Logistic_Regression_AFF.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OBJECTIVE:\n",
    "-->Perform Logistic Regression using 2 fold cross validation, on Amazon Fine food reviews to predict the polarity of the review as either 'Positive' or 'Negative'. The LR is to be performed via L1 and L2 regularization implemetation on following text vectorization models such as:\n",
    "1)Bag of Words\n",
    "2)TF-IDF\n",
    "3)Avg Word2Vec\n",
    "4)TF-IDF Word2Vec\n",
    "\n",
    "-->Come up with the optimal Lambda using the 2-fold cross validation technique and use the same to predict the future TEST data and determine the various metrics such as accuracy,F1 ,precision,confusion matrix and recall scores for the same.\n",
    "\n",
    "-->Perform the cross validation for Lambda using both GridSearch and Random Search.\n",
    "\n",
    "-->Get the most important features for both classes and try to interpret the model.\n",
    "\n",
    "-->Try to analyse the sparsity and Performance trade-off by increasing the values of Lambda for L1 regularizer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\rahul\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# ============================== loading libraries ===========================================\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "from collections import Counter\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn import cross_validation\n",
    "import warnings\n",
    "import sqlite3\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn import metrics\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "# ============================================================================================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading the final dataset of AFF after Data preprocessing and removing duplicates done previously in TSNE\n",
    "\n",
    "con = sqlite3.connect('C:\\\\Users\\\\rahul\\\\Desktop\\\\AI COURSE\\\\Amazon Fine food\\\\amazon-fine-food-reviews (1)\\\\final.sqlite')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(364171, 12)\n",
      "(1000, 12)\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_sql_query(\"\"\"SELECT * FROM REVIEWS \"\"\",con)\n",
    "print(data.shape)\n",
    "\n",
    "#check no of Positive and negative reviews present\n",
    "data['Score'].value_counts()\n",
    "\n",
    "#Performing Time based slicing on the data\n",
    "sorted_data = data.sort_values('Time')\n",
    "\n",
    "#since facing memory issue just taking 100k data points\n",
    "sample_data = sorted_data.head(n=1000)\n",
    "\n",
    "print(sample_data.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label = sample_data['Score']\n",
    "\n",
    "# split the data set into train and test\n",
    "X_train, X_test, y_train, y_test = cross_validation.train_test_split(sample_data, label, test_size=0.3, random_state=0)\n",
    "\n",
    "type(sample_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "BOW Model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Bag of words using Scikit learn\n",
    "count_vect = CountVectorizer(dtype = 'float64')\n",
    "vocab = count_vect.fit(X_train['CleanedText'].values)\n",
    "type(vocab)\n",
    "\n",
    "#Using the vocab obtained in train to vectorize both train and test data\n",
    "X_train = vocab.transform(X_train['CleanedText'].values)\n",
    "X_test = vocab.transform(X_test['CleanedText'].values)\n",
    "epsilon = np.random.normal(loc=0.0, scale=0.001)\n",
    "X_train_per = X_train\n",
    "X_test_per = X_test\n",
    "X_train_per.data = X_train_per.data + epsilon\n",
    "X_test_per.data = X_test_per.data + epsilon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(700, 5439)\n",
      "(300, 5439)\n"
     ]
    }
   ],
   "source": [
    "#Standardise the Data as part of data pre-processing for LR\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "Scaler = StandardScaler(copy=True, with_mean=False, with_std=True).fit(X_train)\n",
    "X_train = Scaler.transform(X_train)\n",
    "X_test = Scaler.transform(X_test)\n",
    "\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import uniform\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "from wordcloud import WordCloud\n",
    "\n",
    "#function to find the optimal lambda in LR for 5 fold cross validation\n",
    "def find_optimal_lambda(x,y,k,search,pen):\n",
    "    \n",
    "    grid_list = [10**-4,10**-3, 10**-2,10**-1, 10**0,10**1, 10**2,10**3, 10**4]\n",
    "    uni_dist = uniform(10**-4,10**4)\n",
    "        \n",
    "    if((search == 'Grid') and (pen == 'l1') ):\n",
    "        tuned_parameters = [{'C': grid_list}]\n",
    "        model = GridSearchCV(LogisticRegression(penalty = 'l1',class_weight= 'balanced'), tuned_parameters, scoring = 'accuracy',n_jobs=-1, cv=k)\n",
    "    \n",
    "    elif((search == 'Grid') and (pen == 'l2') ):\n",
    "        tuned_parameters = [{'C': grid_list}]\n",
    "        model = GridSearchCV(LogisticRegression(penalty = 'l2',class_weight= 'balanced'), tuned_parameters, scoring = 'accuracy',n_jobs=-1, cv=k)\n",
    "    \n",
    "    elif((search == 'Random') and (pen == 'l1')):\n",
    "        \n",
    "        tuned_parameters = {'C': uni_dist}\n",
    "        model = RandomizedSearchCV(LogisticRegression(penalty = 'l1',class_weight= 'balanced'), tuned_parameters, scoring = 'accuracy',n_jobs=-1, cv=k)\n",
    "    \n",
    "    elif((search == 'Random') and (pen == 'l2')):\n",
    "        tuned_parameters = {'C': uni_dist}\n",
    "        model = RandomizedSearchCV(LogisticRegression(penalty = 'l2',class_weight= 'balanced'), tuned_parameters, scoring = 'accuracy',n_jobs=-1, cv=k)\n",
    "    \n",
    "    model.fit(x,y)\n",
    "    \n",
    "\n",
    "      # Get Test Scores Mean and std for each grid search\n",
    "    scores_mean = model.cv_results_['mean_test_score']\n",
    "    uni_list = model.cv_results_['params']\n",
    "    \n",
    "    rand_list = [] \n",
    "    \n",
    "    for i in uni_list:\n",
    "        rand_list.append(i['C'])\n",
    "    \n",
    "    rand_list.sort()\n",
    "    if(search == 'Grid'):\n",
    "        scores_mean = np.array(scores_mean).reshape(len(grid_list))\n",
    "        \n",
    "        errors_mean = 1 - scores_mean\n",
    "\n",
    "        plt.plot(grid_list,errors_mean)\n",
    "\n",
    "        for xy in zip(grid_list, np.round(errors_mean,3)):\n",
    "            plt.annotate('(%s, %s)' % xy, xy=xy, textcoords='data')\n",
    "\n",
    "        plt.xlabel('c - value')\n",
    "        plt.ylabel('Misclassification Error')\n",
    "        plt.show()\n",
    "    \n",
    "    elif(search == 'Random'):\n",
    "        scores_mean = np.array(scores_mean).reshape(len(rand_list))\n",
    "        errors_mean = 1 - scores_mean\n",
    "\n",
    "        plt.plot(rand_list,errors_mean)\n",
    "\n",
    "        for xy in zip(rand_list, np.round(errors_mean,3)):\n",
    "            plt.annotate('(%s, %s)' % xy, xy=xy, textcoords='data')\n",
    "\n",
    "        plt.xlabel('c - value')\n",
    "        plt.ylabel('Misclassification Error')\n",
    "        plt.show()\n",
    "    \n",
    "    \n",
    "    return model\n",
    "        \n",
    "    # ============================== LR with lambda = optimal_lambda ===============================================\n",
    "\n",
    "#function to predict the label of test data and return the same as list \n",
    "def pred_test_data(x_tr,y_tr,x_tst,y_tst,model):\n",
    "    \n",
    "    LR = model\n",
    "\n",
    "    # predict the response\n",
    "    pred = LR.predict(x_tst)\n",
    "    \n",
    "    # evaluate accuracy\n",
    "    acc = accuracy_score(y_tst, pred) * 100\n",
    "    print('\\nThe accuracy of the LR classifier for is %f%%' % ( acc))\n",
    "    \n",
    "    return pred\n",
    "\n",
    "#Function to create Confusion matrix creation and plotting the same\n",
    "def confusion_heatmap(x,y):\n",
    "    cnf_mat = confusion_matrix(x,y)\n",
    "    #labels = ['Negative' , 'Positive']\n",
    "    print(\"Confusion matrix is : \" ,cnf_mat )\n",
    "    ax = sns.heatmap(cnf_mat,cbar_kws={'label': 'colorbar title'},cmap='YlGnBu',linewidths=2, linecolor='yellow')\n",
    "    # X - Y axis labels\n",
    "    ax.set_ylabel('Predicted Polarity')\n",
    "    ax.set_xlabel('Actual Polarity')    \n",
    "    ax.set_xticklabels(['Negative' , 'Positive'])\n",
    "    ax.set_yticklabels(['Negative' , 'Positive'])\n",
    "\n",
    "def metric_score(x,y):\n",
    "    print(\"The precision score for our model is: \", precision_score(x,y)) \n",
    "    print(\"The f1 score for our model is: \", f1_score(x,y))\n",
    "    print(\"The recall score for our model is: \", recall_score(x,y))\n",
    "    print(\"The accuracy of our model is: \", accuracy_score(x,y))\n",
    "\n",
    "def generate_wordcloud(text): # optionally add: stopwords=STOPWORDS and change the arg below\n",
    "    wordcloud = WordCloud(relative_scaling = 1.0 ).generate(text)\n",
    "    plt.imshow(wordcloud)\n",
    "    plt.axis(\"off\")\n",
    "    plt.show()\n",
    "    \n",
    "\n",
    "#Function to increase lambda and check for sparsity and performance \n",
    "def sparsity_perf_tradeoff(x_tr,y_tr,x_tst,y_tst,pen):\n",
    "    # creating a list of alpha for NB\n",
    "    c_list = [10**4,10**3,10**2,10**1,10**0,10**-1,10**-2,10**-3,10**-4]\n",
    "    sparsity_list = []\n",
    "    for c_value in c_list:\n",
    "        clf = LogisticRegression(penalty = pen,C=c_value)\n",
    "        clf.fit(x_tr,y_tr)\n",
    "        y_pred = clf.predict(x_tst)\n",
    "        f1 =  f1_score(y_tst,y_pred)\n",
    "        sparsity_list.append(np.count_nonzero(clf.coef_))\n",
    "        print(\"For Lambda value of {}, The Sparsity is {} and f1 score is {} \".format(1/c_value,np.count_nonzero(clf.coef_),f1))\n",
    "    \n",
    "    plt.plot(c_list, sparsity_list)\n",
    "\n",
    "    for xy in zip(c_list, sparsity_list):\n",
    "        plt.annotate('(%s, %s)' % xy, xy=xy, textcoords='data')\n",
    "\n",
    "    plt.xlabel('C - value')\n",
    "    plt.ylabel('Sparsity value')\n",
    "    plt.show()\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1eccfd4ad30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The accuracy of the LR classifier for is 91.000000%\n",
      "Confusion matrix is :  [[  4  17]\n",
      " [ 10 269]]\n",
      "The precision score for our model is:  0.9405594405594405\n",
      "The f1 score for our model is:  0.9522123893805309\n",
      "The recall score for our model is:  0.96415770609319\n",
      "The accuracy of our model is:  0.91\n"
     ]
    },
    {
     "data": {
      "image/png": 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7Aad02daIiL40KDdeSXoJ8AXgv6kybW4m6SDb353s2EkDvu0PS/ou8NxSdKDtq6bS4IiIfjM4IzocC+xkeymApM2B7wBTD/jFo4C7bZ8kaX1Jm5WVWSIiZoVBGdIBbh8N9sWvgFprhU8a8CV9gGqu55ZUi5evBnwJ2LHzdkZE9Kd+H9KR9Iqye72kc4EzqIbZX0W1hvik6vTw9wK2Ba4EsH1rUiVHxGwzt88DPtWU+FG3Af+37P8OWOfhb3+4OgH/AduWqt87kmrN94yIGCTq8yEd2wdKmgMcavuT3ZyjznWKMyR9EXiMpDcD3weO76ayiIh+NU2LmE9JubN20jTIE6kzS+cYSTsDd1ON4/+b7Qu6rTAioh8N0CydH0n6LPBVVs2Hf+VkB9a5aPtR2/8CXDBOWUTErDBAs3T+oTwe1VJm4AWTHVhnDH9nYGxw322csoiIgTUAF20BsL1Tt8e2y5b5VuBtwOaSrml5aU2gVma2iIhB0e/TMkdJWptqUfTnlaKLgKNs3zXZse16+F+munPraODwlvIVtu/osq0REX1pgIZ0TgSuA/Ypz19HdY/UKyY8omiXLfMu4C5JnwLuaMmWuaakZ9u+bMrNjojoE4PSwwc2t92aHfODkq6uc2CdC9OfB/7U8vyeUhYRMWuMdLDNsPskLRh9ImlH4L46B9a5aCvbf/2tY/svkrI0YkTMKgM0pPNWYHEZyxdwB3BAnQPrBO5fSTqUv/Xq30aVrCciYtYYlAVQbF8NPF3SWuV5rQXMoV7AfwvwaeD9VHM9fwAs7KKdERF9q9/j/UTrjKuszVhnnfE6d9reTrVCekTErDUAQzpTTlrZbh7+e2x/TNJnqHr2q7B96FQrj4joF/0+S2d0nfGpaNfDv6E8LplqJRER/a7fh3RGSdoI+AzVmiQGLgUOs71ssmPbzcM/pzwu7lE7IyL6Vr/38FucRHVj7KvK89eWsp0nO7DdkM45jDOUM8p21yk6IyL6zZyRvh/DH7W+7ZNanp8s6R11Dmw3pHNMeXwF8H+oljUE2A+4qdMWRkT0s0EZ0gF+L+m1wFfK8/2AP9Q5cMLPaPsi2xcB29p+te1zyrY/sGCi4yIiBtGIXHubjKQTJd0u6bqWsnUlXSDpl+VxnVIuSZ+WtFTSNZK2m+T0b6DKo/M/wHJg71I2+Wes8Z71Jf19S6M3A9avc/KIiEHR4xWvTgZ2HVN2OPAD21tQ3c80mpRyN2CLsi1kktQ1tm+2/XLb69vewPaetn9T6zPWeM8/ARdKulDShcAPgVrjRRERg6KXAd/2xVQpD1rtAYxOglkM7NlSfoorP6FaTnb+ROeWtFjSY1qeryPpxDqfsc6NV+dJ2gJ4Uim60fb9dU4eETEoVmv+xqvH2V4OYHu5pA1K+YbALS3vW1bKlk9wnq1t/3H0ie07JW1bpwGT9vAlPQr4Z+AQ2z8DNpH00jonj4gYFJ308CUtlLSkZZtKupnxfjO0+/YZGR3/h+raAPXS5NR600nAFcAO5fky4Ezg23UqiIgYBJ3Mw7e9CFjUYRW3SZpfevfzgdtL+TJg45b3bQTc2uY8x1ItZP41qi+GfYAP12lAnTH8zW1/DHgQwPZ9jP+NFBExsOao/tals4HXl/3XA2e1lP9jma3zHOCu0aGf8dg+BXglcBvwO+AVtk+t04A6PfwHJM2j/MSQtDmQMfyImFV6eaetpK8AzwfWk7SMag3ajwBnSHojcDN/u1P2XGB3YClwL3DgZOe3/XPg5522q07A/wBwHrCxpNOo8jcc0GlFERH9rJfZMm3vN8FLLxznvQYO7lnlbbQN+KoSLd9Idbftc6iGcg6z/ftpaFtExLRZbQgGqtsGfNuW9C3bzwC+M01tioiYdgOUPK1rdS7a/kTSMxtvSUTEDOplaoV+VWcMfyfgLZJuAu6hGtax7a2bbFhExHSawuybgVEn4O/WeCsiImbYMAzptMuH/0iqBcyfAFwLnGB75XQ1LCJiOs0doPzI3WrXw19MdbPVJVS9/K2Aw6ajURER023OAI/N19Uu4G9l+2kAkk4ALp+eJkVETL8h6OC3DfgPju7YXllNyY+ImJ2GegwfeLqku8u+gHnl+egsnbUab11ExDQZ6oBve850NiQiYiYN+xh+RMTQGPZZOhERQ2Ooh3QiIoZJ7rSNiBgSg5wjp64E/IgIMg8/ImJoZAw/ImJIrDYy+4d0Gv8VI+nvJL2o7M+TtGbTdUZEdGpE9bdB1WjAl/Rm4GvAF0vRRsC32rx/oaQlkpYsWtRkyyIiVjUMAb/pIZ2DgWcBlwHY/qWkDSZ6s+1FQAn1Q3DJPCL6Ri7aTt39th8YTbwmaS6QQB4RfWcY8kM2HfAvkvReqsRrOwNvA85puM6IiI4N8lBNXU3/ijkc+B3VilkHAecC72+4zoiIjo10sA2qpnv4ewCn2D6u4XoiIqZEQ3DZsOkvq5cD/yXpVEkvKWP4ERF9Rx1sg6rRgG/7QKpF0M8E9gf+W9LxTdYZEdENqf42qBrvcdt+UNJ3qWbnzKMa5nlT0/VGRHRigON4bU3feLWrpJOBpcDewPHA/CbrjIjoxhzV3wZV0z38A4DTgYNs399wXRERXRvkoZq6Gg34tvdt8vwREb0yBPG+mYAv6VLbCyStYNU7awXY9lpN1BsR0a0E/C7ZXlAekxkzIgZCL++0lXQTsAJ4CFhpe3tJ6wJfBTYFbgL2sX1n72qdXNMXbU+tUxYRMdMamIe/k+1tbG9fnh8O/MD2FsAPyvNp1fSNV09pfVJuvHpGw3VGRHRsRK69dWkPYHHZXwzs2ZOGd6CRgC/piDJ+v7Wku8u2ArgNOKuJOiMipqLHN14ZOF/SFZIWlrLH2V4OUB4nTBXflKbG8I8GjpZ0tO0jmqgjIqKXOun9liC+sKVoUVnPY9SOtm8t639cIOnGnjRyipqapfMk2zcCZ0rabuzrtq9sot6IiG51Mg9/1cWaxn391vJ4u6RvUi0EdZuk+baXS5oP3D61FneuqXn476T69jt2nNcMvKCheiMiutKrSTqSHg2M2F5R9l8MHAWcDbwe+Eh5nPbh7aaGdBaWx52aOH9ERK/1cFrm44BvlpX+5gJftn2epJ8CZ0h6I3Az8Kqe1VhTo3faSnoVcF75pns/sB3wIdtXNVlvRESnehXwbf8KePo45X8AXtibWrrT9LTMfy3BfgGwC9VUpC80XGdERMeSD3/qHiqPLwE+b/ss4BEN1xkR0THJtbdB1XTA/62kLwL7AOdKWn0a6oyI6Fh6+FO3D/A9YFfbfwTWBf654TojIjqWFa+myPa9kv4b2EXSLsAlts9vss6IiG7MmekGTIOmk6cdBpxGdQvxBsCXJL29yTojIrqRHv7UvRF4tu17ACR9FPgx8JmG642I6NAAR/Kamg744m8zdSj7s//fakQMHA1BaGo64J8EXFZySUCVDvSEhuuMiOiYNPsnEDZ90fYTki4EFlD17A/MXbYR0Z/Sw++KpEcCbwGeAFwLfM72yibqiojoBQ3BLUJN9fAXAw8ClwC7AU8G3tFQXRERU5Yhne5tZftpAJJOAC5vqJ6IiB7JkE63Hhzdsb1SgzxxNSKGQmbpdO/pku4u+wLmlecCbHuthuqNiOhKAn6XbA/DXcoRMYtIsz9sNT0PPyJiQKSHHxExFDKkExExNDItMyJiKKSHHxExJIZh+ngCfkQEoCFYAiUBPyICyCydiIghkSGdiIihkYAfETEUkh45ImJopIcfETEURpIPPyJiWCTgR0QMhdxpGxExNGZ/wJftmW7DBNSvDYuIvuMpR2tzQ+2YI548kN8O6eFHRDAcqRX6uIcfoyQttL1optsR/SV/F9Gp2X9ZenZYONMNiL6Uv4voSAJ+RMSQSMCPiBgSCfiDIeO0MZ78XURHctE2ImJIpIcfETEkEvB7TJIlHdvy/N2SjmygnveOef6jXtcRzZD0kKSrJV0n6UxJj+riHMdL2qrs528hasmQTo9J+jOwHHim7d9Lejewhu0je1zPn2yv0ctzxvRo/W8n6TTgCtuf6MX5ItpJD7/3VlJdTPunsS9IWl/S1yX9tGw7tpRfIOlKSV+U9BtJ65XXviXpCknXS1pYyj4CzCu9xNNK2Z/K41cl7d5S58mSXilpjqSPl3qvkXRQ4/8moo5LgCcASHpn6fVfJ+kdpezRkr4j6Wel/NWl/EJJ2+dvITpiO1sPN+BPwFrATcDawLuBI8trXwYWlP1NgBvK/meBI8r+roCB9crzdcvjPOA64LGj9YyttzzuBSwu+48AbinHLgTeX8pXB5YAm830v69h3Fr+W80FzgLeCjwDuBZ4NLAGcD2wLfBK4LiWY9cujxcC2+dvIVsnW3LpNMD23ZJOAQ4F7mt56UXAVi2LJa8laU1gAdX/nNg+T9KdLcccKmmvsr8xsAXwhzbVfxf4tKTVqb48LrZ9n6QXA1tL2ru8b+1yrl93+zmja/MkXV32LwFOoAr637R9D4CkbwDPBc4DjpH0UeDbti/poJ78LcQqEvCb85/AlcBJLWUjwA62W78EUMs3wJjy51N9Sexg+15JFwKPbFep7T+X9+0CvBr4yujpgLfb/l7HnyR67T7b27QWTPQ3YPu/JD0D2B04WtL5to+qU0n+FmKsjOE3xPYdwBnAG1uKzwcOGX0iafR/+kuBfUrZi4F1SvnawJ0l2D8JeE7LuR6UtNoE1Z8OHEjVQxz9n/p7wFtHj5H0REmP7vLjRe9dDOwp6VHlv8tewCWSHg/ca/tLwDHAduMcm7+FqCUBv1nHAuu1PD8U2L5cKPs58JZS/kHgxZKuBHajmuWzgurn/FxJ1wAfAn7Scq5FwDWjF+rGOB94HvB92w+UsuOBnwNXSroO+CL5hdc3bF8JnAxcDlwGHG/7KuBpwOVlCOh9wL+Pc3j+FqKWTMvsA2WM9SHbKyXtAHx+7E/+iIipyrd6f9gEOEPSCPAA8OYZbk9EzELp4UdEDImM4UdEDIkE/IiIIZGAHxExJBLwoxZJe5VMoE+q8d4Dyvzxbut6vqRvT1B+l6SrJN0g6QPdnGeSYx4v6Wtlf5vWXDQRgy4BP+raj+oGsX1rvPcAoOuAP4lLbG8LbA+8ttyF2hOS5tq+1fZoyoFtqO5wjZgVEvBjUpLWAHakumt43zGvvUfStSWb40dKfpbtgdNKBsd5km5qyf65fbndH0nPkvSj0mP/kaQt67ap5Jy5Athc0iMlnVTacZWkncb5DOPWVX6NnCnpHOB8SZuWrJSPAI4CXl0+x6sl/VLS+uW4EUlLRz9XxCDIPPyoY0/gvJLX5Q5J29m+UtJu5bVnl/QP69q+Q9IhwLttLwGYIE0MwI3A88oNZy8C/oMqO+SkJD2WKtXEh4CDAWw/rQw5nS/piR3UtQOwdWn7puVcD0j6N6qMlIeUOp8EvIYqT9KLgJ/Z/n2d9kb0gwT8qGM/qiAHVW6W/agSw70IOMn2vfDX/EGdWBtYLGkLqpTQE+WDafVcSVcBfwE+Yvt6Sf8OfKa04UZJvwHGBvx2dV1Qs+0nUqUz/k/gDayaGC+i7yXgR1ulJ/0C4KmSDMwBLOk9VFkX69y5t5K/DR+2Zvv8EPBD23uVnvWFNc51ie2Xjm1mjePa1XVPjeOxfYuk2yS9AHg2VW8/YmBkDD8mszdwiu2/s72p7Y2p8qYvoErM9QaVNVklrVuOWQGs2XKOm6gW+IBVh2zWBn5b9g+YQhsvpgTfMpSzCfCLMe/ppq6xnwOqxGNfAs6w/VA3jY2YKQn4MZn9gG+OKfs6sL/t84CzgSUlm+O7y+snA18YvWhLlQ30U5IuAVqD5Meocrz/f6pfDt36HDBH0rXAV4EDbN8/5j3d1PVDqgVrrlZZWpDq865BhnNiACWXTkQHJG0PfNL2c2e6LRGdyhh+RE2SDqdaijBj9zGQ0sOPiBgSGcOPiBgSCfgREUMiAT8iYkgk4EdEDIkE/IiIIZGAHxExJP4XLxQN/IIaUysAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ecd2cc1240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find the optimal plane using grid search and L1 regualarisation \n",
    "\n",
    "encoder = preprocessing.LabelEncoder()\n",
    "enc = encoder.fit(y_train)\n",
    "y_en_train= enc.transform(y_train)\n",
    "y_en_test = enc.transform(y_test)\n",
    "\n",
    "\n",
    "opt_plane = find_optimal_lambda(X_train,y_en_train,2,'Grid','l1')\n",
    "y_pred = pred_test_data(X_train,y_en_train,X_test,y_en_test,opt_plane)\n",
    "type(y_pred)\n",
    "\n",
    "confusion_heatmap(y_en_test,y_pred)\n",
    "\n",
    "metric_score(y_en_test,y_pred)\n",
    "\n",
    "coef_org = opt_plane.best_estimator_.coef_\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interpretaion: \n",
    "1) We are getting a FN and FP value of 949 and 2117 while TN and TP value of 2716 and 24218.\n",
    "2) This shows we are doing really well in Postive classification.\n",
    "3) However we have a high False positive value as well which shows higher misclassification of negative class. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PERTUBATION TECHNIQUE:\n",
    "\n",
    "Apply the model on perturbed data and check for multicollinearity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ecdedbc8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The accuracy of the LR classifier for is 92.000000%\n",
      "Confusion matrix is :  [[  5  16]\n",
      " [  8 271]]\n",
      "The precision score for our model is:  0.9442508710801394\n",
      "The f1 score for our model is:  0.9575971731448764\n",
      "The recall score for our model is:  0.9713261648745519\n",
      "The accuracy of our model is:  0.92\n",
      "[[0. 0. 0. ... 0. 0. 0.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1eccfd766a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#perform the model on perturbed data to find multicollinearity\n",
    "# find the optimal plane using grid search and L1 regualarisation \n",
    "\n",
    "epsilon = np.random.normal(loc=0.0, scale=0.001)\n",
    "X_train_per = X_train\n",
    "X_test_per = X_test\n",
    "X_train_per.data = X_train_per.data + epsilon\n",
    "X_test_per.data = X_test_per.data + epsilon\n",
    "\n",
    "opt_plane = find_optimal_lambda(X_train_per,y_en_train,2,'Grid','l1')\n",
    "y_pred = pred_test_data(X_train_per,y_en_train,X_test_per,y_en_test,opt_plane)\n",
    "type(y_pred)\n",
    "\n",
    "confusion_heatmap(y_en_test,y_pred)\n",
    "\n",
    "\n",
    "metric_score(y_en_test,y_pred)\n",
    "\n",
    "coef_per = opt_plane.best_estimator_.coef_\n",
    "print(coef_per)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interpretaion: \n",
    "1) We are getting a FN and FP value of 758 and 2286 while TN and TP value of 2907 and 24049.\n",
    "2) This shows we are doing really well in Postive classification.\n",
    "3) However we have a high False positive value as well which shows higher misclassification of negative class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MULTICOLLINEARITY CHECK:\n",
    "\n",
    "check if more features changes by more than 30%, then do not use plane weights as feature importance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 5439)\n"
     ]
    }
   ],
   "source": [
    "print(coef_org.shape)\n",
    "# diff = (abs((coef_org-coef_per)/coef_org)>.3).sum()\n",
    "# print(diff)\n",
    "# print(\"Only {0:.2f} % of features have weights change more than 30%\".format((diff/len(coef_org[0,:])*100)))\n",
    "\n",
    "# neg_class_wt_sorted = np.argsort(coef_org)\n",
    "# pos_class_wt_sorted = np.argsort(-coef_org)\n",
    "# #If less than 15 % proceed with feature importance\n",
    "# # print(\"The most important features for negative class are :\", np.take(count_vect.get_feature_names(), neg_class_wt_sorted[:,:10]))\n",
    "\n",
    "# # print(\"The most important features for positive class are :\", np.take(count_vect.get_feature_names(), pos_class_wt_sorted[:,:10]))\n",
    "# neg_class_wt_sorted = np.argsort(coef_org)\n",
    "# pos_class_wt_sorted = np.argsort(-coef_org)\n",
    "\n",
    "# neg_text = np.take(count_vect.get_feature_names(), neg_class_wt_sorted[:,:10])\n",
    "# pos_text = np.take(count_vect.get_feature_names(), pos_class_wt_sorted[:,:10])\n",
    "\n",
    "# nega_text = \" \".join(words for words in neg_text[0])\n",
    "# posi_text = \" \".join(words for words in pos_text[0])\n",
    "\n",
    "# print(\"The wordcloud for top 10 negative features are:\")\n",
    "# generate_wordcloud(nega_text)\n",
    "\n",
    "# print(\"The wordcloud for top 10 positive features are:\")\n",
    "# generate_wordcloud(posi_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f0044fec50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The accuracy of the LR classifier for is 90.406667%\n",
      "Confusion matrix is :  [[ 2833   832]\n",
      " [ 2046 24289]]\n",
      "The precision score for our model is:  0.9668802993511405\n",
      "The f1 score for our model is:  0.9440687189054726\n",
      "The recall score for our model is:  0.922308714638314\n",
      "The accuracy of our model is:  0.9040666666666667\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f004f59860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find the optimal plane using grid search and L2 regualarisation \n",
    "\n",
    "opt_plane = find_optimal_lambda(X_train,y_en_train,2,'Grid','l2')\n",
    "y_pred = pred_test_data(X_train,y_en_train,X_test,y_en_test,opt_plane)\n",
    "type(y_pred)\n",
    "\n",
    "\n",
    "confusion_heatmap(y_en_test,y_pred)\n",
    "\n",
    "metric_score(y_en_test,y_pred)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interpretaion: \n",
    "1) We are getting a FN and FP value of 832 and 2046 while TN and TP value of 2833 and 24289.\n",
    "2) This shows we are doing really well in Postive classification.\n",
    "3) However we have a high False positive value as well which shows higher misclassification of negative class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f0044f4e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The accuracy of the LR classifier for is 87.596667%\n",
      "Confusion matrix is :  [[ 2332  1333]\n",
      " [ 2388 23947]]\n",
      "The precision score for our model is:  0.9472705696202531\n",
      "The f1 score for our model is:  0.9279085537150054\n",
      "The recall score for our model is:  0.9093221947977976\n",
      "The accuracy of our model is:  0.8759666666666667\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f005103e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find the optimal plane using Random search and L1 regualarisation \n",
    "\n",
    "opt_plane = find_optimal_lambda(X_train,y_en_train,2,'Random','l1')\n",
    "y_pred = pred_test_data(X_train,y_en_train,X_test,y_en_test,opt_plane)\n",
    "type(y_pred)\n",
    "\n",
    "confusion_heatmap(y_en_test,y_pred)\n",
    "\n",
    "metric_score(y_en_test,y_pred)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interpretaion: \n",
    "1) We are getting a FN and FP value of 1333 and 2388 while TN and TP value of 2332 and 23947.\n",
    "2) This shows we are doing really well in Postive classification.\n",
    "3) However we have a very high False positive value as well which shows higher misclassification of negative class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f004f594a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The accuracy of the LR classifier for is 85.803333%\n",
      "Confusion matrix is :  [[ 2324  1341]\n",
      " [ 2918 23417]]\n",
      "The precision score for our model is:  0.9458356894741093\n",
      "The f1 score for our model is:  0.9166422014757404\n",
      "The recall score for our model is:  0.8891968862730207\n",
      "The accuracy of our model is:  0.8580333333333333\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f07f47d080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find the optimal plane using Random search and L2 regualarisation \n",
    "\n",
    "opt_plane = find_optimal_lambda(X_train,y_en_train,2,'Random','l2')\n",
    "y_pred = pred_test_data(X_train,y_en_train,X_test,y_en_test,opt_plane)\n",
    "type(y_pred)\n",
    "\n",
    "confusion_heatmap(y_en_test,y_pred)\n",
    "   \n",
    "metric_score(y_en_test,y_pred)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interpretaion: \n",
    "1) We are getting a FN and FP value of 1341 and 2918 while TN and TP value of 2324 and 23417.\n",
    "2) This shows we are doing really well in Postive class classification.\n",
    "3) However we have a high False positive value as well which shows higher misclassification of negative class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For Lambda value of 0.0001, The Sparsity is 22991 and f1 score is 0.9303608365892899 \n",
      "For Lambda value of 0.001, The Sparsity is 19579 and f1 score is 0.9322215422276623 \n",
      "For Lambda value of 0.01, The Sparsity is 17100 and f1 score is 0.932876659905859 \n",
      "For Lambda value of 0.1, The Sparsity is 14445 and f1 score is 0.9355862227149356 \n",
      "For Lambda value of 1.0, The Sparsity is 12213 and f1 score is 0.9432956319737995 \n",
      "For Lambda value of 10.0, The Sparsity is 9622 and f1 score is 0.9551698795861836 \n",
      "For Lambda value of 100.0, The Sparsity is 3194 and f1 score is 0.9573917224544143 \n",
      "For Lambda value of 1000.0, The Sparsity is 61 and f1 score is 0.9403236199613374 \n",
      "For Lambda value of 10000.0, The Sparsity is 0 and f1 score is 0.9349427531729831 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f06887ca58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Vary the values of lambda and try to check sparsity performance tradeoff\n",
    "\n",
    "sparsity_perf_tradeoff(X_train,y_en_train,X_test,y_en_test,'l1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Search Type | Regularisation | C-Value | Accuracy |F1-Score|\n",
    "| --- | --- | --- |--- |\n",
    "| Grid | L1 | 0.1 | 89.78 | 94.04 |\n",
    "| Grid | L2 | 0.0001 | 90.40 | 94.41 |\n",
    "| Random | L1 | 5.745 | 87.60 | 92.79 |\n",
    "| Random | L2 | 4200 | 85.80 | 91.86 |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion:\n",
    "\n",
    "1)The LR was performed on AFF using BOW model via L1 regularization and L2 regularization using both Gridsearch and Randomsearch.\n",
    "2)For Gridsearch we get an accuracy of 89.78% for L1 reg and 90.41% for L2 reg.\n",
    "3)For Randomsearch we get an accuracy of 87.60% for L1 reg and 85.80% for L2 reg.\n",
    "4)From above 2 points we can see that L1 reg performs better than L2 reg as L2 reg introduces sparsity to less important features which might lead to performance loss.\n",
    "5)We analyse performance vs Sparsity by increasing the value of lambda. As Lambda increases, the sparsity increases but it also leads to tip in performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TF-IDF MODEL:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "scipy.sparse.csr.csr_matrix"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = cross_validation.train_test_split(sample_data, label, test_size=0.3, random_state=0)\n",
    "#Converting text into vector using tfidf vectorizer\n",
    "tf_idf_vect = TfidfVectorizer(ngram_range=(1,2))\n",
    "vocab = tf_idf_vect.fit(X_train['CleanedText'].values)\n",
    "X_train = vocab.transform(X_train['CleanedText'].values)\n",
    "X_test = vocab.transform(X_test['CleanedText'].values)\n",
    "\n",
    "type(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(70000, 928799)\n",
      "(30000, 928799)\n"
     ]
    }
   ],
   "source": [
    "#Standardise the Data as part of data pre-processing for LR\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "Scaler = StandardScaler(copy=True, with_mean=False, with_std=True).fit(X_train)\n",
    "X_train = Scaler.transform(X_train)\n",
    "X_test = Scaler.transform(X_test)\n",
    "\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f06dd8f5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The accuracy of the LR classifier for is 93.053333%\n",
      "Confusion matrix is :  [[ 2500  1165]\n",
      " [  919 25416]]\n",
      "The precision score for our model is:  0.9561717015913622\n",
      "The f1 score for our model is:  0.9606168266686823\n",
      "The recall score for our model is:  0.9651034744636415\n",
      "The accuracy of our model is:  0.9305333333333333\n",
      "[[ 0.          0.         -0.00089747 ...  0.          0.\n",
      "   0.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f0767c0208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find the optimal plane using grid search and L1 regualarisation \n",
    "\n",
    "encoder = preprocessing.LabelEncoder()\n",
    "enc = encoder.fit(y_train)\n",
    "y_en_train= enc.transform(y_train)\n",
    "y_en_test = enc.transform(y_test)\n",
    "\n",
    "\n",
    "opt_plane = find_optimal_lambda(X_train,y_en_train,2,'Grid','l1')\n",
    "y_pred = pred_test_data(X_train,y_en_train,X_test,y_en_test,opt_plane)\n",
    "type(y_pred)\n",
    "\n",
    "confusion_heatmap(y_en_test,y_pred)\n",
    "\n",
    "\n",
    "metric_score(y_en_test,y_pred)\n",
    "\n",
    "coef_org = opt_plane.best_estimator_.coef_\n",
    "print(coef_org)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interpretaion: \n",
    "1) We are getting a FN and FP value of 1165 and 919 while TN and TP value of 2500 and 25416.\n",
    "2) This shows we are doing phenomenally well in Postive classification.\n",
    "3) However we have a high False negative value as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The wordcloud for top 10 negative features are:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f06d811ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The wordcloud for top 10 positive features are:\n"
     ]
    },
    {
     "data": {
      "image/png": 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7DOis0aLlmBot5UOwWaYujWRSgRypxQrEZUsRmymBIk4AVaoYPAEJhwOwWx2wGO3oadBhoNWA/hY92iuH0VY5DIthar4vUclnftC7iPUYnrH6TsjinDH29QNtOLnlWTcB4Fafw0Xx+t9BKHMe4Kg7TqLuh//67VOekIOCVbcDcK6+Deou1Gx7ETYzfSx2vliJGavvgEDq3B46bBZUfv00rZetZx8AcPSjh8LaBwDMvfYJcLij6jKH3YbD7/2etnzCjPOQNvcir/vBegyTHALXP1+K3MUxIDkhOy4GZN97rfj8sZNB1/tr1QVe9/a+3YJNT1bT1iE5BEouSMC1f53lL5eLF0atFXW7+1GxrQe1u/smRNjNvSQZpRcnIXuByhWAjglmvQ2v/+IImo4MhX1MdCjihShcEYeCc2NRsMw79zIT7DYKn/+5CuVfdcFqmjhhwBNyMGd9IhLz5UjMlyExXwahdGLXyY8t+wHafvO464fDYzgidwIcnhDS2HTXdXfNLloBADjVHr21e5E27xIAgDwhDwRB+q2TXLwaYyfnuu2v+J2cAcBiUKN2+ysoufh+AADJ5SOpeBVO/fg+oz7sNjPjPorX/w4EyQnYBwA3AQAA7eVf++2ju3oH5PFZUKbQh58OxMyV8Vh3Tx5iMqZnQhtFAv0WP2u+Clc8UoSY9ODDb4tkPMxal4hZ6xKx6clq7H07fFnfSA6BsmtScckDheOqL5Bwcfv/FqL1uBpf/a0GLcfUYRubJwuuTMXcS5KQPicqKCHqCw6XwBWPFGHdvfnY8WoT9rzdDJs59B1NwdJY12SfVCBHTLo4KKF6phCRQkCiSgFBjFqvansCJ2HX9o7mVCU5XAjlsTAO9/gsK1YlQxaf7XbPYmCW49ek6XO7js4oRfuxLbAYvH9Qnn10VW5n3EdP7W4kzDgvYB+eWI1a9NQHDiTXcWLruIQAX8zBlY8UYdY670P06YTShxAgSAJr787D0hsyQ564AKBmZ1/gQgxJmiHHVX8uQtIM3/kmgiFtlhK/eLsMhz5px6Ynq8O6ulaliHDOzzKw5Lr0wIWDRCTnYe09eSi9OAnv3HMMPY26kNq74cW5YRrZ9CYihYBA5n4az2SCNuvcIzHyxQpaIZA+9xK3a/1ge1DjM2sHXGMkSA7S51+K+p1v+K2jH2xHZ+V3jPtoPfKlSwj468PzwLnl8Geg7LaA7esH2zHQfBTRGaWMx3TXZ0uQyNBpxmZ2wKSzgS/mgC8KnHZxskkulIPDJWC3OTWRJIfAkyfWhK39XW80Y6AtdOur1b/KxcrbswMXDBKCABZcmYIFV6bgHxfvCXlCBZyf4a2vL0BU0sTq0eNzpLhn0znQD1rw6LnMFlYs9ESkELBbjG7XJJfv18oHgJffgNVEr3aRxKS5XRuHAh8ij8Wg7nQTVNLYjIB1gu3DE7o+PM1a6QSfL0zDwa1UAwmAgTYDDn/ajj1vtbjpwjk8EmVXp2LWukTGQbgMaiv6W/UYbDOiv1WPuj396D0Vvty5BElAHifEUKfzu3b5n8avGvNF9Y7Q8zesuiOHsQAYOYA3DFthtzggVvKhTGRm1XLLf+fj39fug7rbd8Y0pjjsFPb8rxkX3T+DcZ3hHhOMGhscNgdECh7ECh4EEmbTkkTFH+9QWcYQkULAoO52u5aoUtzUPb6QRI9O7JTDDrOO/oCT5PDcrs26waDGZ9K67zp4QqlXGQ7P/QcYbB+e8IRSn+ccnsLPomN+6GcxMlOBAc4zAPp27Pji8ZM4uqkTDru3oYHd6sDed1qw950WZC9Q4eonSmgnqP/8/CC6ajUwaQPvZkJFkeAUAuf+PAPzr0jxWebk9l60VajR12zAcI8JFoMNdhsFVYoYcZkSJOTJkDZLgbis0e+ASWtD89HQD1/P/2WO3+cUBdTu7kPNrj7sf7/VyypJES9E1nwV8pbEYNa6RHC4vnVcslgBbnhxLv69YX/IqqGDH7dj5S9yIFbwvJ5pes1o2D+AzmoNOqo16Kzx/e88cvDNxD5/xnlx4xa4XzzOzMiA7gzmxLfdaDoc2u/apJv473kgIlIImDS9MOsGIZA6Y3fH5pQFFAKxOQtdf2v7mmC3Mj9xH2tGOb7y3j8ursD9UDHYPrwhwBGIYTO5b9s5vLFbbwr2ADumsQTaXbl6JoC1d+f5fGZQW/HyDQfRVadl1FbjwUE8e/WPuPmVeUgq8NZvF6+OD/mHxRRFghBJBXKf79ZcPoQ9bzajYpvvnVVfkx61u0d3UlFJIlz7txJkzIlC3d5+n8IwGBLz/e+6Tnzbje9eaEBPA70aZ7jHhPKvOlH+VSe+fbYOq+7IwfzLfQu7hDwZ1t6Th01P0FtMMcFitGP/+61YcdvoDqa7Touq7b3Y9u96RuazR77owJEvOpBcKMfVT5QgIdd7kTXC0uszxi0EmOYCoBMCTYcHpzyfQDiI2NhBPbV7XX/HZM1FTNY82rKJhcvd7OS7T+4Iqi+HPbgUcQ5b4PIk132rGmwfvvDlNc0Z04/DbgPAfPJhKjDyl8YiNtO3FdCHfzjBWACMoB+04O27jvl8tnhDGuKz6X/04SQxT4Zr/1YCDm/0Z6AbsOCt35TjxY0HaAWAL4Y6jXhx4wH8be0ubH8psCGDP0gOgasfL6Z9/unDVc6DUT8CwBN1lwkf/7HSdQbii8U/SUdaSegx8/e+3QKryY5Dn7bjuWv24ZnL9mLrc8wEwFg6Tmrw/IZ9fg/Ys+arGKu9WHwTkTsBwGnGONRegRnn/9IVCydr8Qa/dTQ9Dajb/mrQEy5PFFyEQL7Ys7z3t9tT/RNsH95QPlU9VpPG9TfJ4YHk8Bi/P5cf+MdDEMC1fymhfV49TguYgTYD7FaH2wQMOHX1d76/CA8v+s7vhBUOlt+S5Xb90ILvYNaHtj0faA3tMJgv5uD/tp0HsdJbnQIAX/+9Fgc+aht3+3+Y9S1+8tQsn9ZdBAH88r0yvPjTAyGps3SDFjw4Nzz5ji1GO16/44hPX48RVv8qFx/+YfqkqYw0InYnADitcKq+eYZRWbvNjNrvXmI0AXoePI84mTFFIHN3evF1CO2pagm2D0+sJr1Pvweb2X3S4Y8JoREILj+wHXxMhgQiue8JKVRHqPYqjc/7fDEHyYWKkNoOFovBHrIACAeLN6TTCoCOkxrsfqMp5D42P13rV121eEMa7bOporPa93cFgE+1IgtzIloIAE6797ETu82sB2W3wWYxwqTtx2DLMTQf/ATHPnnUr3PYWLR9zW7XImVSUGMSK91XUTqP9nwRbB+e0PVh8LA6EinoD3A9Ecr9JxEH4Fc1017J/GDZF/6cldImOZUf00PCiYQggEV+JuCtDHXqgVB3m3DyB3o9euGKwN+LyebEt920z1h1UGhErDpohPR5l4Lk8EBRDrQe2YSemj0IRu/tC21vI5TJo2Zs4qiEoOqPHFiPoOsLvDoTRyVAIImCWc9smy1Suo+Jrg9PHwd5Qg6G2phtjSXRqQHLKP3YfPe3hKb6GGynrz/RtuZjGWgz4Mim4CK2TgTZC6NpJ7S+Jn1Ync/Kv+xE0SrfCwaeMPL8OprL6RcMIjkPHB4Ju3Xq4iJNZyJ6JxCdWYr4gnMBAG1HNqGnZjdCFQAAMNh8zGPXEIJ7KEVhkGbS9ewjJmcB42Zjs0etnfz1YTW6b5OjUotBkIF/xHxJFCTRvi1FxiKS0a8TTNrQDruNGvr6vkwMJ4o9/2sG5Zj6GFolF9AvRqq2Mz+kZkLriYkLGTERdNf6Nz7gCSN6KotoIvqTS5ixDIDT7r+nLnAoBKaY9UMYbDnudk/AUJfuqUIZbKuA2cNvwPXMo4/E0x7ATPqIz1/CqA9P+GIF4nLLApZLLFwOJsKPK6D/ioR6JmA10a/cJjpw1wg2iwOHPwstiU+4yFtMf250cnvozmdj0fSaMdwTqtny5GHSWf2eY5ypSeAng4hWB/GETosaguSgcM2v0Fn5HXT9LbCZDSFnxWrc8zY03fXILLsaADDrsgdhM+nQfuJbDDSXux0ecwUSRGeWIrlkDbj8UTVF29Ev0eXHHNWzD5LLR+mVj4S1DwBoK/8KqXPWu67T518OguSir+GAm3+CQBKF+BnLEJ+/xOV4NjZGky80vfT+Fv5URUyITqWvP1kT1F4P7+apYtbaRNqwxYPthgkJ9tZVo6WNlc8XcSLicxmBopw+KdJo317CRDiCPZ2lRLQQGGg6isSZywE49de5y27wXZCiYLeaMNxdD013vdcES0dfwwGIFPGuHQdXKEXGgiuQPv8yV8IXnkgGkSLea7LsP3UEXSd3RkQf3Sd3QplcCFncqMlj2tyLkTp7HQzqLlhNOpBcPuRjA9pRFOp3vIa85Tf7bdufyWNSAIemQPgLhhaOuDtMiBS1iD/nsL7mifksjH7UebJYQcjmrizTg4gWAh0ntkAclegVJM0LggCHL4IqrQSqtBKklq5He/lm9NTuCdhH65FNsBg0bvH1CYI8HZPHd6TMzsrv0X7sGzA9n3D1UbrelT0sUB8AcOrH9xj1QVEO1P3wKvJX3gZpzGj0RoLD9Xn4S1EONO//COqOwN6hbX4sgAJ5tQYio5ReBUdnPhpu/O10JhM6ZzwAyD8nxq+d/EQQiUH/WCaGiBYCPJEC2r7mwELAAw5XgPT5l0EgVaH1yKaA5burd0CZMgPyeP+xWgCnqWb7sc1BjWekD01PPYrW3cOoD2daySC8f61m1Gx7EUnF5yOx8Dzaw2GzbgBN+z6EpqeBUbv6QQs6qzU+V+2hxl6nswCyGOxom6QVusHP4fRkEmkpDCfSQoggCUQlCRGdJkbKTAUkKj6EEi4EEi74Ig5ECh54AhI8IQdcAQmewHmPLvYRS2hEpBDgCiRIm3sxYjLnYjSwOwWb2eAV6oAgOSA53NN6dPcvScKMZYyEAADUbHsRElUKlKkzoUjIBU+kAE8kg9WogcUwDE13A9Qd1dAPjD9WiGGwA1Wbn5mwPhx2K9qPbUZv/T5kLd4AgVQFnlAGAoDFqEH7sc0YbD0R9HnKsc1dtKoboZQ7riBY/gRIxbbuSUuD6M9CaTKRx3mHBJlKwp0pTqzgYfWvcp0pSPNk7EFuBBFxQiBv+U1QJo8GbBpoLkfjnrcZ108sXI7U0tFDUoLkMJ709IPt0A+2o+P4t8wHHCST0YdFP4SabS8wKnvw7XsDltn5WhMEEq7PsMaPHFiF+h8H8N7vj0M/GDgWEYdLYP3vZ2DxT3w7RR3f3DWpIQAiwUsYAGQxkSUEQmXVL3KwZCO99zNL5BBx4nisAKAcdrQc+iyo+oOtJ9yufYV5ZgmeH16hj+Kauzgav9t8Li78bT7ic3x/3vI4AZZsTMfvNi+lFQAA/Ob9PZOZjBzNk0XBslicf2cOKwCmCRG3ExiLfrA9YE5eT0IP2cziC6vJDqvJTqsrFsl4WHpDJpbekImmw4MwDFth0tmc+lw5D7mLA8eGt5rs0DHYTZxtqLtN0PRO7vfaPM4492t+nYvlt44/E5rDTkHbb4bFYIfFOPqfftCC0otDC73C4puIFgKewdGYIJR7BHczBhfmmIWeN391FNf/e65fBzIAyJyn8vvcFxajHW/eeXS8Q5v2+IqoOsLhT9ux7XlmB/lTySUPFPrd5Y3FqLGi9bgax7/pxmC7AZo+M3QDFr/qOVYITAwRLQSEssCrR0/GetoCnqEbWEKh/scBvHLzIWx4apbPRO3jpa9Jj7fvLkd3feh5bqcrFpMdIhohwBdH9M8UALDkuvSAAkA3aMGRzzpw4ttudFRrIiJUB0sECgGrUQOeyGmJIpTHYd6Gv+Doh3/0GyKawxNCmTwD2edsdLvvmXyeJXSajw7hyZU7AABl16TisofGl5u395QOL248AMNwZFjnTDXtFRpalVlqUWSHSr72ryWYs55+la4ftOCtu8rRdCT0lJss4SfihEDzoU+Ru/R61zXJ4aH0qkeh62uGSdsHm8UIym4HVyAGX6yEQBYDkdLb29ZuNaFhN3OrIhbmcHgkLv6/GSi7xtsRTd1lgkDKgUDMBUEScNgcsJoc6KzRoLdRh45qLRoPDEyaR/B0wRlR1bcQGJu/OBIpuYDe4ZFyUGERAKzz2sStZYkiAAAgAElEQVQRcUJgqLUCLYc+Q/q8S10+AiSXD3liHuSJvvPcemIc7kHDrjdhHA5v5EUWZ8KXG16Yi6z53nr/Pf9rxjf/rIPNzKrggqXDT9IUiYoPRbwwYgO++XPi2vt2S1h2ABKV75hBLKETcUIAAHpq90A/0IrU0ovc4uEEwjDUhe6anRg4dYQ9C5gASA6B6/9d6iUAKAeFjx+qjJhonNORQAl6shaoUP7l1Oc8CJb9H44/FeZYJFGsEJgoIlIIAICuvxXVW5+HUBaDlNlrIVTEgy+UgeQJQJJcWM162K0mmHWDMGn6YFR3ord+/1QP+4xm1R05yF7orbLY+lw9KwBCpKtWC8OwlTaPwoxlsdNOCHTX69DXFJyJNx0JuZGtEpvORKwQGMGk7UfD7remehgsgE+P4c3/qMXO/4ae9/Zsx2Gn8NEDFfj5v0t9Pp+1NhGdNVrseJXeaS/SaCkP30Hwunvzw9ZWuJisnBcTTcR5DLNML7qqWT+McFGzqw/qbnq9/7IbMqeVFy4ZxoBvkagOEkfgmMbDmSHKWFw8Wr6GNjjXH2Z+E/b+wvlDP9tx2Cnse7cVa+/xbQAhVvJwyR8K8d59x30+jzRk0eGJhxTIOXGqSJmpmOohhIXI/HRZpg0FS2MDF2JhzN53WvzmOJh9Ib05ZqSRNlsZcrhxALj0gcLAhaaA1CIFRPLpszOjgxUCZxgf/+EEvnryJA580IrKbd2wmiY2RWDZNalYcGXghPUszLCa7Pj22Tq/ZZbdmDkhfcfnSH2a/o4XsYKHvCX0eZOZsPL2bMy/IjK/X1wBiUXXevvKTDdYddAZxolvutyu79t2Xsi5gP1BkASueKQIZVenofzrTrQeV6OjWsP6CoTA4c86ULw6gXaXte7efKTNUuLzx05C2xd6ZrRFG9Iw79JkpBQpsOuNZpw6NBhymyOsvTsPjQcHgv4+cPkk1v02H0uuSw9ceILxF9dpxW3ZOLmjD9110/dsjBUCLIw5dWiQdqWYPFOO5JmhhzfY/I9aHPms46yPJvr6L47gkQOraC1QilbFo2hVvOt633ut6KzRQt1phG7QAqvZDpGMB76YA4GEC0kUDzHpEhQuj0N0mjisoat/eOUUlt/i258nMV+Gx4+uxkCrAU9fvAd2K70wkKj4yC2LxoanZnk9sxjsqN/Xj5kr433UnFj+95ty3PDCXJ/PeEIO7v5sCRx2ClXf9+DDBypgMfjefQulXEhjBJDFCJBZ6lSVff+fxokcOiNYIcDCmHd/dxy3v7kQMeniCetj3T35WHV7Dr55phb73msFdRbHGHvzzqO4/vlSCCSBf6aLNjCL3jkR7Hq9CQuuTPFrwROdJsYfdy9He4UG3fVOnwiL0Y6oRCEkUXwkz5T7DY/x1l3l0A1YpkQI1OzsQ+txNdJmKWnLkBwCxasTMHNlPDS9Zhg1VtcOQhYjgEjO9dpN9DToWCHAMr3Q9pnx/IZ9uPyRIhSfP3E/Rr6Yg0seKETJmgS8ddcx6IfOzl3BqUODeOn6g7jxpXmQRnDYBMOwFZ89UoWN/5zjt5xI5swrwSS3xFgcdgp1e/tBEPDrUDeRfPJwFX71/qKAlkokh4AyUQhlYmTljPYHezDMwhgOl0ByoRyaHtO48goHS+Y8FW57c8EZYYExXjpOavCvK/ai8WD49PQTQcW2Hmx9rj7s7dosDrx99zEAAEUBTYen5nPortPiowcrzsjw19NiJzDvihTkL41DYoEM0mgBCAJo3D+Auj39OPpFO60ODgCeqFrr+nvHy43Y+i96y4vYTAnu/HiJK3tW2wk1Xtywz+/YODwShSvjcd6t2ZBG8yFW8EBRzsxMtbv70Fo+hJpdfdAECP7FE3Iw68JEt/c0qC3ortUyes+JRqzg4Z4vzoEsdnJz4cZnS7HhqVl47bbDk9pvJKHpNeOVmw5h8YY0nH9nzoQJRZvZgf7m8Yd5+P4/jTAMW7H+voKwJJIfbDfi3d8eQ1vFaFylxoODU6ISAoBjm7tAAbj6seKI9V0YDwQVAUpXgiB8DuK8W7Kx+q5Rxxm7jYJBbQFJEl5RBb/7dz22v+idfUko4+HXny5xWcgMdRjx1OodXuUuuCcfS28aPdx6795jqNjS5VUOcK6Ir3yiBLMuZJbp6MQ3XXj/t8don48VVMDoe0pVfC8762AdvsZaBwVbl+QQuOqxYr8ZnQ592o6Okxr0Nelh0tlg1tvgsPv+ThEEQHJI8EQkhFIeUosVSMyTIXNeFBTx/rfP9xdtGff5wF+rLqB99kDp1mlnycQXcTBrXSJKLkhA9oJov1E86TBpbTh1eBAN+waw952WsI+xaFU8zv15BjJKo4KqZ1Bb8cLG/bQxhwgC+OPuFV7nD48t+wHa/tAtpZhSuDwOi3+Sjpwy1bh8IfpbDKj6vgebn64NaRwURYV8wh+xQqBkXSKufWo2AKBh3wB2vtKIlvIh2CzOH2zxmgSs/k0eotMlrjp0k1zabCVu/V+ZyyLCV7nHKy5w/WMe/KgNnz9cSTveS/9UhAVXj9oHUw4KH9x3HB1Vw9APWUEQgDxeiKU3ZGLGiji8e3c5Gvb5TnDj7z35Yg7yz411e8937y5H5dZu2rF5Ml4hQBDAxn/OcbNAGctQpxFfP1WLiiDG4q+vnLJoXPanmYhO9X3o/OJPD6D5KJuUxBOhlIvUYgXmX54CZZIIykQhBGIueCIOrEY7zAYbLAY7jForBtuMOLmjF32n9Oiu19IK63CiShEhozQKGaVRyF4QDZGcC5GcB8pBwWKyo7dRj8F2A7pqtWg+OoT2ymHYbVM/JzFFouIjZ2E00koUiM+RIaVIDr6IA5JLwm51OHdXrXpo+8wY7DCir0mP3lN6NB4IT8KrM1YISFR83Lt5GYQyp7bqgaJvfK4ChTIu7nh/MWIynBPkH2d/S2uCdt6t2Vj9G+eu4r17ylHxbfeYdnh4aP8qAM4T+xeu+dGvk9XYlbtuwIy3f30UrcfUPstyeCQcNofP8Y99z/3vteLLx6sCvqe6y4in1+7ya2o3lvEKgWU3ZtIG7ard04937jnmNx/seBArefjT3pU+n21/qRHfPhu6zjltzXVQ5s+F3aRH1ct/DLk9puT/9H4IouJ8PqPsNlQ8f19I7Ytik5G6ZiP4chX0nafQ8vXrcFgj60A9b+PvI3p805FwCIGIVGyVXZvmEgBDHUZaNYBJa8PWf47q+Av96Ap3vnoKjfud0vfSh4sgH6N+uPRPzhSJVpMd799bztjL1mGn8Nad9AIAcDqa0I1/5D2HOoz4+q/VjN5TmSjy+57hQBEvxJpf5/p81lmjwVu/KQ+7AACcqgA6UorPjDgtE0Vs6XkQquJBcnmQpeVDkV081UPyItLHd7YSkUJg7mWjbuJHv/Afp75uT59rW5uziN70jHJQ+PD+49APWiCS83DVEyUgCGDOxckoWeuMx/L1X6rR0+A/2Xl02qi64thXnWg7QS8AAjHynke/6Ai4smf6nuFgycZ0Wg/Jr/5aM6GhKOhUAdEpE+ebMBn0HNyGngPfYuD4Huja6mAz+v+eBYvnAoJyTJ0RARMifXxnExFnHVS4Mt4tzMHKO3Kw8o4cRnUz5/qPe6LtM+Pxc7/HE1VrkV0WjccrR9U6j5Ztg0kbeHW76LoMAE6hsunPVYzG5Yux7xnMOwKB3zNU6GLT7H27ZcJNFbtqNEgp8l71i6Omt5mouvaI23XKymugmrkwbO23bX0HbVvfCVt7E8GJZ++Z6iGEnaxF1yI2az6sRi3KP3t0WmY0jLidQChxw5mabXVUuafys9soRgIAgCueu1Fjg8U4/tXMZLxnuOkLwXyQKXQ7kKk0j2VhoUMWkwEA0PQ2TksBAETgTkDgEStF02NibMXANBF33Z5+JI+JBa7uNDIe38hZRag68bHvGcw7AszfM9z409mHi6hk38HuzlavYZbIhid2xsvSD4Qnl/JUEHFCwOqxun79tsPoqQ9vhL5lN7sHuxqr5w/EyIqUL+KENIax7zkR7zgRhMMBKBB0AdPClauWhSWsnD6MsRg1UzyQ8RNx6iDPFV+4PVSFMp7LX0A/JlJl4QpmFjcjq2GRghfSpDj2PSfbC3e8TGU8lPaq8PzIIsAimuUMwqJ3GoZw+RMXrn2iibidQOXWbgz3mFwepGt/W4DnLt8TlrYXXpuGS/7oNAf9+IETOPp5B6TRAvzm83Ow8blSNB0exH9vPOhXNXPww1aUbUgDySGw7r4CbHrs5LjGMvY9w/mO4YByUD69IFf/KhfNR4cm5HA4JkOC299YQPt81+v+k9mTXD5SVl4FZb7vkL/qunJ0bP8INgNzYSJJykL2lXf6KUGdbvdj2C2Tq6LLWH8j5FlFtM9r3ngMFk3w/06y9AIkLL4QotjkgGX7jv6Arj1fTur4Io3q719E0dp7kD73Emh6GmEcDt15crKJuJ0AABz5rN31d2K+LCxtxufKcOF9MwA4J+CjnztNT3UDZnzyxwoAzoBly2/L9tvOWLXN3MtSEJdNH/42ECPvmZgvc5mpRgKtJ4Zpn13zl5Kw7wiKz4/Hne+VjXtHRHJ5yLzkFi8BMNYMUZk3BxkX3QSHhVloAWX+XGRd9guPuxQo+1h1JQFlXilyrr0bXHF4vqdMGajYh+5936D/2C6oa4+EZUKNL7sAmZfcwkgAAIC69ijj8WlbakIeXyRiNelQt/M12Mx6FKy4ZaqHMy4ibicAAHvfbMbi69IhlDktcXIWRdOGXRhBIOHSHtbyhBxseHq2y6rmsz+5h4So2dHr+nv57TloPDCA5iO+QxSMVSfwhBz8/IV5ePOOw+htpLf7Fsq4Pq2Pxr7nZQ8XwaC2BHzPzHmqCY+kePizdqTP9h07XREvxK8/XIwPH6hAza6+kPrJXRSN5bdmI3uBf5PXjgCqoPiyCyBJdgpvm1GHnv1bMNxYAZtBC4LkIGHxOsTMOheS5GyI4gPH3ZckZyN19QYQBAmrVo3eo9uhba52TrQUBQ5fiLS1P4Us3bmoEChjkbH+RjR8+C+Gbx462pZqaFuqXdcxs5ciaeml424vtnQ54hesdl2b1X0Yqj4MQ3czbHoNHDYrxImZkCZnQZZRCJtRB2MfvQ+P5/gAoOTX/xj3+CIVrkACinKg7fgWZCy4HMXr7oFuoA12ixEU6DUKbeVfT+Io/RORQsCoseKD+47jZ8/PBUESuOHl+ajc2o3jX3ehs1oDw7AFEhUf0alixOfKkL0wGtll0fjT3K0+27voD4WuFTtFOdv3pL9Jj5hMCUgOgWuemo1nL9sD47Bva5iKLV0ovsC5co9KEeFXnyzBO78pR3edFkaNFSSHgFjJw+z1Scgui4FYycM/L95N+54/f3EeBFKu13ua9TYI5Vy39+QJOfjvTQfH+9Ey4uimTpx3cxZtHB+Jio8bXpyLlmNq/O/XR6EbYG65I43mI61EiZyyaCzZGDh1oMVox4cPnKB9zlfGIGbOMtd1wwf/dFsVUw47uvZ8CW1zNTIvuQ0k17+/AcnlIW3NdSAI54Kh7p2/eal67BYTmr54BYnnXITY0uUAAHHC1KdBHC/CmCQkLlnvuqYoB2rf+ovXAYpFM3ja34EAVzz+HfCZxNwrH3W7FkclQxwVeCfFCgEG1O7qwycPVuDSh4vA5ZMoviDRNfEGQ/EFiZg3JlH1Hhrd8ge/P47b310EDpeAIl6IK/5cjLd/7Xu7+8mDFRBIuMg715kDlsMj8TOa9HOAU8DQUburDzaLA1w+CYIkAr7nSOgLT2IyJMicp4JAyoVQxoUyQQSBlOsWbfUnz8yBSeuM9GnS2WDSWlH5bbeXyanN4sBnj1Thppfn+Y2QmD5biQd3rsBAqwH9zXr0txpg0lphUFtBcAiQHAICCQciGQ/JhXLEZki8or/6w2Gn8MH9J9BdT7/Lip5Z5pqwAdCqRXTtDeg/vss1adMRNWM+eFLnLoiy2/3q+rv3bYYyrxQ86fQOaRG/YLUzit9puvZ8GeAEnYLNEPnWbCzMiFghADjDKfQ06PDLDxcHLEsXduGyh0cPpzqrNdj6rO98Ah1Vw/juuTqsudsZNK1wZTzKNqRj/3veYXYtRjvevOMIlt6QiaU3ZwWM7241+3d0emnjflz0h0Kk0ahgxtJe6Vtfn7s4Bhc9UOi3btHqBK97Qx1Gn34H9fsG8PXfa7H+vgK/bRIEEJMuDnvKSavJjvfuO4Gq73v8lht7+Gg3+TcjHaw6EFAIqGaWuf7WNvs/9Kfsdmibq6EqctbhyaJg1U6/SKfy7NHP0DTYg/7yXVM4munFsc8fn+ohhExECwHAOTkHGwd/LI+WbWNcduerp7Dz1VOMylIOCjv/ewo7/8usvD86qobxn+v8J68JxL53W7Dv3fDGhd/9ZjN2v9mM7IXRuOrPRbSOXOHk67/XYu9bzYzCCYviUt0ic/af2Ou3vHmoF/qORtf5gSeS5GyI4kZ3jfLs4qD02NKUHAxVH2JcPlIYu5Nq/+59wI8um8Uds376WzhFpHUQS2TReGAAT1+8Z0K9dnsadNj0ZDV2vd7EOJ48R+AulJgEZbOb6b3DuaLQ9NyBzhumA9NxJ8MSGhG/E2CJDKwmO55YuQMzV8Zj9tpE5JRFgy8OzWu68eAgGvYP4OQPveiuC17HTPLdTUopa+CwFv5i2HME7qavlN0GysE8HsyZEBnTztCEluXMgRUCLIyxmR04vrkLxzd3gcMlkDRDjrmXJEOVKoYiTgCxkg+h1JnVinJQsFsdsFspmHQ29DXrMdxtwlCnEQOtBnTVavwe+DKB8pjQCSYrcYJ+8+uwuk+AbdvehbqOPi3omQjJ43t9DizBQXL5IEgOHFbztAgqxwoBlnFht1Foqxh2SwI+6WPwUO1whYEPp/2VsRncD5a5p4ODnU3wpArW8mccEASJ6Iw5yFxwJUiu0wKu6ttnoet3ntNx+WJnsDmHA0ZNr7+mJh1WCLCEnfOK7wWfJ/H57GjDu+jXNISlH0NPKyzqfvCVMQCA6JIl6Dno21cEALhiGaRpvjOmAYCuvR7Gvg6Xx2x82QXoP3Z2WcokL78SDR/8c6qHMY0gULDiFigSnVaFVqPWJQTGYrMYULT2Lgik0Wg/sQUdFcwNViYa9mCYZVoz3DSa2CdQ6IaoGfMB+E/JOlh1wPU3hz91AfMmE13rqNm0OD7t9OfEwoSo1JkuAdB/6hCOfvowbVlNTyMAQB7nPzTNZMMKAZZpzWDVfow1aeTJfPtaCKMTEDd/FYP29rk5nDnr+BccBMmZ1jlzPXdPKSuuCliHwxdOerykSCQ2cx4AQNffgsZ97/staxx2+rwI5XF+y002rDqIZVpjHuzBwIm9iC45BwCQe83d6N6/BZrGCtiMOhAEidh5KxA7dwU4fCHsJj04Qt+qKsDpANay+Q1kX3knSC4fCYvWQZk/F0MnD8LQ1QyrUQs4HBCo4iFUxTvj6aTkgCMQTdv0ifrOU+g7+oPLkY7gcJF77T1Q1x6BobsVNqMOlMMOYUwihNFJkCRnQZqcjfbvP8BQzZEArZ/ZSKKdsaj6GgOHcrGanGctPGFkhdxghQDLtKdr79cQxaVCnJAOrljmXMmuuAqUww6CJDGyktecqsJw4wmknr/Bb3vG3nY0f/ka0tf+DByhGEJVPBLPuWhcY+MroqHILgbJF4IjEIHDF4Ini4IwetR7m+BwkHnxLbBbjLBbzHCYjbBbTOg99J3PNmPmLAPndHskXwAOXwRRfKpbmfR118M83A+H2QS7xQSHxQS72Yj+494xrACga+9X4PCFUBUtAgCI4lLcHOeCgSMQIWb2UrfxeZrfpq+7HjajDlatmtH4IpWRCd2sC+w0FqmWQqwQYJn2OKxmVwTPlJVXQ5KcDZ5EDoCARTOI9u/eh77TGTOK5PKRev61AdvUtdWh6uUHIVDFQ5FdAmlKDgTKGHCEEhAcLizqPpiGemHoaoa2tRam/k6f7YhiU5B4zsUBeiMgy5jhdZdOCCSde0nA8dNN4rSTLEWhfftHaN/+EQiCRNKySyFOyABXIndaVBEEzEN9MKt7YehswvCpSliGfcex4opliF+4JuD4fDHdhIBB3QWJKgWS6FQMd/sOSTNCbLYzX0agcpMNQUVAqiWCIKZ+ECxhY7Ksg1jOLjgkH0K+u9mu3tQ/RaNxkjLrAiQXnQ+LUYPjm56Ew2bBwuueBuBuIhqVMhN5y24EADQd/AS99T+GpX+KovwfWDGA3QmwsLBMC9LiFiA3aYXbva1HH6UpPTn01O5BQv5S8EVyFK66A5VbvM1rk4vOR3Lx+QCcuYj7T01sKPhgiXghcNFfF6JgTWrAcvoBE15Y+dUkjIiFhWUqiJZlTfUQvLCadDi17z3knvtzSKJTUXr5w65nGfMvB8nhQaRw5i+nHHY07HkLDrvv5FdTBWsiysLCEvFwSB6U0sCLwalgsK0CtTv/C5tZD55o1GxWokpxCQCrSYea7S9D2xt61OFwE/E7gcpNLTAMmSFWCiCKEkCk5EOk4EOWEN749SwsLJFLTtJykERoAQsnEnVHNY58/JDreiQ8d6RaBI0l4oVA095uNO3t9rr/u2NXTsFoWFhYpoJIVAX5YzpM/iOw6iAWFpaIRyqKLC/bMwlWCLCwsLCcxUS8OoiFhYVlqslbdkNY26vb+XpY2wsFVgiwTCoUm7+WZRoSlVI01UOYMFghwDKpOByRZSPNEvmkxS2c6iHgwDv30j6TJ+Qgb6nTG7hm+8swaXpht5pAUQ5wuALE5y9BQsEyEASBup2vQdvXPEmjZgYrBAKQdW4iMhfHI2FmFBTJEghlfFiNNgx36NF9cghNe7vRuLsbDtv0sQbwRJYgRv6qZKTOi0V0pgyKFCnMWgv0A2ZoOvVo2teDpt3dGGoLLR0kADgoeiHA5QiRm7QcEmEMRAIVeBwBOCQfFOWA3tQPvXkAw/oODGqboDX2hDyWUOFxRYiSpkMpSYVEGA2FJAVc8nRqQYcVdrsVQ/oWaA09UOvboda1wkFNbR5iLocPmSgRMlEcpKJ4SIUxEPKV4HL44JB8OCg77A4LzFYdLFYd9OZBGMwDMJgGpyTch4ivRLyyYNL7ZQpfEoW8pTcAoFD17bOucNEj2G1mdFZtR3/TERStvRt5592Miq+fgsUwdRn5PJm2sYM8TUTD7TFMckmUXpuN5b+dFbCsfsCE/6z+Gg574NdYdlcxFlyf73bvqdkfj3ucgPdnUf9DJz6/m1lskoufKkPeymQQZOAQJK9dthUDTZqA5fzFDtpX/TK0RneTX6U0DemxCxCnLHDZVwdCb+rH/ppXYHcETi4fbkiCg9KcnyBKlgEiQK6BsdjsFvQN16Gi+dMJHJ1vOCQPsYo8FKVfApIc39rPYB7EoLYZg9omDGqbYLEZwjpGkuRCJoxDYvQsyETxkIniweUIwtqHJzsr/gGzdfyLm4z5lyM+bwk6Kr9D+/Fv/JZNLlqFlFlr0V27By2HPxt3n2NhYwdNEFHpMlz8VBni8hSMykuihbjurRXY9Lv9GO7Q+y1b+UWLlxAQKQUwqseX3JvkeH8HqjY1M6o755ps5J/PPFzwDZ+cj13/qsTBN2sZ1/Fk7KQt5MuRn7IG8UrvCJqBkAhjcM7MO1Hf8T06B0+MezzBQBAkUmPmIjPhHAh4wSdU4XL4SFQVQSxQorZ9G9T6tgkYpTsEQSI1dj6yE5aCxxWF1JZYoIJYoEJKTCkoyoF+TQM6B06gR31yXO0JeFIkRc92TfhiYXRQQjUSUCY5dym6vqaAZXUDrQCAqOQZYRMC4YA1EfUgsViFjW8tZywARkgojMLGt1YgLt93ZqsRBpo06Kp0jz0+44Lxu8NnLklwuzaqzWjc7e1c58ny387Cqv+bE1RfBElg2d3FWPWHOYGSbdFid1hcfy+acfu4BMAIAp4MRRmXoiTzyglfMcrECSjLvxkFqWvHJQDGopCkYEH+DZiZdhE4JC9MI/RGJcvEksI7UJCyJmQB4AlBkIhV5GFW1pUQ8uSBK/igJPMK5CatQELUTEiEMdNOAAAAT+ycJxz2wDtSym53qxMpsDuB0xAkgQ2vn4fkWdFu93c9W4lDb9b6VvUQwKzLs7Ds7mIIpDyIVQL8/INVaC/vx/s37QTl8K0e+viOPbjju/Xg8J0yeOX9s9G0rwdDLdqgxsyXcHHxU2Vu9z68fbff84mEmU5hNVb9U/F5M3b+s4J2NxKdKce6x+cjoTAKADDn6mzMuiIL79+0Ax3HfMeUp8Ni0yNOkY9ZWVeDIMLzo0+IKkRCVCF0xl78WP2fsLQ5Qk7ScmQlnBvWNkdIjpmD5Jg5MFu12FnxTFjbToudj4LUtWFtkw6TNbCK8ExF23sKioQ8ZC/agIpvnoHN7FsTwOWLkbXYmcdC19s8iSMMDLsTOM38n+V5CQCHncKB12rodf0UcPyTU/jw1l1ut1PmxHipfMZi0ljQsNM9CcnM9WlBjzl/VQq4gtF4Kn31w+itUdOW5/BJrPvzfDcBsPfFk9jy8GG/6qiBJg0+uHmn2z2SQ2DF72YHNV6HwwYRX4GijEtpBQBFOWA0D0Fr7IHG0AWDeYCxRZFUFBf21WQwAsBs1UFn6oPW0A2TZdht1+MPAU/mFSc/FJSSFOSn+E/qMhaDecD1eRvNQ7DajIzrMn3HM5W+hgMAnAfERWvvAocn9CoTnTEHM9feBYFE5azTeGBSxxgIdidwmoU3ek/ae/5dyahu98khr3uLbinAgddqaOtUbmpx08cXXpiGPS9UIRgz+sL16V5t+qPoogxEZ7lPNj++xEyfazHYQDkoNwGSMDOK4UhPQxAozfmJT9WNWteGrqFKtPcd9vIlIEAgI2EJUmJKIeL7V7elxy9Cc094EnYkqUoClukfrkefph5DulbojL1ezxNVxYiWZyMxqsjvoXdp9k+wv/bVsCpZGhoAAB60SURBVJjQzki70G9fQ7oW9KprMaRrgd7U7/NwXSqMhVQUB6koDlHSNCgkKT4DuHUNMvuN+KKl9wB6hgJ//9LjyiAS+P6u1bT5P4wNhM0+vrO4EQZajkGVVgJV2iwIJCrMu+oxmA1q146AyxdDIFW5yg+1V6G/uTykPsMNKwROI5Tz3a413QYcfqeecf2OYwNInj26k+CJ/H+0TXu7oe83QRLjXDkokiRImROD9qPMMiXJ4kVInRvjunbYKVRvbvVbZ/bVoQXhat7X43UGEQwkwYFY4L7b0hp7UN22GWod/SEpBQpN3XvQ3PMj0uPK/EaUzElajr7hWuhNwampPJGJ4lGYtp72ec/QSTR27YTO1Oe3na7BCnQNVqCh8wdkJy5DcrTv3ZNUFIe85FWoadsS0rhjFXmQieJpn++veQUaQ1fAdnSmPue7DVUBcFoX5SavQrxyBgS80UTp7f3jTzTfq6ZfJI0lIaqIVgi09h0ad//homHP20idM4SEgqUgCBICSRQEEs/xUuip+xGtRzYhqJXeJMCqg2g4/vEp2C3Mbf+PvOtDYPjRTFAOCic9Ju2ZHit7fxSuS3NblTft7YZ+wOS3juehdeeJ4CbKuu87giofiK7BChyoedWvABgLRTnQ3PMjDte9SbuCIwkOcpNWhjQugiD8mlI6KDuON30cUACMxWQZRlXLJlS1fElbJi12ARSS5KDHO5ZEFb1nq8bQyUgA+MLusKKm7RvsqnwGRxreRufAcQxqm8fd3pkERTnQevRLnNj0F2h6GmE1aeFw2EA57LCadeis+h4nvvwbmg99GpHOkuxOAEBGmfvKSd9vwv5Xma1SRqjd2o72a/qRMmZ1XnxpJio+ozcd2/GPE0ibF4v40weuJZdnonlfD2q3tfvtKy5fiaW/KXZdm4Yt+OLefUGNFwB+fKk6qPL133dgzUNz3e7lLk9C/Q++k6z7w2LTo6J5fGZyan07th//K1aXPuTzeZyyAMUZl42rfQ7Jx9Ki39Ba09R1bENzT/Cf9QgdA+WIlmchIWqmz+cL82/Cwbo3oNb539X5gssRICHKtxDQGnuwv+bVoNv0hKIoDGhOYUATeclRphqTbgDV370w1cMIGnYnACCxROV2PdAUnJXOaD13K4mx6iE6Kr901+MXXhj4gNjzELl6Sxvs1gC7Fh+7EnWQHsBmnbfuWKgcn2lmY9fOwIUCYDR7n8WMEB9VOC6zyLTY+bT1NIaukATACHUd2+DPSTMtdv642uVx6N/XZKE3GGA5u2F3AgDkHlnKBpvHJwQ86ylTfHvNjqV6cytW/n5UT5x1ToJf5zGCJDBjrbsQqPyiOWA/EpW31cLNmy4IWC8QIgU/cCEPbHYTOgaOhdx3c+8+zEhd5/MZSXAQp8gPup/U2Hm0zxq7dgTVFh0miwZ9w7WIowmHEKfIB0lyg1Yd+AvOxyGD/3diOTtghQC8D4VNmvGZvZk07itloSzwD8847N4XySVRsCYF5R80+iyfvjDOdZg8gi/rJE8E8olxSuIJg0/516uuDYtutHPgBPKTV9Pq7uOUBUEJAZUsE0K+b0cevakffcPMDQUC0TVYQSsESJKLaFk2+oaD88y2+gnjIBcngiQ4Ux676EyF5PIRlVIEZVIBCDLwb6Jhz1uTMCpmsEIAgNBjgrQax/dDsRrdJzbhOFbJgPOAmE4IBHN47DYW2cR5pgZL33BdWNqxOyzo09QhXlno83mUNB0ECMbhq+n09ADQG+SEHAi13v+5j0qWHrQQsDusMFt1btY7I3A5QqTEzkNrb2TZqJ8JKBLzkb3kJ+AJvD93OlghEGE4PDx7w+TI6lfvO5Yj79Rj7nW5ruvEYhXOuWOm02/AA88zg6Y9gUNEAABf4iEEKOCtjd+HbK2m6/NvkeSJ2apFjzq4A2l/NHTuoBUCXI4ASdGz0THAzC47JabU532KotDYuWN8A6TBbNVCrWuDUuo7ZEiSahZq27cG3W5N2xbMyvKdf7sgZQ0EXAlOde856528wkXmgisQl7sYADDYVoFT+96H3Rrcb2KqYYUAnNY1Y+GJx/ex8D3qmTXMIlxWftHsJgSAMc5jAahgGCzOc5cCAhhq0fk87J1IfDlUhYLB5PQoplMJSYQxPu8Hg8minhA1itVOP1mMN9ZP73A1jOYhWrv6zIRzkBxTipaefWjrPwKbnzGwBEaekAcA0PQ0oH7XG1M7mHHCWgfBW5c/nsNOwFv9Y9IyW2311g2jt9bdekOR7H2oLFa5W+KYtVY07GBmnqkf8D5oFikn/7DQaAlvHHUKFPRmen8HiTCwhVYg/LUfChMxAVMUhZoAOwg+V4zc5JVYVnw37cE6CzP4p4PB9Z06PMUjGT/sTgDAUKu7qaQqfXxRIlUZ7vXU7f7DSo+lclMLVvzOf0iEvJXujkTVW9oYO7Rpu70PDVWZ8qDGGA4mItiYyaKm9ZINNeInAMTIc2h9EiKRvuFatPUfQWrMXL/lOCQPqbHzIBcnoGPgGLqHqkIOo3DWcVrla9ENBigYubA7AQCdx91XetHZ45s4POPydJ1g/sWo3tzqFf3TM1dA3ir32P+VDFVBAHz6ESQWBRn7Jwz4s2AZL2YrvSDzdUh6NhBMTB2FJAWFaetxXvG9KMm8ArGK3MCVWAAAZr3TMo8rDGwOHqmwQgBAT7W7iaVIKUBskPkE+BKeV0C1YMIyGIbMOOVxyJs+xpNZqOAjdV6s63qwSYuuitBWHxmL6GPMTBQTsdK02umjXvK4YtpnZzIU5UBt+9agzjJIkouEqJmYk70Buckrac8VWEZRdzqNHKKS6S3LIh1WCMD3KvnK588Jygb+qhfPBYfn/nH21Qen//7it/vdnMTWPTYa9nntI/NcOwPKQeGD23b5bMMfw53uK+akktD15cEyEXpwf6GP6QLNnQ209O7Hd+WPY+vRR3Gk4R0MG5iH98iMX4JzZ/4Kq0sfwurSh7Co4NYJHOn0pfXol6ja8i/IE/Ow8Lq/Q5k8A3yxAiSHB4Igaf+LJNgzgdMMtWgRNeYsQBorwoLr87H3P8xCLSd5hJ4YT+J5h82B6m/aULohBwAgjhIgqViFnhq126q95UAvdL3MY76PcOiNOmdWsDHkrUpG3XfhDQznj4nIaU1RwX/WnkTaDzPcDGgaMaBpxLzcn0Ilywy6vkycgIX5N6GmfQuG9ZP3fYl0MuZdBpEiHiSHB4BA/nk3M6p34J17J3ZgQcAKgdPseKYCl/1zsdu9RbfOYCQE4md4H+gefd+3s1cgKjc1u4QA4EwfKVTy3ZLHBHMWMJbjnzZhzrXZbmcX659YiC8dB1C/PbgftvD/2zvz6LbKMw8/V/tmbV7kfSGJTTZIICEESlhawhI4hXYaukHplFMK01I6tJR2Slt6Wsp0OaczndPD0MPSFigEyrRAAgQISyCAgeyb4zRxbMu7LMuWZO2aP27sRLEkW7Zsy9H3nOM/rLu9ur7+fvd7v3exaKheUZxxZdHpGGyz8rafRpwCoUGC4cmVEsk1Pmr+CyZ9CdXFKymzn5VRe0uLsYJVDV+jtbeRQ87XcrIi5kzjaPjEbJswZYQIHOfwmx3s+UcLSz9dO/qZpJC44NZFvP/wwZRv9kuvq+XSu85O+Mx1dJCtv59cs43uAwP0NnsoXiCvSVSdW5wQyhn0hmneknnVTpBnGht/9CFfeuzS0daWSo2C6367mv2bWnn3wf1pi8pZKozUrCqh7sJS5q0pw93qzQkRmEia/nikyyp2unZkpeBdruAd7mF/60YOOV/FYVvM4uprMzq+uvg8bKYadvzzKQJZDvmda7iOTb0G1myTsyLQsLaSwjozWpOKglID2gI1WtOJn1MxFuq4bfM6gt7w8Z8IIW+YwS4/QW+Y3kOecWPqt/xqJ+Vn2SmsO/GmfOFti1j2uTM49LoT504XPlcAvUVDSYOVhssrElxIAOFAlI0/bCQSnHxy0b4XjnHJv8tdrRyLrJjLTyxuNm1un9K5u/e7eeknH7LuF+ed6Ecgyclpi9ZV4zoySNPmdgJDYVQaBRqjCr1Ni722gKpzi9OffAKopqGQmUqRupJpNt5WT9fia5FoCGffDga8bZTbz6a88OwJR1MV6B2cV/9VPmh6+LSZJU2GXCr/MFlyVgTO/eKCCZViPhlTiR5TSfJMyyNbO8cVgZAvwiPXb0Zn1nD1z1cyb00ZAMYiHctvmMfyG+alPNZ9bIjn7/5gTNLXZPjwz4eoXlnMGReVodarErqUvfKzyXdyGuHAS20ceKmN0kU2bnwysQFL4RlmLvhG8jIMpxJP1Xs5DYYsJG+dSoG+JOW2ocDUM5QtxvIpnyOX8QX6aO54neaO10c/W1J7HWW2pSl7QQPoNGYuXvodNm//2UyYKZgmTu/VsEkSGAzx3B3v8sI9Eyu2FfCEeGz9a1kRgBHG6xecDbr2u3n9gZ14nBkkjMXl2cTrD+zkqVsyd5GkqtI5FdKdMxvuCqNu6jOgucbelr/z7v4/TKhwXrKe0fmKpFBisJZhLp2P2TEfg7Vstk0al5ydCTx58xuzbQIHX27D3x9k/sVllC0txFplRFegJuiL4HH66D7gpuW9bv75dmdGrSgnQtOr7fx62bNZPWcytj91mO1PH6ZyWRF1FzooXWyn/OxC1Dol0XCMkD9CcDCEu9XLvhdbaW3swe+efKy/QZPd2HO5b7E95Xb/FHsNg1xmQasuyDu3hz/oYuc/n6bMvoTFNZ9OuQBfalsypV7DpwtGeyWLLv83FKpE96G7fR+d+99gqDd1l8HZJGdFIFdobeyhtTF7Rc+++00L664w0DBfjcko4ffHeeWNYX58v5tjbbMUbRGH9h19tO+YWJP7qWBK47qZDEZ9cdrFZl8gO9/JXlBLZ/+erJxrrtHZv5dINMTyeZ9Pur3IPC/vRaBiyaeoPOvKpCWIbZWLsVUuovPAW7Ruf5FcazQvRGAGMZsV/OyHtlM+k7juagPf/N70FCnLNVRKLUZdIb4svKGD3DMgHR7/xKOXwpHhlNU7iy31eSsCkL4HhNWYvBx2vlC+6DIqz74KgKHeo/j7nYSDPiRJQqU14qi/AJAoW3gJ8ViMtp0bZ9fgUxAiMINse7kc90CMhpXteH3ZdR9NB2fUqti7rZLH/url9ruyN0s4s/JKPj78xJTPo1bqaKi4POX2Lvf+jMRm77HnWT7vhqTbSm2LGRru4mjXuxnbebqQqmS3Rm1ErdKnzdye0nXjuZuPULvyMzjqLyTgdbFn42+IRcZWDm758DkUKg1L132X8sWXodLoONr4t1mwNjliYXiGUKslzqhVsa0xMCcEAODSiyZX03487AV1WanuWVG0PK0rqCfD5jV9g4cIhFJXOa0tuWDSdf5PB9LlUkxn4lg0NrM9LzLBUioX2+tueiepAIwQi4TobnoHALMjtwr0CRGYIUxG2VfY0zt3erx+cs30DHiSpKDWsXrK56kpOT/ltmgsTN9gZj2B4/E4bb0fptyuVuk5s/KqjM55OpEquzgaC0/rQB0Mp05gnG00BrlawPDA+B3+/AMdx4/JfoTcVBAiMEOYjPKtjs2NSQA6rcTFF+rG33GSVBWvnPI50s0mOvv3EIlm3kKxtbcxbRRQmX1JVgTsdCIbEVjpGA65U25TK6fvGc2EOBn8Y2erf22WmBMisOHREvwdtdx/r7yoWlku+6r7j9bQvq+aF592sP668et5Fxcp+fHdVjoPVjNwrIbD26t46uESrr0ydbnhmioV/o5a/B21FNrlELl7v2floy3ldDVV09Ncza53Kli2dGxW6cYNpWx/q4KupmqaPpR7AXztxoLR8438XHV56uuP2LztlXI6D1Yn2DyRZ6lhvppf/tjO1k1leI7J92vH2xVseLSEW786dhC941YzH20pp6e5GptVfjxu/oJpjM3+jlp02sk/zApJiUo5+Uxce0Ft2u2tvY2TOm80FuZwR/rw5Po06xBTwaQrHvd7paPUthjVNA2K6XIB3N7pzWkZ9Kd+y64sXjGt1x6PkF/OQzFYSsfd12ApSzgmV5Cmo6pjxkZIUlojNjxawjVXGOhzya6UWAxuuaOPUDjOogYNd95mprpSxf/8cZC7fzK2xv6Vn9Lz7GMOFAro7I5y5w9c+HxxamtU3HSDifPO1fLmOwGu/UIX0VO8NTVVKg58IA/gt9/Vx4/vtrH1/QDbGgMMDMQodShZsUzL174l23MyjpITcdV2q4KP36zgiWe8/OgXiW82AwMxgqGxt8DbXjtq84OPDHKwOUxxkXLUZoA33wlw9fqx/yTPPFbCurWyuITDcZ5/yc8LL/ux2RTUVau5/BI9ZrPEgnPbE44rKVYiSWDQS+x7T/7eT/+fj3vuG3tfu3uSu7YuWXoXGvXEmmwEw16a2l+hyz1+P2VJUlBTsor55ZeljFmPxsJs2/+HKbex1KiNrD7z62lnG9FYiPa+7bT1foQ/OPHeDgqFCquxisKCOuwFdZgN5aOZuS3d73HI+eqkbL5oybfRn5Q41963Xa4eOnRkUn0cJEnCYV0k9xbQpO56NxMZw+k6uwXDXnYdfZYBb+u023EqRnslS666E5DwdDZxcMtDY/bRGKzMW/15zKULiMdj7Nn4G4Y93Vm5fjwen/K0Yk6JAEBvX5TVazvo6DoxANmsCt7eVEZdtZplFzk5fPSEf9JkVHDggwoK7Uq27wqy9jNd+IcTL/fQ74r48noTt9zRx5PPJvofTxaBYChOb1+U+hWJA+dEKLQradtbxcN/GeJb3x9/+mwyKuhprh7XZgBDecuY4/0dtQAcbA7zL1/p5kjL2IW7ApOCIW/yaezI9YGMo4NSiYDsN44nrcXjC7jo6N9FS/e2pKWhax0XUFW8Iu1gBHCgbRNtvdnp91qgd7B64a0T2tc73ENrbyPDIQ+hsI9YPIyEAo3ahEqpRa3SY9QWYtQVUmRekHJBO5siMEI8HsPjczI43ElX/z68gZ6UoiBJCvQaCzUl51NsqZ9QhvdsiwDI6zk9ngO09+3ANZi8gq9KqUWjMqFVm9BpzOi1No50Zt6X41Rqzr2O0jMvAsDTeQi/Ww4RBVDrTJSeuWb07922axMde19Pea5MyYYIzLkQ0f96cDBBAADcAzHue2CAPz9YzJfWm7jvP0+8ad94g2nUjXPbXa4xgynAf/zczZfXm7jxBtMYETgZrUbi5tunP6EKZLthfJuTMeImCoXjrL+5J6kAACkFYLpo79tOt3s/K+pvGvMmb9QVsqD8MuaXXUogPEgkGiAWj6JSaNBpLBMueZwtAQAYGu4mEg1NyG1l0pewqPqarF07m0iSAqupCqupiuri8wCIxaNEogEi0SDRWAiFpEapVKNVFaStF3QqrT2Tc7tlisfXjsVYmXL7yKzFYV1EIOQhfPz5UUhKFJIKvdY65pnzBnqzIgKt259HoVRRsmA1lrJ6LGX1Y3eKx2nfszmrApAt5sSawMm8+mbyWORNr8q9ay9clei7vHqtHOHS3RNlz/7kC4W9fbKorDwnfQ2U3ftCbGvMfmesZFy9Vj+uzancMQsb5EHrhZf8CbOi2cY1dIQBXxv7W19MuY8kSeg1Fgr0DiyGcoy6ogkLwGAGnbMmykfNfyIUyaC20hxBISnRqIwYtHYK9KUYdYXo1OaMBKDX00STc/M0WnmCfa0vTjgMVXfS81Ogd2DUFU5rh7l4PMbRxmc5uOUh4rGx/5Putj3se+W/ce6ZmXuVKXNuJnCkJfmgNvK2XD8vccA45yx5YHeUKEddJKkw6CUMeinpmzfAxzuz3x83FeecpcVmVYxrMzDG5uXHF6m3vj8zgjURYvEo7qEWADpcu5BQsLgmszr26RjwtbH98F+zdr4RBv2dvHfgIZbWXj+lRdvTjQ7XLva3vpiVrm4TwTvcw95jz7O07nokciu6ZgRPZxMfPfMj9BYHKo3svo4Effj6M3cfzyRzSgRiMfD5069hWCyJkxurJbPJjlotQQoRGPDMzAOvVGZm96k2F9rlYzu7cicnweNrT4gld7p2UF/xqawkX3X07+ZA68Zpi1UPhof4uPkvVBWvZF75JdMWlhiLRfAHpxBuOQMDciQaoKl9M07XzDdT6XLvBeIsqfl00szlWUGSErrSxSIhfK62WTQoc3LkTk4MhUIeHNMNxgMDiduGhmKYzQoONoc55+Kp9UaNzdAaejQq293RHZ2UzZ7j96e4KHearB9yvjbmszd2/xqQ2xbWlKyixLpwwtN2b6CX9w/+ccZaHMaJ09rbOBp6WlG4nFLbIuwFdZPqlhaJBnF7W3ANHsU1dCQrhe627vs9ILt6iszzaahci147taqt8XgMt/cY3QMHsrreMlm63PtGI8mKLQ0sm7d+0jODPk9myYTJWPXF3yT8Hgn58bna8Lpa8bna8blaCQ2nzkLPBeaUCADUz1fT+PFYt8xIMtbB5sS3wZ17Q6y5QEf9PDV2m4J+99zI1tq5N8QnztdNyuY9B+R7sHqllkcen/nyx2/u+W1G+3t8TnYffQ6VUsv88ksx6YoxaO2olDqUCg3xeBRfsA9fwIXH58Q1dATvcPYqu04Gp2sHTtcOVEotFkMFFmMFRZYF6DRmVArt6DpGKOIjEg0SiQbxB934gy56BprwBrqZrsi8WDxKj6eJHk8TapUBs6EMo64Ig9aOxVCOWqVHpdSiVGhk0ZUk4rEo0ViYUMRPKOKlz3MYj78Dj89JNJZ50t1M0Otp4q3dv8VeUIfFWIlJVzwa9aOQlMTiUWKxCJFokKHhLoZDA/gCLnyBPoaGu7JS6+jwu49jtFVgsFVgsJWj1pmwlDVgKWsY3cfdvhefq10Whv52IsHcWmOacyJw5Sf1SUXgmitkt8KpC7ebXvWz5gIdCgV8/StmHvhd9hq/TCcjdk/G5l175ftz/TVGfvrAAM7OzN+WT85bsJpnJn4gEg1ysO3lGblWtohEg7iGjuAaOsKRrq2zbc4YwhG/nCuQImxyrhOK+BNmBzONq2UHrpYdo79bKxZitFVisJVjsFegM9qxVS7BVrlkdJ+gt5+d//jFbJiblDkXHXT7v5pZcEbi4q/NquDe79mIRuGJZxJDPB953Dsa/XPPdyyjCVSnsmypZjRDNhd45HH5e6SzGUiaqTyS8GbQSzzzWAlljuQuFqMh9TQ6HI7T1S2faPVKLQWm3Lk3AkGuMuA8gHPvqzRv/RO7/nE/Hz79A5q3/pmug1sJ+uTQda0pdROk2WBOzQQGh2IoVRJvbyrjptt6CQTiLDpTzZ3fsFBTpeL3Dw2OiYn3+mLcdFsvzz/pQKOWB8V77uunqzuKSiVRXKTgzHoNX/m8iRWXdeAeyI2pr9cXIxyOj9q8Zeswm7cM0+eKjdq8ZrWOuhpV0mSxre8FuGi1jmVLNezZVsnfnvfx2pvD2GwKyhwqVizX8InzddhqU6f8v/iKn1tuKsBRomTTBgff/6kbnVai0K7Ablfwv4/mV6ctgWCiqHUFmIpqMBZW4ViwGpV2Yhn0s8GcEgGlUqJ4/jFqq1Vs2lBKWakSvz/Ozr0hfvJLNxv+ntzX9ta7ASw1x9BpJb58g4kffMc6GkHT54rRdDjM8jVOmg7nTkw9kGDzussNfPsbFgrtilGbH9/g5bkXkn/nKz4rl5JYsVzLjetNXLhKyxc+a8Q9EKPPFWP3/hBf/3b6xcg77nFx7/1u7rzNwrq1el5+1sHgUJzW9khSl5xAkG9Un3OtPNjbq1AoTwyn0XAQX38b3r5j7H7hV4SDuVsJdU6VjYhE4pirp78Bu0AgEEyEVV+SgyDisSh+dwfdzdvw9rUyPNidEDo6XWSjbMSccvRmks0oEAgEM0U0EiQc9BIJDROLBGdEALLFnHIHCQQCQS7RvvsVCoprMRXVYC1fiLV8ISBnCg/1teDtaaGr6W1i0dxtkTmn3EHRKBRUtcyQVQKBQDBBJAmDtZzyxZdSUFw32nEMZFeRr9/JUO9RvH0tDPW2EM5SAlleVhEVCASCnCMex+92cvidxxM+lhRKLGUNmEvOwF5zNmULLwbggyfumg0rkyJEQCAQCLKChK1ysZwoZi3DYC1FW1A0pqxItmYB2WJOuIMEAoEgFymZf7486B8f+JXqscUFPZ1NJ2oJ9bdltb2kcAcJBALBLFK36nMJv0dDw/j6nXKOwPGBP+ibeOvR2UCIgEAgEEySaCSIv9+Jr78dr6vteB2hueXYEO4ggUAgmKPkXbKYQCAQCLKLEAGBQCDIY3LCHSQQCASC2UHMBAQCgSCPESIgEAgEeYwQAYFAIMhjhAgIBAJBHiNEQCAQCPIYIQICgUCQxwgREAgEgjxGiIBAIBDkMUIEBAKBII8RIiAQCAR5jBABgUAgyGOECAgEAkEeI0RAIBAI8hghAgKBQJDHCBEQCASCPEaIgEAgEOQxQgQEAoEgjxEiIBAIBHmMEAGBQCDIY4QICAQCQR4jREAgEAjyGCECAoFAkMcIERAIBII85v8BqWJigLKNTFMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f06dbf6da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "neg_class_wt_sorted = np.argsort(coef_org)\n",
    "pos_class_wt_sorted = np.argsort(-coef_org)\n",
    "#If less than 15 % proceed with feature importance\n",
    "\n",
    "neg_text = np.take(tf_idf_vect.get_feature_names(), neg_class_wt_sorted[:,:10])\n",
    "pos_text = np.take(tf_idf_vect.get_feature_names(), pos_class_wt_sorted[:,:10])\n",
    "\n",
    "nega_text = \" \".join(words for words in neg_text[0])\n",
    "posi_text = \" \".join(words for words in pos_text[0])\n",
    "\n",
    "print(\"The wordcloud for top 10 negative features are:\")\n",
    "generate_wordcloud(nega_text)\n",
    "\n",
    "print(\"The wordcloud for top 10 positive features are:\")\n",
    "generate_wordcloud(posi_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f069ce95f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The accuracy of the LR classifier for is 90.966667%\n",
      "Confusion matrix is :  [[ 1117  2548]\n",
      " [  162 26173]]\n",
      "The precision score for our model is:  0.9112844260297344\n",
      "The f1 score for our model is:  0.9507773902935193\n",
      "The recall score for our model is:  0.9938484906018606\n",
      "The accuracy of our model is:  0.9096666666666666\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f07699a630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find the optimal plane using grid search and L2 regualarisation \n",
    "\n",
    "opt_plane = find_optimal_lambda(X_train,y_en_train,2,'Grid','l2')\n",
    "y_pred = pred_test_data(X_train,y_en_train,X_test,y_en_test,opt_plane)\n",
    "type(y_pred)\n",
    "\n",
    "\n",
    "confusion_heatmap(y_en_test,y_pred)\n",
    "\n",
    "metric_score(y_en_test,y_pred)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interpretaion: \n",
    "1) We are getting a FN and FP value of 2548 and 162 while TN and TP value of 1117 and 26173.\n",
    "2) This shows we are doing really well in Postive classification.\n",
    "3) However we have a high False negative value as well which shows higher misclassification of positive class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f077a352b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The accuracy of the LR classifier for is 88.486667%\n",
      "Confusion matrix is :  [[ 1587  2078]\n",
      " [ 1376 24959]]\n",
      "The precision score for our model is:  0.9231423604689869\n",
      "The f1 score for our model is:  0.9352844187963726\n",
      "The recall score for our model is:  0.9477501423960509\n",
      "The accuracy of our model is:  0.8848666666666667\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f068857c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find the optimal plane using Random search and L1 regualarisation \n",
    "\n",
    "opt_plane = find_optimal_lambda(X_train,y_en_train,2,'Random','l1')\n",
    "y_pred = pred_test_data(X_train,y_en_train,X_test,y_en_test,opt_plane)\n",
    "type(y_pred)\n",
    "\n",
    "confusion_heatmap(y_en_test,y_pred)\n",
    "\n",
    "metric_score(y_en_test,y_pred)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interpretaion: \n",
    "1) We are getting a FN and FP value of 2078 and 1376 while TN and TP value of 1587 and 24959.\n",
    "2) This shows we are doing really well in Postive classification.\n",
    "3) However we have a high False Negative value as well which shows higher misclassification of positive class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f06d95d080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The accuracy of the LR classifier for is 90.410000%\n",
      "Confusion matrix is :  [[  888  2777]\n",
      " [  100 26235]]\n",
      "The precision score for our model is:  0.9042809871777195\n",
      "The f1 score for our model is:  0.9480188628109925\n",
      "The recall score for our model is:  0.9962027719764572\n",
      "The accuracy of our model is:  0.9041\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f06dd8f710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find the optimal plane using Random search and L2 regualarisation \n",
    "\n",
    "opt_plane = find_optimal_lambda(X_train,y_en_train,2,'Random','l2')\n",
    "y_pred = pred_test_data(X_train,y_en_train,X_test,y_en_test,opt_plane)\n",
    "type(y_pred)\n",
    "\n",
    "confusion_heatmap(y_en_test,y_pred)\n",
    "\n",
    "metric_score(y_en_test,y_pred)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interpretaion: \n",
    "1) We are getting a FN and FP value of 2777 and 100 while TN and TP value of 888 and 26235.\n",
    "2) This shows we are doing really well in Postive classification.\n",
    "3) However we have a very high False positive value as well which shows higher misclassification of negative class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Search Type | Regularisation | C-Value | Accuracy |F1-Score|\n",
    "| --- | --- | --- |--- |\n",
    "| Grid | L1 | 0.1 | 93.05 | 96.06 |\n",
    "| Grid | L2 | 0.0001 | 90.96 | 95.07 |\n",
    "| Random | L1 | 9503 | 88.48 | 93.52 |\n",
    "| Random | L2 | 2250 | 90.41 | 94.80 |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion:\n",
    "\n",
    "1)The LR was performed on AFF using TF-IDF model via L1 regularization and L2 regularization using both Gridsearch and Randomsearch.\n",
    "2)For Gridsearch we get an accuracy of 93.05% for L1 reg and 90.96% for L2 reg.\n",
    "3)For Randomsearch we get an accuracy of 88.48% for L1 reg and 90.41% for L2 reg.\n",
    "4)From above 2 points we can see that L1 reg performs better than L2 reg in GridSearch for Tf-IDF but vice-versa in RandomSearch."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "WORD2VEC MODEL:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = cross_validation.train_test_split(sample_data, label, test_size=0.3, random_state=0)\n",
    "#fetching the list of sentences from cleanedtext column of our train data for word2vec model\n",
    "i=0\n",
    "list_of_sent=[]\n",
    "for sent in X_train['CleanedText'].values:\n",
    "    list_of_sent.append(sent.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models import Word2Vec\n",
    "\n",
    "# min_count = 5 considers only words that occured atleast 3 times\n",
    "w2v_model=Word2Vec(list_of_sent,min_count=3,size=50, workers=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70000\n",
      "50\n"
     ]
    }
   ],
   "source": [
    "w2v_words = list(w2v_model.wv.vocab)\n",
    "\n",
    "# average Word2Vec\n",
    "# compute average word2vec for each review.\n",
    "sent_train_vectors = []; # the avg-w2v for each sentence/review is stored in this list\n",
    "for sent in list_of_sent: # for each review/sentence\n",
    "    sent_vec = np.zeros(50) # as word vectors are of zero length\n",
    "    cnt_words =0; # num of words with a valid vector in the sentence/review\n",
    "    for word in sent: # for each word in a review/sentence\n",
    "        if word in w2v_words:\n",
    "            vec = w2v_model.wv[word]\n",
    "            sent_vec += vec\n",
    "            cnt_words += 1\n",
    "    if cnt_words != 0:\n",
    "        sent_vec /= cnt_words\n",
    "    sent_train_vectors.append(sent_vec)\n",
    "print(len(sent_train_vectors))\n",
    "print(len(sent_train_vectors[0]))\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "i=0\n",
    "list_of_sent=[]\n",
    "for sent in X_test['CleanedText'].values:\n",
    "    list_of_sent.append(sent.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30000\n",
      "50\n"
     ]
    }
   ],
   "source": [
    "sent_test_vectors = []; # the avg-w2v for each sentence/review is stored in this list\n",
    "for sent in list_of_sent: # for each review/sentence\n",
    "    sent_vec = np.zeros(50) # as word vectors are of zero length\n",
    "    cnt_words =0; # num of words with a valid vector in the sentence/review\n",
    "    for word in sent: # for each word in a review/sentence\n",
    "        if word in w2v_words:\n",
    "            vec = w2v_model.wv[word]\n",
    "            sent_vec += vec\n",
    "            cnt_words += 1\n",
    "    if cnt_words != 0:\n",
    "        sent_vec /= cnt_words\n",
    "    sent_test_vectors.append(sent_vec)\n",
    "print(len(sent_test_vectors))\n",
    "print(len(sent_test_vectors[0]))\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(70000, 50)\n",
      "(30000, 50)\n"
     ]
    }
   ],
   "source": [
    "X_train = sent_train_vectors\n",
    "X_test = sent_test_vectors\n",
    "\n",
    "Scaler = StandardScaler(copy=True, with_mean=False, with_std=True).fit(X_train)\n",
    "X_train = Scaler.transform(X_train)\n",
    "X_test = Scaler.transform(X_test)\n",
    "\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f06d8115c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The accuracy of the LR classifier for is 81.806667%\n",
      "Confusion matrix is :  [[ 3062   603]\n",
      " [ 4855 21480]]\n",
      "The precision score for our model is:  0.9726939274555088\n",
      "The f1 score for our model is:  0.8872733281011194\n",
      "The recall score for our model is:  0.8156445794569964\n",
      "The accuracy of our model is:  0.8180666666666667\n"
     ]
    },
    {
     "data": {
      "image/png": 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wO1kP0dGpJ9OyZI3bM1NwuZ6sfRiyCfxyG6IldVT2sGpErZLEO7rN37TUcZG5m8zMBopmtklIugj4KLC6pHlkyzhPAoYC16XlE26LiIMjYo6kGWQzbC8BDo2IN9J9DiNbv6cTODci5qRHHANMT2PR7gam5OUjIv4t6S+S1omIJxr5LLWCxC9YuvTQ/djMrG00c4K/iNgzJzn3izydfxJZFX/39CuBK3PSHyXr/VTEmsAcSbdTMWVSr1em6+XcTWZmA0pHZ3/noDS9+i4v0gXWzKztDbTpNorqWqGuUQNvjSUzsxJIKrwNJJK2knSHpBclLZb0Rj0zX7gkYWZG+5YkgJ+S9Yy6BNgc+BJZD6pCSl2ZrhlWGbpzXzzGBpwN+jsD1mbaOEgQEXMldaZeU1Ml3Vr02iIr020AfIisLy/AzsBNDeXUzKxFtXGQeDmNs7hH0qlkc/CtUPTiHns3SbqWbC2IRen4eLJii5lZ2xjSvi20XyRrfz4M+DrZKO7PFb24SJvEOsDiiuPFwLrF89c7Dzx/RV89ylrcmJXfqnocts6EfsyJtZJXnpjelPt0qLTpj/pVRPw9lSTWBS4DHk6zyhZSJEhcANwu6Tdkk0jtCpzfQF7NzFpWMwfTtRJJnwJ+DvyNbHLAUZIOioirilzfY5CIiJMkXQVsm5L2i4i7G82wmVkrat/aJs4AtouIuQCS1gN+DzQnSCTLAwsjYqqkNSSNSotjmJm1hXatbgKe7QoQyaPAs0Uv7jFISDqOrG/tBsBUYBngl8A29eXTzKx1tVt1k6TPpt05kq4EZpA1GexONsNsIUVKErsCmwB3AUTEU5I80Z+ZtZUhbRYkyIYrdHmGt9YD+iewStGbFAkSiyMipKwsJqlw/1ozs4FCbVbdFBH7SeoEDo+IMxu9T5G2mhmSziZbCelA4A/AOY0+0MysFXWo+DZQpBHWhaYEr6ZI76bTJX0cWEjWLvGdiLiuNw81M2s1bdy76VZJPwUuZun1JO4qcnGRhutTIuIY4LqcNDOzttDGvZs+nF5PqEgLYPsiFxdpk/g42VJ5lXbKSTMzG7DasOEagIjYrjfX15oF9hDgK8B6ku6teGs4UHgGQTOzgWAgtTXUQ9JKZGtsfyQl3QicEBEvFLm+VkniV2Qj8k4Gjq1IXxQR8xvIq5lZy2rj6qZzgfuBz6fjL5KNefts1Ssq1JoF9gXgBUk/AuZXzAI7XNKWETGrV9k2M2sh7VqSANaLiMpZX78r6Z6iFxdp0P8Z8GLF8UspzcysbXTUsQ0wr0ga23UgaRvglaIXF2m4VkS8WQ6LiH9L8rKnZtZW2ri66RBgWmqbEDAf2LfoxUW+7B+VdDhvlR6+QjZBlJlZ22jXRYci4h7gA5JGpOOF9VxfJEgcDPwY+DZZ39o/AhPrzKeZWUtrtxgh6cgq6QBExA+K3KfIiOtnAS8DZmZtrQ2rm5oyEWutcRJHR8Spkn5CVoJYSkQc3owMmJm1gnbr3RQR323GfWqVJB5Mr7Ob8SAzs1bWbtVNXSSNBH5CtgZQALcAR0TEvCLX1xoncUV6ndaEfJqZtbR2K0lUmEo2OHr3dLx3Svt4kYtrVTddQU41U5eI6NX0s2ZmraSzo+3aJLqsERFTK47Pk/S1ohfXqm46Pb1+Fvi/ZEuWAuwJPF5PDs3MWl27VjcBz0naG7goHe8J/KvoxbWqm24EkHRiRHyk4q0rJN3USE7NzFpVG/Zu6rI/8FPgTLLaoVtTWiFFxkmsIendEfEogKRRwBoNZNTMrGW1a5tERDxBL1anK1LC+jpwg6QbJN0AXA8Urs8yMxsImrl8qaSvS5oj6X5JF0laTtIoSbMkPSLpYknLpnOHpuO56f11K+4zKaU/LGnHRj6XpGmSVq44XkXSuUWvLzKY7mpJo4ENU9JDEfFa/Vk1M2tdyzSpuknSWsDhwJiIeEXSDLIByZ8EzoyI6ZJ+DhxANt3RAcCCiFhf0gTgFGAPSWPSdRsB7wT+IOk9ad3qemwcEc93HUTEAkmbFL24x5KEpOWBbwCHRcRfgHUkfbrOTJqZtbRmliTI/gAfliZDXR54mmy50EvT+9OAXdL++HRMev9jyubOGA9Mj4jXIuIxYC6wRUMfTVql60DSqhRranjzg/RkKnAnsHU6ngdcAvyujkyambW0ZrVJRMQ/JJ0OPEE2Jfe1ZN+hz0fEknTaPGCttL8W8GS6domkF4DVUvptFbeuvKYeZwC3SrqUrOH688BJRS8u0iaxXkScCrwOEBGvkE03a2bWNjpVfJM0UdLsiu3NSU/TX+3jgVFk1UQrADvlPLKrfivv+zRqpNclIs4HPgc8A/wT+GxEXFD0+iIlicWShnVlTtJ6gNskzKyt1FOSiIjJwOQqb+8APBYR/wSQdBnwYWBlSUNSaWIk8FQ6fx6wNjAvVU+tRLbmQ1d6l8pr6hIRDwAPNHJtkZLEccDVwNqSLiSbKvzoRh5mZtaqOhSFtx48AWwlafnUtvAxsi/o64Hd0jn7AJen/ZnpmPT+n9JCbzOBCan30yhgNHB70z5wQTVLEukDPkQ26norsuLPERHxXB/kzcyszyzTvDaJWan+/y5gCXA3Wanj98B0Sd9LaVPSJVOACyTNJStBTEj3mZN6Rj2Q7nNoAz2beq1mkIiIkPTbiNiM7AOambWlZg6mi4jjyGphKj1KTu+kiHiVtybf6/7eSdTRyFyGItVNt0n6UOk5MTPrR02sbmorRRqutwMOlvQ48BJZlVNExMZlZszMrC91us9mriJBIq/rlplZW2nXuZt6q9Z6EssBBwPrA/cBUyoGgpiZtZUhbTxXeG/UKklMIxtAdzNZaWIMcERfZMrMrK91DrK2hqJqBYkxEfF+AElT6If+uWZmfcUFiXy1gsTrXTtpPpE+yI6ZWf9wm0S+WkHiA5IWpn2RzWi4kLd6N40oPXdmZn3EQSJfreVLO/syI2Zm/cltEvkKzyluZtbO3Lspn4OEmRmubqrGQcLMDI+4rsZBwswMBt2cTEU5SJiZ4XES1ThImJnhNolqHCTMzIBlOlzdlKf0Epakd0naIe0PkzS87GeamdWrQ8W3waTUICHpQOBS4OyUNBL4bY3zJ0qaLWn25GpLjJuZlcBBIl/Z1U2Hki3XNwsgIh6R9I5qJ0fEZLK1YAF3NTCzvuOG63xlB4nXImJx1+SAkoYA/vI3s5bjOUzzlR0kbpT0TbLJAT8OfAW4ouRnmpnVbbBVIxVVdgnrWOCfZCvbHQRcCXy75GeamdWto45tMCm7JDEeOD8iflHyc8zMekVuBs1VdlD8DPBXSRdI+lRqkzAzazmqYxtMSg0SEbEfsD5wCbAX8DdJ55T5TDOzRkjFt8Gk9L/sI+J1SVeR9WoaRlYF9eWyn2tmVo9B9t1fWNmD6cZJOg+YC+wGnAOsWeYzzcwa0ani22BSdkliX2A6cFBEvFbys8zMGjbYqpGKKjVIRMSEMu9vZtYsjhH5SgkSkm6JiLGSFrH0CGsBEREjyniumVmjHCTylRIkImJsevWMr2Y2IHjEdb6yG64vKJJmZtbfPE4iX9kN1xtVHqTBdJuV/Ewzs7p5jet8pZQkJE1K7REbS1qYtkXAM8DlZTzTzKw3mj2YTlKnpLsl/S4dj5I0S9Ijki6WtGxKH5qO56b31624x6SU/rCkHZv/qXtWSpCIiJNTe8RpETEibcMjYrWImFTGM83MeqOECf6OAB6sOD4FODMiRgMLgANS+gHAgohYHzgznYekMcAEshqZccBZkjob+nC9UFZJYsO0e4mkTbtvZTzTzKw3mlmSkDQS+BTZAGKULaqzPdlKnQDTgF3S/vh0THr/Y+n88cD0iHgtIh4jG5S8RXM+bXFltUkcCUwEzsh5L8h+WGZmLaOeBmlJE8m+47pMTitrdvkhcDTQ1cNzNeD5iFiSjucBa6X9tYAnASJiiaQX0vlrAbdV3LPymj5TVhfYiel1uzLub2bWbPV0gV16qeWlSfo08GxE3Cnpo13Jebfp4b1a1/SZsrvA7i5peNr/tqTLJG1S5jPNzBrRoeJbD7YBPiPpcbJpibYnK1msXLFcwkjgqbQ/D1gb3uwBuhIwvzI955o+U/Z6Ev8ZEYskjQV2JKt3+3nJzzQzq1uzxklExKSIGBkR65I1PP8pIr4AXE820SnAPrzV03NmOia9/6eIiJQ+IfV+GgWMBm7v7eesV9lB4o30+ingZxFxObBsyc80M6ubFIW3Bh0DHClpLlmbw5SUPgVYLaUfSbbsMxExB5gBPABcDRwaEW+87a4lK3sw3T8knQ3sAJwiaSiDb4lYMxsAyhhJHRE3ADek/UfJ6Z0UEa8Cu1e5/iTgpBKyVljZX9ifB64BxkXE88CqwDdKfqaZWd28Ml2+sqcKf1nS34Ad02jBmyPi2jKfaWbWiD4fpTZAlN276QjgQuAdafulpK+W+Uwzs0a4JJGv7DaJA4AtI+IlAEmnAH8GflLyc83M6jTIvv0LKjtIiLd6OJH2/S9hZi1H/mrKVXaQmArMkvSbdLwLb3X7MjNrGZI7XuYpu+H6B5JuAMaSlSD2i4i7y3ymmVljXJLIU9Ya18sBBwPrA/cBZ1VMbGVm1nLkIVy5yipJTANeB24GdgLeC3ytpGeZmfWaq5vylRUkxkTE+wEkTaEf5hsxM6uPq5vylBUkXu/aSfOjl/QYM7PmcO+mfGUFiQ9IWpj2BQxLxwIiIkaU9Fwzs4Y4SOQra9Ehj3A3swGlH5aPHhDKHidhZjZAuCSRx0HCzAxXN1XjIGFmBnipm3wOEmZmuCRRjYOEmRngrvr5HCTMzAB52aFcDhJmZoB7N+VzkDAzw9VN1ThImJkBLknkc5AwM8NThVfjIGFmBrgkkc9BwswM6PB6ErkcJMzMAI+4zucgYWaGR1xX4yBhZga4TSKfIqK/81CFWjVjZtZyotff8MGDhb9zxHsHTURxScLMDE/LUU0LlySsi6SJETG5v/NhrcW/F9YX3Jw/MEzs7wxYS/LvhZXOQcLMzKpykDAzs6ocJAYG1ztbHv9eWOnccG1mZlW5JGFmZlU5SDSZpJB0RsXxUZKOL+E53+x2fGuzn2HlkPSGpHsk3S/pEknLN3CPcySNSfv+XbDSuLqpySS9CjwNfCginpN0FLBiRBzf5Oe8GBErNvOe1jcq/+0kXQjcGRE/aMb9zJrNJYnmW0LWoPj17m9IWkPSryXdkbZtKtKvk3SXpLMl/V3S6um930q6U9IcSRNT2veBYemv0QtT2ovp9WJJn6x45nmSPiepU9Jp6bn3Sjqo9J+EFXEzsD6ApCNT6eJ+SV9LaStI+r2kv6T0PVL6DZI29++ClS4ivDVxA14ERgCPAysBRwHHp/d+BYxN++sAD6b9nwKT0v44IIDV0/Gq6XUYcD+wWtdzuj83ve4KTEv7ywJPpmsnAt9O6UOB2cCo/v55Dcat4t9qCHA5cAiwGXAfsAKwIjAH2AT4HPCLimtXSq83AJv7d8Fb2ZvnbipBRCyUdD5wOPBKxVs7AGMqFlwfIWk4MJbsf2gi4mpJCyquOVzSrml/bWA08K8aj78K+LGkoWQB56aIeEXSJ4CNJe2Wzlsp3euxRj+nNWyYpHvS/s3AFLJA8ZuIeAlA0mXAtsDVwOmSTgF+FxE31/Ec/y5YrzlIlOeHwF3A1Iq0DmDriKgMHKgianRL/yhZYNk6Il6WdAOwXK2HRsSr6bwdgT2Ai7puB3w1Iq6p+5NYs70SER+sTKj2OxARf5W0GfBJ4GRJ10bECUUe4t8Fawa3SZQkIuYDM4ADKpKvBQ7rOpDU9UVxC/D5lPYJYJWUvhKwIAWIDYGtKu71uqRlqjx+OrAf2V+iXV8E1wCHdF0j6T2SVmjw41nz3QTsImn59O+yK3CzpHcCL0fEL4HTgU1zrvXvgpXGQaJcZwCrVxwfDmyeGgsfAA5O6d8FPiHpLmAnst5Ri8iqGoZIuhc4Ebit4l6TgXu7Giu7uRb4CPCHiFic0s4BHgDuknQ/cDYuSbaMiLgLOA+4HZgFnBMRdwPvB25P1VPfAr6Xc7l/F6w07gLbAlKd8RsRsUTS1sDPuldHmJn1B//10BrWAWZI6gAWAwf2c37MzACXJMzMrAb9Ta0vAAAC90lEQVS3SZiZWVUOEmZmVpWDhJmZVeUgYYVI2jXNcLthgXP3Tf37G33WRyX9rkr6C5LulvSgpOMauU8P17xT0qVp/4OVcx+ZDUYOElbUnmSD/iYUOHdfoOEg0YObI2ITYHNg7zQauSkkDYmIpyKia7qKD5KNdDYbtBwkrEeSVgS2IRs9PqHbe0dLui/NUvr9NB/Q5sCFaWbSYZIer5jVdvM0VQSStpB0ayoZ3Cppg6J5SnMc3QmsJ2k5SVNTPu6WtF3OZ8h9Vir1XCLpCuBaSeum2VaXBU4A9kifYw9Jj0haI13XIWlu1+cya1ceJ2FF7AJcneYRmi9p04i4S9JO6b0t09Qhq0bEfEmHAUdFxGyAKtMSATwEfCQNItwB+C+yWU97JGk1smlKTgQOBYiI96fqsGslvaeOZ20NbJzyvm6612JJ3yGbafWw9MwNgS+Qzcu1A/CXiHiuSH7NBioHCStiT7IvRsjmAtqTbPLCHYCpEfEyvDlfVT1WAqZJGk02PXq1+YcqbSvpbuDfwPcjYo6k7wE/SXl4SNLfge5BotazriuY93PJpvb+IbA/S0/eaNaWHCSspvQX+/bA+yQF0AmEpKPJZhMtMhpzCW9VbVbOYnsicH1E7Jr+gr+hwL1ujohPd89mgetqPeulAtcTEU9KekbS9sCWZKUKs7bmNgnryW7A+RHxrohYNyLWJlt3YCzZ5HH7K63RLGnVdM0iYHjFPR4nW1QHlq5OWgn4R9rftxd5vIn0hZ2qmdYBHu52TiPP6v45IJsc75fAjIh4o5HMmg0kDhLWkz2B33RL+zWwV0RcDcwEZqdZSo9K758H/Lyr4ZpsltsfSboZqPxiPZVsjYT/T1ZCadRZQKek+4CLgX0j4rVu5zTyrOvJFom6R2nZULLPuyKuarJBwnM3mdVB0ubAmRGxbX/nxawvuE3CrCBJx5ItM+q2CBs0XJIwM7Oq3CZhZmZVOUiYmVlVDhJmZlaVg4SZmVXlIGFmZlU5SJiZWVX/C4qaIuTu/00KAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f021d33438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find the optimal plane using grid search and L1 regualarisation\n",
    "\n",
    "encoder = preprocessing.LabelEncoder()\n",
    "enc = encoder.fit(y_train)\n",
    "y_en_train= enc.transform(y_train)\n",
    "y_en_test = enc.transform(y_test)\n",
    "\n",
    "opt_plane = find_optimal_lambda(X_train,y_en_train,2,'Grid','l1')\n",
    "y_pred = pred_test_data(X_train,y_en_train,X_test,y_en_test,opt_plane)\n",
    "type(y_pred)\n",
    "\n",
    "confusion_heatmap(y_en_test,y_pred)\n",
    "\n",
    "metric_score(y_en_test,y_pred)\n",
    "\n",
    "coef_org = opt_plane.best_estimator_.coef_\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interpretaion: \n",
    "1) We are getting a FN and FP value of 603 and 4855 while TN and TP value of 3062 and 21480.\n",
    "2) This shows we are doing really well in Postive classification.\n",
    "3) However we have a high False positive value as well which shows higher misclassification of negative class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f0689bbbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The accuracy of the LR classifier for is 81.823333%\n",
      "Confusion matrix is :  [[ 3059   606]\n",
      " [ 4847 21488]]\n",
      "The precision score for our model is:  0.9725717389336471\n",
      "The f1 score for our model is:  0.8874021763819199\n",
      "The recall score for our model is:  0.8159483576988799\n",
      "The accuracy of our model is:  0.8182333333333334\n"
     ]
    },
    {
     "data": {
      "image/png": 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nGyE6Oo1kWpqsc3tGCi7Xk/UPQzaBX25HtKSOyhFWjahVk3hXt/mbljguMneTmdlA0cw+CUkXAR8HVpU0j2wZ50nAUOC6tHzCbRFxYETMljSdbIbtxcDBEfFmus8hZOv3dALnRsTslMVRwLT0LNrdwDl55YiIf0v6q6S1IuLxRj5LrSDxS5asPXQ/NjNrG82c4C8ids9Jzv0iT+efSNbE3z39SuDKnPRHyEY/FbE6MFvS7VRMmdTrlel6OXeTmdmA0tHZ3yUoTa++y4sMgTUza3sDbbqNorpWqGvUwFtjycysBJIKbwOJpC0k3SHpRUmvS3qznpkvXJMwM6N9axLAz8hGRl0CbAp8hWwEVSGlrkzXDCsN3bEvsrEBZ73+LoC1mTYOEkTEXEmdadTUFEm3Fr22yMp06wEfIRvLC7AjcFNDJTUza1FtHCReTs9Z3CPpFLI5+JYrenGPo5skXUu2FsSidHwcWbXFzKxtDGnfHtovk/U/HwJ8k+wp7i8UvbhIn8RawOsVx68DaxcvX+/Mef6KvsrKWtz7V3y76XHYWhP6sSTWSl55fFpT7tOh0qY/6lcR8Y9Uk1gbuAx4KM0qW0iRIHEBcLuk35JNIrUzcH4DZTUza1nNfJiulUj6DPAL4O9kkwOOknRARFxV5Poeg0REnCjpKmDrlLRPRNzdaIHNzFpR+7Y2cTqwTUTMBZC0DvAHoDlBIlkWWBgRUyStJmlUWhzDzKwttGtzE/BMV4BIHgGeKXpxj0FC0rFkY2vXA6YASwG/Araqr5xmZq2r3ZqbJH0+7c6WdCUwnazLYFeyGWYLKVKT2BnYCLgLICKelOSJ/sysrQxpsyBB9rhCl6d5ez2gZ4GVit6kSJB4PSJCyupikgqPrzUzGyjUZs1NEbGPpE7g0Ig4o9H7FOmrmS7pLLKVkPYH/gic3WiGZmatqEPFt4EiPWFdaErwaoqMbjpN0ieBhWT9Et+NiOt6k6mZWatp49FNt0r6GXAxS64ncVeRi4t0XJ8cEUcB1+WkmZm1hTYe3fTR9Hp8RVoA2xa5uEifxCfJlsqrtENOmpnZgNWGHdcARMQ2vbm+1iywBwFfA9aRdG/FW8OBwjMImpkNBAOpr6EeklYgW2P7YynpRuD4iHihyPW1ahK/Jnsi7yTg6Ir0RRExv4Gympm1rDZubjoXuB/4Yjr+Mtkzb5+vekWFWrPAvgC8IOnHwPyKWWCHS9o8Imb2qthmZi2kXWsSwDoRUTnr6/ck3VP04iId+j8HXqw4fimlmZm1jY46tgHmFUljuw4kbQW8UvTiIh3Xioi36mER8W9JXvbUzNpKGzc3HQRMTX0TAuYDexe9uMiX/SOSDuXt2sPXyCaIMjNrG+266FBE3AN8SNKIdLywnuuLBIkDgZ8Ax5CNrf0TMLHOcpqZtbR2ixGSDq+SDkBE/LDIfYo8cf0M4GXAzKyttWFzU1MmYq31nMSREXGKpJ+S1SCWEBGHNqMAZmatoN1GN0XE95pxn1o1iTnpdVYzMjIza2Xt1tzURdJI4KdkawAFcAtwWETMK3J9reckrkivU5tQTjOzltZuNYkKU8gejt41He+Z0j5Z5OJazU1XkNPM1CUiejX9rJlZK+nsaLs+iS6rRcSUiuPzJH2j6MW1mptOS6+fB/4v2ZKlALsDj9VTQjOzVteuzU3Ac5L2BC5Kx7sD/yp6ca3mphsBJJ0QER+reOsKSTc1UlIzs1bVhqObuuwL/Aw4g6x16NaUVkiR5yRWk/TeiHgEQNIoYLUGCmpm1rLatU8iIh6nF6vTFalhfRO4QdINkm4ArgcKt2eZmQ0EzVy+VNI3Jc2WdL+kiyQtI2mUpJmSHpZ0saSl07lD0/Hc9P7aFfeZlNIfkrR9I59L0lRJK1YcryTp3KLXF3mY7mpJo4H1U9KDEfFa/UU1M2tdSzWpuUnSGsChwJiIeEXSdLIHkj8NnBER0yT9AtiPbLqj/YAFEbGupAnAycBuksak6zYA3g38UdL70rrV9dgwIp7vOoiIBZI2KnpxjzUJScsC3wIOiYi/AmtJ+mydhTQza2nNrEmQ/QE+LE2GuizwFNlyoZem96cCO6X98emY9P4nlM2dMR6YFhGvRcSjwFxgs4Y+mrRS14GklSnW1fDWB+nJFOBOYMt0PA+4BPh9HYU0M2tpzeqTiIh/SjoNeJxsSu5ryb5Dn4+Ixem0ecAaaX8N4Il07WJJLwCrpPTbKm5deU09TgdulXQpWcf1F4ETi15cpE9inYg4BXgDICJeIZtu1sysbXSq+CZpoqRZFdtbk56mv9rHA6PImomWA3bIybKrfSvv+zRqpNclIs4HvgA8DTwLfD4iLih6fZGaxOuShnUVTtI6gPskzKyt1FOTiIjJwOQqb28HPBoRzwJIugz4KLCipCGpNjESeDKdPw9YE5iXmqdWIFvzoSu9S+U1dYmIB4AHGrm2SE3iWOBqYE1JF5JNFX5kI5mZmbWqDkXhrQePA1tIWjb1LXyC7Av6emCXdM5ewOVpf0Y6Jr3/57TQ2wxgQhr9NAoYDdzetA9cUM2aRPqAD5I9db0FWfXnsIh4rg/KZmbWZ5ZqXp/EzNT+fxewGLibrNbxB2CapO+ntHPSJecAF0iaS1aDmJDuMzuNjHog3efgBkY29VrNIBERIel3EbEJ2Qc0M2tLzXyYLiKOJWuFqfQIOaOTIuJV3p58r/t7J1JHJ3MZijQ33SbpI6WXxMysHzWxuamtFOm43gY4UNJjwEtkTU4RERuWWTAzs77U6TGbuYoEibyhW2ZmbaVd527qrVrrSSwDHAisC9wHnFPxIIiZWVsZ0sZzhfdGrZrEVLIH6G4mq02MAQ7ri0KZmfW1zkHW11BUrSAxJiI+CCDpHPphfK6ZWV9xRSJfrSDxRtdOmk+kD4pjZtY/3CeRr1aQ+JCkhWlfZDMaLuTt0U0jSi+dmVkfcZDIV2v50s6+LIiZWX9yn0S+wnOKm5m1M49uyucgYWaGm5uqcZAwM8NPXFfjIGFmBoNuTqaiHCTMzPBzEtU4SJiZ4T6JahwkzMyApTrc3JSn9BqWpPdI2i7tD5M0vOw8zczq1aHi22BSapCQtD9wKXBWShoJ/K7G+RMlzZI0a3K1JcbNzErgIJGv7Oamg8mW65sJEBEPS3pXtZMjYjLZWrCAhxqYWd9xx3W+soPEaxHxetfkgJKGAP7yN7OW4zlM85UdJG6U9G2yyQE/CXwNuKLkPM3M6jbYmpGKKruGdTTwLNnKdgcAVwLHlJynmVndOurYBpOyaxLjgfMj4pcl52Nm1ityN2iusoPi54C/SbpA0mdSn4SZWctRHdtgUmqQiIh9gHWBS4A9gL9LOrvMPM3MGiEV3waT0v+yj4g3JF1FNqppGFkT1FfLztfMrB6D7Lu/sLIfphsn6TxgLrALcDawepl5mpk1olPFt8Gk7JrE3sA04ICIeK3kvMzMGjbYmpGKKjVIRMSEMu9vZtYsjhH5SgkSkm6JiLGSFrHkE9YCIiJGlJGvmVmjHCTylRIkImJsevWMr2Y2IPiJ63xld1xfUCTNzKy/+TmJfGV3XG9QeZAeptuk5DzNzOrmNa7zlVKTkDQp9UdsKGlh2hYBTwOXl5GnmVlvNPthOkmdku6W9Pt0PErSTEkPS7pY0tIpfWg6npveX7viHpNS+kOStm/+p+5ZKUEiIk5K/RGnRsSItA2PiFUiYlIZeZqZ9UYJE/wdBsypOD4ZOCMiRgMLgP1S+n7AgohYFzgjnYekMcAEshaZccCZkjob+nC9UFZNYv20e4mkjbtvZeRpZtYbzaxJSBoJfIbsAWKULaqzLdlKnQBTgZ3S/vh0THr/E+n88cC0iHgtIh4leyh5s+Z82uLK6pM4HJgInJ7zXpD9sMzMWkY9HdKSJpJ9x3WZnFbW7PIj4Eiga4TnKsDzEbE4Hc8D1kj7awBPAETEYkkvpPPXAG6ruGflNX2mrCGwE9PrNmXc38ys2eoZArvkUstLkvRZ4JmIuFPSx7uS827Tw3u1rukzZQ+B3VXS8LR/jKTLJG1UZp5mZo3oUPGtB1sBn5P0GNm0RNuS1SxWrFguYSTwZNqfB6wJb40AXQGYX5mec02fKXs9if+MiEWSxgLbk7W7/aLkPM3M6tas5yQiYlJEjIyItck6nv8cEV8Crieb6BRgL94e6TkjHZPe/3NEREqfkEY/jQJGA7f39nPWq+wg8WZ6/Qzw84i4HFi65DzNzOomReGtQUcBh0uaS9bncE5KPwdYJaUfTrbsMxExG5gOPABcDRwcEW++464lK/thun9KOgvYDjhZ0lAG3xKxZjYAlPEkdUTcANyQ9h8hZ3RSRLwK7Frl+hOBE0soWmFlf2F/EbgGGBcRzwMrA98qOU8zs7p5Zbp8ZU8V/rKkvwPbp6cFb46Ia8vM08ysEX3+lNoAUfbopsOAC4F3pe1Xkr5eZp5mZo1wTSJf2X0S+wGbR8RLAJJOBv4C/LTkfM3M6jTIvv0LKjtIiLdHOJH2/S9hZi1H/mrKVXaQmALMlPTbdLwTbw/7MjNrGZIHXuYpu+P6h5JuAMaS1SD2iYi7y8zTzKwxrknkKWuN62WAA4F1gfuAMysmtjIzaznyI1y5yqpJTAXeAG4GdgDeD3yjpLzMzHrNzU35ygoSYyLigwCSzqEf5hsxM6uPm5vylBUk3ujaSfOjl5SNmVlzeHRTvrKCxIckLUz7AoalYwERESNKytfMrCEOEvnKWnTIT7ib2YDSD8tHDwhlPydhZjZAuCaRx0HCzAw3N1XjIGFmBnipm3wOEmZmuCZRjYOEmRngofr5HCTMzAB52aFcDhJmZoBHN+VzkDAzw81N1ThImJkBrknkc5AwM8NThVfjIGFmBrgmkc9BwswM6PB6ErkcJMzMAD9xnc9BwswMP3FdjYOEmRngPol8ioj+LkMVatWCmVnLiV5/wwdzCn/niPcPmojimoSZGZ6Wo5oWrklYF0kTI2Jyf5fDWot/L6wvuDt/YJjY3wWwluTfCyudg4SZmVXlIGFmZlU5SAwMbne2PP69sNK549rMzKpyTcLMzKpykGgySSHp9IrjIyQdV0I+3+52fGuz87BySHpT0j2S7pd0iaRlG7jH2ZLGpH3/Llhp3NzUZJJeBZ4CPhIRz0k6Alg+Io5rcj4vRsTyzbyn9Y3KfztJFwJ3RsQPm3E/s2ZzTaL5FpN1KH6z+xuSVpP0G0l3pG2rivTrJN0l6SxJ/5C0anrvd5LulDRb0sSU9gNgWPpr9MKU9mJ6vVjSpyvyPE/SFyR1Sjo15XuvpANK/0lYETcD6wJIOjzVLu6X9I2UtpykP0j6a0rfLaXfIGlT/y5Y6SLCWxM34EVgBPAYsAJwBHBceu/XwNi0vxYwJ+3/DJiU9scBAayajldOr8OA+4FVuvLpnm963RmYmvaXBp5I104EjknpQ4FZwKj+/nkNxq3i32oIcDlwELAJcB+wHLA8MBvYCPgC8MuKa1dIrzcAm/p3wVvZm+duKkFELJR0PnAo8ErFW9sBYyoWXB8haTgwlux/aCLiakkLKq45VNLOaX9NYDTwrxrZXwX8RNJQsoBzU0S8IulTwIaSdknnrZDu9Wijn9MaNkzSPWn/ZuAcskDx24h4CUDSZcDWwNXAaZJOBn4fETfXkY9/F6zXHCTK8yPgLmBKRVoHsGVEVAYOVBE1uqV/nCywbBkRL0u6AVimVqYR8Wo6b3tgN+CirtsBX4+Ia+r+JNZsr0TEhysTqv0ORMTfJG0CfBo4SdK1EXF8kUz8u2DN4D6JkkTEfGA6sF9F8rXAIV0Hkrq+KG4BvpjSPgWslNJXABakALE+sEXFvd6QtFSV7KcB+5D9Jdr1RXANcFDXNZLeJ2m5Bj+eNd9NwE6Slk3/LjsDN0t6N/ByRPwKOA3YOOda/y5YaRwkynU6sGrF8aHApqmz8AHgwJT+PeBTku4CdiAbHbWIrKlhiKR7gROA2yruNRm4t6uzsptrgY8Bf4yI11Pa2cADwF2S7gfOwjXJlhERdwHnAbcDM4GzI+Ju4IPA7al56jvA93Mu9++ClcZDYFtAajN+MyIWS9oS+Hn35ggzs/7gvx5aw1rAdEkdwOvA/v1cHjMzwDUJMzOrwX0jMvK2AAAC9klEQVQSZmZWlYOEmZlV5SBhZmZVOUhYIZJ2TjPcrl/g3L3T+P5G8/q4pN9XSX9B0t2S5kg6tpH79HDNuyVdmvY/XDn3kdlg5CBhRe1O9tDfhALn7g00HCR6cHNEbARsCuyZnkZuCklDIuLJiOiaruLDZE86mw1aDhLWI0nLA1uRPT0+odt7R0q6L81S+oM0H9CmwIVpZtJhkh6rmNV20zRVBJI2k3RrqhncKmm9omVKcxzdCawjaRlJU1I57pa0Tc5nyM0r1XoukXQFcK2ktdNsq0sDxwO7pc+xm6SHJa2WruuQNLfrc5m1Kz8nYUXsBFyd5hGaL2njiLhL0g7pvc3T1CErR8R8SYcAR0TELIAq0xIBPAh8LD1EuB3wX2SznvZI0ipk05ScABwMEBEfTM1h10p6Xx15bQlsmMq+drrX65K+SzbT6iEpz/WBL5HNy7Ud8NeIeK5Iec0GKgcJK2J3si9GyOYC2p1s8sLtgCkR8TK8NV9VPVYApkoaTTY9erX5hyptLelu4N/ADyJitqTvAz9NZXhQ0j+A7kGiVl7XFSz7uWRTe/8I2JclJ280a0sOElZT+ot9W+ADkgLoBELSkWSziRZ5GnMxbzdtVs5iewJwfUTsnP6Cv6HAvW6OiM92L2aB62rl9VKB64mIJyQ9LWlbYHOyWoVZW3OfhPVkF+D8iHhPRKwdEWuSrTswlmzyuH2V1miWtHK6ZhEwvOIej5EtqgNLNietAPwz7e/dizLeRPrCTs1MawEPdTunkby6fw7IJsf7FTA9It5spLBmA4mDhPVkd+C33dJ+A+wREVcDM4BZaZbSI9L75wG/6Oq4Jpvl9seSbgYqv1hPIVsj4f+T1VAadSbQKek+4GJg74h4rds5jeR1PdkiUfcoLRtK9nmXx01NNkh47iazOkjaFDgjIrbu77KY9QX3SZgVJOlosmVG3Rdhg4ZrEmZmVpX7JMzMrCoHCTMzq8pBwszMqnKQMDOzqhwkzMysKgcJMzOr6n8BFv0taDiyqY8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f021d33550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find the optimal plane using grid search and L2 regualarisation \n",
    " \n",
    "opt_plane = find_optimal_lambda(X_train,y_en_train,2,'Grid','l2')\n",
    "y_pred = pred_test_data(X_train,y_en_train,X_test,y_en_test,opt_plane)\n",
    "type(y_pred)\n",
    "\n",
    "\n",
    "confusion_heatmap(y_en_test,y_pred)\n",
    "\n",
    "metric_score(y_en_test,y_pred)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interpretaion: \n",
    "1) We are getting a FN and FP value of 606 and 4847 while TN and TP value of 3059 and 21488.\n",
    "2) This shows we are doing really well in Postive classification.\n",
    "3) However we have a high False positive value as well which shows higher misclassification of negative class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f01f17f908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The accuracy of the LR classifier for is 81.820000%\n",
      "Confusion matrix is :  [[ 3063   602]\n",
      " [ 4852 21483]]\n",
      "The precision score for our model is:  0.9727416798732171\n",
      "The f1 score for our model is:  0.8873605947955391\n",
      "The recall score for our model is:  0.8157584962977027\n",
      "The accuracy of our model is:  0.8182\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f01efa7668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find the optimal plane using Random search and L1 regualarisation \n",
    "\n",
    "opt_plane = find_optimal_lambda(X_train,y_en_train,2,'Random','l1')\n",
    "y_pred = pred_test_data(X_train,y_en_train,X_test,y_en_test,opt_plane)\n",
    "type(y_pred)\n",
    "\n",
    "confusion_heatmap(y_en_test,y_pred)\n",
    "\n",
    "metric_score(y_en_test,y_pred)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interpretaion: \n",
    "1) We are getting a FN and FP value of 602 and 4852 while TN and TP value of 3063 and 21483.\n",
    "2) This shows we are doing really well in Postive classification.\n",
    "3) However we have a high False positive value as well which shows higher misclassification of negative class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f01f14a048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The accuracy of the LR classifier for is 81.823333%\n",
      "Confusion matrix is :  [[ 3064   601]\n",
      " [ 4852 21483]]\n",
      "The precision score for our model is:  0.9727857272233291\n",
      "The f1 score for our model is:  0.8873789214977592\n",
      "The recall score for our model is:  0.8157584962977027\n",
      "The accuracy of our model is:  0.8182333333333334\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f01ef1f9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find the optimal plane using Random search and L2 regualarisation \n",
    "\n",
    "opt_plane = find_optimal_lambda(X_train,y_en_train,2,'Random','l2')\n",
    "y_pred = pred_test_data(X_train,y_en_train,X_test,y_en_test,opt_plane)\n",
    "type(y_pred)\n",
    "\n",
    "confusion_heatmap(y_en_test,y_pred)\n",
    "\n",
    "metric_score(y_en_test,y_pred)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interpretaion: \n",
    "1) We are getting a FN and FP value of 601 and 4852 while TN and TP value of 3064 and 21483.\n",
    "2) This shows we are doing really well in Postive classification.\n",
    "3) However we have a very high False positive value as well which shows higher misclassification of negative class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Search Type | Regularisation | C-Value | Accuracy |F1-Score|\n",
    "| --- | --- | --- |--- |\n",
    "| Grid | L1 | 0.1 | 81.81 | 88.93 |\n",
    "| Grid | L2 | 0.1 | 81.82 | 88.74 |\n",
    "| Random | L1 | 3600 | 81.82 | 88.73 |\n",
    "| Random | L2 | 3000 | 81.82 | 88.74 |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion:\n",
    "\n",
    "1)The LR was performed on AFF using Word2Vec model via L1 regularization and L2 regularization using both Gridsearch and Randomsearch.\n",
    "2)For Gridsearch we get an accuracy of 81.81% for L1 reg and 81.82% for L2 reg.\n",
    "3)For Randomsearch we get an accuracy of 81.82% for L1 reg and 81.82% for L2 reg.\n",
    "4)From above 2 points we can see that L1 reg and L2 reg in both search methods for Word2vec  are almost identical.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TF-IDF weighted WORD2VEC:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TF-IDF weighted Word2Vec\n",
    "X_train, X_test, y_train, y_test = cross_validation.train_test_split(sample_data, label, test_size=0.3, random_state=0)\n",
    "#fetching the list of sentences from cleanedtext column of our train data for word2vec model\n",
    "i=0\n",
    "list_of_sent=[]\n",
    "for sent in X_train['CleanedText'].values:\n",
    "    list_of_sent.append(sent.split())\n",
    "    \n",
    "tf_idf_vect = TfidfVectorizer(ngram_range=(1,2))\n",
    "\n",
    "# final_tf_idf is the sparse matrix with row= sentence, col=word and cell_val = tfidf\n",
    "vocab = tf_idf_vect.fit(X_train['CleanedText'].values)\n",
    "X_train_tfidf = vocab.transform(X_train['CleanedText'].values)\n",
    "X_test_tfidf = vocab.transform(X_test['CleanedText'].values)\n",
    "\n",
    "tfidf_feat = tf_idf_vect.get_feature_names() # tfidf words/col-names\n",
    "\n",
    "# we are converting a dictionary with word as a key, and the idf as a value\n",
    "dictionary = dict(zip(tfidf_feat, list(tf_idf_vect.idf_)))\n",
    "type(X_train)\n",
    "\n",
    "tfidf_train_vectors = []; # the tfidf-w2v for each sentence/review is stored in this list\n",
    "row=0;\n",
    "for sent in list_of_sent: # for each review/sentence \n",
    "    sent_vec = np.zeros(50) # as word vectors are of zero length\n",
    "    weight_sum =0; # num of words with a valid vector in the sentence/review\n",
    "    for word in sent: # for each word in a review/sentence\n",
    "        if word in w2v_words:\n",
    "            vec = w2v_model.wv[word]\n",
    "            # obtain the tf_idfidf of a word in a sentence/review\n",
    "#             tf_idf = X_train_tfidf[row, tfidf_feat.index(word)]\n",
    "#             sent_vec += (vec * tf_idf)\n",
    "#             weight_sum += tf_idf\n",
    "            tf_idf = dictionary[word]*(sent.count(word)/len(sent))\n",
    "            sent_vec += (vec * tf_idf)\n",
    "            weight_sum += tf_idf\n",
    "    if weight_sum != 0:\n",
    "        sent_vec /= weight_sum\n",
    "    tfidf_train_vectors.append(sent_vec)\n",
    "    row += 1\n",
    "\n",
    "X_train = tfidf_train_vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TF-IDF weighted Word2Vec\n",
    "#fetching the list of sentences from cleanedtext column of our train data for word2vec model\n",
    "i=0\n",
    "list_of_sent=[]\n",
    "for sent in X_test['CleanedText'].values:\n",
    "    list_of_sent.append(sent.split())\n",
    "    \n",
    "type(X_train)\n",
    "\n",
    "tfidf_train_vectors = []; # the tfidf-w2v for each sentence/review is stored in this list\n",
    "row=0;\n",
    "for sent in list_of_sent: # for each review/sentence \n",
    "    sent_vec = np.zeros(50) # as word vectors are of zero length\n",
    "    weight_sum =0; # num of words with a valid vector in the sentence/review\n",
    "    for word in sent: # for each word in a review/sentence\n",
    "        if word in w2v_words:\n",
    "            vec = w2v_model.wv[word]\n",
    "            # obtain the tf_idfidf of a word in a sentence/review\n",
    "#             tf_idf = X_train_tfidf[row, tfidf_feat.index(word)]\n",
    "#             sent_vec += (vec * tf_idf)\n",
    "#             weight_sum += tf_idf\n",
    "            tf_idf = dictionary[word]*(sent.count(word)/len(sent))\n",
    "            sent_vec += (vec * tf_idf)\n",
    "            weight_sum += tf_idf\n",
    "    if weight_sum != 0:\n",
    "        sent_vec /= weight_sum\n",
    "    tfidf_train_vectors.append(sent_vec)\n",
    "    row += 1\n",
    "\n",
    "X_test = tfidf_test_vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "Scaler = StandardScaler(copy=True, with_mean=False, with_std=True).fit(X_train)\n",
    "X_train = Scaler.transform(X_train)\n",
    "X_test = Scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f028a4a668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The accuracy of the LR classifier for is 79.710000%\n",
      "Confusion matrix is :  [[ 2922   743]\n",
      " [ 5344 20991]]\n",
      "The precision score for our model is:  0.9658139320879727\n",
      "The f1 score for our model is:  0.873369531298758\n",
      "The recall score for our model is:  0.7970761344218721\n",
      "The accuracy of our model is:  0.7971\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f01ef1f048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find the optimal plane using grid search and L1 regualarisation\n",
    "\n",
    "opt_plane = find_optimal_lambda(X_train,y_en_train,2,'Grid','l1')\n",
    "y_pred = pred_test_data(X_train,y_en_train,X_test,y_en_test,opt_plane)\n",
    "type(y_pred)\n",
    "\n",
    "confusion_heatmap(y_en_test,y_pred)\n",
    "\n",
    "\n",
    "metric_score(y_en_test,y_pred)\n",
    "\n",
    "coef_org = opt_plane.best_estimator_.coef_\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interpretaion: \n",
    "1) We are getting a FN and FP value of 743 and 5344 while TN and TP value of 2922 and 20991.\n",
    "2) This shows we are doing really well in Postive classification.\n",
    "3) However we have a very high False positive value as well which shows higher misclassification of negative class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f0299b7278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The accuracy of the LR classifier for is 79.796667%\n",
      "Confusion matrix is :  [[ 2921   744]\n",
      " [ 5317 21018]]\n",
      "The precision score for our model is:  0.9658119658119658\n",
      "The f1 score for our model is:  0.8739838243549494\n",
      "The recall score for our model is:  0.7981013859882286\n",
      "The accuracy of our model is:  0.7979666666666667\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f06afcae48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find the optimal plane using grid search and L2 regualarisation \n",
    "\n",
    "opt_plane = find_optimal_lambda(X_train,y_en_train,2,'Grid','l2')\n",
    "y_pred = pred_test_data(X_train,y_en_train,X_test,y_en_test,opt_plane)\n",
    "type(y_pred)\n",
    "\n",
    "confusion_heatmap(y_en_test,y_pred)\n",
    "\n",
    "metric_score(y_en_test,y_pred)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interpretaion: \n",
    "1) We are getting a FN and FP value of 744 and 5317 while TN and TP value of 2921 and 21018.\n",
    "2) This shows we are doing really well in Postive classification.\n",
    "3) However we have a very high False positive value as well which shows higher misclassification of negative class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f0339ddf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The accuracy of the LR classifier for is 79.570000%\n",
      "Confusion matrix is :  [[ 2914   751]\n",
      " [ 5378 20957]]\n",
      "The precision score for our model is:  0.9654044591855537\n",
      "The f1 score for our model is:  0.8724267843390296\n",
      "The recall score for our model is:  0.7957850768938675\n",
      "The accuracy of our model is:  0.7957\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f01ef1f780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find the optimal plane using Random search and L1 regualarisation \n",
    "\n",
    "opt_plane = find_optimal_lambda(X_train,y_en_train,2,'Random','l1')\n",
    "y_pred = pred_test_data(X_train,y_en_train,X_test,y_en_test,opt_plane)\n",
    "type(y_pred)\n",
    "\n",
    "confusion_heatmap(y_en_test,y_pred)\n",
    "\n",
    "metric_score(y_en_test,y_pred)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interpretaion: \n",
    "1) We are getting a FN and FP value of 751 and 5378 while TN and TP value of 2914 and 20957.\n",
    "2) This shows we are doing really well in Postive classification.\n",
    "3) However we have a very high False positive value as well which shows higher misclassification of negative class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f0778332b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The accuracy of the LR classifier for is 79.573333%\n",
      "Confusion matrix is :  [[ 2914   751]\n",
      " [ 5377 20958]]\n",
      "The precision score for our model is:  0.9654060527891658\n",
      "The f1 score for our model is:  0.872450253933894\n",
      "The recall score for our model is:  0.795823049174103\n",
      "The accuracy of our model is:  0.7957333333333333\n"
     ]
    },
    {
     "data": {
      "image/png": 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hTRUiImJJ62efhKTTgZ8CG5Q/svekarZfAbhE0k2SvgVg+zbgLKp18S4E9rb9TKkl7EO16vYdwFnlWqiCzf6S5lD1URzfx1/FIro1Ny20fSeA7WskjXkGdkTERNfPyXS2d2mT3PGL3PZhVH+Yj0z/MfDjNun3UI1+6krSEHCz7ZeNdm0n3YLE80es37TIeZ21myIiJotBm3ENYPsvkn4haV3b943lHt2CxHdYdP2mkecREQNjABf4G7YmcJuka2lZMmncO9ONc+2miIhJZWjKki5BY8b1XV5nCGxExMAbxOYmeHaHurHKpLeICKo9rusek4mkzST9XNIfJS2Q9EwvK1+kJhERweDWJKiG3u4MnA1sAvwT1VyMWhrdma4fVlpmu8XxmJh0NljSBYgBM8BBAttzJE2x/QxwoqSr6+atszPdBsBrqGYFArwDuGJMJY2ImKAGOEg8UWZs3yTpSKotTZ9XN/Ooo5skXUy1F8T8cn4wVbUlImJgTB3cHtp/pOp/3gf4BNV6UO+um7lOn8S6wIKW8wXAevXLNz43/uH8xfWomOBetdo7/vp62ro7L8GSxETy5H1n9OU+Q/qbhVQHgu3flJrEesAPgLvK/hS11AkSpwLXSvoh1XK0OwCnjKGsERET1qBOppP0NuBbwK+olhmfLunDti/onrMyapCwfZikC4DXlaQ9bN841gJHRExEg9vaxNHAVrbnAEh6EfBfPLvpUVd1h8A+F3jc9omS1pA0vWyzFxExEAa1uQl4aDhAFPcAD9XNPGqQkHQQ1djaDYATgecA3wW26K2cERET16A1N0l6V3l5m6QfUy1HbmAnqr0qaqlTk9gBeBVwA4DtB7JseEQMmqkDFiSopisMexD4P+X174FV6t6kTpBYYNtSVReTVHt8bUTEZKEBa26yvYekKcC+to8Z633q9NWcJenbVHuqfgj4b+C4sT4wImIiGlL9Y7IoM6xrLQneSZ3RTUdJejPwOFW/xL/avmQ8D42ImGgGeHTT1ZK+AZzJovtJ3FAnc52O6yNsfwa4pE1aRMRAGODRTf9Qfh7SkmbgjXUy1+mTeDPVptuttmuTFhExaQ1gxzUAtrcaT/5uq8B+BPgo8CJJN7e8tQJQewXBiIjJYDL1NfRC0krAQcDrS9LlwCG2H6uTv1tN4ntUM/IOBw5oSZ9v+5ExlDUiYsIa4OamE4BbgfeU83+kmvP2ro45WnRbBfYx4DFJXwUeaVkFdgVJr7V9zbiKHRExgQxqTQJ4ke3WVV+/IOmmupnrdOh/E/hjy/mfSlpExMAY6uGYZJ6UtOXwiaQtgCfrZq7TcS3bf62H2f6LpGx7GhEDZYCbmz4CnFz6JgQ8AuxeN3OdL/t7JO3Ls7WHj1ItEBURMTAGddMh2zcBr5C0Yjl/vJf8dYLEXsDXgM9Tja39H2BWj+WMiJjQBi1GSNq/QzoAtr9S5z51Zlw/BGQbsIgYaAPY3NSXhVi7zZP4tO0jJX2dqgaxCNv79qMAERETwaCNbrL9hX7cp1tN4o7y87p+PCgiYiIbtOamYZLWBr5OtQeQgauA/WzPrZO/2zyJ88vPk/tQzoiICW3QahItTqSaHL1TOX9/SXtznczdmpvOp00z0zDb41p+NiJiIpkyNHB9EsPWsH1iy/lJkj5eN3O35qajys93Af+bastSgF2AX/dSwoiIiW5Qm5uAhyW9Hzi9nO8C/KFu5m7NTZcDSDrU9utb3jpf0hVjKWlExEQ1gKObhn0A+AZwDFXr0NUlrZY6wXMNSX83fCJpOrBGj4WMiJjQ+rkznaRPSLpN0q2STpe0nKTpkq6RdLekMyUtU65dtpzPKe+v13KfA0v6XZK2Gcvnsn2f7XfaXsP2821vb/s3dfPXCRKfAC6TdJmky4BLgdrtWRERk0G/goSktYB9gU1svwyYQjXX7AjgGNszgHnAniXLnsA82+tT/bV/RLnPhiXfS4FtgWPLntU9kXSypJVbzleRdELd/HUm010oaQbwkpJ0p+2nei1oRMRE9pz+NjdNBaZJehp4LvA7qp3gdi3vnwwcTLXc0czyGuAc4BuqpkXPBM4o37f3SpoDbAr8tMeybGT70eET2/Mkvapu5lFrEpKeC3wK2Mf2L4B1Jb29x0JGRExo/apJ2P4t1cCf+6iCw2PA9cCjtheWy+YCa5XXawH3l7wLy/Wrtaa3ydPbR5NWGT6RtCr1lmSqMte45kRgAbB5OZ8LfLGXEkZETHS9BAlJsyRd13L8dT278oU8E5gOvAB4HtWWzyMNV13ahR13Se/V0cDVkg6VdAhVx/WRdTPXiSYvsv1eSbsA2H5SwytERUQMiCk9fKvZng3M7vD21sC9tn8PIOkHwD8AK0uaWmoLawMPlOvnAusAc8s2DCtRLec9nD6sNU8vZT1F0nVUzV0C3mX79rr569QkFkiaRolgkl4EpE8iIgZKH0c33QdsJum55Q/qNwG3Uw362bFcsxtwbnl9XjmnvP+TsofPecDOZfTTdGAGcO1YPpvt221/w/bXewkQUK8mcRBwIbCOpNOo1v/YvfdiRkRMXP2aJ2H7GknnADcAC4EbqWod/wWcIemLJe34kuV44NTSMf0IZdVt27dJOosqwCwE9rb9TF8K2YOuQaJEwTupZl1vRlVV2c/2w4uhbBERi81z+tiIbvsgqj+wW91DNTpp5LV/5tl1lUa+dxhwWP9K1ruuQcK2Jf3I9qupomBExEAa4AX+xqVOn8TPJL2m8ZJERCxBQ3LtY2lSp09iK2AvSb8G/kTV5GTbGzVZsIiIxamX0U1LkzpBot343oiIgZLmpva67SexHLAXsD5wC3B8y2zBiIiBMnWA1wofj241iZOBp4ErqWoTGwL7LY5CRUQsblOWsr6GuroFiQ1tvxxA0vGMcRJHRMRkkIpEe92CxNPDL2wvzEocETHI0ifRXrcg8QpJj5fXolr29nGeHd20YuOli4hYTBIk2uu2fWnPm1tERExW6ZNor/aa4hERgyyjm9pLkIiIIM1NnSRIRESQGdedJEhERNC/pcIHTYJERASZJ9FJgkREBOmT6CRBIiICeM5QmpvaabyGJemFkrYur6dJWqHpZ0ZE9KqPe1wPlEaDhKQPAecA3y5JawM/6nL9LEnXSbpu9uwmSxYRsagEifaabm7am2pP12sAbN8t6fmdLrY9m2rDcCBDDSJi8UnHdXtNB4mnbC8YXhxQ0lQgX/4RMeFkDdP2mg4Sl0v6LNXigG8GPgqc3/AzIyJ6trQ1I9XVdA3rAOD3VDvbfRj4MfD5hp8ZEdGzoR6OpUnTNYmZwCm2v9PwcyIixkXpBm2r6aD4TuCXkk6V9LbSJxERMeGoh2Np0miQsL0HsD5wNrAr8CtJxzX5zIiIsZDqH0uTxv+yt/20pAuoRjVNo2qC+mDTz42I6MVS9t1fW9OT6baVdBIwB9gROA5Ys8lnRkSMxRTVP5YmTdckdgfOAD5s+6mGnxURMWZLWzNSXY0GCds7N3n/iIh+SYxor5EgIekq21tKms+iM6wF2PaKTTw3ImKsEiTaayRI2N6y/MyKrxExKWTGdXtNd1yfWictImJJyzyJ9pruuH5p60mZTPfqhp8ZEdGz7HHdXiM1CUkHlv6IjSQ9Xo75wIPAuU08MyJiPPo9mU7SFEk3SvrPcj5d0jWS7pZ0pqRlSvqy5XxOeX+9lnscWNLvkrRN/z/16BoJErYPL/0RX7a9YjlWsL2a7QObeGZExHg0sMDffsAdLedHAMfYngHMA/Ys6XsC82yvDxxTrkPShsDOVC0y2wLHSpoypg83Dk3VJF5SXp4taeORRxPPjIgYj37WJCStDbyNagIxqjbVeSPVTp0AJwPbl9czyznl/TeV62cCZ9h+yva9VJOSN+3Pp62vqT6J/YFZwNFt3jPVLysiYsLoc4f0vwOfBoZHeK4GPGp7YTmfC6xVXq8F3A9ge6Gkx8r1awE/a7lna57FpqkhsLPKz62auH9ERL/1MgRW0iyqP4SHzS7bLyPp7cBDtq+X9IbhLG1u41He65ZnsWl0dJOknYALbc+X9HlgY+BQ2zc2+dyIiF71EiRKQJjd4e0tgHdKeiuwHLAiVc1iZUlTS21ibeCBcv1cYB1gbhkBuhLwSEv6sNY8i03T+0n8SwkQWwLbULW7favhZ0ZE9Kxf8yRsH2h7bdvrUXU8/8T2+4BLqRY6BdiNZ0d6nlfOKe//xLZL+s5l9NN0YAZw7Xg/Z6+aDhLPlJ9vA75p+1xgmYafGRHRM8m1jzH6DLC/pDlUfQ7Hl/TjgdVK+v5U2z5j+zbgLOB24EJgb9vP/M1dG9b0ZLrfSvo2sDVwhKRlWfq2iI2ISaCJmdS2LwMuK6/voc3oJNt/BnbqkP8w4LAGilZb01/Y7wEuAra1/SiwKvCphp8ZEdGz7EzXXtNLhT8h6VfANmW24JW2L27ymRERY7HYZ6lNEk0v8LcfcBrw/HJ8V9LHmnxmRMRYpCbRXtN9EnsCr7X9JwBJRwA/Bb7e8HMjInq0lH3719R0kBDPjnCivM5/iYiYcJSvpraaDhInAtdI+mE5355nh31FREwYUgZettN0x/VXJF0GbElVg9gjs60jYmJKTaKdpva4Xg7YC1gfuAU4tmVhq4iICUeZwtVWUzWJk4GngSuB7YC/Bz7e0LMiIsYtzU3tNRUkNrT9cgBJx7ME1huJiOhNmpvaaSpIPD38oqyP3tBjIiL6I6Ob2msqSLxC0uPltYBp5VyAba/Y0HMjIsYkQaK9pjYdygz3iJhUlsD20ZNC0/MkIiImidQk2kmQiIggzU2dJEhERADZ6qa9BImICFKT6CRBIiICyFD99hIkIiIAZduhthIkIiKAjG5qL0EiIoI0N3WSIBERAaQm0V6CREQEWSq8kwSJiAggNYn2EiQiIoCh7CfRVoJERASQGdftJUhERJAZ150kSEREAOmTaE+2l3QZOtBELVhETDge9ze8uaP2d474+6UmoqQmERFBluXoZALXJGKYpFm2Zy/pcsTEkn8XsTikO39ymLWkCxATUv5dROMSJCIioqMEiYiI6ChBYnJIu3O0k38X0bh0XEdEREepSUREREcJEn0myZKObjn/pKSDG3jOZ0ecX93vZ0QzJD0j6SZJt0o6W9Jzx3CP4yRtWF7n30I0Js1NfSbpz8DvgNfYfljSJ4HlbR/c5+f80fby/bxnLB6t/+0knQZcb/sr/bhfRL+lJtF/C6k6FD8x8g1Ja0j6vqSfl2OLlvRLJN0g6duSfiNp9fLejyRdL+k2SbNK2peAaeWv0dNK2h/LzzMlvbXlmSdJerekKZK+XJ57s6QPN/6biDquBNYHkLR/qV3cKunjJe15kv5L0i9K+ntL+mWSNsm/hWic7Rx9PIA/AisCvwZWAj4JHFze+x6wZXm9LnBHef0N4MDyelvAwOrlfNXycxpwK7Da8HNGPrf83AE4ubxeBri/5J0FfL6kLwtcB0xf0r+vpfFo+W81FTgX+AjwauAW4HnA8sBtwKuAdwPfacm7Uvl5GbBJ/i3kaPrI2k0NsP24pFOAfYEnW97aGtiwZcP1FSWtAGxJ9T80ti+UNK8lz76Sdiiv1wFmAH/o8vgLgK9JWpYq4Fxh+0lJbwE2krRjuW6lcq97x/o5Y8ymSbqpvL4SOJ4qUPzQ9p8AJP0AeB1wIXCUpCOA/7R9ZQ/Pyb+FGLcEieb8O3ADcGJL2hCwue3WwIFaosaI9DdQBZbNbT8h6TJguW4Ptf3nct02wHuB04dvB3zM9kU9f5Lotydtv7I1odO/Adu/lPRq4K3A4ZIutn1InYfk30L0Q/okGmL7EeAsYM+W5IuBfYZPJA1/UVwFvKekvQVYpaSvBMwrAeIlwGYt93pa0nM6PP4MYA+qv0SHvwguAj4ynEfSiyU9b4wfL/rvCmB7Sc8t/112AK6U9ALgCdvfBY4CNm6TN/8WojEJEs06Gli95XxfYJPSWXg7sFdJ/wLwFkk3ANtRjY6aT9XUMFXSzcChwM9a7jUbuHm4s3KEi4HXA/9te0FJOw64HbhB0q3At0lNcsKwfQNwEnAtcA1wnO0bgZcD15bmqc8BX2yTPf8WojEZAjsBlDbjZ2wvlLQ58M2RzREREUt365nLAAADEElEQVRC/nqYGNYFzpI0BCwAPrSEyxMRAaQmERERXaRPIiIiOkqQiIiIjhIkIiKiowSJqEXSDmWF25fUuHb3Mr5/rM96g6T/7JD+mKQbJd0h6aCx3GeUPC+QdE55/crWtY8ilkYJElHXLlST/nauce3uwJiDxCiutP0qYBPg/WU2cl9Immr7AdvDy1W8kmqmc8RSK0EiRiVpeWALqtnjO49479OSbimrlH6prAe0CXBaWZl0mqRft6xqu0lZKgJJm0q6utQMrpa0Qd0ylTWOrgdeJGk5SSeWctwoaas2n6Hts0qt52xJ5wMXS1qvrLa6DHAI8N7yOd4r6W5Ja5R8Q5LmDH+uiEGVeRJRx/bAhWUdoUckbWz7BknblfdeW5YOWdX2I5L2AT5p+zqADssSAdwJvL5MItwa+DeqVU9HJWk1qmVKDgX2BrD98tIcdrGkF/fwrM2BjUrZ1yv3WiDpX6lWWt2nPPMlwPuo1uXaGviF7YfrlDdiskqQiDp2ofpihGotoF2oFi/cGjjR9hPw1/WqerEScLKkGVTLo3daf6jV6yTdCPwF+JLt2yR9Efh6KcOdkn4DjAwS3Z51Sc2yn0C1tPe/Ax9g0cUbIwZSgkR0Vf5ifyPwMkkGpgCW9Gmq1UTrzMZcyLNNm62r2B4KXGp7h/IX/GU17nWl7bePLGaNfN2e9aca+bF9v6QHJb0ReC1VrSJioKVPIkazI3CK7RfaXs/2OlT7DmxJtXjcB1T2aJa0askzH1ih5R6/ptpUBxZtTloJ+G15vfs4yngF5Qu7NDOtC9w14pqxPGvk54BqcbzvAmfZfmYshY2YTBIkYjS7AD8ckfZ9YFfbFwLnAdeVVUo/Wd4/CfjWcMc11Sq3X5V0JdD6xXok1R4J/5eqhjJWxwJTJN0CnAnsbvupEdeM5VmXUm0SdZPKtqFUn3d50tQUS4ms3RTRA0mbAMfYft2SLkvE4pA+iYiaJB1Atc1o+iJiqZGaREREdJQ+iYiI6ChBIiIiOkqQiIiIjhIkIiKiowSJiIjoKEEiIiI6+v/dHT08ttgUXAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f06ead7a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find the optimal plane using Random search and L2 regualarisation \n",
    "\n",
    "opt_plane = find_optimal_lambda(X_train,y_en_train,2,'Random','l2')\n",
    "y_pred = pred_test_data(X_train,y_en_train,X_test,y_en_test,opt_plane)\n",
    "type(y_pred)\n",
    "\n",
    "confusion_heatmap(y_en_test,y_pred)\n",
    "\n",
    "metric_score(y_en_test,y_pred)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interpretaion: \n",
    "1) We are getting a FN and FP value of 751 and 5377 while TN and TP value of 2914 and 20958.\n",
    "2) This shows we are doing really well in Postive classification.\n",
    "3) However we have a high False positive value as well which shows higher misclassification of negative class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Search Type | Regularisation | C-Value | Accuracy |F1-Score|\n",
    "| --- | --- | --- |--- |\n",
    "| Grid | L1 | 0.1 | 79.71 | 87.33 |\n",
    "| Grid | L2 | 1 | 79.79 | 87.39 |\n",
    "| Random | L1 | 2800 | 79.57 | 87.24 |\n",
    "| Random | L2 | 3700 | 79.51 | 87.24 |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion:\n",
    "\n",
    "1)The LR was performed on AFF using TF-IDF weighted Word2Vec model via L1 regularization and L2 regularization using both Gridsearch and Randomsearch.\n",
    "2)For Gridsearch we get an accuracy of 79.71% for L1 reg and 79.79% for L2 reg.\n",
    "3)For Randomsearch we get an accuracy of 79.57% for L1 reg and 79.51% for L2 reg.\n",
    "4)From above 2 points we can see that L1 reg and L2 reg in both search methods for TF-IDF weighted Word2vec  are almost identical."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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