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.bar(state_approval_counts.index, state_approval_counts.values, color='skyblue')\n",
    "\n",
    "plt.xlabel('School State') # Defining the x-axis\n",
    "plt.ylabel('Number of Approved Projects') # Defining the y-axis\n",
    "plt.title('Number of Approved Projects by State')\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c1aa5d46-9299-44a4-b593-c8798063b12e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Group by teacher_prefix and sum project_is_approved to get the number of approved projects per prefix\n",
    "prefix_approval_counts = df.groupby('teacher_prefix')['project_is_approved'].sum().sort_values()\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(prefix_approval_counts.index, prefix_approval_counts.values, color='lightcoral')\n",
    "plt.xlabel('Teacher Prefix')\n",
    "plt.ylabel('Number of Approved Projects')\n",
    "plt.title('Number of Approved Projects by Teacher Prefix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "6b0bb2c8-76d0-48ca-be86-da1e23a093b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Group by project_grade_category and sum project_is_approved to get the number of approved projects per grade category\n",
    "grade_category_approval_counts = df.groupby('project_grade_category')['project_is_approved'].sum().sort_values()\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.bar(grade_category_approval_counts.index, grade_category_approval_counts.values, color='orchid')\n",
    "plt.xlabel('Project Grade Category')\n",
    "plt.ylabel('Number of Approved Projects')\n",
    "plt.title('Number of Approved Projects by Project Grade Category')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d32c03a1-dc65-4167-bf88-b7682dfe5959",
   "metadata": {},
   "source": [
    "# Checking For Outliers\n",
    "* Outliers: These are data points that differ significantly from other observations. They can distort statistical analyses and model performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4b3a5842-f444-488f-9c9e-c4dd6b5b58ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Helper function to calculate percentiles, IQR, and plot boxplot\n",
    "def analyze_feature(df, feature_name):\n",
    "    # Calculate percentiles\n",
    "    q1 = np.percentile(df[feature_name], 25)\n",
    "    q2 = np.percentile(df[feature_name], 50)\n",
    "    q3 = np.percentile(df[feature_name], 75)\n",
    "    q90 = np.percentile(df[feature_name], 90)\n",
    "    q95 = np.percentile(df[feature_name], 95)\n",
    "    q99 = np.percentile(df[feature_name], 99)\n",
    "    q100 = np.percentile(df[feature_name], 100)\n",
    "\n",
    "    # Print percentiles\n",
    "    print(f'{feature_name} - 25th percentile:', q1)\n",
    "    print(f'{feature_name} - 50th percentile:', q2)\n",
    "    print(f'{feature_name} - 75th percentile:', q3)\n",
    "    print(f'{feature_name} - 90th percentile:', q90)\n",
    "    print(f'{feature_name} - 95th percentile:', q95)\n",
    "    print(f'{feature_name} - 99th percentile:', q99)\n",
    "    print(f'{feature_name} - 100th percentile:', q100)\n",
    "\n",
    "    # Generate a boxplot\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    sns.boxplot(x=df[feature_name])\n",
    "    plt.title(f'Boxplot of {feature_name}')\n",
    "    plt.xlabel(feature_name)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "34e3a5c7-42db-4b0a-84ea-c5dddcbdcf66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "quantity - 25th percentile: 4.0\n",
      "quantity - 50th percentile: 9.0\n",
      "quantity - 75th percentile: 21.0\n",
      "quantity - 90th percentile: 39.0\n",
      "quantity - 95th percentile: 58.0\n",
      "quantity - 99th percentile: 120.0\n",
      "quantity - 100th percentile: 930.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "analyze_feature(df, 'quantity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "5c17a2e8-adae-4c97-b441-a6129e53f9e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "price - 25th percentile: 104.31\n",
      "price - 50th percentile: 206.22\n",
      "price - 75th percentile: 379.0\n",
      "price - 90th percentile: 619.4530000000001\n",
      "price - 95th percentile: 839.9865\n",
      "price - 99th percentile: 1583.449999999999\n",
      "price - 100th percentile: 9999.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "analyze_feature(df, 'price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "13e1afa1-b042-42fd-8ed7-8790a932b320",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "teacher_number_of_previously_posted_projects - 25th percentile: 0.0\n",
      "teacher_number_of_previously_posted_projects - 50th percentile: 2.0\n",
      "teacher_number_of_previously_posted_projects - 75th percentile: 9.0\n",
      "teacher_number_of_previously_posted_projects - 90th percentile: 28.0\n",
      "teacher_number_of_previously_posted_projects - 95th percentile: 53.0\n",
      "teacher_number_of_previously_posted_projects - 99th percentile: 139.0\n",
      "teacher_number_of_previously_posted_projects - 100th percentile: 451.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "analyze_feature(df, 'teacher_number_of_previously_posted_projects')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0b06a12-39eb-49b6-b037-9cf6caa3ee80",
   "metadata": {},
   "source": [
    "# Handling Textual Data\n",
    "\n",
    "Steps for textual data:\n",
    "* Tokenization: Splits each text into individual words or tokens using word_tokenize from NLTK.\n",
    "* Lowercasing: Converts all tokens to lowercase to ensure consistency in text processing.\n",
    "* Removing Punctuation: Strips out non-alphanumeric characters using list comprehension with word.isalnum().\n",
    "* Removing Stopwords: Filters out common English stopwords using stopwords.words('english').\n",
    "* Lemmatization: Reduces words to their base or root form using WordNetLemmatizer from NLTK."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "a549b1f3-3446-4383-9fc0-9dfdd884ea54",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize lemmatizer\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "stop_words = set(stopwords.words('english'))\n",
    "# Function to preprocess text with lemmatization\n",
    "def preprocess_text_lemmatize(text):\n",
    "    # Tokenization\n",
    "    lst=[]\n",
    "    for i in text.split():\n",
    "        i=i.lower() \n",
    "        if i.isalnum() and i not in stop_words:\n",
    "           lst.append(lemmatizer.lemmatize(i)) \n",
    "\n",
    "    return \" \".join(lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "817e32c9-4137-4073-bba7-5764f232a15d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████| 109248/109248 [01:26<00:00, 1265.28it/s]\n"
     ]
    }
   ],
   "source": [
    "lst=[ ]\n",
    "for i in tqdm(df['cleaned_essays']):\n",
    "    lst.append(preprocess_text_lemmatize(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "44feafef-2ff1-49f1-831e-93e94eaaf071",
   "metadata": {},
   "outputs": [],
   "source": [
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    "def preprocess_text_lemmatize(text):\n",
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    "    lst=[]\n",
    "    for i in text.split(\"_\"):\n",
    "        i=i.lower() \n",
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    "\n",
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       "      <th>4</th>\n",
       "      <td>p104768</td>\n",
       "      <td>mrs</td>\n",
       "      <td>tx</td>\n",
       "      <td>grades_prek_2</td>\n",
       "      <td>math_science</td>\n",
       "      <td>mathematics</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>67.98</td>\n",
       "      <td>4</td>\n",
       "      <td>interactive math tools</td>\n",
       "      <td>second grade classroom next year made around 2...</td>\n",
       "      <td>students_need_hands_practice_mathematics_fun_p...</td>\n",
       "      <td>0</td>\n",
       "      <td>second grade classroom next year made around 2...</td>\n",
       "      <td>interactive math tool</td>\n",
       "      <td>student need hand practice mathematics fun per...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id teacher_prefix school_state project_grade_category  \\\n",
       "0  p253737            mrs           in          grades_prek_2   \n",
       "1  p258326             mr           fl             grades_6_8   \n",
       "2  p182444             ms           az             grades_6_8   \n",
       "3  p246581            mrs           ky          grades_prek_2   \n",
       "4  p104768            mrs           tx          grades_prek_2   \n",
       "\n",
       "       project_subject_categories project_subject_subcategories  \\\n",
       "0               literacy_language                  esl_literacy   \n",
       "1    history_civics_health_sports  civics_government_teamsports   \n",
       "2                   health_sports    health_wellness_teamsports   \n",
       "3  literacy_language_math_science          literacy_mathematics   \n",
       "4                    math_science                   mathematics   \n",
       "\n",
       "   teacher_number_of_previously_posted_projects  project_is_approved   price  \\\n",
       "0                                             0                    0  154.60   \n",
       "1                                             7                    1  299.00   \n",
       "2                                             1                    0  516.85   \n",
       "3                                             4                    1  232.90   \n",
       "4                                             1                    1   67.98   \n",
       "\n",
       "   quantity                                   cleaned_titles  \\\n",
       "0        23        educational support english learners home   \n",
       "1         1                 wanted projector hungry learners   \n",
       "2        22  soccer equipment awesome middle school students   \n",
       "3         4                           techie kindergarteners   \n",
       "4         4                           interactive math tools   \n",
       "\n",
       "                                      cleaned_essays  \\\n",
       "0  students english learners working english seco...   \n",
       "1  students arrive school eager learn polite gene...   \n",
       "2  true champions not always ones win guts mia ha...   \n",
       "3  work unique school filled esl english second l...   \n",
       "4  second grade classroom next year made around 2...   \n",
       "\n",
       "                                     cleaned_summary  isdigit_summary  \\\n",
       "0  students_need_opportunities_practice_beginning...                0   \n",
       "1  students_need_projector_help_viewing_education...                0   \n",
       "2  students_need_shine_guards_athletic_socks_socc...                0   \n",
       "3  students_need_engage_reading_math_way_inspire_...                0   \n",
       "4  students_need_hands_practice_mathematics_fun_p...                0   \n",
       "\n",
       "                    cleaned_essays_tokens_lemmatized  \\\n",
       "0  student english learner working english second...   \n",
       "1  student arrive school eager learn polite gener...   \n",
       "2  true champion always one win gut mia hamm quot...   \n",
       "3  work unique school filled esl english second l...   \n",
       "4  second grade classroom next year made around 2...   \n",
       "\n",
       "                 cleaned_titles_tokens_lemmatized  \\\n",
       "0        educational support english learner home   \n",
       "1                 wanted projector hungry learner   \n",
       "2  soccer equipment awesome middle school student   \n",
       "3                           techie kindergartener   \n",
       "4                           interactive math tool   \n",
       "\n",
       "                   cleaned_summary_tokens_lemmatized  \n",
       "0  student need opportunity practice beginning re...  \n",
       "1  student need projector help viewing educationa...  \n",
       "2  student need shine guard athletic sock soccer ...  \n",
       "3  student need engage reading math way inspire m...  \n",
       "4  student need hand practice mathematics fun per...  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "24b27807-91fe-423e-a61a-198f4c816bb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['text']=df['cleaned_titles'] + \" \" + df['cleaned_summary']+\" \"+df['cleaned_essays']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "c99b1d1a-d677-4c97-9fc8-42ccda0d81e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of columns to drop\n",
    "columns_to_drop = [\n",
    "    'cleaned_essays_tokens_lemmatized',\n",
    "    'cleaned_summary_tokens_lemmatized',\n",
    "    'cleaned_titles_tokens_lemmatized',\n",
    "    'isdigit_summary',\n",
    "    'cleaned_titles',\n",
    "    'cleaned_summary',\n",
    "    'cleaned_essays'\n",
    "]\n",
    "\n",
    "# Drop columns from the DataFrame\n",
    "df.drop(columns=columns_to_drop, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "3572a652-3996-454c-99fc-328a16b28a03",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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_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>teacher_number_of_previously_posted_projects</th>\n",
       "      <th>project_is_approved</th>\n",
       "      <th>price</th>\n",
       "      <th>quantity</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>p253737</td>\n",
       "      <td>mrs</td>\n",
       "      <td>in</td>\n",
       "      <td>grades_prek_2</td>\n",
       "      <td>literacy_language</td>\n",
       "      <td>esl_literacy</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>154.60</td>\n",
       "      <td>23</td>\n",
       "      <td>educational support english learners home stud...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>p258326</td>\n",
       "      <td>mr</td>\n",
       "      <td>fl</td>\n",
       "      <td>grades_6_8</td>\n",
       "      <td>history_civics_health_sports</td>\n",
       "      <td>civics_government_teamsports</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>299.00</td>\n",
       "      <td>1</td>\n",
       "      <td>wanted projector hungry learners students_need...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>p182444</td>\n",
       "      <td>ms</td>\n",
       "      <td>az</td>\n",
       "      <td>grades_6_8</td>\n",
       "      <td>health_sports</td>\n",
       "      <td>health_wellness_teamsports</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>516.85</td>\n",
       "      <td>22</td>\n",
       "      <td>soccer equipment awesome middle school student...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>p246581</td>\n",
       "      <td>mrs</td>\n",
       "      <td>ky</td>\n",
       "      <td>grades_prek_2</td>\n",
       "      <td>literacy_language_math_science</td>\n",
       "      <td>literacy_mathematics</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>232.90</td>\n",
       "      <td>4</td>\n",
       "      <td>techie kindergarteners students_need_engage_re...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>p104768</td>\n",
       "      <td>mrs</td>\n",
       "      <td>tx</td>\n",
       "      <td>grades_prek_2</td>\n",
       "      <td>math_science</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>67.98</td>\n",
       "      <td>4</td>\n",
       "      <td>interactive math tools students_need_hands_pra...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id teacher_prefix school_state project_grade_category  \\\n",
       "0  p253737            mrs           in          grades_prek_2   \n",
       "1  p258326             mr           fl             grades_6_8   \n",
       "2  p182444             ms           az             grades_6_8   \n",
       "3  p246581            mrs           ky          grades_prek_2   \n",
       "4  p104768            mrs           tx          grades_prek_2   \n",
       "\n",
       "       project_subject_categories project_subject_subcategories  \\\n",
       "0               literacy_language                  esl_literacy   \n",
       "1    history_civics_health_sports  civics_government_teamsports   \n",
       "2                   health_sports    health_wellness_teamsports   \n",
       "3  literacy_language_math_science          literacy_mathematics   \n",
       "4                    math_science                   mathematics   \n",
       "\n",
       "   teacher_number_of_previously_posted_projects  project_is_approved   price  \\\n",
       "0                                             0                    0  154.60   \n",
       "1                                             7                    1  299.00   \n",
       "2                                             1                    0  516.85   \n",
       "3                                             4                    1  232.90   \n",
       "4                                             1                    1   67.98   \n",
       "\n",
       "   quantity                                               text  \n",
       "0        23  educational support english learners home stud...  \n",
       "1         1  wanted projector hungry learners students_need...  \n",
       "2        22  soccer equipment awesome middle school student...  \n",
       "3         4  techie kindergarteners students_need_engage_re...  \n",
       "4         4  interactive math tools students_need_hands_pra...  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ecc47a3-d929-495a-bbd2-9c14ff67c1ec",
   "metadata": {},
   "source": [
    "# Handling Outliers\n",
    "\n",
    "* by Filtering: This is a common method for handling outliers when you believe that values above a certain threshold are not representative or are likely errors.\n",
    "\n",
    "Since, we have sufficient size of the dataset this method can be useful as there is less chance of significant data loss. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "4269654e-87bb-4be8-b7c4-7e1ddb2b31f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of outliers in teacher_number_of_previously_posted_projects: 1073\n"
     ]
    }
   ],
   "source": [
    "outliers_teacher = df[df['teacher_number_of_previously_posted_projects'] > 139]\n",
    "print(\"Number of outliers in teacher_number_of_previously_posted_projects:\", len(outliers_teacher))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "b66b7220-cd1e-4b8f-a5dd-e507342005a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of outliers in quantity: 1085\n"
     ]
    }
   ],
   "source": [
    "outliers_quantity = df[df['quantity'] > 120]\n",
    "print(\"Number of outliers in quantity:\", len(outliers_quantity))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "7494f4fc-dde0-4a1c-9f92-c65545a5e09d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of outliers in price: 1093\n"
     ]
    }
   ],
   "source": [
    "outliers_price = df[df['price'] > 1583.45]\n",
    "print(\"Number of outliers in price:\", len(outliers_price))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "e98ac559-d013-49ff-b7ce-b00470e1c8fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['quantity'] <= 120]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "018549ed-2b4b-4767-b09d-5c37a6a18223",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['teacher_number_of_previously_posted_projects'] <= 139]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "b956cff0-9c68-4f75-bd5a-c5f705ad0b19",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['price'] <= 1583.45]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "f630052b-8651-4bd6-9e26-216a852eafdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(106027, 11)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "9cc87c02-acc2-4e40-bbc7-b3b2dcefa81a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "project_is_approved\n",
       "1    89967\n",
       "0    16060\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['project_is_approved'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "003f3466-90ff-48bf-a518-14dcc9010285",
   "metadata": {},
   "source": [
    "# Splitting Of Dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "d69da466-7b9d-4526-8f92-e8c28ab744e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['project_is_approved','id'], axis = 1) # independent varirables\n",
    "y = df['project_is_approved'] # dependent variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "45eddf13-f2d7-4bc1-b703-a667a21e7e80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(106027, 9)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "b60682cc-6030-40da-a326-e4280e3f589f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    }\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",
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       "      <th>2</th>\n",
       "      <td>ms</td>\n",
       "      <td>az</td>\n",
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       "      <td>22</td>\n",
       "      <td>soccer equipment awesome middle school student...</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mrs</td>\n",
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       "      <td>literacy_language_math_science</td>\n",
       "      <td>literacy_mathematics</td>\n",
       "      <td>4</td>\n",
       "      <td>232.90</td>\n",
       "      <td>4</td>\n",
       "      <td>techie kindergarteners students_need_engage_re...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mrs</td>\n",
       "      <td>tx</td>\n",
       "      <td>grades_prek_2</td>\n",
       "      <td>math_science</td>\n",
       "      <td>mathematics</td>\n",
       "      <td>1</td>\n",
       "      <td>67.98</td>\n",
       "      <td>4</td>\n",
       "      <td>interactive math tools students_need_hands_pra...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  teacher_prefix school_state project_grade_category  \\\n",
       "0            mrs           in          grades_prek_2   \n",
       "1             mr           fl             grades_6_8   \n",
       "2             ms           az             grades_6_8   \n",
       "3            mrs           ky          grades_prek_2   \n",
       "4            mrs           tx          grades_prek_2   \n",
       "\n",
       "       project_subject_categories project_subject_subcategories  \\\n",
       "0               literacy_language                  esl_literacy   \n",
       "1    history_civics_health_sports  civics_government_teamsports   \n",
       "2                   health_sports    health_wellness_teamsports   \n",
       "3  literacy_language_math_science          literacy_mathematics   \n",
       "4                    math_science                   mathematics   \n",
       "\n",
       "   teacher_number_of_previously_posted_projects   price  quantity  \\\n",
       "0                                             0  154.60        23   \n",
       "1                                             7  299.00         1   \n",
       "2                                             1  516.85        22   \n",
       "3                                             4  232.90         4   \n",
       "4                                             1   67.98         4   \n",
       "\n",
       "                                                text  \n",
       "0  educational support english learners home stud...  \n",
       "1  wanted projector hungry learners students_need...  \n",
       "2  soccer equipment awesome middle school student...  \n",
       "3  techie kindergarteners students_need_engage_re...  \n",
       "4  interactive math tools students_need_hands_pra...  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "3b9e1400-c7cf-4580-9a70-bd8ee2fc1e92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(106027,)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "eb52c281-8a6d-4f94-ae43-435a4c79bf66",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, \n",
    "    test_size=0.2,  \n",
    "    random_state=42,  \n",
    "    stratify=y \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "090eb328-bf33-41fa-b0f6-fadda166272f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original train data shape (84821, 9)\n",
      "Original test data shape (21206, 9)\n"
     ]
    }
   ],
   "source": [
    "print('Original train data shape',X_train.shape)\n",
    "print('Original test data shape',X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b6dbde5-292f-47a5-8b68-8b990bc66a4f",
   "metadata": {},
   "source": [
    "# TF-IDF For Textual Data\n",
    "\n",
    "In this section, we apply the TF-IDF (Term Frequency-Inverse Document Frequency) transformation to the text data. This process converts the textual data into numerical features that can be used by machine learning models.\n",
    "\n",
    "Term Frequency (TF): Measures how frequently a term appears in a document.\n",
    "Formula:\n",
    "𝑇𝐹(𝑡,𝑑)=(Number of times term 𝑡 appears in document 𝑑)/(Total number of terms in document 𝑑)\n",
    "\n",
    "Example: If the word \"data\" appears 3 times in a document with 100 words, TF(data, doc) = 3/100 = 0.03.\n",
    "\n",
    "Inverse Document Frequency (IDF): Measures how important a term is across all documents.\n",
    "Formula:\n",
    "𝐼𝐷𝐹(𝑡,𝐷)=log(Total number of documents 𝑁/Number of documents containing term 𝑡)\n",
    "\n",
    "Example: If the word \"data\" appears in 5 out of 1000 documents, IDF(data) = log(1000/5) ≈ 2.3.\n",
    "\n",
    "TF-IDF: The product of TF and IDF gives the TF-IDF score for a term in a document.\n",
    "Formula:\n",
    "𝑇𝐹-𝐼𝐷𝐹(𝑡,𝑑,𝐷)=𝑇𝐹(𝑡,𝑑)×𝐼𝐷𝐹(𝑡,𝐷)\n",
    "\n",
    "Example: Using the previous values, TF-IDF(data, doc) = 0.03 * 2.3 ≈ 0.069."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "f1c8c891-8254-4c58-85b8-ca4eeb18fed5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TF-IDF Titles Train shape: (84821, 20416)\n",
      "TF-IDF Titles Test shape: (21206, 20416)\n"
     ]
    }
   ],
   "source": [
    "tfidf_vectorizer = TfidfVectorizer(min_df=5 ,max_features=50000)\n",
    "tfidf_text_train = tfidf_vectorizer.fit_transform(X_train['text'])\n",
    "tfidf_text_test = tfidf_vectorizer.transform(X_test['text'])\n",
    "\n",
    "print('TF-IDF Titles Train shape:', tfidf_text_train.shape)\n",
    "print('TF-IDF Titles Test shape:', tfidf_text_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "22b6fecf-39d9-43ec-867c-4500b98a3869",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 20229)\t0.04151420140797907\n",
      "  (0, 6334)\t0.053866239318803175\n",
      "  (0, 13389)\t0.09229245395436585\n",
      "  (0, 14628)\t0.059030455049535226\n",
      "  (0, 15018)\t0.10264197565313872\n",
      "  (0, 6593)\t0.04837164117129428\n",
      "  (0, 6269)\t0.09172607511577786\n",
      "  (0, 6433)\t0.046370658960977106\n",
      "  (0, 4435)\t0.0981430229180117\n",
      "  (0, 11042)\t0.03440665625537908\n",
      "  (0, 12614)\t0.08219776900428172\n",
      "  (0, 1180)\t0.06080126237263298\n",
      "  (0, 17566)\t0.13523007563653874\n",
      "  (0, 18274)\t0.12249718562596332\n",
      "  (0, 19982)\t0.16859672505765258\n",
      "  (0, 17483)\t0.10015484542727057\n",
      "  (0, 3748)\t0.062120505965657845\n",
      "  (0, 7357)\t0.13033188522738118\n",
      "  (0, 18430)\t0.12205519731562123\n",
      "  (0, 9550)\t0.06995489445006393\n",
      "  (0, 17784)\t0.06928663113586898\n",
      "  (0, 3146)\t0.1260354894946952\n",
      "  (0, 1772)\t0.18316132854514738\n",
      "  (0, 1925)\t0.1806209453130236\n",
      "  (0, 4267)\t0.09793497234973253\n",
      "  :\t:\n",
      "  (0, 10860)\t0.06422167644358966\n",
      "  (0, 422)\t0.06792282684775398\n",
      "  (0, 466)\t0.05238674049540669\n",
      "  (0, 14558)\t0.06440705314977971\n",
      "  (0, 20024)\t0.0667603672547921\n",
      "  (0, 16508)\t0.07948325009948164\n",
      "  (0, 8788)\t0.04373704759251259\n",
      "  (0, 1747)\t0.04959759026743037\n",
      "  (0, 5986)\t0.06638806931502418\n",
      "  (0, 16744)\t0.08257505184101716\n",
      "  (0, 10621)\t0.059026214814354896\n",
      "  (0, 19474)\t0.17555510937468763\n",
      "  (0, 3736)\t0.030806424935394094\n",
      "  (0, 17537)\t0.1616219535972325\n",
      "  (0, 7968)\t0.08360517313335768\n",
      "  (0, 11)\t0.07964426841925108\n",
      "  (0, 2444)\t0.08442321819722104\n",
      "  (0, 46)\t0.10741462041349009\n",
      "  (0, 10986)\t0.13174670580757686\n",
      "  (0, 8135)\t0.049758196789386304\n",
      "  (0, 16026)\t0.11464249456269528\n",
      "  (0, 177)\t0.10430916635731427\n",
      "  (0, 3472)\t0.033358773115622145\n",
      "  (0, 14624)\t0.26224979372583845\n",
      "  (0, 15581)\t0.05497105061913828\n"
     ]
    }
   ],
   "source": [
    "print(tfidf_text_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "5363f4f0-b995-4c30-9cc9-8621dfa74bd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tfidf_vectorizer.get_params())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "244e6fbe-4fe5-4ec6-b5bb-a5d9de0a91a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20416"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tfidf_vectorizer.get_feature_names_out())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "fd878033-14f1-4cd3-b0ef-f12b87106b5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['00', '000', '00am', ..., 'zoos', 'zuma', 'zumba'], dtype=object)"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_vectorizer.get_feature_names_out()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "78603e3c-4dfa-477f-929b-a59f7856ea0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Combined Train shape: (84821, 20416)\n",
      "Combined Test shape: (21206, 20416)\n"
     ]
    }
   ],
   "source": [
    "# Combine TF-IDF features for each split\n",
    "X_train_combined = hstack([tfidf_text_train])\n",
    "X_test_combined = hstack([tfidf_text_test])\n",
    "\n",
    "# Print shapes\n",
    "print('Combined Train shape:', X_train_combined.shape)\n",
    "print('Combined Test shape:', X_test_combined.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f375a3ca-41a8-4492-8aa0-a8215109ee5e",
   "metadata": {},
   "source": [
    "# One-hot Encoding for Categorical Data\n",
    "\n",
    "In this section, we will discuss One-Hot Encoding, a method used to convert categorical data into a numerical format that can be used by machine learning models. One-Hot Encoding is a technique used to transform categorical variables into a format that can be provided to machine learning algorithms. It converts each categorical value into a new binary column (0 or 1). Each category is represented by a unique column where the presence of the category is marked with a 1 and the absence is marked with a 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "ecb9b02d-ebca-4b89-bdb7-1cbc01d25980",
   "metadata": {},
   "outputs": [],
   "source": [
    "def one_hot_encode_feature(X_train, X_test, feature_name):\n",
    "    encoder = OneHotEncoder(sparse_output=False, handle_unknown='ignore') \n",
    "    X_train_encoded = encoder.fit_transform(X_train[feature_name].values.reshape(-1, 1))\n",
    "    X_test_encoded = encoder.transform(X_test[feature_name].values.reshape(-1, 1))\n",
    "    \n",
    "    return X_train_encoded, X_test_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "5c79e335-7e03-4813-a0cb-cf8ca28b6c14",
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of categorical features\n",
    "categorical_features = [\n",
    "    'teacher_prefix',\n",
    "    'school_state',\n",
    "    'project_grade_category',\n",
    "    'project_subject_categories',\n",
    "    'project_subject_subcategories'\n",
    "]\n",
    "\n",
    "# Initialize encoded feature containers\n",
    "encoded_train = []\n",
    "encoded_test = []\n",
    "\n",
    "# Apply encoding function to each feature\n",
    "for feature in categorical_features:\n",
    "    train_enc, test_enc = one_hot_encode_feature(X_train, X_test, feature)\n",
    "    encoded_train.append(train_enc)\n",
    "    encoded_test.append(test_enc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "5c1b8318-8e30-4d03-a39b-c244e46b260a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Combined Train shape: (84821, 20921)\n",
      "Combined Test shape: (21206, 20921)\n"
     ]
    }
   ],
   "source": [
    "# Convert lists to arrays and combine them\n",
    "X_train_combined = hstack([X_train_combined] + encoded_train)\n",
    "X_test_combined = hstack([X_test_combined] + encoded_test)\n",
    "\n",
    "# Print shapes\n",
    "print('Combined Train shape:', X_train_combined.shape)\n",
    "print('Combined Test shape:', X_test_combined.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "852bb54c-6cec-4edd-bf21-ef1cb97c0963",
   "metadata": {},
   "source": [
    "# Normalization For Numerical Values\n",
    "\n",
    "In this section, we'll discuss Normalization, a technique used to rescale numerical features to a standard range, typically [0,1]. This process ensures that the data is on a similar scale, which is crucial for many machine learning algorithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "22592886-54d8-44a6-a100-f6d26424aa3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original train data shape for teacher_number_of_previously_posted_projects: (84821, 1)\n",
      "Original test data shape for teacher_number_of_previously_posted_projects: (21206, 1)\n",
      "[[0.        ]\n",
      " [0.        ]\n",
      " [0.00719424]\n",
      " [0.05035971]\n",
      " [0.02158273]]\n",
      "Original train data shape for quantity: (84821, 1)\n",
      "Original test data shape for quantity: (21206, 1)\n",
      "[[0.03361345]\n",
      " [0.67226891]\n",
      " [0.        ]\n",
      " [0.06722689]\n",
      " [0.1092437 ]]\n",
      "Original train data shape for price: (84821, 1)\n",
      "Original test data shape for price: (21206, 1)\n",
      "[[0.19727943]\n",
      " [0.0439691 ]\n",
      " [0.06389932]\n",
      " [0.41646123]\n",
      " [0.06554911]]\n"
     ]
    }
   ],
   "source": [
    "# List of numerical features to normalize\n",
    "numerical_features = ['teacher_number_of_previously_posted_projects', 'quantity', 'price']\n",
    "\n",
    "# Initialize the scaler\n",
    "scaled = MinMaxScaler()\n",
    "\n",
    "# Containers for the normalized features\n",
    "normalized_train_features = []\n",
    "normalized_test_features = []\n",
    "\n",
    "# Normalize each numerical feature and store the results\n",
    "for feature in numerical_features:\n",
    "    # Fit the scaler on the training data and transform both train and test data\n",
    "    scaled.fit(X_train[feature].values.reshape(-1, 1))\n",
    "    \n",
    "    train_transformed = scaled.transform(X_train[feature].values.reshape(-1, 1))\n",
    "    test_transformed = scaled.transform(X_test[feature].values.reshape(-1, 1))\n",
    "    \n",
    "    # Store the transformed features\n",
    "    normalized_train_features.append(train_transformed)\n",
    "    normalized_test_features.append(test_transformed)\n",
    "    \n",
    "    # Print the results for the current feature\n",
    "    print(f'Original train data shape for {feature}:', train_transformed.shape)\n",
    "    print(f'Original test data shape for {feature}:', test_transformed.shape)\n",
    "    print(train_transformed[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "fb3e5b12-a6a6-4574-ac8f-1ba9c06b31c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Combined Train shape: (84821, 20924)\n",
      "Combined Test shape: (21206, 20924)\n"
     ]
    }
   ],
   "source": [
    "# Combine One-Hot Encoded Features with TF-IDF Features and normalized numerical features\n",
    "X_train_combined = hstack([X_train_combined] + normalized_train_features).tocsr()\n",
    "X_test_combined = hstack([X_test_combined] + normalized_test_features).tocsr()\n",
    "\n",
    "# Print shapes\n",
    "print('Combined Train shape:', X_train_combined.shape)\n",
    "print('Combined Test shape:', X_test_combined.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "287a9f45-eb85-4170-a21b-6ee79338f6ce",
   "metadata": {},
   "source": [
    "# Logistic Regression\n",
    "\n",
    "Logistic regression is inherently designed for binary classification tasks, where the goal is to predict one of two possible outcomes. In our case, you are predicting whether a project is approved or not, which is a binary outcome (1 for approved, 0 for not approved). Logistic regression is naturally suited to this type of problem.\n",
    "\n",
    "Logistic regression models the probability that a given input belongs to a particular class. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "5a00d165-e3bf-4aaa-a7e9-3f1a1f5af1a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=1000, random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=1000, random_state=42)"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize Logistic Regression\n",
    "logistic_regression = LogisticRegression(max_iter=1000, random_state=42)\n",
    "\n",
    "# Train the model\n",
    "logistic_regression.fit(X_train_combined, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "8d67c5ab-9f69-4fcf-ac3c-a8ce7e0f76f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy: 0.8626165690100329\n",
      "Train Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.13      0.22     12848\n",
      "           1       0.86      0.99      0.92     71973\n",
      "\n",
      "    accuracy                           0.86     84821\n",
      "   macro avg       0.82      0.56      0.57     84821\n",
      "weighted avg       0.85      0.86      0.82     84821\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Predict on the Training Set\n",
    "y_train_pred = logistic_regression.predict(X_train_combined)\n",
    "train_accuracy = accuracy_score(y_train, y_train_pred)\n",
    "train_report = classification_report(y_train, y_train_pred)\n",
    "\n",
    "print(f'Train Accuracy: {train_accuracy}')\n",
    "print('Train Classification Report:')\n",
    "print(train_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "5e216012-1aae-4153-b4ff-e4c6a43fee49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 0.8494294067716684\n",
      "Test Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.52      0.08      0.14      3212\n",
      "           1       0.86      0.99      0.92     17994\n",
      "\n",
      "    accuracy                           0.85     21206\n",
      "   macro avg       0.69      0.53      0.53     21206\n",
      "weighted avg       0.81      0.85      0.80     21206\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Predict on the Test set\n",
    "y_test_pred = logistic_regression.predict(X_test_combined)\n",
    "test_accuracy = accuracy_score(y_test, y_test_pred)\n",
    "test_report = classification_report(y_test, y_test_pred)\n",
    "\n",
    "print(f'Test Accuracy: {test_accuracy}')\n",
    "print('Test Classification Report:')\n",
    "print(test_report)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "406203f2-8800-4f02-b08d-30f576e07ce4",
   "metadata": {},
   "source": [
    "# Logistic Regresson with Hyperparameter Tuning\n",
    "\n",
    "Logistic regression doesn't require extensive hyperparameter tuning. The primary parameter to adjust is the regularization strength (C), which controls the balance between fitting the data well and keeping the model simple."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "48cb8571-e320-4074-8a94-273ae27f36ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'C': 100}\n",
      "Best cross-validation score: 0.7239481058447795\n"
     ]
    }
   ],
   "source": [
    "# The range of C values to test\n",
    "param_grid = {'C': [0.01, 0.1, 1, 10, 100, 1000]}\n",
    "\n",
    "# Initialize Logistic Regression model\n",
    "model = LogisticRegression(max_iter=1500, class_weight='balanced')\n",
    "\n",
    "# Initialize GridSearchCV\n",
    "grid_search = GridSearchCV(estimator=model, param_grid=param_grid, cv=5, scoring='accuracy')\n",
    "\n",
    "# Fit GridSearchCV\n",
    "grid_search.fit(X_train_combined, y_train)\n",
    "\n",
    "# Get the best parameters\n",
    "best_params = grid_search.best_params_\n",
    "best_score = grid_search.best_score_\n",
    "\n",
    "print(\"Best parameters:\", best_params)\n",
    "print(\"Best cross-validation score:\", best_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "52349852-0cf1-418a-921a-12ea56b40b5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy: 0.8472430176489313\n",
      "Train Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.50      0.91      0.64     12848\n",
      "           1       0.98      0.84      0.90     71973\n",
      "\n",
      "    accuracy                           0.85     84821\n",
      "   macro avg       0.74      0.87      0.77     84821\n",
      "weighted avg       0.91      0.85      0.86     84821\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model with the best parameters on the training set\n",
    "best_model = grid_search.best_estimator_\n",
    "y_train_pred = best_model.predict(X_train_combined)\n",
    "print(\"Train Accuracy:\", accuracy_score(y_train, y_train_pred))\n",
    "print(\"Train Classification Report:\\n\", classification_report(y_train, y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "450cd3a9-938c-4f4d-8744-526defa3e9c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 0.7159294539281336\n",
      "Test Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.26      0.48      0.34      3212\n",
      "           1       0.89      0.76      0.82     17994\n",
      "\n",
      "    accuracy                           0.72     21206\n",
      "   macro avg       0.58      0.62      0.58     21206\n",
      "weighted avg       0.80      0.72      0.75     21206\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Evaluate on the test set\n",
    "y_test_pred = best_model.predict(X_test_combined)\n",
    "print(\"Test Accuracy:\", accuracy_score(y_test, y_test_pred))\n",
    "print(\"Test Classification Report:\\n\", classification_report(y_test, y_test_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c006a0a-0539-41c8-bf7a-c225e44abd86",
   "metadata": {},
   "source": [
    "# Decision Tree Classifier\n",
    "\n",
    "A decision tree can help classify whether a project is likely to be approved or not based on features such as project subject, teacher's previous projects, and funding amount. The tree splits the data to find patterns that separate approved from non-approved projects.\n",
    "\n",
    "The decision tree classifier with class_weight='balanced' helps handle class imbalance effectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "afd7309d-d92a-470a-b823-74ec5309e513",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy: 0.9999764209334953\n",
      "Train Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     12848\n",
      "           1       1.00      1.00      1.00     71973\n",
      "\n",
      "    accuracy                           1.00     84821\n",
      "   macro avg       1.00      1.00      1.00     84821\n",
      "weighted avg       1.00      1.00      1.00     84821\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Initialize Decision Tree Classifier\n",
    "decision_tree = DecisionTreeClassifier(random_state=42, class_weight='balanced')\n",
    "\n",
    "# Train the model\n",
    "decision_tree.fit(X_train_combined, y_train)\n",
    "y_train_pred=decision_tree.predict(X_train_combined)\n",
    "print(\"Train Accuracy:\", accuracy_score(y_train, y_train_pred))\n",
    "print(\"Train Classification Report:\\n\", classification_report(y_train, y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "335eb33e-4d89-43ba-8f94-fb852a430843",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 0.754361973026502\n",
      "Test Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.24      0.29      0.26      3212\n",
      "           1       0.87      0.84      0.85     17994\n",
      "\n",
      "    accuracy                           0.75     21206\n",
      "   macro avg       0.56      0.56      0.56     21206\n",
      "weighted avg       0.77      0.75      0.76     21206\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Predict on the Test set\n",
    "y_test_pred = decision_tree.predict(X_test_combined)\n",
    "test_accuracy = accuracy_score(y_test, y_test_pred)\n",
    "test_report = classification_report(y_test, y_test_pred)\n",
    "\n",
    "print(f'Test Accuracy: {test_accuracy}')\n",
    "print('Test Classification Report:')\n",
    "print(test_report)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f39189a6-8406-48a5-b4fd-d0abbbb34b81",
   "metadata": {},
   "source": [
    "# Random Forest\n",
    "\n",
    "Random Forest is an ensemble learning method used for classification and regression tasks. It constructs multiple decision trees during training and outputs the mode of the classes (for classification) or mean prediction (for regression) of the individual trees. Random Forest can achieve higher accuracy than a single decision tree. It reduces the risk of overfitting and improves generalization.\n",
    "\n",
    "In our case, predicting project approval, a Random Forest Classifier can be used to determine whether a project is likely to be approved based on various features. The model combines the predictions from multiple decision trees, leading to a more accurate and robust classifier compared to a single decision tree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "0a70bba3-d5ed-4e8a-8cab-8846e11436d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy: 0.9999646314002428\n",
      "Train Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     12848\n",
      "           1       1.00      1.00      1.00     71973\n",
      "\n",
      "    accuracy                           1.00     84821\n",
      "   macro avg       1.00      1.00      1.00     84821\n",
      "weighted avg       1.00      1.00      1.00     84821\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Initialize Random Forest Classifier\n",
    "random_forest = RandomForestClassifier(random_state=42)  \n",
    "\n",
    "# Train the model\n",
    "random_forest.fit(X_train_combined, y_train)\n",
    "y_train_pred=random_forest.predict(X_train_combined)\n",
    "print(\"Train Accuracy:\", accuracy_score(y_train, y_train_pred))\n",
    "print(\"Train Classification Report:\\n\", classification_report(y_train, y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "b1d73588-095a-4c06-9b43-a718230bd662",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 0.8477789304913703\n",
      "Test Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.21      0.00      0.00      3212\n",
      "           1       0.85      1.00      0.92     17994\n",
      "\n",
      "    accuracy                           0.85     21206\n",
      "   macro avg       0.53      0.50      0.46     21206\n",
      "weighted avg       0.75      0.85      0.78     21206\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Predict on the Test set\n",
    "y_test_pred = random_forest.predict(X_test_combined)\n",
    "test_accuracy = accuracy_score(y_test, y_test_pred)\n",
    "test_report = classification_report(y_test, y_test_pred)\n",
    "\n",
    "print(f'Test Accuracy: {test_accuracy}')\n",
    "print('Test Classification Report:')\n",
    "print(test_report)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f096c6d-0b6b-4b05-b4a1-0bc4acf4c003",
   "metadata": {},
   "source": [
    "# XGBoost\n",
    "\n",
    "XGBoost is built on the gradient boosting framework, where trees are built sequentially to correct errors made by previous trees. Each new tree improves upon the errors of the existing ensemble. In our case, predicting project approval, XGBoost can be used to handle complex patterns and interactions in the data, potentially yielding better performance compared to traditional models like logistic regression or decision trees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "d821e045-0cdb-4adb-a870-0a809cc7babd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy: 0.8772709588427394\n",
      "Train Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.22      0.36     12848\n",
      "           1       0.88      0.99      0.93     71973\n",
      "\n",
      "    accuracy                           0.88     84821\n",
      "   macro avg       0.87      0.61      0.64     84821\n",
      "weighted avg       0.88      0.88      0.84     84821\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Initialize XGBoost Classifier\n",
    "xgb_classifier = xgb.XGBClassifier(random_state=42, eval_metric='error')\n",
    "\n",
    "# Train the model\n",
    "xgb_classifier.fit(X_train_combined, y_train)\n",
    "# Predict on the training set\n",
    "y_train_pred = xgb_classifier.predict(X_train_combined)\n",
    "print(\"Train Accuracy:\", accuracy_score(y_train, y_train_pred))\n",
    "print(\"Train Classification Report:\\n\", classification_report(y_train, y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "e9099a5e-6774-4b62-9d48-5f58a9a0e9c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 0.8498066584928794\n",
      "Test Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.52      0.10      0.16      3212\n",
      "           1       0.86      0.98      0.92     17994\n",
      "\n",
      "    accuracy                           0.85     21206\n",
      "   macro avg       0.69      0.54      0.54     21206\n",
      "weighted avg       0.81      0.85      0.80     21206\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Predict on the Test set\n",
    "y_test_pred = xgb_classifier.predict(X_test_combined)\n",
    "test_accuracy = accuracy_score(y_test, y_test_pred)\n",
    "test_report = classification_report(y_test, y_test_pred)\n",
    "\n",
    "print(f'Test Accuracy: {test_accuracy}')\n",
    "print('Test Classification Report:')\n",
    "print(test_report)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "479d3168-b6b3-42f9-ae50-7fd68b5d9d75",
   "metadata": {},
   "source": [
    "# XGBoost with Hyperparamter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "5d917d8a-6abd-4946-b972-f90aeef8a31e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters for n_iter=5: {'gamma': 0.1, 'max_depth': 5, 'n_estimators': 314}\n",
      "Best Score for n_iter=5: 0.8504733415387074\n"
     ]
    }
   ],
   "source": [
    "# Initialize the XGBoost Classifier\n",
    "xgb_classifier = xgb.XGBClassifier(random_state=42, eval_metric='error')\n",
    "\n",
    "# Define the parameter distribution for hyperparameter tuning\n",
    "param_dist = {\n",
    "    'n_estimators': randint(100, 500),  \n",
    "    'max_depth': randint(3, 10),         \n",
    "    'gamma': [0, 0.1, 0.2, 0.3, 0.4, 0.5] \n",
    "}\n",
    "\n",
    "# Initialize RandomizedSearchCV with n_iter=5\n",
    "random_search= RandomizedSearchCV(estimator=xgb_classifier, param_distributions=param_dist, scoring='accuracy', cv=2, n_iter=3, random_state=42)\n",
    "\n",
    "# Fit RandomizedSearchCV\n",
    "random_search.fit(X_train_combined, y_train)\n",
    "\n",
    "# Best parameters and best score for n_iter=5\n",
    "print(\"Best Parameters for n_iter=5:\", random_search.best_params_)\n",
    "print(\"Best Score for n_iter=5:\", random_search.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "990e6897-1d55-4188-aac5-17d1337c7b8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy: 0.898657172162554\n",
      "Train Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.36      0.52     12848\n",
      "           1       0.90      0.99      0.94     71973\n",
      "\n",
      "    accuracy                           0.90     84821\n",
      "   macro avg       0.91      0.68      0.73     84821\n",
      "weighted avg       0.90      0.90      0.88     84821\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Train the model with the best parameters\n",
    "best_xgb_classifier = random_search.best_estimator_\n",
    "\n",
    "# Predict on the training set\n",
    "y_train_pred = best_xgb_classifier.predict(X_train_combined)\n",
    "print(\"Train Accuracy:\", accuracy_score(y_train, y_train_pred))\n",
    "print(\"Train Classification Report:\\n\", classification_report(y_train, y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "a852d49a-bf50-4675-8389-8e1f2785b8f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 0.8493350938413656\n",
      "Test Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.51      0.13      0.21      3212\n",
      "           1       0.86      0.98      0.92     17994\n",
      "\n",
      "    accuracy                           0.85     21206\n",
      "   macro avg       0.69      0.55      0.56     21206\n",
      "weighted avg       0.81      0.85      0.81     21206\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Predict on the Test set\n",
    "y_test_pred = best_xgb_classifier.predict(X_test_combined)\n",
    "test_accuracy = accuracy_score(y_test, y_test_pred)\n",
    "test_report = classification_report(y_test, y_test_pred)\n",
    "\n",
    "print(f'Test Accuracy: {test_accuracy}')\n",
    "print('Test Classification Report:')\n",
    "print(test_report)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e00ba412-b986-4b78-9da9-67b0ed8e7a28",
   "metadata": {},
   "source": [
    "# RESULT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "4061e35c-6773-4259-96c1-a6c4c6439229",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = {\n",
    "    'Model': [\n",
    "        'Logistic Regression (No Tuning)',\n",
    "        'Logistic Regression (Tuned)',\n",
    "        'Decision Tree',\n",
    "        'Random Forest',\n",
    "        'XGBoost (No Tuning)',\n",
    "        'XGBoost (Tuned)'\n",
    "    ],\n",
    "    'Train AUC': [0.86, 0.85, 0.99, 0.99, 0.88, 0.90],\n",
    "    'Test AUC': [0.85, 0.72, 0.75, 0.85, 0.85, 0.85]\n",
    "}\n",
    "\n",
    "# Create DataFrame\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# Create a plot with the table\n",
    "fig, ax = plt.subplots(figsize=(10, 4)) # set size frame\n",
    "ax.axis('tight')\n",
    "ax.axis('off')\n",
    "table = ax.table(cellText=df.values, colLabels=df.columns, cellLoc='center', loc='center', bbox=[0, 0, 1, 1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f21dc9c-db1d-46d3-9bd2-fb5e081f166b",
   "metadata": {},
   "source": [
    "OBSERVATIONS \n",
    "\n",
    "* Models like Logistic Regression (No Tuning), Random Forest, and XGBoost (both tuned and untuned) exhibit consistent performance between training and   test sets, indicating good generalization.\n",
    "* The Decision Tree model clearly overfits the training data, as seen by the large gap between the training and test AUC. This highlights the importance of using ensemble methods like Random Forest or tuning techniques like in XGBoost to mitigate overfitting.\n",
    "* XGBoost demonstrates robustness, performing well even without tuning, and showing only slight improvements after tuning. This makes it a reliable choice, especially when computational resources for extensive tuning are limited."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71346118-f98c-4ed4-bb20-192d7e8c1c13",
   "metadata": {},
   "source": [
    "# CONCLUSION\n",
    "\n",
    "* The Random Forest and XGBoost models, both with and without tuning, show strong generalization capabilities with high test AUC scores. These models are less prone to overfitting compared to the Decision Tree model.\n",
    "*  Given the performance across both datasets, XGBoost emerges as a top contender due to its balance between complexity and generalization. While the Random Forest is also a strong candidate, the marginal improvement in training AUC for tuned XGBoost suggests it might edge out in capturing more subtle patterns.\n",
    "\n",
    "In conclusion, XGBoost (both tuned and untuned) and Random Forest stand out as the models with the best trade-offs between training performance and generalization, making them strong candidates for deployment."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.4"
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 },
 "nbformat": 4,
 "nbformat_minor": 5
}