code/nb4_model_playground.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling Experimental Playground\n",
    "\n",
    "Trying out multiple types of models and then selecting the most promising one for hyperparameter tuning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import string\n",
    "\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk import word_tokenize, FreqDist\n",
    "from nltk.tokenize import TweetTokenizer\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, f1_score, plot_confusion_matrix, classification_report\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.pipeline import Pipeline \n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "playground_tweet_tokenizer = TweetTokenizer(preserve_case=True, strip_handles=True, reduce_len=True)\n",
    "playground_lemmatizer = WordNetLemmatizer()\n",
    "\n",
    "def playground_tokenize_lemmatize_tweet(tweet):\n",
    "    ''' \n",
    "    Tokenize then Lemmatize the given tweet \n",
    "    '''\n",
    "    tokens = playground_tweet_tokenizer.tokenize(tweet)\n",
    "    lemmatized_tokens = [playground_lemmatizer.lemmatize(token) for token in tokens]\n",
    "    return lemmatized_tokens\n",
    "\n",
    "def calc_and_display_performance_metric(classifer, \n",
    "                                        Xtest, \n",
    "                                        ytest, \n",
    "                                        Xtrain, \n",
    "                                        ytrain, \n",
    "                                        classifier_predictions, \n",
    "                                        class_label_names):\n",
    "    '''\n",
    "    Calcuate and display peformance metrics for a classifier\n",
    "    '''\n",
    "    # Metrics - on how well the classifer performed on test data AND getting metrics on training data to check for model overfitting.\n",
    "    train_predictions = classifer.predict(Xtrain)\n",
    "    show_peformance_metrics(ytrain, train_predictions, Xtest, ytest, classifier_predictions, class_label_names)\n",
    "    # Confusion Matrix\n",
    "    plot_pretty_cf(classifer, Xtest, ytest, normalize='true')\n",
    "\n",
    "\n",
    "def show_peformance_metrics(y_train, \n",
    "                            y_train_pred, \n",
    "                            X_test, \n",
    "                            y_test, \n",
    "                            y_pred, names):\n",
    "    ''' \n",
    "    Print out peformance metrics\n",
    "    '''\n",
    "    train_acc_core = accuracy_score(y_train, y_train_pred)\n",
    "    test_acc_score = accuracy_score(y_test, y_pred)\n",
    "    train_wf1_score = f1_score(y_train, y_train_pred, average='weighted')\n",
    "    test_wf1_score = f1_score(y_test, y_pred, average='weighted')\n",
    "    print('--------------------------------------------------------------------------')\n",
    "    print('Training Accuracy:{:.4}\\t\\t Testing Accuracy: {:.4}'.format(train_acc_core, test_acc_score))\n",
    "    print('Training Weighted F1:{:.4}\\t\\t Testing Weighted F1: {:.4}'.format(train_wf1_score, test_wf1_score))\n",
    "    print('--------------------------------------------------------------------------')\n",
    "    print(classification_report(y_test, y_pred, target_names=names))\n",
    "    print('--------------------------------------------------------------------------')\n",
    "\n",
    "\n",
    "def plot_pretty_cf(predictor, Xtest, ytest, cmap='Blues', normalize=None, title=None):\n",
    "    ''' Plot a pretty confusion matrix '''\n",
    "    fig, ax = plt.subplots(figsize=(6, 6))\n",
    "    plot_confusion_matrix(predictor, Xtest, ytest, cmap=cmap, normalize=normalize, ax=ax)\n",
    "    ax.set_title(title, size='large')\n",
    "    ax.set_xlabel('Predicted Label', size='medium')\n",
    "    ax.set_ylabel('True Label', size='medium')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>Unnamed: 0.1.1</th>\n",
       "      <th>content</th>\n",
       "      <th>user_loc</th>\n",
       "      <th>user_screen_name</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>fav_count</th>\n",
       "      <th>created_at</th>\n",
       "      <th>char_count</th>\n",
       "      <th>sentiment_method</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>_id</th>\n",
       "      <th>id_str</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>“Instructional Considerations for the 2020-21 ...</td>\n",
       "      <td>Illinois, USA</td>\n",
       "      <td>Erik_Youngman</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2020-08-02 00:10:26</td>\n",
       "      <td>276</td>\n",
       "      <td>tools</td>\n",
       "      <td>neutral</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>With all the uncertainty of what September wil...</td>\n",
       "      <td>Lyndhurst, NJ</td>\n",
       "      <td>Renee_LoBue</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-08-01 23:57:31</td>\n",
       "      <td>264</td>\n",
       "      <td>tools</td>\n",
       "      <td>positive</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Check this out on Wakelet - Digital learning a...</td>\n",
       "      <td>Cary, NC</td>\n",
       "      <td>SupriyaVasu</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-08-01 23:20:38</td>\n",
       "      <td>133</td>\n",
       "      <td>tools</td>\n",
       "      <td>neutral</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Happy Friendship Day!\\n#rdnums #nagaland #kohi...</td>\n",
       "      <td>Kohima, India</td>\n",
       "      <td>rdnums</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-08-01 23:17:09</td>\n",
       "      <td>264</td>\n",
       "      <td>tools</td>\n",
       "      <td>positive</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Beat the summer heat with over 400 cool games ...</td>\n",
       "      <td>Providence, RI</td>\n",
       "      <td>ABCyaGames</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2020-08-01 23:00:00</td>\n",
       "      <td>146</td>\n",
       "      <td>tools</td>\n",
       "      <td>positive</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1  Unnamed: 0.1.1  \\\n",
       "0           9           9.0             9.0   \n",
       "1          10          10.0            10.0   \n",
       "2          11          11.0            11.0   \n",
       "3          12          12.0            12.0   \n",
       "4          13          13.0            13.0   \n",
       "\n",
       "                                             content        user_loc  \\\n",
       "0  “Instructional Considerations for the 2020-21 ...   Illinois, USA   \n",
       "1  With all the uncertainty of what September wil...   Lyndhurst, NJ   \n",
       "2  Check this out on Wakelet - Digital learning a...        Cary, NC   \n",
       "3  Happy Friendship Day!\\n#rdnums #nagaland #kohi...   Kohima, India   \n",
       "4  Beat the summer heat with over 400 cool games ...  Providence, RI   \n",
       "\n",
       "  user_screen_name  retweet_count  fav_count           created_at  char_count  \\\n",
       "0    Erik_Youngman              0          2  2020-08-02 00:10:26         276   \n",
       "1      Renee_LoBue              0          0  2020-08-01 23:57:31         264   \n",
       "2      SupriyaVasu              0          0  2020-08-01 23:20:38         133   \n",
       "3           rdnums              2          1  2020-08-01 23:17:09         264   \n",
       "4       ABCyaGames              0          2  2020-08-01 23:00:00         146   \n",
       "\n",
       "  sentiment_method sentiment  _id  id_str  \n",
       "0            tools   neutral  NaN     NaN  \n",
       "1            tools  positive  NaN     NaN  \n",
       "2            tools   neutral  NaN     NaN  \n",
       "3            tools  positive  NaN     NaN  \n",
       "4            tools  positive  NaN     NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jun_2020_tweets = pd.read_csv('../data/us_only_external_data_tweets_TOOL_labeled.csv')\n",
    "jan_2021_tweets_qh = pd.read_csv('../data/jan_2021_queried_tweets_HUMAN_labeled.csv')\n",
    "jan_2021_tweets_q = pd.read_csv('../data/jan_2021_queried_tweets_TOOL_labeled.csv')\n",
    "jan_2021_tweets_s = pd.read_csv('../data/jan_2021_streaming_tweets_TOOL_labeled.csv')\n",
    "\n",
    "tweets_df = jun_2020_tweets.append(jan_2021_tweets_qh)\n",
    "tweets_df.append(jan_2021_tweets_q)\n",
    "tweets_df.append(jan_2021_tweets_s)\n",
    "tweets_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 30599 entries, 0 to 30598\n",
      "Data columns (total 14 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   Unnamed: 0        30599 non-null  int64 \n",
      " 1   Unnamed: 0.1      30599 non-null  int64 \n",
      " 2   content           30599 non-null  object\n",
      " 3   user_loc          30599 non-null  object\n",
      " 4   user_screen_name  30599 non-null  object\n",
      " 5   retweet_count     30599 non-null  int64 \n",
      " 6   fav_count         30599 non-null  int64 \n",
      " 7   created_at        30599 non-null  object\n",
      " 8   char_count        30599 non-null  int64 \n",
      " 9   sentiment_method  30599 non-null  object\n",
      " 10  sentiment         30599 non-null  object\n",
      " 11  loc_state         30599 non-null  object\n",
      " 12  loc_state_vetted  30599 non-null  object\n",
      " 13  region            30599 non-null  object\n",
      "dtypes: int64(5), object(9)\n",
      "memory usage: 3.3+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(30599, 14)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_df = pd.read_csv('../data/vetted_usa_tweets.csv')\n",
    "tweets_df.info()\n",
    "tweets_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 30599 entries, 0 to 30598\n",
      "Data columns (total 2 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   content    30599 non-null  object\n",
      " 1   sentiment  30599 non-null  object\n",
      "dtypes: object(2)\n",
      "memory usage: 478.2+ KB\n"
     ]
    }
   ],
   "source": [
    "tweets_df = tweets_df[['content', 'sentiment']]\n",
    "tweets_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "positive    22618\n",
       "neutral      4708\n",
       "negative     3273\n",
       "Name: sentiment, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_df['sentiment'].value_counts()\n",
    "# We have class imbalance here with many more positive tweets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling Experimentation Steps:\n",
    "\n",
    "* Create TRAIN and TEST data sets\n",
    "\n",
    "* Create a Pipeline to try out different classifiers\n",
    "    * Apply light text processing and vectorize TRAIN data using TF-IDF \n",
    "    * Use the chosen classifier\n",
    "\n",
    "* Create predictions for TEST set\n",
    "\n",
    "* Examine performance metrics: Weighted F1 Scores\n",
    "    * Compare the performance metricx for TRAIN and TEST  (If model does __much__ better on TRAIN than on TEST set, then we’re likely overfitting)\n",
    "\n",
    "* Choose the most promosing classifer to move forward with hyperparameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get TRAIN and TEST data and target and prep for processing steps\n",
    "\n",
    "three_label_names = ['Negative', 'Neutral', 'Positive']\n",
    "pg_X = tweets_df['content']\n",
    "pg_y = tweets_df['sentiment']\n",
    "\n",
    "pg_X_train, pg_X_test, pg_y_train, pg_y_test = train_test_split(pg_X,\n",
    "                                                                pg_y, \n",
    "                                                                test_size=0.3, \n",
    "                                                                random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Text Processing - Vectorize the Tweets using - TF-IDF\n",
    "# Use TF_IDF to focus on words that appear heavily in tweets from a given class, but rarely appear in tweets from other class/classes.\n",
    "# Use custom function as tokenzier - uses TweetTokenier and NLTK Wordnet Lemmatizier on the resulting tokens\n",
    "# Use english stopwords. Not removing/stripping punctuation as it may be useful for sentiment classifification\n",
    "\n",
    "playground_vectorizer = TfidfVectorizer(tokenizer=playground_tokenize_lemmatize_tweet,\n",
    "                             stop_words=stopwords.words('english'),\n",
    "                             decode_error='ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Try out RandomForest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------\n",
      "Training Accuracy:1.0\t\t Testing Accuracy: 0.8385\n",
      "Training Weighted F1:1.0\t\t Testing Weighted F1: 0.81\n",
      "--------------------------------------------------------------------------\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Negative       0.98      0.25      0.40       953\n",
      "     Neutral       0.91      0.50      0.65      1420\n",
      "    Positive       0.83      0.99      0.90      6807\n",
      "\n",
      "    accuracy                           0.84      9180\n",
      "   macro avg       0.90      0.58      0.65      9180\n",
      "weighted avg       0.86      0.84      0.81      9180\n",
      "\n",
      "--------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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   ],
   "source": [
    "#  RandomForest\n",
    "rf_clf = Pipeline([('tfidf', playground_vectorizer),\n",
    "                    ('clf', RandomForestClassifier(n_estimators=200, \n",
    "                                                   random_state=42, \n",
    "                                                   class_weight='balanced'))\n",
    "                    ])\n",
    "\n",
    "rf_clf.fit(pg_X_train, pg_y_train)\n",
    "\n",
    "rf_predictions = rf_clf.predict(pg_X_test)\n",
    "\n",
    "calc_and_display_performance_metric(classifer=rf_clf, \n",
    "                                    Xtest=pg_X_test, \n",
    "                                    ytest=pg_y_test,\n",
    "                                    Xtrain=pg_X_train,\n",
    "                                    ytrain=pg_y_train,\n",
    "                                    classifier_predictions=rf_predictions,\n",
    "                                    class_label_names=three_label_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Weighted F1 score for Random Forest was lack-luster. The F1 scores for Negative and Neutral classes were both very poor. Let's consider other model types."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Try out SGDClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------\n",
      "Training Accuracy:0.9682\t\t Testing Accuracy: 0.9038\n",
      "Training Weighted F1:0.9684\t\t Testing Weighted F1: 0.9031\n",
      "--------------------------------------------------------------------------\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Negative       0.77      0.75      0.76       953\n",
      "     Neutral       0.83      0.79      0.81      1420\n",
      "    Positive       0.94      0.95      0.94      6807\n",
      "\n",
      "    accuracy                           0.90      9180\n",
      "   macro avg       0.84      0.83      0.84      9180\n",
      "weighted avg       0.90      0.90      0.90      9180\n",
      "\n",
      "--------------------------------------------------------------------------\n"
     ]
    },
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   ],
   "source": [
    "# SGDClassifier: Linear classifiers (SVM, logistic regression, etc.) \n",
    "# with SGD training.\n",
    "# \n",
    "# The model it fits can be controlled with the loss parameter; \n",
    "# by default, it fits a linear support vector machine (SVM).\n",
    "# \n",
    "# loss set equal to hinge will yield behaviour such as that of a SVC with a linear kernel\n",
    "# penalty defaults to ‘l2’ which is the standard regularizer for linear SVM models.\n",
    "# \n",
    "\n",
    "sgd_clf = Pipeline([('tfidf', playground_vectorizer),\n",
    "                    ('clf', SGDClassifier(loss='hinge', \n",
    "                                         penalty='l2', \n",
    "                                         random_state=42, \n",
    "                                         max_iter=2000, \n",
    "                                         class_weight='balanced'))\n",
    "                    ])\n",
    "\n",
    "sgd_clf.fit(pg_X_train, pg_y_train)\n",
    "sgd_predictions = sgd_clf.predict(pg_X_test)\n",
    "\n",
    "calc_and_display_performance_metric(classifer=sgd_clf, \n",
    "                                    Xtest=pg_X_test, \n",
    "                                    ytest=pg_y_test,\n",
    "                                    Xtrain=pg_X_train,\n",
    "                                    ytrain=pg_y_train,\n",
    "                                    classifier_predictions=sgd_predictions,\n",
    "                                    class_label_names=three_label_names)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Weighted F1 score for SGDClassifier was better but the F1 scores for Negative and Neutral classes were both poor. Let's try out LinearSVC."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Try out LinearSVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------\n",
      "Training Accuracy:0.9968\t\t Testing Accuracy: 0.9203\n",
      "Training Weighted F1:0.9968\t\t Testing Weighted F1: 0.9189\n",
      "--------------------------------------------------------------------------\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Negative       0.83      0.76      0.80       953\n",
      "     Neutral       0.88      0.80      0.84      1420\n",
      "    Positive       0.94      0.97      0.95      6807\n",
      "\n",
      "    accuracy                           0.92      9180\n",
      "   macro avg       0.88      0.84      0.86      9180\n",
      "weighted avg       0.92      0.92      0.92      9180\n",
      "\n",
      "--------------------------------------------------------------------------\n"
     ]
    },
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      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
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     },
     "metadata": {
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   ],
   "source": [
    "# Linear SVC with no hyperparameter tuning\n",
    "not_tuned_lsvc_clf = Pipeline([('tfidf', playground_vectorizer),\n",
    "                               ('clf', LinearSVC(class_weight='balanced', max_iter=2000))\n",
    "                               ])\n",
    "\n",
    "not_tuned_lsvc_clf.fit(pg_X_train, pg_y_train)\n",
    "not_tuned_lsvc_predictions = not_tuned_lsvc_clf.predict(pg_X_test)\n",
    "\n",
    "calc_and_display_performance_metric(classifer=not_tuned_lsvc_clf, \n",
    "                                    Xtest=pg_X_test, \n",
    "                                    ytest=pg_y_test,\n",
    "                                    Xtrain=pg_X_train,\n",
    "                                    ytrain=pg_y_train,\n",
    "                                    classifier_predictions=not_tuned_lsvc_predictions,\n",
    "                                    class_label_names=three_label_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear SVC is promising! \n",
    "\n",
    "Let's move forward with this one. See the modeling.ipynb notebook for hyperparameter tuning and final model performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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