03_reddit_modeling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ba6b67c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import plot_confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3bb2ff54",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>selftext</th>\n",
       "      <th>subreddit</th>\n",
       "      <th>created_utc</th>\n",
       "      <th>post_word_count</th>\n",
       "      <th>post_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Anyone graduated/currently enrolled in U Washi...</td>\n",
       "      <td>Would like to connect and clarify some doubts....</td>\n",
       "      <td>1</td>\n",
       "      <td>1637254319</td>\n",
       "      <td>10</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>possible implications in returning to work in ...</td>\n",
       "      <td>I've held off my cs studies for most of covid ...</td>\n",
       "      <td>1</td>\n",
       "      <td>1637254205</td>\n",
       "      <td>80</td>\n",
       "      <td>415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>VR Career Advice</td>\n",
       "      <td>My background is in designing and programming ...</td>\n",
       "      <td>1</td>\n",
       "      <td>1637253634</td>\n",
       "      <td>468</td>\n",
       "      <td>2681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Professional resume writing service?</td>\n",
       "      <td>[removed]</td>\n",
       "      <td>1</td>\n",
       "      <td>1637253631</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Has being completely honest and upfront about ...</td>\n",
       "      <td>Hello,\\n\\nI hate playing the game of salary ne...</td>\n",
       "      <td>1</td>\n",
       "      <td>1637253246</td>\n",
       "      <td>78</td>\n",
       "      <td>440</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               title  \\\n",
       "0  Anyone graduated/currently enrolled in U Washi...   \n",
       "1  possible implications in returning to work in ...   \n",
       "2                                   VR Career Advice   \n",
       "3               Professional resume writing service?   \n",
       "4  Has being completely honest and upfront about ...   \n",
       "\n",
       "                                            selftext  subreddit  created_utc  \\\n",
       "0  Would like to connect and clarify some doubts....          1   1637254319   \n",
       "1  I've held off my cs studies for most of covid ...          1   1637254205   \n",
       "2  My background is in designing and programming ...          1   1637253634   \n",
       "3                                          [removed]          1   1637253631   \n",
       "4  Hello,\\n\\nI hate playing the game of salary ne...          1   1637253246   \n",
       "\n",
       "   post_word_count  post_length  \n",
       "0               10           65  \n",
       "1               80          415  \n",
       "2              468         2681  \n",
       "3                1            9  \n",
       "4               78          440  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Subreddit column is the target of this data. \n",
    "# 1: r/CSCareerQuestions\n",
    "# 2: r/PersonalFinance\n",
    "\n",
    "df = pd.read_csv('data/clean_subreddit_data.csv')\n",
    "df.head()\n",
    "# Remember our target of the modeling will be \"subreddit\" column and\n",
    "# our features will be the \"selftext\" column\n",
    "# for subreddits: 1 represents r/CSCareerQuestions, 0 represents r/personalfinance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9107e83c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.515014\n",
       "1    0.484986\n",
       "Name: subreddit, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# X is features, y is target\n",
    "# Note: In this classification problem we only use one column (text) for training\n",
    "X = df['selftext']\n",
    "y = df['subreddit']\n",
    "y.value_counts(normalize=True)\n",
    "# BASELINE ACCURACY FOR OUR MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cae3e921",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training and Testing split of the data set for our model\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,random_state=42,stratify = y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bf31fb32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training messages shape is:(1823,)\n",
      "Testing messages shape is:(608,)\n",
      "Training targets shape is:(1823,)\n",
      "Testing targets shape is:(608,)\n"
     ]
    }
   ],
   "source": [
    "# Show the sizes of training and test sets for X and y\n",
    "print(f'Training messages shape is:{X_train.shape}')\n",
    "print(f'Testing messages shape is:{X_test.shape}')\n",
    "print(f'Training targets shape is:{y_train.shape}')\n",
    "print(f'Testing targets shape is:{y_test.shape}')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b631934b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Initiate Count vectorizer with english stop words\n",
    "cvec = CountVectorizer(stop_words='english')\n",
    "X_train_cvec = cvec.fit_transform(X_train)\n",
    "X_test_cvec = cvec.transform(X_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "24c6d858",
   "metadata": {},
   "outputs": [
    {
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       "   00  000  000k  000lbs  001  008  00s  01  02  025  ...  zf5cp8u  zillow  \\\n",
       "0   0    0     0       0    0    0    0   0   0    0  ...        0       0   \n",
       "1   0    0     0       0    0    0    0   0   0    0  ...        0       0   \n",
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       "4   0    0     0       0    0    0    0   0   0    0  ...        0       0   \n",
       "\n",
       "   zip  zoho  zone  zoned  zoom  zuckerberg  être  cs_or_pf  \n",
       "0    0     0     0      0     0           0     0         0  \n",
       "1    0     0     0      0     0           0     0         1  \n",
       "2    0     0     0      0     0           0     0         0  \n",
       "3    0     0     0      0     0           0     0         0  \n",
       "4    0     0     0      0     0           0     0         1  \n",
       "\n",
       "[5 rows x 11146 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cteate a data frame from the cvec matrix by applying .todense()\n",
    "X_train_df = pd.DataFrame(X_train_cvec.todense(),columns = cvec.get_feature_names())\n",
    "X_test_df = pd.DataFrame(X_test_cvec.todense(),columns = cvec.get_feature_names())\n",
    "X_train_df['cs_or_pf'] = y_train.values\n",
    "X_train_df.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4b1d91f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dcb31e92",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>cs_or_pf</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>credit</th>\n",
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       "      <td>4</td>\n",
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       "      <th>money</th>\n",
       "      <td>553</td>\n",
       "      <td>70</td>\n",
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       "      <th>just</th>\n",
       "      <td>517</td>\n",
       "      <td>531</td>\n",
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       "      <td>308</td>\n",
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       "      <th>pay</th>\n",
       "      <td>501</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>account</th>\n",
       "      <td>440</td>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>like</th>\n",
       "      <td>423</td>\n",
       "      <td>617</td>\n",
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       "      <th>car</th>\n",
       "      <td>379</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>don</th>\n",
       "      <td>364</td>\n",
       "      <td>431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>years</th>\n",
       "      <td>355</td>\n",
       "      <td>316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>343</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ve</th>\n",
       "      <td>342</td>\n",
       "      <td>443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <td>322</td>\n",
       "      <td>419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>know</th>\n",
       "      <td>313</td>\n",
       "      <td>375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>card</th>\n",
       "      <td>297</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000</th>\n",
       "      <td>291</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>make</th>\n",
       "      <td>272</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>want</th>\n",
       "      <td>271</td>\n",
       "      <td>352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>need</th>\n",
       "      <td>262</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new</th>\n",
       "      <td>255</td>\n",
       "      <td>277</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cs_or_pf    0    1\n",
       "credit    582    4\n",
       "money     553   70\n",
       "just      517  531\n",
       "year      517  308\n",
       "pay       501  122\n",
       "account   440   12\n",
       "like      423  617\n",
       "car       379    3\n",
       "don       364  431\n",
       "years     355  316\n",
       "month     343   89\n",
       "ve        342  443\n",
       "time      322  419\n",
       "know      313  375\n",
       "card      297    3\n",
       "000       291   24\n",
       "make      272  170\n",
       "want      271  352\n",
       "need      262  162\n",
       "new       255  277"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the count of the personal finance and cs/Career Questions \n",
    "# 1 is for r/CSCareerQuestions, 0 is for r/personalfinance\n",
    "X_train_df.groupby('cs_or_pf').sum().T.sort_values(0,ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1e43d360",
   "metadata": {},
   "outputs": [],
   "source": [
    "#initial pipeline framework with countvectorizer and logistic regression \n",
    "pipe = Pipeline([\n",
    "    ('cvec',CountVectorizer()),\n",
    "    ('tfidf', TfidfTransformer()),\n",
    "    ('model',LogisticRegression())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6e8e1514",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9632500376335994\n",
      "{}\n"
     ]
    }
   ],
   "source": [
    "params = {\n",
    "    \n",
    "}# Baseline parameters - RUN 1\n",
    "gs = GridSearchCV(pipe,param_grid=params)\n",
    "gs.fit(X_train,y_train)\n",
    "print(gs.best_score_)\n",
    "print(gs.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a6b22ab3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9610537407797682\n",
      "{'cvec__stop_words': 'english'}\n"
     ]
    }
   ],
   "source": [
    "params2 = {\n",
    "    'cvec__stop_words':['english']\n",
    "}# Baseline parameters - RUN 1\n",
    "gs = GridSearchCV(pipe,param_grid=params2)\n",
    "gs.fit(X_train,y_train)\n",
    "print(gs.best_score_)\n",
    "print(gs.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "340c2495",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9490131578947368"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds = gs.predict(X_test)\n",
    "gs.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3923556d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9621526418786693\n",
      "{'cvec__ngram_range': (1, 2), 'cvec__stop_words': 'english', 'model__solver': 'newton-cg', 'tfidf__use_idf': True}\n"
     ]
    }
   ],
   "source": [
    "params3 = {\n",
    "    'cvec__ngram_range': [(1,1),(1, 2),(1,3),(2,2),(3,3)], \n",
    "    'cvec__stop_words':['english'],\n",
    "    'model__solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'], \n",
    "    'tfidf__use_idf': [True,False]\n",
    "}\n",
    "gs = GridSearchCV(pipe,param_grid=params3)\n",
    "gs.fit(X_train,y_train)\n",
    "print(gs.best_score_)\n",
    "print(gs.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9e00c8e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9424342105263158"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds = gs.predict(X_test)\n",
    "gs.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0de50bc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create confusion Matrix\n",
    "plot_confusion_matrix(gs,X_test,y_test,display_labels = ['r/PersonalFinance','r/CSCareerQuestions']);\n",
    "plt.savefig('./images/model_1_confusion_matrix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "eedb5c59",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe2 = Pipeline([\n",
    "    ('cvec',CountVectorizer()),\n",
    "    ('tfidf', TfidfTransformer()),\n",
    "    ('model',KNeighborsClassifier())\n",
    "])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5a07801a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9221210296552762\n",
      "{}\n"
     ]
    }
   ],
   "source": [
    "params = {}\n",
    "gs = GridSearchCV(pipe2,param_grid=params)\n",
    "gs.fit(X_train,y_train)\n",
    "print(gs.best_score_)\n",
    "print(gs.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bd5aa1a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9144736842105263"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds = gs.predict(X_test)\n",
    "gs.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b3ab79ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9369275929549902\n",
      "{'cvec__stop_words': 'english', 'tfidf__use_idf': True}\n"
     ]
    }
   ],
   "source": [
    "params2 = {\n",
    "    'cvec__stop_words':['english'],\n",
    "    'tfidf__use_idf': [True,False]\n",
    "    \n",
    "}# Baseline parameters - RUN 1\n",
    "gs = GridSearchCV(pipe2,param_grid=params2)\n",
    "gs.fit(X_train,y_train)\n",
    "print(gs.best_score_)\n",
    "print(gs.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b59532f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9276315789473685"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds = gs.predict(X_test)\n",
    "gs.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c0921d6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9566716844799037\n",
      "{'cvec__ngram_range': (1, 3), 'cvec__stop_words': 'english', 'model__n_neighbors': 19, 'tfidf__use_idf': True}\n"
     ]
    }
   ],
   "source": [
    "params3 = {\n",
    "    'cvec__ngram_range': [(1,1),(1, 2),(1,3),(2,2),(3,3)], \n",
    "    'cvec__stop_words':['english'],\n",
    "    'model__n_neighbors': list(range(1, 21, 2)), \n",
    "    'tfidf__use_idf': [True,False]\n",
    "}\n",
    "gs = GridSearchCV(pipe2,param_grid=params3)\n",
    "gs.fit(X_train,y_train)\n",
    "print(gs.best_score_)\n",
    "print(gs.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "75636494",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9391447368421053"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds = gs.predict(X_test)\n",
    "gs.score(X_test,y_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c8efaef4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create confusion Matrix\n",
    "plot_confusion_matrix(gs,X_test,y_test,display_labels = ['r/PersonalFinance','r/CSCareerQuestions']);\n",
    "plt.savefig('./images/model_2_confusion_matrix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95314c2b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}