MVP.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Riiid Project: Minimally Viable Product (MVP)  \n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table of Contents <a id='toc'></a>\n",
    "### 1. [Acquire](#acquire)\n",
    "### 2. [Prepare](#prepare)\n",
    "### 3. [Explore](#explore)\n",
    "### 4. [Modeling](#modeling)\n",
    "### 5. [Conclusions](#conclusions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## About Riiid Labs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Riiid Labs, an AI solutions provider delivering creative disruption to the education market. With a strong belief in equal opportunity in education, Riiid launched an AI tutor based on deep-learning algorithms in 2017 that attracted more than one million South Korean students. This year, the company released EdNet, the world’s largest open database for AI education containing more than 100 million student interactions. [Source](https://www.kaggle.com/c/riiid-test-answer-prediction/overview/description)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Our Goal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal of this project is to create a machine learning model that can predict whether or not a user will answer a problem correctly using data from Riiid's EdNet Database. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# General Imports \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "%matplotlib inline\n",
    "\n",
    "# Train Validate Test\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Modeling Imports\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "# Acquire and Prepare Files\n",
    "import acquire, prepare, explore, model\n",
    "from explore import rfe_ranker\n",
    "\n",
    "# Sklearn \n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.feature_selection import SelectKBest, f_regression\n",
    "from sklearn.metrics import plot_roc_curve, plot_precision_recall_curve, roc_auc_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "# Warnings \n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Acquire<a id='acquire'></a>\n",
    "Acqure the data used for this project. \n",
    "\n",
    "[Return to Top](#toc)\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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     "execution_count": 2,
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   "source": [
    "# Reading in data from local files\n",
    "df_train = pd.read_csv('train.csv')\n",
    "df_validate = pd.read_csv('validate.csv')\n",
    "df_test = pd.read_csv('test.csv')\n",
    "\n",
    "df_train.shape, df_validate.shape, df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "   timestamp  user_id  content_id  content_type_id  task_container_id  \\\n",
       "0          0  1864702        5720                0                  0   \n",
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       "\n",
       "   user_answer  answered_correctly  prior_question_elapsed_time  \\\n",
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       "  prior_question_had_explanation  lecture_id  tag  lecture_part type_of  \\\n",
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Train Data\n",
    "df_train.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
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       "0  15625124241  1864702        3209                0               2152   \n",
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       "\n",
       "   user_answer  answered_correctly  prior_question_elapsed_time  \\\n",
       "0            3                   1                      34660.0   \n",
       "1            3                   1                      34660.0   \n",
       "\n",
       "   prior_question_had_explanation  lecture_id  tag  lecture_part type_of  \\\n",
       "0                            True         NaN  NaN           NaN     NaN   \n",
       "1                            True         NaN  NaN           NaN     NaN   \n",
       "\n",
       "   question_id  bundle_id  correct_answer  question_part            tags  \n",
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       "1       3208.0     3207.0             3.0            4.0  113 169 162 38  "
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Validate Data\n",
    "df_validate.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
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       "     timestamp  user_id  content_id  content_type_id  task_container_id  \\\n",
       "0  20170378604  1864702        8811                0               2494   \n",
       "1  20170394313  1864702        8643                0               2495   \n",
       "\n",
       "   user_answer  answered_correctly  prior_question_elapsed_time  \\\n",
       "0            1                   1                       3000.0   \n",
       "1            3                   1                       4000.0   \n",
       "\n",
       "   prior_question_had_explanation  lecture_id  tag  lecture_part type_of  \\\n",
       "0                            True         NaN  NaN           NaN     NaN   \n",
       "1                            True         NaN  NaN           NaN     NaN   \n",
       "\n",
       "   question_id  bundle_id  correct_answer  question_part tags  \n",
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       "1       8643.0     8643.0             3.0            5.0   15  "
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test Data\n",
    "df_test.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Acquire Takeaways\n",
    "- Data initially acquired from Kaggle and saved as local files, lectures.csv, questions.csv, and train.csv.\n",
    "- Imported data from saved local files. \n",
    "- We used a random sample of 100K users for our analysis. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Prepare<a id='prepare'></a>\n",
    "Preparing the data for future exploration and modeling.\n",
    "\n",
    "[Return to Top](#toc)\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run Prepare Function (found in prepare.py module)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Produce train/validate/test\n",
    "# Produce scaled train/validate/test\n",
    "train, validate, test, train_s, validate_s, test_s = prepare.prep_riiid(df_train, df_validate, df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   answered_correctly  question_had_explanation  user_acc_mean  \\\n",
       "0                   1                         0       0.630049   \n",
       "1                   0                         0       0.630049   \n",
       "2                   1                         0       0.630049   \n",
       "3                   1                         0       0.630049   \n",
       "4                   0                         0       0.630049   \n",
       "\n",
       "   user_lectures_running_total   q_time  avg_user_q_time  \\\n",
       "0                            0  45951.0       11917302.0   \n",
       "1                            0  28391.0       11917302.0   \n",
       "2                            0  22436.0       11917302.0   \n",
       "3                            0  36191.0       11917302.0   \n",
       "4                            0  24322.0       11917302.0   \n",
       "\n",
       "   mean_content_accuracy  mean_task_accuracy  \n",
       "0               0.818182            0.682248  \n",
       "1               0.550000            0.534988  \n",
       "2               0.444444            0.445216  \n",
       "3               0.406250            0.544008  \n",
       "4               0.687500            0.485282  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the shape of train\n",
    "print(train.shape)\n",
    "\n",
    "# Display first 5 rows of train\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(403377, 7)\n"
     ]
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       "      <th></th>\n",
       "      <th>answered_correctly</th>\n",
       "      <th>question_had_explanation</th>\n",
       "      <th>user_acc_mean</th>\n",
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       "      <th>mean_task_accuracy</th>\n",
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      ],
      "text/plain": [
       "   answered_correctly  question_had_explanation  user_acc_mean  \\\n",
       "0                   1                         0       0.630049   \n",
       "1                   0                         0       0.630049   \n",
       "2                   1                         0       0.630049   \n",
       "3                   1                         0       0.630049   \n",
       "4                   0                         0       0.630049   \n",
       "\n",
       "   mean_content_accuracy  mean_task_accuracy  \\\n",
       "0               0.818182            0.682248   \n",
       "1               0.550000            0.534988   \n",
       "2               0.444444            0.445216   \n",
       "3               0.406250            0.544008   \n",
       "4               0.687500            0.485282   \n",
       "\n",
       "   user_lectures_running_total_scaled  avg_user_q_time_scaled  \n",
       "0                                 0.0                0.001202  \n",
       "1                                 0.0                0.001202  \n",
       "2                                 0.0                0.001202  \n",
       "3                                 0.0                0.001202  \n",
       "4                                 0.0                0.001202  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the shape of train scaled\n",
    "print(train_s.shape)\n",
    "\n",
    "# Display first 5 rows of train scaled \n",
    "train_s.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49945, 7)\n"
     ]
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       "      <th></th>\n",
       "      <th>answered_correctly</th>\n",
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      ],
      "text/plain": [
       "   answered_correctly  question_had_explanation  user_acc_mean  \\\n",
       "0                   1                         1       0.630049   \n",
       "1                   1                         1       0.630049   \n",
       "2                   1                         1       0.630049   \n",
       "3                   1                         1       0.630049   \n",
       "4                   1                         1       0.630049   \n",
       "\n",
       "   user_lectures_running_total  avg_user_q_time  mean_content_accuracy  \\\n",
       "0                            0       11917302.0               0.604167   \n",
       "1                            0       11917302.0               0.708333   \n",
       "2                            0       11917302.0               0.687500   \n",
       "3                            0       11917302.0               0.735294   \n",
       "4                            0       11917302.0               0.852941   \n",
       "\n",
       "   mean_task_accuracy  \n",
       "0            0.478261  \n",
       "1            0.478261  \n",
       "2            0.478261  \n",
       "3            0.619048  \n",
       "4            0.619048  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the shape of validate \n",
    "print(validate.shape)\n",
    "\n",
    "# Display first 5 rows of validate\n",
    "validate.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49945, 7)\n"
     ]
    },
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      ],
      "text/plain": [
       "   answered_correctly  question_had_explanation  user_acc_mean  \\\n",
       "0                   1                         1       0.630049   \n",
       "1                   1                         1       0.630049   \n",
       "2                   1                         1       0.630049   \n",
       "3                   1                         1       0.630049   \n",
       "4                   1                         1       0.630049   \n",
       "\n",
       "   mean_content_accuracy  mean_task_accuracy  \\\n",
       "0               0.604167            0.478261   \n",
       "1               0.708333            0.478261   \n",
       "2               0.687500            0.478261   \n",
       "3               0.735294            0.619048   \n",
       "4               0.852941            0.619048   \n",
       "\n",
       "   user_lectures_running_total_scaled  avg_user_q_time_scaled  \n",
       "0                                 0.0                0.001202  \n",
       "1                                 0.0                0.001202  \n",
       "2                                 0.0                0.001202  \n",
       "3                                 0.0                0.001202  \n",
       "4                                 0.0                0.001202  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the shape of validate scaled \n",
    "print(validate_s.shape)\n",
    "\n",
    "# Display first 5 rows of validate scaled \n",
    "validate_s.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "(51971, 7)\n"
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      "text/plain": [
       "   answered_correctly  question_had_explanation  user_acc_mean  \\\n",
       "0                   1                         1       0.630049   \n",
       "1                   1                         1       0.630049   \n",
       "2                   0                         1       0.630049   \n",
       "3                   1                         1       0.630049   \n",
       "4                   0                         1       0.630049   \n",
       "\n",
       "   user_lectures_running_total  avg_user_q_time  mean_content_accuracy  \\\n",
       "0                            0       11917302.0               0.468750   \n",
       "1                            0       11917302.0               0.290323   \n",
       "2                            0       11917302.0               0.387755   \n",
       "3                            0       11917302.0               0.740741   \n",
       "4                            0       11917302.0               0.428571   \n",
       "\n",
       "   mean_task_accuracy  \n",
       "0            0.529412  \n",
       "1            0.578947  \n",
       "2            0.588235  \n",
       "3            0.714286  \n",
       "4            0.687500  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the shape of test\n",
    "print(test.shape)\n",
    "\n",
    "# Display first 5 rows of test\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(51971, 7)\n"
     ]
    },
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.630049</td>\n",
       "      <td>0.290323</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.630049</td>\n",
       "      <td>0.387755</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.630049</td>\n",
       "      <td>0.740741</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.630049</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001202</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   answered_correctly  question_had_explanation  user_acc_mean  \\\n",
       "0                   1                         1       0.630049   \n",
       "1                   1                         1       0.630049   \n",
       "2                   0                         1       0.630049   \n",
       "3                   1                         1       0.630049   \n",
       "4                   0                         1       0.630049   \n",
       "\n",
       "   mean_content_accuracy  mean_task_accuracy  \\\n",
       "0               0.468750            0.529412   \n",
       "1               0.290323            0.578947   \n",
       "2               0.387755            0.588235   \n",
       "3               0.740741            0.714286   \n",
       "4               0.428571            0.687500   \n",
       "\n",
       "   user_lectures_running_total_scaled  avg_user_q_time_scaled  \n",
       "0                                 0.0                0.001202  \n",
       "1                                 0.0                0.001202  \n",
       "2                                 0.0                0.001202  \n",
       "3                                 0.0                0.001202  \n",
       "4                                 0.0                0.001202  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the shape of test scaled\n",
    "print(test_s.shape)\n",
    "\n",
    "# Display first 5 rows of test scaled\n",
    "test_s.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare Takeaways\n",
    "Used __prep_riiid__ function from __prepare.py__ to make the following changes:\n",
    "- Filled missing boolean values in question_had_explanation with False. Missing values indicated that the question did not have an explanation or the user viewed a lecture.\n",
    "- Filled missing values in prior_question_elapsed_time with 0. Missing values indicated that a user viewed a lecture before answering the first question in a bundle.\n",
    "- Dropped columns: lecture_id, tag, lecture_part, type_of, question_id, bundle_id, correct_answer, question_part, and tags\n",
    "- Dropped rows that represent lectures\n",
    "- Converted null and inf values to 0's\n",
    "- Converted True and False values to 1's and 0's respectively\n",
    "- Created scaled versions of the train, validate and test sets \n",
    "\n",
    "<b>Feature Engineering: (all in prepare.py file)</b> \n",
    "- Added column, user_lectures_running_total, that keeps running total of number of lectures viewed by user\n",
    "- Added column, user_acc_mean, that reflects the avg accuracy of a user on all questions they answered\t\n",
    "- Added column, mean_content_accuracy, that reflects avg accuracy of all users answering a particular question\n",
    "- Added column, mean_task_accuracy, that reflects avg accuracy of all users answering a particular group of questions\n",
    "- Added column to train, q_time, that reflects the amount of milliseconds it took the user to answer the current question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Explore<a id='explore'></a>\n",
    "Plotted graphs, ran feature selection, and performed hypothesis testing in order to discover drivers of 'answered_correctly'.\n",
    "\n",
    "[Return to Top](#toc)\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 403377 entries, 0 to 403376\n",
      "Data columns (total 8 columns):\n",
      " #   Column                       Non-Null Count   Dtype  \n",
      "---  ------                       --------------   -----  \n",
      " 0   answered_correctly           403377 non-null  int64  \n",
      " 1   question_had_explanation     403377 non-null  int64  \n",
      " 2   user_acc_mean                403377 non-null  float64\n",
      " 3   user_lectures_running_total  403377 non-null  int64  \n",
      " 4   q_time                       403377 non-null  float64\n",
      " 5   avg_user_q_time              403377 non-null  float64\n",
      " 6   mean_content_accuracy        403377 non-null  float64\n",
      " 7   mean_task_accuracy           403377 non-null  float64\n",
      "dtypes: float64(5), int64(3)\n",
      "memory usage: 27.7 MB\n"
     ]
    }
   ],
   "source": [
    "# Check data types and null value counts\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "answered_correctly has 2 unique values.\n",
      "question_had_explanation has 2 unique values.\n",
      "user_lectures_running_total has 249 unique values.\n"
     ]
    }
   ],
   "source": [
    "# Checking # of unique values in int64 data type columns\n",
    "for col in train:\n",
    "    if train[col].dtypes == 'int64': \n",
    "        print(f'{col} has {train[col].nunique()} unique values.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot distribution of all train columns \n",
    "train.hist(figsize=(15, 10),\n",
    "        grid=True, color='#0080ff', ec='black') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Plotting all variables to get a general view of their distributions  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# User_id vs Timestamp\n",
    "# Had to convert time from in milliseconds to weeks\n",
    "ts = df_train['timestamp']/(31536000000/52)\n",
    "fig = plt.figure(figsize=(12,6))\n",
    "ts.plot.hist(bins=100, color='#0080ff', ec='black') \n",
    "plt.title(\"Most Users Leave After First Week\", fontsize=20)\n",
    "plt.xlabel(\"Weeks Of Interaction\", fontsize=15)\n",
    "plt.ylabel(\"Number of Users (in Thousands)\", fontsize=15)\n",
    "plt.xticks(np.linspace(0,100,11),rotation=0, fontsize=15)\n",
    "plt.yticks([20000,40000,60000,80000,100000,120000],labels=['20','40','60','80','100','120'], rotation=0, fontsize=15)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Most users stop using the platform after the first week.\n",
    "- The rest of users stop using the platform after ten weeks.\n",
    "- This shows us who are our users and platform usage behavior. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count of Questions Answered Correct\n",
    "plt.figure(figsize=(10,6))\n",
    "train.answered_correctly.value_counts().sort_index().plot.bar(grid=True, color=['#d55e00', '#009e73'], ec='black')\n",
    "answer_correctly = train.answered_correctly.mean()\n",
    "plt.title(\"Count of Questions Answered Correctly\",fontsize=20) \n",
    "plt.xlabel('Answered Correctly',fontsize=15)\n",
    "plt.ylabel('Count (in Thousands)',fontsize=15)\n",
    "plt.xticks([0,1],labels=['False','True'],rotation=0,fontsize=15) \n",
    "plt.yticks([50000,100000,150000,200000,250000],labels=['50','100','150','200','250'],rotation=0,fontsize=15) \n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Of the total count of questions answered, most questions are answered correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Function to create plot \n",
    "# Plot displays how students perform on questions with and without explanations\n",
    "explore.question_explanation_graph(train) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Users perform better on questions with explanations, than questions without explanations. \n",
    "    - A possible reason for this could be that the guidance from the explanations are actually helpful. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Percent of Questions Answered Correct vs. Number of Questions Answered Per User\n",
    "user_percent = df_train[df_train.answered_correctly != -1].groupby('user_id')['answered_correctly'].agg(Mean='mean', Answers='count')\n",
    "user_percent = user_percent.query('Answers <= 1000')\n",
    "\n",
    "fig = plt.figure(figsize=(12,6))\n",
    "x = user_percent.Answers\n",
    "y = user_percent.Mean\n",
    "plt.scatter(x, y, marker='o',color='#0080ff')\n",
    "plt.title(\"Answering More Questions Leads to Slightly Higher Accuracy\", fontsize=20)\n",
    "plt.xlabel(\"Number of Questions Answered\", fontsize=15)\n",
    "plt.ylabel(\"Percent Answered Correct\", fontsize=15)\n",
    "plt.xticks(rotation=0, fontsize=15)\n",
    "plt.yticks(rotation=0, fontsize=15)\n",
    "\n",
    "z = np.polyfit(x, y, 1)\n",
    "p = np.poly1d(z)\n",
    "plt.plot(x,p(x),\"r--\")\n",
    "\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- There is a slight positive linear trend for the relationship between percentage answered correct and the number of the questions answered. \n",
    "\n",
    "- Most users answer less than 200 question on average. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis Testing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hypothesis Test 1\n",
    "\n",
    "### General Inquiry\n",
    "<br>\n",
    "Does a student receiving an explanation prior to a question have a connection with whether or not they'll answer it correctly?\n",
    "\n",
    "### Chi-Squared Test\n",
    "\n",
    "$H$0: question_had_explanation and answered_correctly are independent\n",
    "<br>\n",
    "$H$a: question_had_explanation and answered_correctly are dependant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>question_had_explanation</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>answered_correctly</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21210</td>\n",
       "      <td>119596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21974</td>\n",
       "      <td>240597</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "question_had_explanation      0       1\n",
       "answered_correctly                     \n",
       "0                         21210  119596\n",
       "1                         21974  240597"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating crosstab of data reflecting if question was answered correctly \n",
    "# and if it had an explanation.\n",
    "observed_pqhe = pd.crosstab(train.answered_correctly, \n",
    "                            train.question_had_explanation)\n",
    "\n",
    "# Displaying crosstab\n",
    "observed_pqhe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha = .05\n",
      "\n",
      "p = 0.0000\n"
     ]
    }
   ],
   "source": [
    "# Calculating chi^2, p value, degrees of freedom and expected values\n",
    "chi2, p, degf, expected = stats.chi2_contingency(observed_pqhe)\n",
    "\n",
    "# Printing results\n",
    "print('alpha = .05\\n')\n",
    "print(f'p = {p:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- P is less than alpha so we <b>reject</b> the null hypothesis that answered_correctly and question_had_explanation are independent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hypothesis Test 2\n",
    "\n",
    "### General Inquiry\n",
    "<br>\n",
    "Is there a linear relationship between the average time it takes a user to answer a question and their average accuracy?\n",
    "\n",
    "### Pearson Correlation Test\n",
    "\n",
    "$H$0: There is no linear relationship between avg_user_q_time and user_acc_mean\n",
    "<br>\n",
    "$H$a: There is a linear relationship between avg_user_q_time and user_acc_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha = .05\n",
      "\n",
      "r correlation coefficient -0.053798627856180434\n",
      "p value                    3.0491148951959335e-256\n"
     ]
    }
   ],
   "source": [
    "# Performing pearson correlation test\n",
    "r, p = stats.pearsonr(train.avg_user_q_time, train.user_acc_mean)\n",
    "\n",
    "# Displaying alpha and test results\n",
    "print(\"alpha = .05\\n\")\n",
    "print(\"r correlation coefficient\", r)\n",
    "print(\"p value                   \", p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- P is less than alpha so we <b>reject</b> the null hypothesis that there is no linear relationship between the variables.\n",
    "\n",
    "- Given the r correlation coefficient of -.05, we see that there is almost no relationship between the variables.\n",
    "\n",
    "- Although the relationship between these variables is weak, it was still worth exploring because to needed to see the correlation between these variables. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hypothesis Test 3\n",
    "\n",
    "### General Inquiry\n",
    "<br>\n",
    "Does users with high accuracy take less time to answer difficult questions than other users?\n",
    "\n",
    "### Two-Sample Two-Tailed T-Test\n",
    "$H$0: The avg time that users with above avg accuracy spend on questions with below avg content accuracy is equal to or greater than the avg time that users with avg or below accuracy spend on them.\n",
    "<br><br>\n",
    "$H$a: The avg time that users with above avg accuracy spend on questions with below avg content accuracy is less than the avg time that users with avg or below accuracy spend on them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating df of all questions that had less than average mean_content_accuracy\n",
    "# ie. all questions that users answered incorrectly more often than average\n",
    "hard_questions = train[train.mean_content_accuracy < train.mean_content_accuracy.mean()]\n",
    "\n",
    "# Filtering DF above for all users with above avg accuracy\n",
    "hi_acc_users = hard_questions[hard_questions.user_acc_mean > hard_questions.user_acc_mean.mean()]\n",
    "\n",
    "# Filtering DF above for all users with avg or below accuracy\n",
    "low_acc_users = hard_questions[hard_questions.user_acc_mean <= hard_questions.user_acc_mean.mean()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha = .05\n",
      "\n",
      "test statistic    -2.4486883290862065\n",
      "p value            0.014338813349406287\n"
     ]
    }
   ],
   "source": [
    "# Performing two sample, two tailed t-test\n",
    "t, p = stats.ttest_ind(hi_acc_users.q_time, low_acc_users.q_time, equal_var = False)\n",
    "\n",
    "# Printing results\n",
    "print('alpha = .05\\n')\n",
    "print(\"test statistic   \", t)\n",
    "print(\"p value           \", p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- P is less than alpha so we <b>reject</b> the null hypothesis. \n",
    "- The sub-alpha p value and negative test statistic suggest that users with higher accuracy spend less time on difficult questions than others. \n",
    "    - The explanation for this might be that many high accuracy users are more well prepared for difficult questions than others and not that they are spending more time on hard questions to improve their performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hypothesis Test 4\n",
    "\n",
    "### General Hypothesis\n",
    "<br>\n",
    "Is their a linear relationship between the amount of lectures a user has viewed and their task performance? Task performance is measured by a users overall performance on each bundle of questions. A bundle can contain 1 or more questions.\n",
    "\n",
    "### Pearson Correlation Test\n",
    "\n",
    "$H$0: There is no linear relationship between user_lectures_running_total and mean_task_accuracy\n",
    "<br>\n",
    "$H$a: There is a linear relationship between user_lectures_running_total and mean_task_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha = .05\n",
      "\n",
      "r correlation coefficient 0.20740509858727074\n",
      "p value                   0.0\n"
     ]
    }
   ],
   "source": [
    "# performing pearson correlation test\n",
    "r, p = stats.pearsonr(train.user_lectures_running_total, train.mean_task_accuracy)\n",
    "\n",
    "# printing alpha and results\n",
    "print(\"alpha = .05\\n\")\n",
    "print(\"r correlation coefficient\", r)\n",
    "print(\"p value                  \", p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Since p is less than alpha we will <b>reject</b> the null hypothesis that there is no linear relationship between mean_task_accuracy and user_lectures_running_total.    \n",
    "\n",
    "\n",
    "\n",
    "- The r correlation coefficient of .207 indicates that there is a weak linear relationship between these variables.\n",
    "    - As the amount of lectures that a user has seen increases, so does their task accuracy on average.\n",
    "    - Viewing lectures may be having a positive, albeit weak impact on user performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seperate the Features and Target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating train, validate, test DFs that only include non-target variables\n",
    "X_train = train_s.drop(columns='answered_correctly')\n",
    "y_train = train_s['answered_correctly']\n",
    "\n",
    "X_validate = validate_s.drop(columns='answered_correctly')\n",
    "y_validate = validate_s['answered_correctly']\n",
    "\n",
    "X_test = test_s.drop(columns='answered_correctly')\n",
    "y_test = test_s['answered_correctly']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>question_had_explanation</th>\n",
       "      <th>user_acc_mean</th>\n",
       "      <th>mean_content_accuracy</th>\n",
       "      <th>mean_task_accuracy</th>\n",
       "      <th>user_lectures_running_total_scaled</th>\n",
       "      <th>avg_user_q_time_scaled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.630049</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.682248</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.630049</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.534988</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.630049</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.445216</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001202</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   question_had_explanation  user_acc_mean  mean_content_accuracy  \\\n",
       "0                         0       0.630049               0.818182   \n",
       "1                         0       0.630049               0.550000   \n",
       "2                         0       0.630049               0.444444   \n",
       "\n",
       "   mean_task_accuracy  user_lectures_running_total_scaled  \\\n",
       "0            0.682248                                 0.0   \n",
       "1            0.534988                                 0.0   \n",
       "2            0.445216                                 0.0   \n",
       "\n",
       "   avg_user_q_time_scaled  \n",
       "0                0.001202  \n",
       "1                0.001202  \n",
       "2                0.001202  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the X_train\n",
    "X_train.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    0\n",
       "2    1\n",
       "Name: answered_correctly, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the y_train\n",
    "y_train.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select Top 5 Features by Select K Best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['question_had_explanation',\n",
       " 'user_acc_mean',\n",
       " 'mean_content_accuracy',\n",
       " 'mean_task_accuracy',\n",
       " 'user_lectures_running_total_scaled']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute the top 5 features\n",
    "f_features = explore.KBest_ranker(X_train, y_train, 5)\n",
    "\n",
    "# Print the top 5 features\n",
    "f_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select Top 5 Features by Recursive Feature Elimination (RFE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Feature</th>\n",
       "      <th>Rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mean_content_accuracy</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>user_acc_mean</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mean_task_accuracy</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>avg_user_q_time_scaled</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>question_had_explanation</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Feature  Rank\n",
       "2     mean_content_accuracy     1\n",
       "1             user_acc_mean     2\n",
       "3        mean_task_accuracy     3\n",
       "5    avg_user_q_time_scaled     4\n",
       "0  question_had_explanation     5"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using function from explore.py to rank using RFE\n",
    "rfe_ranker(train_s).head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore Takeaways\n",
    "### Graphs:\n",
    "- Most users leave after first week.\n",
    "- In general, most questions are answered correctly. \n",
    "- Questions that have explanations are more likely to be answered correctly. \n",
    "- Answering more questions leads to slight higher accuracy for students.      \n",
    "    \n",
    "### Hypothesis Testing:\n",
    "<br>\n",
    "<b>Hypothesis 1:</b> Chi^2 test results show that a question having an explanation and the question being answered correctly are dependant.\n",
    "<br>\n",
    "<br>\n",
    "<b>Hypothesis 2:</b> Pearson correlation test results reflect that there is an extremely weak inverse linear relationship between the average time a user spends on question and their average accuracy. \n",
    "<br>\n",
    "<br>\n",
    "<b>Hypothesis 3:</b> Two-sample one-tailed t-test results suggest that high accuracy users spend less time on hard questions than other users.\n",
    "<br>\n",
    "<br>\n",
    "<b>Hypothesis 4:</b> Pearson correlation test results reflect that there is a weak linear relationship between average task accuracy and the amount of lectures a user has viewed.     \n",
    "<br>\n",
    "\n",
    "### Feature Selection:\n",
    "The top 4 features that __Select K Best__ and __RFE__ rankers found in common were:\n",
    "- mean_content_accuracy\n",
    "- user_acc_mean\n",
    "- mean_task_accuracy\n",
    "- prior_question_had_explanation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Modeling<a id='modeling'></a>\n",
    "Create baseline and alternate models that will predict if a student answers a question correctly. \n",
    "\n",
    "[Return to Top](#toc)\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute the AUC score of the baseline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use the randomly generated classes as the baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, ..., 1, 0, 1])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute the size of y_train\n",
    "size = y_train.size\n",
    "\n",
    "# Construct y_baseline\n",
    "y_baseline_random = np.random.randint(2, size=size)\n",
    "\n",
    "# Print the y_baseline\n",
    "y_baseline_random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.500980258055526"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute the AUC score \n",
    "roc_auc_score(y_train, y_baseline_random)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use the most frequent class as the baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.650932\n",
       "0    0.349068\n",
       "Name: answered_correctly, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute the most frequent class\n",
    "y_train.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 1, 1, 1])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Construct the y_baseline_frequency\n",
    "y_baseline_frequency = np.array([1]*size)\n",
    "\n",
    "# Check the baseline values\n",
    "y_baseline_frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute the AUC score \n",
    "roc_auc_score(y_train, y_baseline_frequency)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Baseline Results**\n",
    "1. Two methods (randomly generated class and most frequent class) were used to generate the baseline values.\n",
    "2. The baseline AUC score is 0.5 regardless of the chosen methods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model the train/validate/test using various classification algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a list of names of the classifiers\n",
    "# Make sure all the classifiers have the method: predict_proba\n",
    "names = ['LogisticRegression',\n",
    "         'Decision Tree',\n",
    "         'Random Forest',\n",
    "         'Ada Boost',\n",
    "         'Gradient Boost',\n",
    "         'Nearest Neighbors',\n",
    "         'Naive Bayes',\n",
    "         'Neural Net'\n",
    "        ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a list of classifiers\n",
    "classifiers = [LogisticRegression(), \n",
    "               DecisionTreeClassifier(max_depth=3),\n",
    "               RandomForestClassifier(max_depth=3),\n",
    "               AdaBoostClassifier(),\n",
    "               GradientBoostingClassifier(),\n",
    "               KNeighborsClassifier(3),\n",
    "               GaussianNB(), \n",
    "               MLPClassifier() \n",
    "              ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sanity check: the size between the two should be the same\n",
    "len(names) == len(classifiers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Currently running on model LogisticRegression\n",
      "LogisticRegression has completed\n",
      "Currently running on model Decision Tree\n",
      "Decision Tree has completed\n",
      "Currently running on model Random Forest\n",
      "Random Forest has completed\n",
      "Currently running on model Ada Boost\n",
      "Ada Boost has completed\n",
      "Currently running on model Gradient Boost\n",
      "Gradient Boost has completed\n",
      "Currently running on model Nearest Neighbors\n",
      "Nearest Neighbors has completed\n",
      "Currently running on model Naive Bayes\n",
      "Naive Bayes has completed\n",
      "Currently running on model Neural Net\n",
      "Neural Net has completed\n"
     ]
    }
   ],
   "source": [
    "# Use the classifiers created above to model the train, validate and test\n",
    "metrics = model.model_multiple_algos(names, classifiers, \n",
    "                                     X_train, y_train, \n",
    "                                     X_validate, y_validate, \n",
    "                                     X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>AUC score</th>\n",
       "      <th>Algo</th>\n",
       "      <th>dataset</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.692036</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.691238</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.687428</td>\n",
       "      <td>Neural Net</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.686307</td>\n",
       "      <td>Gradient Boost</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.678628</td>\n",
       "      <td>Ada Boost</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.676666</td>\n",
       "      <td>Naive Bayes</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.675218</td>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.626234</td>\n",
       "      <td>Nearest Neighbors</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    AUC score                Algo dataset\n",
       "8    0.692036       Random Forest    test\n",
       "2    0.691238  LogisticRegression    test\n",
       "23   0.687428          Neural Net    test\n",
       "14   0.686307      Gradient Boost    test\n",
       "11   0.678628           Ada Boost    test\n",
       "20   0.676666         Naive Bayes    test\n",
       "5    0.675218       Decision Tree    test\n",
       "17   0.626234   Nearest Neighbors    test"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rank the auc scores on test dataset in the descending order\n",
    "mask = (metrics.dataset == 'test')\n",
    "metrics[mask].sort_values(by=\"AUC score\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Modeling Results**\n",
    "- The highest AUC score on the test dataset is produced by Random Forest.\n",
    "- The AUC score from the top model is 0.692.\n",
    "- The AUC score for the baseline is 0.500.\n",
    "- The top model surpassed the baseline by 0.192, which is a 38.4% improvement (which is a comparison of the difference between the scores divided by the baseline)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the ROC curve on the top model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the lr object and fit on train\n",
    "lr = RandomForestClassifier(max_depth=3)\n",
    "lr = lr.fit(X_train, y_train)\n",
    "\n",
    "# Plot the ROC curve on test\n",
    "model.auc_curve_plot1(lr, \"Random Forest\", X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modeling Takeaways\n",
    "- <b>Baseline:</b> 0.5 AUC Score  \n",
    "\n",
    "\n",
    "- <b>Top 3 performing models: (results are from the test set)</b> \n",
    "    - Random Forest: 0.692   \n",
    "    - Logistic Regression: 0.691\n",
    "    - Neural Net: 0.687    \n",
    "    \n",
    "    \n",
    "\n",
    "- <b>Features used on all models:</b>\n",
    "    - mean_content_accuracy\n",
    "    - user_acc_mean\n",
    "    - mean_task_accuracy\n",
    "    - question_had_explanation\n",
    "    - user_lectures_running_total\n",
    "    - avg_user_q_time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Conclusions<a id='conclusions'></a>\n",
    "Summary of data science pipeline takeaways and results.\n",
    "\n",
    "[Return to Top](#toc)\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Acquire: \n",
    "- Data initially acquired from Kaggle and saved as local files: lectures.csv, questions.csv, and train.csv.\n",
    "- Imported data from saved local files.\n",
    "\n",
    "### Prepare: \n",
    "- Filled missing values with appropriate values varying according to column.\n",
    "- Dropped columns that were not needed. \n",
    "- Dropped rows that represent lectures.\n",
    "- Converted True and False values to 1's and 0's, respectively.\n",
    "- Created scaled versions of the train, validate and test sets.\n",
    "- Added feature engineering columns.\n",
    "\n",
    "### Explore: \n",
    "\n",
    "__Graphs:__\n",
    "- Most users leave after first week.\n",
    "<br>\n",
    "- Most questions are answered correctly.\n",
    "<br>\n",
    "- Questions that have explanations are more likely to be answered correctly.\n",
    "<br>\n",
    "- Answering more questions leads to slight higher accuracy for students.\n",
    "\n",
    "__Hypothesis Testing:__\n",
    "\n",
    "- Hypothesis 1: Chi^2 test results show that a question having an explanation and the question being answered correctly are dependant. \n",
    "\n",
    "- Hypothesis 2: Pearson correlation test results reflect that there is an extremely weak inverse linear relationship between the average time a user spends on question and their average accuracy. \n",
    "\n",
    "- Hypothesis 3: Two-sample one-tailed t-test results suggest that high accuracy users \n",
    "\n",
    "- Hypothesis 4: Pearson correlation test results reflect that there is a weak linear relationship between average task accuracy and the amount of lectures a user has viewed. \n",
    "\n",
    "__Feature Selection:__\n",
    "The top 4 features that Select K Best and RFE rankers found in common were:\n",
    "- mean_content_accuracy\n",
    "- user_acc_mean\n",
    "- mean_task_accuracy\n",
    "- prior_question_had_explanation (renamed to 'question_had_explanation')\n",
    "\n",
    "### Modeling: \n",
    "\n",
    "__Overall:__ \n",
    "- The highest AUC score on the test dataset is produced by Random Forest.\n",
    "- The top model surpassed the baseline by 0.192, which is a 38.4% improvement (which is a comparison of the difference between the scores divided by the baseline).\n",
    "\n",
    "__Recommendations:__\n",
    "- Riiid should provide more questions with explanations for students. \n",
    "- Focus on improving user engagement after the first week to increase user retention. \n",
    "- Using our project, Riiid can assess: \n",
    "    - ... question difficulty level based on population performance (mean_content_accuracy and mean_task_accuracy).\n",
    "    - ... an individual's performance based on their personal performance (user_acc_mean). \n",
    "    - ... how time spent on a question reflects on a student's accuracy (avg_user_q_time_scaled). \n",
    "\n",
    "- Using our model, Riiid can predict future performance of current students. \n",
    "\n",
    "__Expectations:__\n",
    "- Providing more explanations could improve overall answer accuracy. \n",
    "- Increasing user retention would provide a better representation of student long-term performance. \n",
    "\n",
    "\n",
    "### Future Investigations\n",
    "- Use this predictive model on Riiid's other educational programs. \n",
    "- Explore more features and different modeling like xgboost and lightlgbm.\n",
    "- Improve model to predict new student performance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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