{ "cells": [ { "cell_type": "markdown", "id": "cloudy-schedule", "metadata": { "_kg_hide-input": false, "_kg_hide-output": true, "execution": { "iopub.execute_input": "2021-06-09T08:08:42.846187Z", "iopub.status.busy": "2021-06-09T08:08:42.845821Z", "iopub.status.idle": "2021-06-09T08:10:23.759465Z", "shell.execute_reply": "2021-06-09T08:10:23.758484Z", "shell.execute_reply.started": "2021-06-09T08:08:42.846105Z" }, "id": "ptkHaiID_EWG", "papermill": { "duration": 0.087484, "end_time": "2021-06-13T15:26:14.738261", "exception": false, "start_time": "2021-06-13T15:26:14.650777", "status": "completed" }, "tags": [] }, "source": [ "# **Machine Learning approach to predict real or fake tweets about disaster**\n", "---" ] }, { "cell_type": "markdown", "id": "swiss-motivation", "metadata": { "papermill": { "duration": 0.084464, "end_time": "2021-06-13T15:26:14.907687", "exception": false, "start_time": "2021-06-13T15:26:14.823223", "status": "completed" }, "tags": [] }, "source": [ "# Project Description\n", "\n", "[Twitter](https://twitter.com/?lang=en) has become an important communication channel in times of emergency. \n", "The ubiquitousness of smartphones enables people to announce an emergency they’re observing in real-time. \n", "Because of this, more agencies are interested in programatically monitoring Twitter (i.e. disaster relief organizations and news agencies).\n", "\n", "# Objective \n", "\n", "[Sentiment analysis](https://en.wikipedia.org/wiki/Sentiment_analysis) is a common use case of [NLP](https://machinelearningmastery.com/natural-language-processing/) where the idea is to classify the tweet as positive, negative or neutral depending upon the text in the tweet. \n", "This problem goes a way ahead and expects us to also determine the words in the tweet which decide the polarity of the tweet.\n", "\n", "In this project [Machine Learning](https://www.geeksforgeeks.org/machine-learning/) models are implemented for predicting that a tweet regarding a disaster is real or fake \n", "Whole code below is in [Python](https://www.python.org/) using various libraries. Open source library [Scikit-Learn](https://scikit-learn.org/) is used for creating the models.\n", "\n", "

\n", "
\n", " \"Tweets\"\n", "

" ] }, { "cell_type": "markdown", "id": "natural-services", "metadata": { "papermill": { "duration": 0.083493, "end_time": "2021-06-13T15:26:15.077181", "exception": false, "start_time": "2021-06-13T15:26:14.993688", "status": "completed" }, "tags": [] }, "source": [ "# Table of Contents\n", "\n", "\n", " 1. Dependancies and Dataset\n", "\n", " 2. Data Exploration\n", " \n", " 3. Data Cleaning\n", "\n", " 4. Extra Data Exploration and Analysis on Cleaned Text\n", "\n", " 5. Spliting data\n", "\n", " 6. Machine Learning Models\n", "\n", " 7. Comparing accuracies of all models\n", " \n", " 8. Conclusion\n" ] }, { "cell_type": "markdown", "id": "square-tradition", "metadata": { "papermill": { "duration": 0.083262, "end_time": "2021-06-13T15:26:15.244023", "exception": false, "start_time": "2021-06-13T15:26:15.160761", "status": "completed" }, "tags": [] }, "source": [ "# 1. Dependancies and Dataset" ] }, { "cell_type": "markdown", "id": "published-advance", "metadata": { "papermill": { "duration": 0.082685, "end_time": "2021-06-13T15:26:15.409999", "exception": false, "start_time": "2021-06-13T15:26:15.327314", "status": "completed" }, "tags": [] }, "source": [ "## 1.1 Importing dependancies" ] }, { "cell_type": "code", "execution_count": 1, "id": "speaking-engine", "metadata": { "_kg_hide-output": true, "execution": { "iopub.execute_input": "2021-06-13T15:26:15.603129Z", "iopub.status.busy": "2021-06-13T15:26:15.602386Z", "iopub.status.idle": "2021-06-13T15:26:34.460793Z", "shell.execute_reply": "2021-06-13T15:26:34.460094Z", "shell.execute_reply.started": "2021-06-13T14:48:41.221060Z" }, "papermill": { "duration": 18.967857, "end_time": "2021-06-13T15:26:34.460944", "exception": false, "start_time": "2021-06-13T15:26:15.493087", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting chart_studio\r\n", " Downloading chart_studio-1.1.0-py3-none-any.whl (64 kB)\r\n", "\u001b[K |████████████████████████████████| 64 kB 274 kB/s \r\n", "\u001b[?25hRequirement already satisfied: retrying>=1.3.3 in /opt/conda/lib/python3.7/site-packages (from chart_studio) (1.3.3)\r\n", "Requirement already satisfied: plotly in /opt/conda/lib/python3.7/site-packages (from chart_studio) (4.14.3)\r\n", "Requirement already satisfied: requests in /opt/conda/lib/python3.7/site-packages (from chart_studio) (2.25.1)\r\n", "Requirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from chart_studio) (1.15.0)\r\n", "Requirement already satisfied: idna<3,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests->chart_studio) (2.10)\r\n", "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests->chart_studio) (1.26.4)\r\n", "Requirement already satisfied: chardet<5,>=3.0.2 in /opt/conda/lib/python3.7/site-packages (from requests->chart_studio) (4.0.0)\r\n", "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests->chart_studio) (2020.12.5)\r\n", "Installing collected packages: chart-studio\r\n", "Successfully installed chart-studio-1.1.0\r\n" ] }, { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import tensorflow as tf\n", "\n", "#libraries for NLP\n", "import nltk\n", "import re\n", "from nltk.corpus import stopwords\n", "from nltk.stem.porter import PorterStemmer\n", "from nltk.stem.snowball import SnowballStemmer\n", "from nltk.stem import WordNetLemmatizer\n", "from wordcloud import WordCloud,STOPWORDS\n", "from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer\n", "from sklearn.feature_extraction.text import TfidfVectorizer\n", "from IPython.core.interactiveshell import InteractiveShell\n", "InteractiveShell.ast_node_interactivity = 'all'\n", "\n", "from IPython.display import HTML\n", "!pip install chart_studio\n", "import plotly\n", "import plotly.graph_objs as go\n", "import chart_studio.plotly as py\n", "import plotly.figure_factory as ff\n", "from plotly.offline import iplot\n", "plotly.offline.init_notebook_mode(connected=True)\n", "import cufflinks\n", "cufflinks.go_offline()\n", "cufflinks.set_config_file(world_readable=True, theme='pearl')\n", "import plotly.express as px\n", "from collections import defaultdict\n", "from tensorflow import keras\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import classification_report,accuracy_score,confusion_matrix\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.ensemble import RandomForestClassifier\n", "from xgboost import XGBClassifier\n", "from catboost import CatBoostClassifier\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.naive_bayes import MultinomialNB\n", "from sklearn.ensemble import VotingClassifier\n", "from sklearn.svm import SVC\n", "\n", "import warnings\n", "warnings.filterwarnings('ignore')" ] }, { "cell_type": "markdown", "id": "surrounded-testing", "metadata": { "papermill": { "duration": 0.086936, "end_time": "2021-06-13T15:26:34.635641", "exception": false, "start_time": "2021-06-13T15:26:34.548705", "status": "completed" }, "tags": [] }, "source": [ "## 1.2 Reading and preparation of data" ] }, { "cell_type": "markdown", "id": "intimate-salvation", "metadata": { "papermill": { "duration": 0.086489, "end_time": "2021-06-13T15:26:34.809004", "exception": false, "start_time": "2021-06-13T15:26:34.722515", "status": "completed" }, "tags": [] }, "source": [ "Reading [data](https://www.kaggle.com/c/nlp-getting-started/data) and choosing important columns using [pandas](https://pandas.pydata.org/)" ] }, { "cell_type": "code", "execution_count": 2, "id": "polish-jumping", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:26:34.989466Z", "iopub.status.busy": "2021-06-13T15:26:34.988788Z", "iopub.status.idle": "2021-06-13T15:26:35.037547Z", "shell.execute_reply": "2021-06-13T15:26:35.036976Z", "shell.execute_reply.started": "2021-06-13T14:48:58.917846Z" }, "id": "maaZD2m7GrYZ", "papermill": { "duration": 0.141549, "end_time": "2021-06-13T15:26:35.037696", "exception": false, "start_time": "2021-06-13T15:26:34.896147", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "data = pd.read_csv('../input/nlp-getting-started/train.csv')" ] }, { "cell_type": "markdown", "id": "surgical-suspension", "metadata": { "papermill": { "duration": 0.087618, "end_time": "2021-06-13T15:26:35.213279", "exception": false, "start_time": "2021-06-13T15:26:35.125661", "status": "completed" }, "tags": [] }, "source": [ "Displaying first 10 rows of our data using [DataFrame.head()](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.head.html)" ] }, { "cell_type": "code", "execution_count": 3, "id": "tracked-siemens", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:26:35.392837Z", "iopub.status.busy": "2021-06-13T15:26:35.392196Z", "iopub.status.idle": "2021-06-13T15:26:35.416859Z", "shell.execute_reply": "2021-06-13T15:26:35.417375Z", "shell.execute_reply.started": "2021-06-13T14:48:58.966698Z" }, "id": "NluCZIHYHDQU", "outputId": "5e7f9692-a804-44e3-f2bf-f91d8de296a4", "papermill": { "duration": 0.115569, "end_time": "2021-06-13T15:26:35.417550", "exception": false, "start_time": "2021-06-13T15:26:35.301981", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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01NaNNaNOur Deeds are the Reason of this #earthquake M...1
14NaNNaNForest fire near La Ronge Sask. Canada1
25NaNNaNAll residents asked to 'shelter in place' are ...1
36NaNNaN13,000 people receive #wildfires evacuation or...1
47NaNNaNJust got sent this photo from Ruby #Alaska as ...1
58NaNNaN#RockyFire Update => California Hwy. 20 closed...1
610NaNNaN#flood #disaster Heavy rain causes flash flood...1
713NaNNaNI'm on top of the hill and I can see a fire in...1
814NaNNaNThere's an emergency evacuation happening now ...1
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2All residents asked to 'shelter in place' are ...1
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.bar(x=[\"0\",\"1\"], y=data[\"target\"].value_counts(),color=[\"red\", \"goldenrod\"])\n", "\n", "#Change this value for bar widths\n", "for dt in fig.data:\n", " dt[\"width\"] = 0.4 \n", "\n", "fig.update_layout(\n", " title_text = \"Counts for Disaster and Non-Disaster Tweets\",\n", " title_x=0.5,\n", " width=800,\n", " height=550,\n", " xaxis_title=\"Targets\",\n", " yaxis_title=\"Count\",\n", " showlegend=False\n", ").show()\n", "\n", "# py.plot(fig,filename='Counts for Disaster and Non-Disaster Tweets',auto_open=False,show_link=False)" ] }, { "cell_type": "markdown", "id": "occupied-shock", "metadata": { "papermill": { "duration": 0.095937, "end_time": "2021-06-13T15:26:37.995265", "exception": false, "start_time": "2021-06-13T15:26:37.899328", "status": "completed" }, "tags": [] }, "source": [ "The plot shows that our data is quite balanced, you can also click on the plot to explore more about [interactive plots](https://plotly.com/) " ] }, { "cell_type": "markdown", "id": "selective-ghana", "metadata": { "_kg_hide-input": true, "_kg_hide-output": true, "id": "q5FZ58sah2jH", "papermill": { "duration": 0.094239, "end_time": "2021-06-13T15:26:38.185960", "exception": false, "start_time": "2021-06-13T15:26:38.091721", "status": "completed" }, "tags": [] }, "source": [ "\n", "
\n", " \"Counts\n", " \n", "
" ] }, { "cell_type": "markdown", "id": "dirty-array", "metadata": { "papermill": { "duration": 0.093634, "end_time": "2021-06-13T15:26:38.374232", "exception": false, "start_time": "2021-06-13T15:26:38.280598", "status": "completed" }, "tags": [] }, "source": [ "## 2.2 Visualising lengths of tweets" ] }, { "cell_type": "markdown", "id": "located-backup", "metadata": { "papermill": { "duration": 0.094536, "end_time": "2021-06-13T15:26:38.564273", "exception": false, "start_time": "2021-06-13T15:26:38.469737", "status": "completed" }, "tags": [] }, "source": [ "Analyzing lengths of words in a tweets according to it being real or fake target value by ploting [histograms](https://plotly.com/python/histograms/)" ] }, { "cell_type": "code", "execution_count": 7, "id": "collective-export", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:26:38.810024Z", "iopub.status.busy": "2021-06-13T15:26:38.809385Z", "iopub.status.idle": "2021-06-13T15:26:38.946294Z", 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"anchor": "y", "domain": [ 0, 0.45 ] }, "xaxis2": { "anchor": "y2", "domain": [ 0.55, 1 ] }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ] }, "yaxis2": { "anchor": "x2", "domain": [ 0, 1 ] } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from plotly.subplots import make_subplots\n", "\n", "word_len_dis = data[data['target']==1]['text'].str.split().map(lambda x : len(x))\n", "\n", "word_len_non_dis = data[data['target']==0]['text'].str.split().map(lambda x : len(x))\n", "\n", "fig = make_subplots(rows=1, cols=2,subplot_titles=(\"Disaster Tweets\", \"Non-Disaster Tweets\"))\n", "\n", "fig.add_trace(\n", " go.Histogram(x=word_len_dis,marker_line=dict(color='black'),marker_line_width=1.2),\n", " row=1, col=1\n", ").add_trace(\n", " go.Histogram(x=word_len_non_dis,marker_line=dict(color='black'),marker_line_width=1.2),\n", " row=1, col=2\n", ").update_layout(title_text=\"Length of words in Tweets\",title_x=0.5,showlegend=False).show()\n", "\n", "# py.plot(fig,filename='Length of words in Tweets',auto_open=False,show_link=False)" ] }, { "cell_type": "markdown", "id": "adverse-evening", "metadata": { "papermill": { "duration": 0.117115, "end_time": "2021-06-13T15:26:39.182406", "exception": false, "start_time": "2021-06-13T15:26:39.065291", "status": "completed" }, "tags": [] }, "source": [ "From the plot we can say that the number of words in the tweets ranges from 2 to 30 in both cases" ] }, { "cell_type": "markdown", "id": "coastal-drilling", "metadata": { "_kg_hide-input": true, "_kg_hide-output": true, "id": "xM2GJvWTmOSh", "papermill": { "duration": 0.115736, "end_time": "2021-06-13T15:26:39.414743", "exception": false, "start_time": "2021-06-13T15:26:39.299007", "status": "completed" }, "tags": [] }, "source": [ "
\n", " \"Length\n", " \n", "
\n" ] }, { "cell_type": "markdown", "id": "confused-portfolio", "metadata": { "papermill": { "duration": 0.117021, "end_time": "2021-06-13T15:26:39.648931", "exception": false, "start_time": "2021-06-13T15:26:39.531910", "status": "completed" }, "tags": [] }, "source": [ "## 2.3 Visualising average word lengths of tweets" ] }, { "cell_type": "markdown", "id": "ongoing-tackle", "metadata": { "papermill": { "duration": 0.117745, "end_time": "2021-06-13T15:26:39.914287", "exception": false, "start_time": "2021-06-13T15:26:39.796542", "status": "completed" }, "tags": [] }, "source": [ "Checking average word length for both type of tweets" ] }, { "cell_type": "code", "execution_count": 8, "id": "analyzed-chemistry", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:26:40.161175Z", "iopub.status.busy": "2021-06-13T15:26:40.160533Z", "iopub.status.idle": "2021-06-13T15:26:40.955575Z", "shell.execute_reply": "2021-06-13T15:26:40.956200Z", "shell.execute_reply.started": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def avgwordlen(strlist):\n", " sum=[]\n", " for i in strlist:\n", " sum.append(len(i))\n", " return sum\n", "\n", "avgword_len_dis = data[data['target']==1]['text'].str.split().apply(avgwordlen).map(lambda x: np.mean(x))\n", "\n", "avgword_len_non_dis = data[data['target']==0]['text'].str.split().apply(avgwordlen).map(lambda x: np.mean(x))\n", "\n", "group_labels = ['Disaster', 'Non-Disaster']\n", "colors = ['rgb(0, 0, 100)', 'rgb(0, 200, 200)']\n", "\n", "fig = ff.create_distplot([avgword_len_dis, avgword_len_non_dis], group_labels, bin_size=.2, colors=colors,)\n", "\n", "fig.update_layout(title_text=\"Average word length in tweets\",title_x=0.5,xaxis_title=\"Text\",yaxis_title=\"Density\").show()\n", "\n", "# py.plot(fig,filename='Average word length in tweets',auto_open=False,show_link=False)" ] }, { "cell_type": "markdown", "id": "enormous-elder", "metadata": { "papermill": { "duration": 0.198248, "end_time": "2021-06-13T15:26:41.354351", "exception": false, "start_time": "2021-06-13T15:26:41.156103", "status": "completed" }, "tags": [] }, "source": [ "From the [distplot](https://plotly.com/python/distplot/), average word countss for real disaster tweets are found to be in the range(5-7.5) \n", "while for fake disaster tweets are in the range of (4-6)." ] }, { "cell_type": "markdown", "id": "apart-judges", "metadata": { "_kg_hide-input": true, "_kg_hide-output": true, "id": "WsevjsJpqbAU", "papermill": { "duration": 0.196201, "end_time": "2021-06-13T15:26:41.747065", "exception": false, "start_time": "2021-06-13T15:26:41.550864", "status": "completed" }, "tags": [] }, "source": [ "
\n", " \"Average\n", " \n", "
\n" ] }, { "cell_type": "markdown", "id": "extensive-mitchell", "metadata": { "papermill": { "duration": 0.19797, "end_time": "2021-06-13T15:26:42.141711", "exception": false, "start_time": "2021-06-13T15:26:41.943741", "status": "completed" }, "tags": [] }, "source": [ "## 2.4 Visualising most common stop words in the text data" ] }, { "cell_type": "markdown", "id": "meaning-square", "metadata": { "papermill": { "duration": 0.196792, "end_time": "2021-06-13T15:26:42.535351", "exception": false, "start_time": "2021-06-13T15:26:42.338559", "status": "completed" }, "tags": [] }, "source": [ "\n", "### What is a corpus?\n", "\n", "In linguistics and NLP, corpus (literally Latin for body) refers to a collection of texts. \n", "Such collections may be formed of a single language of texts, or can span multiple languages\n", " \n", "Function for creating sample [corpus](https://21centurytext.wordpress.com/home-2/special-section-window-to-corpus/what-is-corpus/) for further analysis. " ] }, { "cell_type": "code", "execution_count": 9, "id": "acceptable-southwest", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:26:42.935011Z", "iopub.status.busy": "2021-06-13T15:26:42.934374Z", "iopub.status.idle": "2021-06-13T15:26:42.936251Z", "shell.execute_reply": "2021-06-13T15:26:42.936729Z", "shell.execute_reply.started": "2021-06-13T14:49:00.757988Z" }, "id": "rtAFdb6xpDK9", "papermill": { "duration": 0.204247, "end_time": "2021-06-13T15:26:42.936890", "exception": false, "start_time": "2021-06-13T15:26:42.732643", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "def create_corpus(target):\n", " corpus = []\n", " for i in data[data['target']==target]['text'].str.split():\n", " for x in i:\n", " corpus.append(x)\n", " return corpus" ] }, { "cell_type": "markdown", "id": "patient-construction", "metadata": { "papermill": { "duration": 0.195555, "end_time": "2021-06-13T15:26:43.331823", "exception": false, "start_time": "2021-06-13T15:26:43.136268", "status": "completed" }, "tags": [] }, "source": [ "### What are stopwords?\n", "\n", "In computing, stop words are words that are filtered out before or after the natural language data (text) are processed. \n", "While “stop words” typically refers to the most common words in a language, all-natural language processing tools don't use a single universal list of stop words. \n", "\n", "Analysing most occuring [stop words](https://en.wikipedia.org/wiki/Stop_word) in the text using corpus creating function(create_corpus)" ] }, { "cell_type": "code", "execution_count": 10, "id": "honey-algorithm", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:26:43.736494Z", "iopub.status.busy": "2021-06-13T15:26:43.735841Z", "iopub.status.idle": "2021-06-13T15:26:43.842586Z", "shell.execute_reply": "2021-06-13T15:26:43.843084Z", "shell.execute_reply.started": "2021-06-13T14:49:00.764779Z" }, "id": "mAAtFo91xRBw", "papermill": { "duration": 0.314878, 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "values_list = []\n", "\n", "def analyze_stopwords(data,func,targetlist):\n", " \n", " for label in range(0,len(targetlist)):\n", " corpus = func(targetlist[label])\n", " dic = defaultdict(int)\n", " \n", " for word in corpus:\n", " dic[word] += 1\n", " \n", " top = sorted(dic.items(),key = lambda x: x[1],reverse=True)[:10]\n", " x_items,y_values = zip(*top)\n", " values_list.append(x_items)\n", " values_list.append(y_values)\n", "\n", "#analyzing stopwords for 0 and 1 target labels\n", "analyze_stopwords(data,create_corpus,[0,1])\n", "\n", "fig = make_subplots(rows=1, cols=2,subplot_titles=(\"Disaster Tweets\", \"Non-Disaster Tweets\"))\n", "\n", "fig.add_trace(\n", " go.Bar(x=values_list[1],y=values_list[0],orientation='h',marker=dict(color= 'rgba(152, 255, 74,0.8)'),\n", " marker_line=dict(color='black'),marker_line_width=1.2),\n", " row=1, col=1\n", ").add_trace(\n", " go.Bar(x=values_list[3],y=values_list[2],orientation='h',marker=dict(color= 'rgba(255, 143, 92,0.8)'),\n", " marker_line=dict(color='black'),marker_line_width=1.2),\n", " row=1, col=2\n", ").update_layout(title_text=\"Top stop words in the text\",title_x=0.5,showlegend=False).show()\n", "\n", "# py.plot(fig,filename='Top stop words in the text',auto_open=False,show_link=False)" ] }, { "cell_type": "markdown", "id": "alternative-canberra", "metadata": { "papermill": { "duration": 0.202748, "end_time": "2021-06-13T15:26:44.248996", "exception": false, "start_time": "2021-06-13T15:26:44.046248", "status": "completed" }, "tags": [] }, "source": [ "The [Bar Charts](https://plotly.com/python/bar-charts/) displays the top 10 stop words in tweets where **'the'** is most frequent in both groups" ] }, { "cell_type": "markdown", "id": "close-wholesale", "metadata": { "_kg_hide-input": true, "_kg_hide-output": true, "id": "zPUJ70ZnqipE", "papermill": { "duration": 0.202917, "end_time": "2021-06-13T15:26:44.654365", "exception": false, "start_time": "2021-06-13T15:26:44.451448", "status": "completed" }, "tags": [] }, "source": [ "
\n", " \"Top\n", " \n", "
" ] }, { "cell_type": "markdown", "id": "inner-remark", "metadata": { "papermill": { "duration": 0.203468, "end_time": "2021-06-13T15:26:45.062281", "exception": false, "start_time": "2021-06-13T15:26:44.858813", "status": "completed" }, "tags": [] }, "source": [ "## 2.5 Visualising most common punctuations in the text data" ] }, { "cell_type": "markdown", "id": "wrapped-sitting", "metadata": { "papermill": { "duration": 0.203007, "end_time": "2021-06-13T15:26:45.468372", "exception": false, "start_time": "2021-06-13T15:26:45.265365", "status": "completed" }, "tags": [] }, "source": [ "Now let's have a look at the punctuations inside our data" ] }, { "cell_type": "code", "execution_count": 11, "id": "entire-baptist", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:26:45.887773Z", "iopub.status.busy": "2021-06-13T15:26:45.887080Z", "iopub.status.idle": "2021-06-13T15:26:45.972889Z", "shell.execute_reply": "2021-06-13T15:26:45.973375Z", "shell.execute_reply.started": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#The above Bar Charts displays the top 10 stop words in tweets where the occurs the most in both groups\n", "# Anaysing Punctuations\n", "from string import punctuation\n", "values_list = []\n", "def analyze_punctuations(data,func,targetlist):\n", " \n", " for label in range(0,len(targetlist)):\n", " corpus = func(targetlist[label])\n", " dic = defaultdict(int)\n", " \n", " for word in corpus:\n", " if word in punctuation:\n", " dic[word] += 1 \n", " x_items, y_values = zip(*dic.items())\n", " values_list.append(x_items)\n", " values_list.append(y_values)\n", "\n", "#analyzing punctuations for 0 and 1 target labels\n", "analyze_punctuations(data,create_corpus,[0,1])\n", "\n", "fig = make_subplots(rows=1, cols=2,subplot_titles=(\"Disaster Tweets\", \"Non-Disaster Tweets\"))\n", " \n", "fig.add_trace(\n", " go.Bar(x=values_list[0],y=values_list[1],\n", " marker=dict(color= 'rgba(196, 94, 255,0.8)'),\n", " marker_line=dict(color='black'),marker_line_width=1.2),\n", " row=1, col=1\n", ").add_trace(\n", " go.Bar(x=values_list[2],y=values_list[3],\n", " marker=dict(color= 'rgba(255, 163, 102,0.8)'),\n", " marker_line=dict(color='black'),marker_line_width=1.2),\n", " row=1, col=2\n", ").update_layout(title_text=\"Top Punctuations in the text\",title_x=0.5,showlegend=False).show()\n", "\n", "# py.plot(fig,filename='Top Punctuations in the text',auto_open=False,show_link=False)" ] }, { "cell_type": "markdown", "id": "prospective-programmer", "metadata": { "_kg_hide-input": true, "id": "cpQiMex1qqbg", "papermill": { "duration": 0.211259, "end_time": "2021-06-13T15:26:46.394606", "exception": false, "start_time": "2021-06-13T15:26:46.183347", "status": "completed" }, "tags": [] }, "source": [ "
\n", " \"Top\n", " \n", "
\n" ] }, { "cell_type": "markdown", "id": "supported-mistress", "metadata": { "papermill": { "duration": 0.208775, "end_time": "2021-06-13T15:26:46.812563", "exception": false, "start_time": "2021-06-13T15:26:46.603788", "status": "completed" }, "tags": [] }, "source": [ "# 3. Data Cleaning" ] }, { "cell_type": "markdown", "id": "colonial-adoption", "metadata": { "papermill": { "duration": 0.211855, "end_time": "2021-06-13T15:26:47.232980", "exception": false, "start_time": "2021-06-13T15:26:47.021125", "status": "completed" }, "tags": [] }, "source": [ "## 3.1 Removing unwanted text using regular expressions" ] }, { "cell_type": "markdown", "id": "meaningful-drink", "metadata": { "papermill": { "duration": 0.210034, "end_time": "2021-06-13T15:26:47.653400", "exception": false, "start_time": "2021-06-13T15:26:47.443366", "status": "completed" }, "tags": [] }, "source": [ "### What is Stemming? \n", "Stemming is the process of reducing a word to its word stem that affixes to suffixes and prefixes or to the roots of words known as a lemma. \n", "Stemming is important in natural language understanding (NLU) and natural language processing (NLP). Here we use SnowballStemmer.\n", "\n", "Function for cleaning the data, we use [RegEx](https://docs.python.org/3/library/re.html) i.e. re python library and [SnowballStemmer()](https://www.nltk.org/_modules/nltk/stem/snowball.html) to stem the words." ] }, { "cell_type": "code", "execution_count": 12, "id": "convertible-imaging", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:26:48.100940Z", "iopub.status.busy": "2021-06-13T15:26:48.092865Z", "iopub.status.idle": "2021-06-13T15:27:02.742026Z", "shell.execute_reply": "2021-06-13T15:27:02.742513Z", "shell.execute_reply.started": "2021-06-13T14:49:00.947959Z" }, "id": "sckzCfiLegfL", "papermill": { "duration": 14.87848, "end_time": "2021-06-13T15:27:02.742698", "exception": false, "start_time": "2021-06-13T15:26:47.864218", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "stemmer = SnowballStemmer(\"english\")\n", "\n", "def preprocess_data(data):\n", " \n", " #removal of url\n", " text = re.sub(r'https?://\\S+|www\\.\\S+|http?://\\S+',' ',data) \n", " \n", " #decontraction\n", " text = re.sub(r\"won\\'t\", \" will not\", text)\n", " text = re.sub(r\"won\\'t've\", \" will not have\", text)\n", " text = re.sub(r\"can\\'t\", \" can not\", text)\n", " text = re.sub(r\"don\\'t\", \" do not\", text) \n", " text = re.sub(r\"can\\'t've\", \" can not have\", text)\n", " text = re.sub(r\"ma\\'am\", \" madam\", text)\n", " text = re.sub(r\"let\\'s\", \" let us\", text)\n", " text = re.sub(r\"ain\\'t\", \" am not\", text)\n", " text = re.sub(r\"shan\\'t\", \" shall not\", text)\n", " text = re.sub(r\"sha\\n't\", \" shall not\", text)\n", " text = re.sub(r\"o\\'clock\", \" of the clock\", text)\n", " text = re.sub(r\"y\\'all\", \" you all\", text)\n", " text = re.sub(r\"n\\'t\", \" not\", text)\n", " text = re.sub(r\"n\\'t've\", \" not have\", text)\n", " text = re.sub(r\"\\'re\", \" are\", text)\n", " text = re.sub(r\"\\'s\", \" is\", text)\n", " text = re.sub(r\"\\'d\", \" would\", text)\n", " text = re.sub(r\"\\'d've\", \" would have\", text)\n", " text = re.sub(r\"\\'ll\", \" will\", text)\n", " text = re.sub(r\"\\'ll've\", \" will have\", text)\n", " text = re.sub(r\"\\'t\", \" not\", text)\n", " text = re.sub(r\"\\'ve\", \" have\", text)\n", " text = re.sub(r\"\\'m\", \" am\", text)\n", " text = re.sub(r\"\\'re\", \" are\", text)\n", " \n", " #removal of html tags\n", " text = re.sub(r'<.*?>',' ',text) \n", " \n", " # Match all digits in the string and replace them by empty string\n", " text = re.sub(r'[0-9]', '', text)\n", " text = re.sub(\"[\"\n", " u\"\\U0001F600-\\U0001F64F\" # removal of emoticons\n", " u\"\\U0001F300-\\U0001F5FF\" # symbols & pictographs\n", " u\"\\U0001F680-\\U0001F6FF\" # transport & map symbols\n", " u\"\\U0001F1E0-\\U0001F1FF\" # flags (iOS)\n", " u\"\\U00002702-\\U000027B0\"\n", " u\"\\U000024C2-\\U0001F251\"\n", " \"]+\",' ',text)\n", " \n", " # filtering out miscellaneous text.\n", " text = re.sub('[^a-zA-Z]',' ',text) \n", " text = re.sub(r\"\\([^()]*\\)\", \"\", text)\n", " \n", " # remove mentions\n", " text = re.sub('@\\S+', '', text) \n", " \n", " # remove punctuations\n", " text = re.sub('[%s]' % re.escape(\"\"\"!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~\"\"\"), '', text) \n", " \n", "\n", " # Lowering all the words in text\n", " text = text.lower()\n", " text = text.split()\n", " \n", " text = [stemmer.stem(words) for words in text if words not in stopwords.words('english')]\n", " \n", " # Removal of words with length<2\n", " text = [i for i in text if len(i)>2] \n", " text = ' '.join(text)\n", " return text\n", "\n", "data[\"Cleaned_text\"] = data[\"text\"].apply(preprocess_data)" ] }, { "cell_type": "markdown", "id": "shaped-affairs", "metadata": { "papermill": { "duration": 0.214498, "end_time": "2021-06-13T15:27:03.167469", "exception": false, "start_time": "2021-06-13T15:27:02.952971", "status": "completed" }, "tags": [] }, "source": [ "Displaying Cleaned Data " ] }, { "cell_type": "code", "execution_count": 13, "id": "perceived-sword", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:27:03.634304Z", "iopub.status.busy": "2021-06-13T15:27:03.633623Z", "iopub.status.idle": "2021-06-13T15:27:03.644055Z", "shell.execute_reply": "2021-06-13T15:27:03.643581Z", "shell.execute_reply.started": "2021-06-13T14:49:13.615650Z" }, "id": "iwX5VY5bK13S", "outputId": "7ecbefb1-5c8c-4e22-aebb-8264be851a15", "papermill": { "duration": 0.265663, "end_time": "2021-06-13T15:27:03.644211", "exception": false, "start_time": "2021-06-13T15:27:03.378548", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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0Our Deeds are the Reason of this #earthquake M...1deed reason earthquak may allah forgiv
1Forest fire near La Ronge Sask. Canada1forest fire near rong sask canada
2All residents asked to 'shelter in place' are ...1resid ask ishelt place notifi offic evacu shel...
313,000 people receive #wildfires evacuation or...1peopl receiv wildfir evacu order california
4Just got sent this photo from Ruby #Alaska as ...1got sent photo rubi alaska smoke wildfir pour ...
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" ], "text/plain": [ " text target \\\n", "0 Our Deeds are the Reason of this #earthquake M... 1 \n", "1 Forest fire near La Ronge Sask. Canada 1 \n", "2 All residents asked to 'shelter in place' are ... 1 \n", "3 13,000 people receive #wildfires evacuation or... 1 \n", "4 Just got sent this photo from Ruby #Alaska as ... 1 \n", "\n", " Cleaned_text \n", "0 deed reason earthquak may allah forgiv \n", "1 forest fire near rong sask canada \n", "2 resid ask ishelt place notifi offic evacu shel... \n", "3 peopl receiv wildfir evacu order california \n", "4 got sent photo rubi alaska smoke wildfir pour ... " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head()" ] }, { "cell_type": "markdown", "id": "viral-anthropology", "metadata": { "papermill": { "duration": 0.211025, "end_time": "2021-06-13T15:27:04.065557", "exception": false, "start_time": "2021-06-13T15:27:03.854532", "status": "completed" }, "tags": [] }, "source": [ "# 4. Extra Data Exploration and Analysis on Cleaned Text" ] }, { "cell_type": "markdown", "id": "expected-tension", "metadata": { "papermill": { "duration": 0.21049, "end_time": "2021-06-13T15:27:04.485869", "exception": false, "start_time": "2021-06-13T15:27:04.275379", "status": "completed" }, "tags": [] }, "source": [ "## 4.1 Creating function and data for visualising words" ] }, { "cell_type": "markdown", "id": "constitutional-acrobat", "metadata": { "papermill": { "duration": 0.210449, "end_time": "2021-06-13T15:27:04.906001", "exception": false, "start_time": "2021-06-13T15:27:04.695552", "status": "completed" }, "tags": [] }, "source": [ "Using the popular [WordCloud](https://www.python-graph-gallery.com/wordcloud/) python library for visulaising the cleaned data" ] }, { "cell_type": "code", "execution_count": 14, "id": "revised-medication", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:27:05.341888Z", "iopub.status.busy": "2021-06-13T15:27:05.341222Z", "iopub.status.idle": "2021-06-13T15:27:05.345595Z", "shell.execute_reply": "2021-06-13T15:27:05.346060Z", "shell.execute_reply.started": "2021-06-13T14:49:13.626664Z" }, "id": "rA860VD7K5zQ", "papermill": { "duration": 0.226395, "end_time": "2021-06-13T15:27:05.346239", "exception": false, "start_time": "2021-06-13T15:27:05.119844", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "def wordcloud(data,title):\n", " words = ' '.join(data['Cleaned_text'].astype('str').tolist())\n", " stopwords = set(STOPWORDS)\n", " wc = WordCloud(stopwords = stopwords,width= 512, height = 512).generate(words)\n", " plt.figure(figsize=(10,8),frameon=True)\n", " plt.imshow(wc)\n", " plt.axis('off')\n", " plt.title(title,fontsize=20)\n", " plt.show()\n", " \n", "data_disaster = data[data['target'] == 1]\n", "data_non_disaster = data[data['target'] == 0]" ] }, { "cell_type": "markdown", "id": "impossible-preview", "metadata": { "papermill": { "duration": 0.216104, "end_time": "2021-06-13T15:27:05.772898", "exception": false, "start_time": "2021-06-13T15:27:05.556794", "status": "completed" }, "tags": [] }, "source": [ "## 4.2 Visualising words inside Real Disaster Tweets" ] }, { "cell_type": "markdown", "id": "short-portsmouth", "metadata": { "papermill": { "duration": 0.211219, "end_time": "2021-06-13T15:27:06.194749", "exception": false, "start_time": "2021-06-13T15:27:05.983530", "status": "completed" }, "tags": [] }, "source": [ "we can see that most common words in disaster tweets are fire,storm,flood , police etc. " ] }, { "cell_type": "code", "execution_count": 15, "id": "urban-hazard", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:27:06.617663Z", "iopub.status.busy": "2021-06-13T15:27:06.616677Z", "iopub.status.idle": "2021-06-13T15:27:07.849474Z", "shell.execute_reply": "2021-06-13T15:27:07.848896Z", "shell.execute_reply.started": "2021-06-13T14:49:13.638736Z" }, "id": "zH60osphMhQ0", "outputId": "9f045705-2762-4571-ebf5-3e946c14e2a4", "papermill": { "duration": 1.444856, "end_time": "2021-06-13T15:27:07.849631", "exception": false, "start_time": "2021-06-13T15:27:06.404775", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "wordcloud(data_disaster,\"Disaster Tweets\")" ] }, { "cell_type": "markdown", "id": "hidden-mention", "metadata": { "papermill": { "duration": 0.218851, "end_time": "2021-06-13T15:27:08.286647", "exception": false, "start_time": "2021-06-13T15:27:08.067796", "status": "completed" }, "tags": [] }, "source": [ "## 4.3 Visualising words inside Fake Disaster Tweets" ] }, { "cell_type": "markdown", "id": "packed-silly", "metadata": { "papermill": { "duration": 0.214732, "end_time": "2021-06-13T15:27:08.716702", "exception": false, "start_time": "2021-06-13T15:27:08.501970", "status": "completed" }, "tags": [] }, "source": [ "love,new,time etc are the most common words as we can see in wordcloud of Non-disaster tweets" ] }, { "cell_type": "code", "execution_count": 16, "id": "adult-friendly", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:27:09.188389Z", "iopub.status.busy": "2021-06-13T15:27:09.183299Z", "iopub.status.idle": "2021-06-13T15:27:10.417277Z", "shell.execute_reply": "2021-06-13T15:27:10.417802Z", "shell.execute_reply.started": "2021-06-13T14:49:14.750107Z" }, "id": "6USUwvWKNFXX", "outputId": "9c6d292d-3f95-4252-ad04-d85b51e3ba75", "papermill": { "duration": 1.4857, "end_time": "2021-06-13T15:27:10.417976", "exception": false, "start_time": "2021-06-13T15:27:08.932276", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "wordcloud(data_non_disaster,\"Non-Disaster Tweets\")" ] }, { "cell_type": "markdown", "id": "rocky-black", "metadata": { "papermill": { "duration": 0.223803, "end_time": "2021-06-13T15:27:10.866045", "exception": false, "start_time": "2021-06-13T15:27:10.642242", "status": "completed" }, "tags": [] }, "source": [ "## 4.4 Removing unwanted words with high frequency" ] }, { "cell_type": "markdown", "id": "equal-authorization", "metadata": { "papermill": { "duration": 0.221683, "end_time": "2021-06-13T15:27:11.309702", "exception": false, "start_time": "2021-06-13T15:27:11.088019", "status": "completed" }, "tags": [] }, "source": [ "Our cleaned text still contains some unnecessary words (such as: like, amp, get, would etc.) that aren't relevant and can confuse our model, \n", "resulting in false prediction. Now, we will further remove some words with high frequency from text based on above charts." ] }, { "cell_type": "code", "execution_count": 17, "id": "lightweight-brazil", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:27:11.795965Z", "iopub.status.busy": "2021-06-13T15:27:11.785701Z", "iopub.status.idle": "2021-06-13T15:27:11.805784Z", "shell.execute_reply": "2021-06-13T15:27:11.805298Z", "shell.execute_reply.started": "2021-06-13T14:49:15.879474Z" }, "id": "-_-USeyzNoPr", "papermill": { "duration": 0.275324, "end_time": "2021-06-13T15:27:11.805944", "exception": false, "start_time": "2021-06-13T15:27:11.530620", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "common_words = ['via','like','build','get','would','one','two','feel','lol','fuck','take','way','may','first','latest'\n", " 'want','make','back','see','know','let','look','come','got','still','say','think','great','pleas','amp']\n", "\n", "def text_cleaning(data):\n", " return ' '.join(i for i in data.split() if i not in common_words)\n", "\n", "data[\"Cleaned_text\"] = data[\"Cleaned_text\"].apply(text_cleaning)" ] }, { "cell_type": "markdown", "id": "interim-ensemble", "metadata": { "papermill": { "duration": 0.235639, "end_time": "2021-06-13T15:27:12.264441", "exception": false, "start_time": "2021-06-13T15:27:12.028802", "status": "completed" }, "tags": [] }, "source": [ "## 4.5 Analysing top 10 N-grams where N is 1,2,3" ] }, { "cell_type": "markdown", "id": "acting-efficiency", "metadata": { "papermill": { "duration": 0.253156, "end_time": "2021-06-13T15:27:12.768302", "exception": false, "start_time": "2021-06-13T15:27:12.515146", "status": "completed" }, "tags": [] }, "source": [ "### What do you mean by N-grams? \n", "N-grams of texts are extensively used in text mining and natural language processing tasks. They are basically a set of co-occurring words within a given window and when computing the n-grams you typically move one word forward (although you can move X words forward in more advanced scenarios). \n", "\n", "For example, for the sentence “The cow jumps over the moon”. If N=2 (known as bigrams), then the ngrams would be: \n", "* the cow \n", "* cow jumps \n", "* jumps over \n", "* over the \n", "* the moon\n", "\n", "Below we perform [N-grams](https://en.wikipedia.org/wiki/N-gram#:~:text=In%20the%20fields%20of%20computational,a%20text%20or%20speech%20corpus.) analysis on cleaned data" ] }, { "cell_type": "code", "execution_count": 18, "id": "interpreted-identifier", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:27:13.223434Z", "iopub.status.busy": "2021-06-13T15:27:13.222567Z", "iopub.status.idle": "2021-06-13T15:27:13.225014Z", "shell.execute_reply": "2021-06-13T15:27:13.225749Z", "shell.execute_reply.started": "2021-06-13T14:49:15.925884Z" }, "id": "7FrdJlJNQGCa", "papermill": { "duration": 0.232529, "end_time": "2021-06-13T15:27:13.225914", "exception": false, "start_time": "2021-06-13T15:27:12.993385", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "def top_ngrams(data,n,grams):\n", " count_vec = CountVectorizer(ngram_range=(grams,grams)).fit(data)\n", " bow = count_vec.transform(data)\n", " add_words = bow.sum(axis=0)\n", " word_freq = [(word, add_words[0, idx]) for word, idx in count_vec.vocabulary_.items()]\n", " word_freq = sorted(word_freq, key = lambda x: x[1], reverse=True) \n", " return word_freq[:n]" ] }, { "cell_type": "markdown", "id": "animated-scanning", "metadata": { "papermill": { "duration": 0.26245, "end_time": "2021-06-13T15:27:13.713994", "exception": false, "start_time": "2021-06-13T15:27:13.451544", "status": "completed" }, "tags": [] }, "source": [ "Creating data of top 10 n-grams for n = 1, 2, 3" ] }, { "cell_type": "code", "execution_count": 19, "id": "great-mississippi", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:27:14.186961Z", "iopub.status.busy": "2021-06-13T15:27:14.181743Z", "iopub.status.idle": "2021-06-13T15:27:15.285271Z", "shell.execute_reply": "2021-06-13T15:27:15.285790Z", "shell.execute_reply.started": "2021-06-13T14:49:15.932465Z" }, "id": "BmAP6cJhXQGU", "papermill": { "duration": 1.352174, "end_time": "2021-06-13T15:27:15.285955", "exception": false, "start_time": "2021-06-13T15:27:13.933781", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "common_uni = top_ngrams(data[\"Cleaned_text\"],10,1)\n", "common_bi = top_ngrams(data[\"Cleaned_text\"],10,2)\n", "common_tri = top_ngrams(data[\"Cleaned_text\"],10,3)\n", "common_uni_df = pd.DataFrame(common_uni,columns=['word','freq'])\n", "common_bi_df = pd.DataFrame(common_bi,columns=['word','freq'])\n", "common_tri_df = pd.DataFrame(common_tri,columns=['word','freq'])" ] }, { "cell_type": "markdown", "id": "fluid-fiber", "metadata": { "papermill": { "duration": 0.224521, "end_time": "2021-06-13T15:27:15.729942", "exception": false, "start_time": "2021-06-13T15:27:15.505421", "status": "completed" }, "tags": [] }, "source": [ "## 4.6 Visualising top 10 N-grams for N = 1, 2, 3" ] }, { "cell_type": "code", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = make_subplots(rows=3, cols=1,subplot_titles=(\"Top 20 Unigrams in Text\", \"Top 20 Bigrams in Text\",\"Top 20 Trigrams in Text\"))\n", " \n", "fig.add_trace(\n", " go.Bar(x=common_uni_df[\"word\"],y=common_uni_df[\"freq\"],\n", " marker=dict(color= 'rgba(255, 170, 59,0.8)'),\n", " marker_line=dict(color='black'),marker_line_width=1.2),\n", " row=1, col=1\n", ").add_trace(\n", " go.Bar(x=common_bi_df[\"word\"],y=common_bi_df[\"freq\"],\n", " marker=dict(color= 'rgba(89, 255, 147,0.8)'),\n", " marker_line=dict(color='black'),marker_line_width=1.2),\n", " row=2, col=1\n", ").add_trace(\n", " go.Bar(x=common_tri_df[\"word\"],y=common_tri_df[\"freq\"],\n", " marker=dict(color= 'rgba(89, 153, 255,0.8)'),\n", " marker_line=dict(color='black'),marker_line_width=1.2),\n", " row=3, col=1\n", ").update_layout(title_text=\"Visualization of Top 20 Unigrams, Bigrams and Trigrams\",\n", " title_x=0.5,showlegend=False,width=800,height=1600,).update_xaxes(tickangle=-90).show()\n", "\n", "# py.plot(fig,filename='Visualization of Top 20 Unigrams, Bigrams and Trigrams',auto_open=False,show_link=False)" ] }, { "cell_type": "markdown", "id": "banner-baseball", "metadata": { "_kg_hide-input": true, "id": "GvvSq0MZq98J", "papermill": { "duration": 0.227724, "end_time": "2021-06-13T15:27:16.704751", "exception": false, "start_time": "2021-06-13T15:27:16.477027", "status": "completed" }, "tags": [] }, "source": [ "
\n", " \"Visualization\n", " \n", "
\n" ] }, { "cell_type": "markdown", "id": "tutorial-nevada", "metadata": { "papermill": { "duration": 0.231758, "end_time": "2021-06-13T15:27:17.165308", "exception": false, "start_time": "2021-06-13T15:27:16.933550", "status": "completed" }, "tags": [] }, "source": [ "# 5. Data Preprocessing " ] }, { "cell_type": "markdown", "id": "annual-circulation", "metadata": { "papermill": { "duration": 0.22656, "end_time": "2021-06-13T15:27:17.620134", "exception": false, "start_time": "2021-06-13T15:27:17.393574", "status": "completed" }, "tags": [] }, "source": [ "## 5.1 Spliting original data after cleaning " ] }, { "cell_type": "code", "execution_count": 21, "id": "reasonable-patch", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:27:18.079792Z", "iopub.status.busy": "2021-06-13T15:27:18.078783Z", "iopub.status.idle": "2021-06-13T15:27:18.083405Z", "shell.execute_reply": "2021-06-13T15:27:18.083927Z", "shell.execute_reply.started": "2021-06-13T14:49:16.777343Z" }, "papermill": { "duration": 0.236981, "end_time": "2021-06-13T15:27:18.084303", "exception": false, "start_time": "2021-06-13T15:27:17.847322", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "X_inp_clean = data['Cleaned_text']\n", "X_inp_original = data['text']\n", "y_inp = data['target']" ] }, { "cell_type": "markdown", "id": "typical-digest", "metadata": { "papermill": { "duration": 0.235044, "end_time": "2021-06-13T15:27:18.549434", "exception": false, "start_time": "2021-06-13T15:27:18.314390", "status": "completed" }, "tags": [] }, "source": [ "Using [scikit-learn's train_test_split](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) to split the data into training and validation dataset" ] }, { "cell_type": "code", "execution_count": 22, "id": "environmental-brief", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:27:19.010963Z", "iopub.status.busy": "2021-06-13T15:27:19.009997Z", "iopub.status.idle": "2021-06-13T15:27:19.022218Z", "shell.execute_reply": "2021-06-13T15:27:19.022706Z", "shell.execute_reply.started": "2021-06-13T14:49:16.784027Z" }, "papermill": { "duration": 0.246245, "end_time": "2021-06-13T15:27:19.022872", "exception": false, "start_time": "2021-06-13T15:27:18.776627", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "X_train, X_valid, y_train, y_valid = train_test_split(X_inp_clean, y_inp, test_size=0.2, random_state=42, stratify=y_inp)\n", "y_train = np.array(y_train)\n", "y_valid = np.array(y_valid)" ] }, { "cell_type": "markdown", "id": "empty-growing", "metadata": { "papermill": { "duration": 0.22577, "end_time": "2021-06-13T15:27:19.474589", "exception": false, "start_time": "2021-06-13T15:27:19.248819", "status": "completed" }, "tags": [] }, "source": [ "checking size of data after train test split" ] }, { "cell_type": "code", "execution_count": 23, "id": "posted-dakota", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:27:19.934621Z", "iopub.status.busy": "2021-06-13T15:27:19.933964Z", "iopub.status.idle": "2021-06-13T15:27:19.938999Z", "shell.execute_reply": "2021-06-13T15:27:19.939593Z", "shell.execute_reply.started": "2021-06-13T14:49:16.806005Z" }, "papermill": { "duration": 0.239556, "end_time": "2021-06-13T15:27:19.939753", "exception": false, "start_time": "2021-06-13T15:27:19.700197", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "((6090,), (1523,), (6090,), (1523,))" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train.shape, X_valid.shape, y_train.shape, y_valid.shape" ] }, { "cell_type": "markdown", "id": "lightweight-hebrew", "metadata": { "papermill": { "duration": 0.228234, "end_time": "2021-06-13T15:27:20.395540", "exception": false, "start_time": "2021-06-13T15:27:20.167306", "status": "completed" }, "tags": [] }, "source": [ "## 5.2 Creating function to encode data using BoW or TF-IDF" ] }, { "cell_type": "markdown", "id": "polyphonic-values", "metadata": { "papermill": { "duration": 0.228521, "end_time": "2021-06-13T15:27:20.851396", "exception": false, "start_time": "2021-06-13T15:27:20.622875", "status": "completed" }, "tags": [] }, "source": [ "### What is BoW? \n", "BoW stands for \"*bag of words*\" which is a representation of text that describes the occurrence of words within a document. \n", "We just keep track of word counts and disregard the grammatical details and the word order. \n", "It is called a “bag” of words because any information about the order or structure of words in the document is discarded. \n", "The model is only concerned with whether known words occur in the document, not where in the document.\n", " \n", " \n", "### What is TF-IDF?\n", "TF-IDF which means Term Frequency and Inverse Document Frequency, is a scoring measure widely used in information retrieval (IR) or summarization. \n", "TF-IDF is intended to reflect how relevant a term is in a given document. It is a technique in Natural Language Processing for converting words in Vectors and with some semantic information and it gives weighted to uncommon words , used in various NLP applications. \n", "\n", "For [BoW](https://www.mygreatlearning.com/blog/bag-of-words/) approach we use scikit-learn's [CountVectorizer](http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html) and for [TF-IDF](https://towardsdatascience.com/natural-language-processing-feature-engineering-using-tf-idf-e8b9d00e7e76) we use [TfidfVectorizer](http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html)" ] }, { "cell_type": "code", "execution_count": 24, "id": "random-current", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:27:21.313725Z", "iopub.status.busy": "2021-06-13T15:27:21.313097Z", "iopub.status.idle": "2021-06-13T15:27:21.320467Z", "shell.execute_reply": "2021-06-13T15:27:21.320956Z", "shell.execute_reply.started": "2021-06-13T14:49:16.814681Z" }, "id": "-Cw5J87AnyN4", "papermill": { "duration": 0.240863, "end_time": "2021-06-13T15:27:21.321138", "exception": false, "start_time": "2021-06-13T15:27:21.080275", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "def encoding(train_data,valid_data,bow=False,n=1,tf_idf=False):\n", " if bow==True:\n", " cv = CountVectorizer(ngram_range=(n,n))\n", " cv_df_train = cv.fit_transform(train_data).toarray()\n", " train_df = pd.DataFrame(cv_df_train,columns=cv.get_feature_names())\n", " cv_df_valid = cv.transform(valid_data).toarray()\n", " valid_df = pd.DataFrame(cv_df_valid,columns=cv.get_feature_names())\n", " \n", " elif tf_idf==True:\n", " \n", " tfidf = TfidfVectorizer(ngram_range=(n, n), use_idf=1,smooth_idf=1,sublinear_tf=1) \n", " tf_df_train = tfidf.fit_transform(train_data).toarray()\n", " train_df = pd.DataFrame(tf_df_train,columns=tfidf.get_feature_names())\n", " tf_df_valid = tfidf.transform(valid_data).toarray()\n", " valid_df = pd.DataFrame(tf_df_valid,columns=tfidf.get_feature_names())\n", " \n", " return train_df,valid_df " ] }, { "cell_type": "markdown", "id": "iraqi-cornwall", "metadata": { "papermill": { "duration": 0.227488, "end_time": "2021-06-13T15:27:21.777032", "exception": false, "start_time": "2021-06-13T15:27:21.549544", "status": "completed" }, "tags": [] }, "source": [ "## 5.3 Encoding training and validation data" ] }, { "cell_type": "markdown", "id": "suitable-jacksonville", "metadata": { "papermill": { "duration": 0.232687, "end_time": "2021-06-13T15:27:22.239397", "exception": false, "start_time": "2021-06-13T15:27:22.006710", "status": "completed" }, "tags": [] }, "source": [ "We encode our data in all possible combinations provided by our function" ] }, { "cell_type": "code", "execution_count": 25, "id": "vertical-privilege", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:27:22.722915Z", "iopub.status.busy": "2021-06-13T15:27:22.717940Z", "iopub.status.idle": "2021-06-13T15:27:25.141330Z", "shell.execute_reply": "2021-06-13T15:27:25.140757Z", "shell.execute_reply.started": "2021-06-13T14:49:16.825028Z" }, "papermill": { "duration": 2.673646, "end_time": "2021-06-13T15:27:25.141489", "exception": false, "start_time": "2021-06-13T15:27:22.467843", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "X_train_bow1 , X_valid_bow1 = encoding(X_train,X_valid,bow=True) \n", "X_train_bow2 , X_valid_bow2 = encoding(X_train,X_valid,bow=True,n=2) \n", "X_train_bow3 , X_valid_bow3 = encoding(X_train,X_valid,bow=True,n=3) \n", "X_train_tfidf1 , X_valid_tfidf1 = encoding(X_train,X_valid,tf_idf=True) \n", "X_train_tfidf2 , X_valid_tfidf2 = encoding(X_train,X_valid,tf_idf=True,n=2) \n", "X_train_tfidf3 , X_valid_tfidf3 = encoding(X_train,X_valid,tf_idf=True,n=3)" ] }, { "cell_type": "markdown", "id": "human-canvas", "metadata": { "papermill": { "duration": 0.228851, "end_time": "2021-06-13T15:27:25.600602", "exception": false, "start_time": "2021-06-13T15:27:25.371751", "status": "completed" }, "tags": [] }, "source": [ "# 6. Training and tuning Machine Learining Models" ] }, { "cell_type": "markdown", "id": "military-aluminum", "metadata": { "papermill": { "duration": 0.231934, "end_time": "2021-06-13T15:27:26.064383", "exception": false, "start_time": "2021-06-13T15:27:25.832449", "status": "completed" }, "tags": [] }, "source": [ "### What is a classification report?\n", "A Classification report is used to measure the quality of predictions from a classification algorithm. \n", "The report shows the main classification metrics precision, recall and f1-score on a per-class basis. The metrics are calculated by using true and false positives, true and false negatives. Positive and negative in this case are generic names for the predicted classes. \n", "### What is a confusion matrix?\n", "A confusion matrix is a table that is often used to describe the performance of a classification model (or \"classifier\") on a set of test data for which the true values are known. The confusion matrix itself is relatively simple to understand, but the related terminology can be confusing. \n", "\n", "#### In a confusion matrix there are 4 basic terminologies :\n", "* **true positives (TP)** : We predicted yes (they are real tweets), and they are actually real.\n", "* **true negatives (TN)** : We predicted no, and they are fake.\n", "* **false positives (FP)**: We predicted yes, but they are't actually real. (Also known as a \"Type I error.\")\n", "* **false negatives (FN)**: We predicted no, but they are real. (Also known as a \"Type II error.\")\n", "\n", "Now let's create functions to display model's [classification report](https://datascience.stackexchange.com/questions/64441/how-to-interpret-classification-report-of-scikit-learn) and [confusion matrix](https://machinelearningmastery.com/confusion-matrix-machine-learning/)" ] }, { "cell_type": "code", "execution_count": 26, "id": "mental-trade", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:27:26.532823Z", "iopub.status.busy": "2021-06-13T15:27:26.532011Z", "iopub.status.idle": "2021-06-13T15:27:26.535077Z", "shell.execute_reply": "2021-06-13T15:27:26.534574Z", "shell.execute_reply.started": "2021-06-13T14:49:18.902232Z" }, "papermill": { "duration": 0.240061, "end_time": "2021-06-13T15:27:26.535256", "exception": false, "start_time": "2021-06-13T15:27:26.295195", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "def c_report(y_true,y_pred):\n", " print(\"Classifictaion Report\")\n", " print(classification_report(y_true, y_pred))\n", " acc_scr = accuracy_score(y_true, y_pred)\n", " print(\"Accuracy : \"+ str(acc_scr))\n", " return acc_scr\n", "\n", "def plot_cm(y_true,y_pred,cmap = \"Blues\"):\n", " mtx = confusion_matrix(y_true, y_pred)\n", " sns.heatmap(mtx, annot = True, fmt='d', linewidth=0.5,\n", " cmap=cmap, cbar = False)\n", " plt.xlabel('Predicted Label')\n", " plt.ylabel('True Label')" ] }, { "cell_type": "markdown", "id": "indonesian-congo", "metadata": { "papermill": { "duration": 0.27104, "end_time": "2021-06-13T15:27:27.034391", "exception": false, "start_time": "2021-06-13T15:27:26.763351", "status": "completed" }, "tags": [] }, "source": [ "## 6.1 Logistic Regression" ] }, { "cell_type": "markdown", "id": "increased-cleaner", "metadata": { "papermill": { "duration": 0.228686, "end_time": "2021-06-13T15:27:27.491701", "exception": false, "start_time": "2021-06-13T15:27:27.263015", "status": "completed" }, "tags": [] }, "source": [ "### About Logistic Regression \n", "\n", "Logistic regression is a statistical model that in its basic form uses a logistic function to model a binary dependent variable, although many more complex extensions exist. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic model (a form of binary regression).\n", "\n", "Logistic regression is the appropriate regression analysis to conduct when the dependent variable is dichotomous (binary). Like all regression analyses, the logistic regression is a predictive analysis. Logistic regression is used to describe data and to explain the relationship between one dependent binary variable and one or more nominal, ordinal, interval or ratio-level independent variables.\n", "\n", "Now let's create a [Logistic Regression](https://medium.com/analytics-vidhya/applying-text-classification-using-logistic-regression-a-comparison-between-bow-and-tf-idf-1f1ed1b83640) model and train it." ] }, { "cell_type": "code", "execution_count": 27, "id": "frozen-strip", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:27:27.983763Z", "iopub.status.busy": "2021-06-13T15:27:27.967441Z", "iopub.status.idle": "2021-06-13T15:27:33.733948Z", "shell.execute_reply": "2021-06-13T15:27:33.734823Z", "shell.execute_reply.started": "2021-06-13T14:49:18.909462Z" }, "papermill": { "duration": 6.014244, "end_time": "2021-06-13T15:27:33.735093", "exception": false, "start_time": "2021-06-13T15:27:27.720849", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "LogisticRegression()" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_bow1_logreg = LogisticRegression()\n", "model_bow1_logreg.fit(X_train_bow1,y_train)\n", "pred_bow1_logreg = model_bow1_logreg.predict(X_valid_bow1)" ] }, { "cell_type": "markdown", "id": "coordinate-hydrogen", "metadata": { "papermill": { "duration": 0.22907, "end_time": "2021-06-13T15:27:34.241988", "exception": false, "start_time": "2021-06-13T15:27:34.012918", "status": "completed" }, "tags": [] }, "source": [ "Printing classification report and ploting confusion matrix for the predictions made by LogisticRegression(BoW,n-grams=1) model" ] }, { "cell_type": "code", "execution_count": 28, "id": "beginning-provision", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:27:34.722751Z", "iopub.status.busy": "2021-06-13T15:27:34.722023Z", "iopub.status.idle": "2021-06-13T15:27:34.868478Z", "shell.execute_reply": "2021-06-13T15:27:34.869337Z", "shell.execute_reply.started": "2021-06-13T14:49:24.491086Z" }, "papermill": { "duration": 0.399098, "end_time": "2021-06-13T15:27:34.869600", "exception": false, "start_time": "2021-06-13T15:27:34.470502", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Classifictaion Report\n", " precision recall f1-score support\n", "\n", " 0 0.81 0.88 0.84 869\n", " 1 0.82 0.73 0.77 654\n", "\n", " accuracy 0.82 1523\n", " macro avg 0.82 0.81 0.81 1523\n", "weighted avg 0.82 0.82 0.81 1523\n", "\n", "Accuracy : 0.8161523309258043\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "acc_bow1_logreg = c_report(y_valid,pred_bow1_logreg)\n", "plot_cm(y_valid,pred_bow1_logreg)" ] }, { "cell_type": "markdown", "id": "leading-tomato", "metadata": { "papermill": { "duration": 0.231973, "end_time": "2021-06-13T15:27:35.340842", "exception": false, "start_time": "2021-06-13T15:27:35.108869", "status": "completed" }, "tags": [] }, "source": [ "Now training another Logistic Regression model with n-grams=2 and BoW " ] }, { "cell_type": "code", "execution_count": 29, "id": "emotional-peripheral", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:27:35.808975Z", "iopub.status.busy": "2021-06-13T15:27:35.808276Z", "iopub.status.idle": "2021-06-13T15:27:47.166323Z", "shell.execute_reply": "2021-06-13T15:27:47.167178Z", "shell.execute_reply.started": "2021-06-13T14:49:24.641376Z" }, "papermill": { "duration": 11.596431, "end_time": "2021-06-13T15:27:47.167468", "exception": false, "start_time": "2021-06-13T15:27:35.571037", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "LogisticRegression()" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_bow2_logreg = LogisticRegression()\n", "model_bow2_logreg.fit(X_train_bow2,y_train)\n", "pred_bow2_logreg = model_bow2_logreg.predict(X_valid_bow2)" ] }, { "cell_type": "markdown", "id": "religious-creek", "metadata": { "papermill": { "duration": 0.236741, "end_time": "2021-06-13T15:27:47.686081", "exception": false, "start_time": "2021-06-13T15:27:47.449340", "status": "completed" }, "tags": [] }, "source": [ "Printing classification report and ploting confusion matrix for the predictions made by LogisticRegression(BoW,n-grams=2) model" ] }, { "cell_type": "code", "execution_count": 30, "id": "logical-isolation", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:27:48.154177Z", "iopub.status.busy": "2021-06-13T15:27:48.153554Z", "iopub.status.idle": "2021-06-13T15:27:48.290751Z", "shell.execute_reply": "2021-06-13T15:27:48.291619Z", "shell.execute_reply.started": "2021-06-13T14:49:35.238968Z" }, "papermill": { "duration": 0.375166, "end_time": "2021-06-13T15:27:48.291812", "exception": false, "start_time": "2021-06-13T15:27:47.916646", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Classifictaion Report\n", " precision recall f1-score support\n", "\n", " 0 0.70 0.96 0.81 869\n", " 1 0.89 0.45 0.60 654\n", "\n", " accuracy 0.74 1523\n", " macro avg 0.79 0.71 0.70 1523\n", "weighted avg 0.78 0.74 0.72 1523\n", "\n", "Accuracy : 0.7406434668417596\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "acc_bow2_logreg = c_report(y_valid,pred_bow2_logreg)\n", "plot_cm(y_valid,pred_bow2_logreg)" ] }, { "cell_type": "markdown", "id": "legendary-morris", "metadata": { "papermill": { "duration": 0.230668, "end_time": "2021-06-13T15:27:48.757804", "exception": false, "start_time": "2021-06-13T15:27:48.527136", "status": "completed" }, "tags": [] }, "source": [ "We can observe that as n is increasing model accuracy is decreasing \n", "let's try to increase n one last time just to be sure" ] }, { "cell_type": "code", "execution_count": 31, "id": "handed-associate", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:27:49.234608Z", "iopub.status.busy": "2021-06-13T15:27:49.231887Z", "iopub.status.idle": "2021-06-13T15:27:58.928379Z", "shell.execute_reply": "2021-06-13T15:27:58.927452Z", "shell.execute_reply.started": "2021-06-13T14:49:35.387048Z" }, "papermill": { "duration": 9.938016, "end_time": "2021-06-13T15:27:58.928645", "exception": false, "start_time": "2021-06-13T15:27:48.990629", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "LogisticRegression()" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_bow3_logreg = LogisticRegression()\n", "model_bow3_logreg.fit(X_train_bow3,y_train)\n", "pred_bow3_logreg = model_bow3_logreg.predict(X_valid_bow3)" ] }, { "cell_type": "markdown", "id": "hungarian-religious", "metadata": { "papermill": { "duration": 0.233443, "end_time": "2021-06-13T15:27:59.448971", "exception": false, "start_time": "2021-06-13T15:27:59.215528", "status": "completed" }, "tags": [] }, "source": [ "Printing classification report and ploting confusion matrix for the predictions made by LogisticRegression(BoW,n-grams=3) model" ] }, { "cell_type": "code", "execution_count": 32, "id": "cordless-nowhere", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:27:59.919688Z", "iopub.status.busy": "2021-06-13T15:27:59.918995Z", "iopub.status.idle": "2021-06-13T15:28:00.057991Z", "shell.execute_reply": "2021-06-13T15:28:00.059036Z", "shell.execute_reply.started": "2021-06-13T14:49:44.820469Z" }, "papermill": { "duration": 0.376984, "end_time": "2021-06-13T15:28:00.059328", "exception": false, "start_time": "2021-06-13T15:27:59.682344", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Classifictaion Report\n", " precision recall f1-score support\n", "\n", " 0 0.66 0.98 0.79 869\n", " 1 0.92 0.33 0.49 654\n", "\n", " accuracy 0.70 1523\n", " macro avg 0.79 0.65 0.64 1523\n", "weighted avg 0.77 0.70 0.66 1523\n", "\n", "Accuracy : 0.7005909389363099\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "acc_bow3_logreg = c_report(y_valid,pred_bow3_logreg)\n", "plot_cm(y_valid,pred_bow3_logreg)" ] }, { "cell_type": "markdown", "id": "expired-struggle", "metadata": { "execution": { "iopub.execute_input": "2021-06-09T08:11:24.298447Z", "iopub.status.busy": "2021-06-09T08:11:24.298095Z", "iopub.status.idle": "2021-06-09T08:11:24.306226Z", "shell.execute_reply": "2021-06-09T08:11:24.305086Z", "shell.execute_reply.started": "2021-06-09T08:11:24.29841Z" }, "papermill": { "duration": 0.235929, "end_time": "2021-06-13T15:28:00.578611", "exception": false, "start_time": "2021-06-13T15:28:00.342682", "status": "completed" }, "tags": [] }, "source": [ "From the above results it's clear that using n = 1 will always give us more accuray, \n", "now let's use tfidf approach with n = 1 to train our Logistic Regression model" ] }, { "cell_type": "code", "execution_count": 33, "id": "boring-shooting", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:28:01.048866Z", "iopub.status.busy": "2021-06-13T15:28:01.048175Z", "iopub.status.idle": "2021-06-13T15:28:03.938586Z", "shell.execute_reply": "2021-06-13T15:28:03.937272Z", "shell.execute_reply.started": "2021-06-13T14:49:45.022632Z" }, "papermill": { "duration": 3.128408, "end_time": "2021-06-13T15:28:03.938817", "exception": false, "start_time": "2021-06-13T15:28:00.810409", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "LogisticRegression()" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_tfidf1_logreg = LogisticRegression(C=1.0)\n", "model_tfidf1_logreg.fit(X_train_tfidf1,y_train)\n", "pred_tfidf1_logreg = model_tfidf1_logreg.predict(X_valid_tfidf1)" ] }, { "cell_type": "markdown", "id": "sapphire-tracker", "metadata": { "papermill": { "duration": 0.234482, "end_time": "2021-06-13T15:28:04.456836", "exception": false, "start_time": "2021-06-13T15:28:04.222354", "status": "completed" }, "tags": [] }, "source": [ "Printing classification report and ploting confusion matrix for the predictions made by LogisticRegression(TF-IDF,n-grams=1) model" ] }, { "cell_type": "code", "execution_count": 34, "id": "judicial-grass", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:28:04.931653Z", "iopub.status.busy": "2021-06-13T15:28:04.930942Z", "iopub.status.idle": "2021-06-13T15:28:05.031845Z", "shell.execute_reply": "2021-06-13T15:28:05.032381Z", "shell.execute_reply.started": "2021-06-13T14:49:47.773193Z" }, "papermill": { "duration": 0.341235, "end_time": "2021-06-13T15:28:05.032557", "exception": false, "start_time": "2021-06-13T15:28:04.691322", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Classifictaion Report\n", " precision recall f1-score support\n", "\n", " 0 0.81 0.90 0.85 869\n", " 1 0.85 0.71 0.77 654\n", "\n", " accuracy 0.82 1523\n", " macro avg 0.83 0.81 0.81 1523\n", "weighted avg 0.82 0.82 0.82 1523\n", "\n", "Accuracy : 0.8207485226526592\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "acc_tfidf1_logreg = c_report(y_valid,pred_tfidf1_logreg)\n", "plot_cm(y_valid,pred_tfidf1_logreg)" ] }, { "cell_type": "markdown", "id": "suitable-flower", "metadata": { "papermill": { "duration": 0.23522, "end_time": "2021-06-13T15:28:05.503606", "exception": false, "start_time": "2021-06-13T15:28:05.268386", "status": "completed" }, "tags": [] }, "source": [ "From Logistic Regression we saw n-grams = 1 gives the best results " ] }, { "cell_type": "markdown", "id": "colored-catalog", "metadata": { "papermill": { "duration": 0.232464, "end_time": "2021-06-13T15:28:05.974625", "exception": false, "start_time": "2021-06-13T15:28:05.742161", "status": "completed" }, "tags": [] }, "source": [ "## 6.2 Multinomial Naive Bayes" ] }, { "cell_type": "markdown", "id": "intended-relay", "metadata": { "papermill": { "duration": 0.234059, "end_time": "2021-06-13T15:28:06.442598", "exception": false, "start_time": "2021-06-13T15:28:06.208539", "status": "completed" }, "tags": [] }, "source": [ "### About Multinomial Naive Bayes \n", "\n", "Multinomial Naive Bayes algorithm is a probabilistic learning method that is mostly used in Natural Language Processing (NLP). The algorithm is based on the Bayes theorem and predicts the tag of a text such as a piece of email or newspaper article. It calculates the probability of each tag for a given sample and then gives the tag with the highest probability as output.\n", "\n", "Naive Bayes classifier is a collection of many algorithms where all the algorithms share one common principle, and that is each feature being classified is not related to any other feature. The presence or absence of a feature does not affect the presence or absence of the other feature.\n", " \n", "Now let's create [MultinomialNB](https://www.upgrad.com/blog/multinomial-naive-bayes-explained/) model and train it." ] }, { "cell_type": "code", "execution_count": 35, "id": "preceding-virgin", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:28:06.943258Z", "iopub.status.busy": "2021-06-13T15:28:06.926031Z", "iopub.status.idle": "2021-06-13T15:28:08.764044Z", "shell.execute_reply": "2021-06-13T15:28:08.764946Z", "shell.execute_reply.started": "2021-06-13T14:49:47.905824Z" }, "papermill": { "duration": 2.088335, "end_time": "2021-06-13T15:28:08.765231", "exception": false, "start_time": "2021-06-13T15:28:06.676896", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "MultinomialNB(alpha=0.7)" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_bow1_NB = MultinomialNB(alpha=0.7)\n", "model_bow1_NB.fit(X_train_bow1,y_train)\n", "pred_bow1_NB = model_bow1_NB.predict(X_valid_bow1)" ] }, { "cell_type": "markdown", "id": "hourly-present", "metadata": { "papermill": { "duration": 0.234655, "end_time": "2021-06-13T15:28:09.284218", "exception": false, "start_time": "2021-06-13T15:28:09.049563", "status": "completed" }, "tags": [] }, "source": [ "Printing classification report and ploting confusion matrix for the predictions of MultinomialNB(BoW,n-grams=1) model" ] }, { "cell_type": "code", "execution_count": 36, "id": "ecological-majority", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:28:09.759857Z", "iopub.status.busy": "2021-06-13T15:28:09.758829Z", "iopub.status.idle": "2021-06-13T15:28:09.851942Z", "shell.execute_reply": "2021-06-13T15:28:09.852847Z", "shell.execute_reply.started": "2021-06-13T14:49:49.618243Z" }, "papermill": { "duration": 0.333449, "end_time": "2021-06-13T15:28:09.853138", "exception": false, "start_time": "2021-06-13T15:28:09.519689", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Classifictaion Report\n", " precision recall f1-score support\n", "\n", " 0 0.82 0.85 0.84 869\n", " 1 0.79 0.76 0.78 654\n", "\n", " accuracy 0.81 1523\n", " macro avg 0.81 0.80 0.81 1523\n", "weighted avg 0.81 0.81 0.81 1523\n", "\n", "Accuracy : 0.8108995403808273\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "acc_bow1_NB = c_report(y_valid,pred_bow1_NB)\n", "plot_cm(y_valid,pred_bow1_NB)" ] }, { "cell_type": "markdown", "id": "electrical-large", "metadata": { "papermill": { "duration": 0.235389, "end_time": "2021-06-13T15:28:10.348908", "exception": false, "start_time": "2021-06-13T15:28:10.113519", "status": "completed" }, "tags": [] }, "source": [ "Creating a MultinomialNB model and training it with TF-IDF approach" ] }, { "cell_type": "code", "execution_count": 37, "id": "earlier-valentine", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:28:10.835293Z", "iopub.status.busy": "2021-06-13T15:28:10.830444Z", "iopub.status.idle": "2021-06-13T15:28:11.200972Z", "shell.execute_reply": "2021-06-13T15:28:11.201865Z", "shell.execute_reply.started": "2021-06-13T14:49:49.768093Z" }, "papermill": { "duration": 0.618329, "end_time": "2021-06-13T15:28:11.202178", "exception": false, "start_time": "2021-06-13T15:28:10.583849", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "MultinomialNB(alpha=0.7)" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_tfidf1_NB = MultinomialNB(alpha=0.7)\n", "model_tfidf1_NB.fit(X_train_tfidf1,y_train)\n", "pred_tfidf1_NB = model_tfidf1_NB.predict(X_valid_tfidf1)" ] }, { "cell_type": "markdown", "id": "common-breeding", "metadata": { "papermill": { "duration": 0.23629, "end_time": "2021-06-13T15:28:11.726308", "exception": false, "start_time": "2021-06-13T15:28:11.490018", "status": "completed" }, "tags": [] }, "source": [ "Printing classification report and ploting confusion matrix for the predictions of MultinomialNB(TF-IDF,n-grams=1) model" ] }, { "cell_type": "code", "execution_count": 38, "id": "according-kennedy", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:28:12.207036Z", "iopub.status.busy": "2021-06-13T15:28:12.206421Z", "iopub.status.idle": "2021-06-13T15:28:12.311296Z", "shell.execute_reply": "2021-06-13T15:28:12.310710Z", "shell.execute_reply.started": "2021-06-13T14:49:50.090451Z" }, "papermill": { "duration": 0.346965, "end_time": "2021-06-13T15:28:12.311456", "exception": false, "start_time": "2021-06-13T15:28:11.964491", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Classifictaion Report\n", " precision recall f1-score support\n", "\n", " 0 0.80 0.90 0.85 869\n", " 1 0.84 0.70 0.76 654\n", "\n", " accuracy 0.81 1523\n", " macro avg 0.82 0.80 0.80 1523\n", "weighted avg 0.82 0.81 0.81 1523\n", "\n", "Accuracy : 0.8135259356533159\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEGCAYAAABmXi5tAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Z1A+gAAAACXBIWXMAAAsTAAALEwEAmpwYAAAShUlEQVR4nO3de5SVZb3A8e8PBg6DAgICAYGSImiKCioapoGlWJ400RLzeEgMPWVlZmktz1ErO6bp8XjQlLxRS00RNc2W11DwFnjPS4J3ARHiJjqDwMxz/pjNOAgzDjp7Nuzn+1mLteZ933354cLvvPPsvd+JlBKSpPLXptQDSJJah8GXpEwYfEnKhMGXpEwYfEnKREWpB2iCbx+SpI0XjR3YlINP5e4nlXoEaYOqn5zIyjWlnkJaX4cmqu6SjiRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRloqLUA+iTG7hNT/7w6+Pqtwf07c4vfnsHw4cMYOC2vQDYqlMly1ZUs/dR59Kuoi0TzxjL0J36U5tqOfW8qcx4fE6pxldmrv3DZKbeNIWUEmOOOJJjjh3H8mXL+MmpP2T+vHn06duX8y+4iM5dupR61LJj8MvAnNcXsvdR5wLQpk3w8l3ncNu0p5l43f31tzn3lK+x/N1qAI47fAQAe379V/TouiW3TvwO+x5zPimlVp9deZkzZzZTb5rCtX+cQrt27fjOCcez3/4jmTrlBvYavg/jvz2BK383iSuvmMQPf/TjUo9bdoq2pBMRgyPitIi4uPDntIjYsVjPpzoj9xrEq3MX8cZbS9fZP+ZLQ7nxzscBGPyZT3H/rBcBWLT0XZavqGbYTv1bfVbl59VXXmaXIUOorKykoqKCYXvsyX333s20affx1cMOA+Crhx3GtL/eW9pBy1RRgh8RpwF/BAKYWfgTwPURcXoxnlN1jjxoWH3Y1xoxdDveXrKCl99YBMDfZ8/jkP13oW3bNmzTpzu779SPT3+qaynGVWa2334Hnnj8cZYtW0p1dTUPzpjOggULWLJ4MT169ARg6617sGTx4hJPWp6KtaQzHvhsSml1w50RcSHwHHDuhu4UEROACQCXX355kUYrX+0q2vKV/Xfhv/7vtnX2f330Hky587H67cl/eoTBA3rx0LU/4Y23lvDo069SU1Pb2uMqQ5/Zbju+Nf54Tvz2eCorKxk0eDBt26x73hkREFGiCctbsYJfC/QBXv/Q/t6FYxuUUpoETFq7+YPfnlSc6crUQfvuxFP/eJOFS1bU72vbtg2HjtqVEUefV7+vpqaWn1xwc/32tGtOYc4bC1t1VuXr8DFHcviYIwG4+KIL6dWrF926d2fRooX06NGTRYsW0q1btxJPWZ6KFfyTgfsiYg7wZmFff2B7wIoXyddH77Hecs6o4YOY/drbzFu4rH5fZYd2BEHVylWMGj6YNTW1/OOVBa08rXK1ePFiunfvzlvz53PfvXfzh+tuZN68udx2662M//YEbrv1VkaOPKDUY5alogQ/pXRnROwA7AX0LeyeB8xKKdUU4zlz17FDe0YNH8xJv7x+nf0bWtPv0bUTt1/6XWprE/MXLWP8GZNbc1Rl7kcnf4/ly5ZRUVHBz844k86dO3Pc8RP48Sknc+vNN9G7Tx/Ov+CiUo9ZlmITfiteqtzdHwa0aap+ciIr15R6Cml9HSpo9AUQP2krSZkw+JKUCYMvSZkw+JKUCYMvSZkw+JKUCYMvSZkw+JKUCYMvSZkw+JKUCYMvSZkw+JKUCYMvSZkw+JKUCYMvSZkw+JKUCYMvSZkw+JKUCYMvSZkw+JKUCYMvSZkw+JKUCYMvSZkw+JKUCYMvSZmoaOxARAxt6o4ppSdafhxJUrE0GnzggiaOJWBUC88iSSqiRoOfUhrZmoNIkorrI9fwI6JjRJwREZMK2wMj4pDijyZJaknNedH2amAV8LnC9jzgl0WbSJJUFM0J/nYppfOA1QAppSogijqVJKnFNSf4qyKikroXaomI7YD3izqVJKnFNfUunbXOBO4E+kXEtcAIYFwxh5IktbyPDH5K6Z6IeALYm7qlnB+klP5Z9MkkSS2qOWf4APsD+1K3rNMOuKVoE0mSiqI5b8u8FDgR+DvwLHBCRFxS7MEkSS2rOWf4o4AdU0prX7SdDDxX1KkkSS2uOe/SeQno32C7X2GfJGkz0tTF026nbs2+E/BCRMwsbA8HZrbOeJKkltLUks5vWm0KSVLRNXXxtAdacxBJUnE15106e0fErIh4NyJWRURNRLzTGsNJklpOc160nQiMBeYAlcDxgG/LlKTNTLN+xWFK6SWgbUqpJqV0NTC6uGNJklpac96HXxUR7YGnIuI84C38XbiStNlpTrj/rXC7k4D3qHsf/uHFHEqS1PKac/G01wtfrgTOBoiIG4BvFHEuSVIL+7hLM/u06BSSpKJzLV6SMhGFa6KtfyBiaGP3Af6cUupdtKnqbHgwSVJTGv0VtE0Ff1pTj5hSGvkJh/ooafbbVUV+Cunj2aFXR8Zd/0ypx5DWc83YIY0Gv6lLKxQ76JKkVuQaviRlwuBLUiYMviRlojlXy4yIOCYi/quw3T8i9ir+aJKkltScM/xLqfug1djC9gq8WqYkbXaac/G04SmloRHxJEBKaWnhYmqSpM1Ic87wV0dEWwofhIqIHkBtUaeSJLW45gT/YuAWoGdEnAM8CPyqqFNJklpcc66WeW1EPA4cQN1Hdg9LKb1Q9MkkSS3qI4MfEf2BKuD2hvtSSm8UczBJUstqzou2d1C3fh9AB2AA8CLw2SLOJUlqYc1Z0tml4XbhKprfKdpEkqSi2OhP2qaUngCGF2EWSVIRNWcN/5QGm22AocD8ok0kSSqK5qzhd2rw9Rrq1vSnFmccSVKxNBn8wgeuOqWUTm2leSRJRdLoGn5EVKSUaoARrTiPJKlImjrDn0ndev1TEXEbMAV4b+3BlNLNRZ5NktSCmrOG3wFYDIzig/fjJ8DgS9JmpKng9yy8Q+dZPgj9Whv+zeeSpE1WU8FvC2zJuqFfy+BL0mamqeC/lVL6eatNIkkqqqY+abuhM3tJ0maqqeAf0GpTSJKKrtHgp5SWtOYgkqTi2uiLp0mSNk8GX5IyYfAlKRMGX5IyYfAlKRMGX5IyYfAlKRMGX5IyYfAlKRMGX5IyYfAlKRMGX5IyYfAlKRMGX5IyYfAlKRMGX5IyYfAlKRMGX5IyYfAlKRMGX5IyYfAlKRMGX5IyUVHqAdQy/vfcs5j18HS6dO3GJZNvAuDVl17kkgvOYWVVNT179+HU/zyHjltsWX+fhW+/xXePHcPYcSdy+NhjSzW6MhABZx00kKVVq7lo+msAjBnSiz37bUVtSvz1pcXcO3sxAN8c2ochfTqxqqaWKx6dy+tLq0s4eXnxDL9MHDD6Xznr/EvW2XfxeT/n30/4PhMnT2Gfz4/k5usnr3P8yokXMGz4iNYcU5k6cIetmb98Zf32vgO60q1je356x4v87C+z+dvrywAY0rsTvTq157Q/v8g1M+dx7B59SzRxeTL4ZWLn3YbRqXOXdfbNf/MNdt51GAC77bE3Dz9wX/2xR2ZMo1fvvvTfdrtWnVP56VrZjl37dGL6K0vq940a2J0/Pfs2qbC94v0aAHb/dGceem0ZAC8vrqJj+7Z06eBCREsx+GWs/7af4dEH7wfgofvv4Z8L3waguqqKqdddzdhxJ5RwOuXi6KG9ueGpBaT0wb6eW7ZneP+tOPPA7Tll/23ptWV7oO6bw5L3VtXfbmnVKrp2bNfaI5etVg9+RHyriWMTIuKxiHhs0qRJrTlWWfr+6Wfxl1tu5OTjj6a6qoqKdnX/41x39WUceuQxVHbsWOIJVe527dOJd95fs946fEWbYHVtLWff/RIPvLyE44b3K9GEeSnFz0pnA1dv6EBKaRKwtvRp9ttVrTZUOeq3zQB+ceFvAZj35uvMemQGALNfeJaHH7iXay67iPfeXUFEG9q3b88hY44q5bgqQwN7bMHufTuza+/OtGsbdGjXlgn79GNp9Woee3M5AI/PfYfxheAvrV5Nty3awz/r/t/v2rE9S6tWl2z+clOU4EfEM40dAnoV4zm1vmVLl7BV127U1tZyw+9/x8GHHgHArydeVX+b6666jA6VHY29iuKmpxdw09MLABjccwtGD+7BpEfe5MhdP8WOvbZkxitLGdxzCxaseB+Ap+a9wwEDu/O315exXfeOVK+uYfnKNaX8K5SVYp3h9wIOApZ+aH8ADxfpObN2/tmn8/cnH+ed5csYN+Ygjv7WiaysruaOW24AYJ/9RvHFLx9a4imlOnc8v5AT9unPgYO25v01tVw9cy4AT89fwZDenTjvkEG8X1PLlX+bW+JJy0ukhq+ktNSDRlwJXJ1SenADx65LKR3djIdxSUebrB16dWTc9Y39ICuVzjVjh0Rjx4pyhp9SGt/EsebEXpLUwnxbpiRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlwuBLUiYMviRlIlJKpZ5BrSAiJqSUJpV6DunD/LfZejzDz8eEUg8gNcJ/m63E4EtSJgy+JGXC4OfDNVJtqvy32Up80VaSMuEZviRlwuBLUiYMfpmLiNER8WJEvBQRp5d6HmmtiLgqIhZGxLOlniUXBr+MRURb4BLgYGAnYGxE7FTaqaR61wCjSz1ETgx+edsLeCml9EpKaRXwR+DQEs8kAZBSmg4sKfUcOTH45a0v8GaD7bmFfZIyZPAlKRMGv7zNA/o12P50YZ+kDBn88jYLGBgRAyKiPXAUcFuJZ5JUIga/jKWU1gAnAXcBLwA3ppSeK+1UUp2IuB54BBgUEXMjYnypZyp3XlpBkjLhGb4kZcLgS1ImDL4kZcLgS1ImDL4kZcLga5MWETUR8VREPBsRUyKi4yd4rGsi4ojC11c0dSG5iPhCRHzuYzzHaxGxdXP3N/IY4yJiYks8r9SQwdemrjqltFtKaWdgFXBiw4MRUfFxHjSldHxK6fkmbvIFYKODL23KDL42JzOA7Qtn3zMi4jbg+YhoGxHnR8SsiHgmIk4AiDoTC78P4F6g59oHioj7I2KPwtejI+KJiHg6Iu6LiG2p+8byw8JPF5+PiB4RMbXwHLMiYkThvt0j4u6IeC4irgCiuX+ZiNgrIh6JiCcj4uGIGNTgcL/CjHMi4swG9zkmImYW5rq8cAlsqVk+1tmR1NoKZ/IHA3cWdg0Fdk4pvRoRE4DlKaU9I+JfgIci4m5gd2AQdb8LoBfwPHDVhx63B/A7YL/CY3VLKS2JiMuAd1NKvync7jrgf1JKD0ZEf+o+vbwjcCbwYErp5xHxFWBjPi36D+DzKaU1EfFF4FfAmMKxvYCdgSpgVkTcAbwHfAMYkVJaHRGXAt8Efr8Rz6mMGXxt6ioj4qnC1zOAK6lbapmZUnq1sP9AYMja9XmgCzAQ2A+4PqVUA8yPiL9u4PH3BqavfayUUmPXZ/8isFNE/Ql854jYsvAchxfue0dELN2Iv1sXYHJEDAQS0K7BsXtSSosBIuJmYF9gDTCMum8AAJXAwo14PmXO4GtTV51S2q3hjkLs3mu4C/heSumuD93uyy04Rxtg75TSyg3M8nH9ApiWUvpaYRnp/gbHPnzNk0Td33NySumnn+RJlS/X8FUO7gL+IyLaAUTEDhGxBTAd+EZhjb83MHID930U2C8iBhTu262wfwXQqcHt7ga+t3YjInYrfDkdOLqw72Cg60bM3YUPLlc97kPHvhQR3SKiEjgMeAi4DzgiInqunTUittmI51PmDL7KwRXUrc8/UfiF2JdT99PrLcCcwrHfU3dlxnWklBYBE4CbI+Jp4IbCoduBr6190Rb4PrBH4UXh5/ng3UJnU/cN4znqlnbeaGLOZwpXhZwbERcC5wH/HRFPsv5P2zOBqcAzwNSU0mOFdxWdAdwdEc8A9wC9m/nfSPJqmZKUC8/wJSkTBl+SMmHwJSkTBl+SMmHwJSkTBl+SMmHwJSkT/w8VSkl0kUCrTgAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "acc_tfidf1_NB = c_report(y_valid,pred_tfidf1_NB)\n", "plot_cm(y_valid,pred_tfidf1_NB)" ] }, { "cell_type": "markdown", "id": "demonstrated-hurricane", "metadata": { "papermill": { "duration": 0.236203, "end_time": "2021-06-13T15:28:12.786584", "exception": false, "start_time": "2021-06-13T15:28:12.550381", "status": "completed" }, "tags": [] }, "source": [ "## 6.3 Random Forest Classifier" ] }, { "cell_type": "markdown", "id": "surprising-exploration", "metadata": { "papermill": { "duration": 0.236736, "end_time": "2021-06-13T15:28:13.259250", "exception": false, "start_time": "2021-06-13T15:28:13.022514", "status": "completed" }, "tags": [] }, "source": [ "### About Random Forest Classifier \n", "\n", "Random forests is a supervised learning algorithm. It can be used both for classification and regression. It is also the most flexible and easy to use algorithm. A forest is comprised of trees. It is said that the more trees it has, the more robust a forest is. Random forests creates decision trees on randomly selected data samples, gets prediction from each tree and selects the best solution by means of voting. It also provides a pretty good indicator of the feature importance.\n", "\n", "Random forests has a variety of applications, such as recommendation engines, image classification and feature selection. It can be used to classify loyal loan applicants, identify fraudulent activity and predict diseases. It lies at the base of the Boruta algorithm, which selects important features in a dataset. \n", "\n", "Now let's create a [RandomForestClassifier](https://www.geeksforgeeks.org/random-forest-classifier-using-scikit-learn/) model and train it." ] }, { "cell_type": "code", "execution_count": 39, "id": "domestic-spotlight", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:28:13.761349Z", "iopub.status.busy": "2021-06-13T15:28:13.750213Z", "iopub.status.idle": "2021-06-13T15:29:39.125031Z", "shell.execute_reply": "2021-06-13T15:29:39.124465Z", "shell.execute_reply.started": "2021-06-13T14:49:50.236639Z" }, "papermill": { "duration": 85.628823, "end_time": "2021-06-13T15:29:39.125201", "exception": false, "start_time": "2021-06-13T15:28:13.496378", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "RandomForestClassifier()" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_tfidf1_RFC = RandomForestClassifier()\n", "model_tfidf1_RFC.fit(X_train_tfidf1,y_train)\n", "pred_tfidf1_RFC = model_tfidf1_RFC.predict(X_valid_tfidf1)" ] }, { "cell_type": "markdown", "id": "hazardous-alloy", "metadata": { "papermill": { "duration": 0.237065, "end_time": "2021-06-13T15:29:39.600315", "exception": false, "start_time": "2021-06-13T15:29:39.363250", "status": "completed" }, "tags": [] }, "source": [ "Printing classification report and ploting confusion matrix for predictions of RandomForestClassifier model" ] }, { "cell_type": "code", "execution_count": 40, "id": "advanced-wilderness", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:29:40.082751Z", "iopub.status.busy": "2021-06-13T15:29:40.082070Z", "iopub.status.idle": "2021-06-13T15:29:40.185284Z", "shell.execute_reply": "2021-06-13T15:29:40.184643Z", "shell.execute_reply.started": "2021-06-13T14:51:16.633065Z" }, "papermill": { "duration": 0.344789, "end_time": "2021-06-13T15:29:40.185453", "exception": false, "start_time": "2021-06-13T15:29:39.840664", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Classifictaion Report\n", " precision recall f1-score support\n", "\n", " 0 0.79 0.87 0.83 869\n", " 1 0.80 0.70 0.75 654\n", "\n", " accuracy 0.80 1523\n", " macro avg 0.80 0.79 0.79 1523\n", "weighted avg 0.80 0.80 0.80 1523\n", "\n", "Accuracy : 0.7977675640183848\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "acc_tfidf1_RFC = c_report(y_valid,pred_tfidf1_RFC)\n", "plot_cm(y_valid,pred_tfidf1_RFC)" ] }, { "cell_type": "markdown", "id": "forced-banana", "metadata": { "papermill": { "duration": 0.238242, "end_time": "2021-06-13T15:29:40.663959", "exception": false, "start_time": "2021-06-13T15:29:40.425717", "status": "completed" }, "tags": [] }, "source": [ "## 6.4 eXtreme Gradient Boosting Classifier" ] }, { "cell_type": "markdown", "id": "incorporate-timothy", "metadata": { "papermill": { "duration": 0.283763, "end_time": "2021-06-13T15:29:41.185856", "exception": false, "start_time": "2021-06-13T15:29:40.902093", "status": "completed" }, "tags": [] }, "source": [ "### About XGBClassifier\n", "The XGBoost stands for eXtreme Gradient Boosting, which is a boosting algorithm based on gradient boosted decision trees algorithm. \n", "XGBoost applies a better regularization technique to reduce overfitting, and it is one of the differences from the gradient boosting. \n", "\n", "Now let's create a [XGBClassifier](https://machinelearningmastery.com/develop-first-xgboost-model-python-scikit-learn/) model and train it." ] }, { "cell_type": "code", "execution_count": 41, "id": "defined-drilling", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:29:41.666964Z", "iopub.status.busy": "2021-06-13T15:29:41.665957Z", "iopub.status.idle": "2021-06-13T15:31:20.914664Z", "shell.execute_reply": "2021-06-13T15:31:20.915242Z", "shell.execute_reply.started": "2021-06-13T14:51:16.759244Z" }, "papermill": { "duration": 99.491415, "end_time": "2021-06-13T15:31:20.915423", "exception": false, "start_time": "2021-06-13T15:29:41.424008", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n", " colsample_bynode=1, colsample_bytree=1, eval_metric='mlogloss',\n", " gamma=0, gpu_id=-1, importance_type='gain',\n", " interaction_constraints='', learning_rate=0.300000012,\n", " max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,\n", " monotone_constraints='()', n_estimators=100, n_jobs=4,\n", " num_parallel_tree=1, random_state=0, reg_alpha=0, reg_lambda=1,\n", " scale_pos_weight=1, subsample=1, tree_method='exact',\n", " validate_parameters=1, verbosity=None)" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_tfidf1_XGB = XGBClassifier(eval_metric='mlogloss')\n", "model_tfidf1_XGB.fit(X_train_tfidf1,y_train)\n", "pred_tfidf1_XGB = model_tfidf1_XGB.predict(X_valid_tfidf1)" ] }, { "cell_type": "markdown", "id": "hawaiian-opera", "metadata": { "papermill": { "duration": 0.237615, "end_time": "2021-06-13T15:31:21.390653", "exception": false, "start_time": "2021-06-13T15:31:21.153038", "status": "completed" }, "tags": [] }, "source": [ "Printing classification report and ploting confusion matrix for the predictions made by the XGBClassifier model" ] }, { "cell_type": "code", "execution_count": 42, "id": "unlimited-selling", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:31:21.876940Z", "iopub.status.busy": "2021-06-13T15:31:21.876290Z", "iopub.status.idle": "2021-06-13T15:31:21.970037Z", "shell.execute_reply": "2021-06-13T15:31:21.969205Z", "shell.execute_reply.started": "2021-06-13T14:52:50.889659Z" }, "papermill": { "duration": 0.34054, "end_time": "2021-06-13T15:31:21.970276", "exception": false, "start_time": "2021-06-13T15:31:21.629736", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Classifictaion Report\n", " precision recall f1-score support\n", "\n", " 0 0.78 0.90 0.84 869\n", " 1 0.83 0.67 0.74 654\n", "\n", " accuracy 0.80 1523\n", " macro avg 0.81 0.78 0.79 1523\n", "weighted avg 0.80 0.80 0.79 1523\n", "\n", "Accuracy : 0.799080761654629\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "acc_tfidf1_XGB = c_report(y_valid,pred_tfidf1_XGB)\n", "plot_cm(y_valid,pred_tfidf1_XGB)" ] }, { "cell_type": "markdown", "id": "catholic-portfolio", "metadata": { "papermill": { "duration": 0.240372, "end_time": "2021-06-13T15:31:22.477933", "exception": false, "start_time": "2021-06-13T15:31:22.237561", "status": "completed" }, "tags": [] }, "source": [ "## 6.5 CatBoostClassifier" ] }, { "cell_type": "markdown", "id": "automated-spanking", "metadata": { "papermill": { "duration": 0.24115, "end_time": "2021-06-13T15:31:22.960130", "exception": false, "start_time": "2021-06-13T15:31:22.718980", "status": "completed" }, "tags": [] }, "source": [ "### About CatBoostClassifier\n", "\n", "CatBoost is a recently open-sourced machine learning algorithm from Yandex. It can easily integrate with deep learning frameworks like Google’s TensorFlow and Apple’s Core ML. It can work with diverse data types to help solve a wide range of problems that businesses face today. To top it up, it provides best-in-class accuracy. \n", "\n", "It yields state-of-the-art results without extensive data training typically required by other machine learning methods. \n", " \n", "“Boost” comes from gradient boosting machine learning algorithm as this library is based on gradient boosting library. \n", "Gradient boosting is a powerful machine learning algorithm that is widely applied to multiple types of business challenges like fraud detection, recommendation items, forecasting and it performs well also. It can also return very good result with relatively less data, unlike DL models that need to learn from a massive amount of data.\n", "\n", "\n", "Now let's create a [CatBoostClassifier](https://catboost.ai/docs/concepts/python-reference_catboostclassifier.html) model with 100 iterations and training it" ] }, { "cell_type": "code", "execution_count": 43, "id": "innocent-filename", "metadata": { "_kg_hide-output": true, "execution": { "iopub.execute_input": "2021-06-13T15:31:23.477094Z", "iopub.status.busy": "2021-06-13T15:31:23.467016Z", "iopub.status.idle": "2021-06-13T15:31:33.343888Z", "shell.execute_reply": "2021-06-13T15:31:33.343326Z", "shell.execute_reply.started": "2021-06-13T14:52:51.019156Z" }, "papermill": { "duration": 10.143559, "end_time": "2021-06-13T15:31:33.344042", "exception": false, "start_time": "2021-06-13T15:31:23.200483", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Learning rate set to 0.184063\n", 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"output_type": "execute_result" } ], "source": [ "model_tfidf1_CBC = CatBoostClassifier(iterations=100)\n", "model_tfidf1_CBC.fit(X_train_tfidf1,y_train)\n", "pred_tfidf1_CBC = model_tfidf1_CBC.predict(X_valid_tfidf1)\n" ] }, { "cell_type": "markdown", "id": "supreme-jungle", "metadata": { "papermill": { "duration": 0.248945, "end_time": "2021-06-13T15:31:33.843724", "exception": false, "start_time": "2021-06-13T15:31:33.594779", "status": "completed" }, "tags": [] }, "source": [ "Printing classification report and ploting confusion matrix for the predictions made by the above model" ] }, { "cell_type": "code", "execution_count": 44, "id": "approved-beaver", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:31:34.394745Z", "iopub.status.busy": "2021-06-13T15:31:34.393710Z", "iopub.status.idle": "2021-06-13T15:31:34.534226Z", "shell.execute_reply": "2021-06-13T15:31:34.534851Z", "shell.execute_reply.started": "2021-06-13T15:04:50.627319Z" }, "papermill": { "duration": 0.408179, "end_time": "2021-06-13T15:31:34.535026", "exception": false, "start_time": "2021-06-13T15:31:34.126847", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Classifictaion Report\n", " precision recall f1-score support\n", "\n", " 0 0.78 0.89 0.83 869\n", " 1 0.82 0.66 0.73 654\n", "\n", " accuracy 0.79 1523\n", " macro avg 0.80 0.78 0.78 1523\n", "weighted avg 0.80 0.79 0.79 1523\n", "\n", "Accuracy : 0.793827971109652\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "acc_tfidf1_CBC = c_report(y_valid,pred_tfidf1_CBC)\n", "plot_cm(y_valid,pred_tfidf1_CBC)" ] }, { "cell_type": "markdown", "id": "eastern-millennium", "metadata": { "papermill": { "duration": 0.247809, "end_time": "2021-06-13T15:31:35.041010", "exception": false, "start_time": "2021-06-13T15:31:34.793201", "status": "completed" }, "tags": [] }, "source": [ "## 6.6 Support Vector CLassifier " ] }, { "cell_type": "markdown", "id": "clear-hormone", "metadata": { "papermill": { "duration": 0.248069, "end_time": "2021-06-13T15:31:35.539255", "exception": false, "start_time": "2021-06-13T15:31:35.291186", "status": "completed" }, "tags": [] }, "source": [ "### About SVC\n", "SVM offers very high accuracy compared to other classifiers such as logistic regression, and decision trees. It is known for its kernel trick to handle nonlinear input spaces. It is used in a variety of applications such as face detection, intrusion detection, classification of emails, news articles and web pages, classification of genes, and handwriting recognition.\n", "\n", "SVM is an exciting algorithm and the concepts are relatively simple. The classifier separates data points using a hyperplane with the largest amount of margin. That's why an SVM classifier is also known as a discriminative classifier. SVM finds an optimal hyperplane which helps in classifying new data points. \n", "\n", "Now let's create a [SVC](https://pythonprogramming.net/linear-svc-example-scikit-learn-svm-python/) model and training it" ] }, { "cell_type": "code", "execution_count": 45, "id": "active-creator", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:31:36.047928Z", "iopub.status.busy": "2021-06-13T15:31:36.042766Z", "iopub.status.idle": "2021-06-13T15:35:00.431587Z", "shell.execute_reply": "2021-06-13T15:35:00.432394Z", "shell.execute_reply.started": "2021-06-13T14:53:00.507994Z" }, "papermill": { "duration": 204.642933, "end_time": "2021-06-13T15:35:00.432667", "exception": false, "start_time": "2021-06-13T15:31:35.789734", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "SVC(gamma='auto', kernel='linear')" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_tfidf1_SVC = SVC(kernel='linear', degree=3, gamma='auto')\n", "model_tfidf1_SVC.fit(X_train_tfidf1,y_train)\n", "pred_tfidf1_SVC = model_tfidf1_SVC.predict(X_valid_tfidf1)" ] }, { "cell_type": "markdown", "id": "persistent-bicycle", "metadata": { "papermill": { "duration": 0.249041, "end_time": "2021-06-13T15:35:00.984465", "exception": false, "start_time": "2021-06-13T15:35:00.735424", "status": "completed" }, "tags": [] }, "source": [ "Printing classification report and ploting confusion matrix for the SVC model" ] }, { "cell_type": "code", "execution_count": 46, "id": "governing-saying", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:35:01.532033Z", "iopub.status.busy": "2021-06-13T15:35:01.531405Z", "iopub.status.idle": "2021-06-13T15:35:01.667272Z", "shell.execute_reply": "2021-06-13T15:35:01.668222Z", "shell.execute_reply.started": "2021-06-13T14:56:18.027351Z" }, "papermill": { "duration": 0.388307, "end_time": "2021-06-13T15:35:01.668492", "exception": false, "start_time": "2021-06-13T15:35:01.280185", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Classifictaion Report\n", " precision recall f1-score support\n", "\n", " 0 0.82 0.88 0.85 869\n", " 1 0.83 0.74 0.78 654\n", "\n", " accuracy 0.82 1523\n", " macro avg 0.82 0.81 0.81 1523\n", "weighted avg 0.82 0.82 0.82 1523\n", "\n", "Accuracy : 0.8214051214707814\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "acc_tfidf1_SVC = c_report(y_valid,pred_tfidf1_SVC)\n", "plot_cm(y_valid,pred_tfidf1_SVC)" ] }, { "cell_type": "markdown", "id": "vulnerable-technology", "metadata": { "papermill": { "duration": 0.252538, "end_time": "2021-06-13T15:35:02.179289", "exception": false, "start_time": "2021-06-13T15:35:01.926751", "status": "completed" }, "tags": [] }, "source": [ "## 6.7 Voting Classifier" ] }, { "cell_type": "markdown", "id": "polished-services", "metadata": { "papermill": { "duration": 0.250215, "end_time": "2021-06-13T15:35:02.678059", "exception": false, "start_time": "2021-06-13T15:35:02.427844", "status": "completed" }, "tags": [] }, "source": [ "### About Voting Classifier\n", "\n", "A Voting Classifier is a machine learning model that trains on an ensemble of numerous models and predicts an output (class) based on \n", "their highest probability of chosen class as the output. It simply aggregates the findings of each classifier passed into Voting Classifier \n", "and predicts the output class based on the highest majority of voting. The idea is instead of creating separate dedicated models and finding the accuracy for each them, we create a single model which trains by these models and predicts output based on their combined majority of voting for each output class.\n", "\n", "#### Voting Classifier supports two types of votings : \n", "\n", "\n", "* **Hard Voting** : In hard voting, the predicted output class is a class with the highest majority of votes i.e the class which had the highest probability of being predicted by each of the classifiers. Suppose three classifiers predicted the output class(A, A, B), so here the majority predicted A as output. Hence A will be the final prediction.\n", "\n", "\n", "* **Soft Voting** : In soft voting, the output class is the prediction based on the average of probability given to that class. Suppose given some input to three models, the prediction probability for class A = (0.30, 0.47, 0.53) and B = (0.20, 0.32, 0.40). So the average for class A is 0.4333 and B is 0.3067, the winner is clearly class A because it had the highest probability averaged by each classifier\n", " \n", " \n", "Now let's create a [VotingClassifier](https://www.geeksforgeeks.org/ml-voting-classifier-using-sklearn/) with soft voting and train it " ] }, { "cell_type": "code", "execution_count": 47, "id": "particular-agriculture", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:35:03.185182Z", "iopub.status.busy": "2021-06-13T15:35:03.184180Z", "iopub.status.idle": "2021-06-13T15:36:47.390483Z", "shell.execute_reply": "2021-06-13T15:36:47.391064Z", "shell.execute_reply.started": "2021-06-13T15:01:30.048290Z" }, "papermill": { "duration": 104.463086, "end_time": "2021-06-13T15:36:47.391421", "exception": false, "start_time": "2021-06-13T15:35:02.928335", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "VotingClassifier(estimators=[('LR', LogisticRegression()),\n", " ('NB', MultinomialNB(alpha=0.7)),\n", " ('XBG',\n", " XGBClassifier(base_score=None, booster=None,\n", " colsample_bylevel=None,\n", " colsample_bynode=None,\n", " colsample_bytree=None,\n", " eval_metric='mlogloss', gamma=None,\n", " gpu_id=None, importance_type='gain',\n", " interaction_constraints=None,\n", " learning_rate=None,\n", " max_delta_step=None, max_depth=None,\n", " min_child_weight=None, missing=nan,\n", " monotone_constraints=None,\n", " n_estimators=100, n_jobs=None,\n", " num_parallel_tree=None,\n", " random_state=None, reg_alpha=None,\n", " reg_lambda=None,\n", " scale_pos_weight=None,\n", " subsample=None, tree_method=None,\n", " validate_parameters=None,\n", " verbosity=None))],\n", " voting='soft')" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "estimators = []\n", "estimators.append(('LR', \n", " LogisticRegression()))\n", "estimators.append(('NB', MultinomialNB(alpha=0.7)))\n", "estimators.append(('XBG', XGBClassifier(eval_metric='mlogloss')))\n", "\n", "model_tfidf1_VC = VotingClassifier(estimators=estimators,voting='soft')\n", "model_tfidf1_VC.fit(X_train_tfidf1,y_train)\n", "pred_tfidf1_VC = model_tfidf1_VC.predict(X_valid_tfidf1)" ] }, { "cell_type": "markdown", "id": "regional-granny", "metadata": { "papermill": { "duration": 0.25035, "end_time": "2021-06-13T15:36:47.891056", "exception": false, "start_time": "2021-06-13T15:36:47.640706", "status": "completed" }, "tags": [] }, "source": [ "Printing classification report and ploting confusion matrix for VotingClasssifier" ] }, { "cell_type": "code", "execution_count": 48, "id": "little-fancy", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:36:48.398937Z", "iopub.status.busy": "2021-06-13T15:36:48.397914Z", "iopub.status.idle": "2021-06-13T15:36:48.558044Z", "shell.execute_reply": "2021-06-13T15:36:48.558672Z", "shell.execute_reply.started": "2021-06-13T15:03:13.706183Z" }, "papermill": { "duration": 0.416539, "end_time": "2021-06-13T15:36:48.558838", "exception": false, "start_time": "2021-06-13T15:36:48.142299", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Classifictaion Report\n", " precision recall f1-score support\n", "\n", " 0 0.81 0.92 0.86 869\n", " 1 0.87 0.72 0.78 654\n", "\n", " accuracy 0.83 1523\n", " macro avg 0.84 0.82 0.82 1523\n", "weighted avg 0.84 0.83 0.83 1523\n", "\n", "Accuracy : 0.8312541037426132\n" ] }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "acc_tfidf1_VC = c_report(y_valid,pred_tfidf1_VC)\n", "plot_cm(y_valid,pred_tfidf1_VC,cmap = \"Greens\")" ] }, { "cell_type": "markdown", "id": "educational-desert", "metadata": { "papermill": { "duration": 0.250601, "end_time": "2021-06-13T15:36:49.059955", "exception": false, "start_time": "2021-06-13T15:36:48.809354", "status": "completed" }, "tags": [] }, "source": [ "# 7. Comparing the Accuracy of all models" ] }, { "cell_type": "code", "execution_count": 49, "id": "piano-strike", "metadata": { "execution": { "iopub.execute_input": "2021-06-13T15:36:49.574930Z", "iopub.status.busy": "2021-06-13T15:36:49.573959Z", "iopub.status.idle": "2021-06-13T15:36:49.620799Z", "shell.execute_reply": "2021-06-13T15:36:49.621290Z", "shell.execute_reply.started": "2021-06-13T15:05:09.493304Z" }, "papermill": { "duration": 0.307919, "end_time": "2021-06-13T15:36:49.621462", "exception": false, "start_time": "2021-06-13T15:36:49.313543", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Models Accuracy Score
9Voting Tf-Idf10.831254
8SVC Tf-Idf10.821405
3Logistic Regression Tf-Idf10.820749
0Logistic Regression BoW10.816152
4Naive Bayes Tf-Idf10.813526
6XGBClassifier Tf-Idf10.799081
5Random Forest Tf-Idf10.797768
7CatBoost Tf-Idf10.793828
1Logistic Regression BoW20.740643
2Logistic Regression BoW30.700591
" ], "text/plain": [ "" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results = pd.DataFrame([[\"Logistic Regression BoW1\",acc_bow1_logreg],[\"Logistic Regression BoW2\",acc_bow2_logreg],\n", " [\"Logistic Regression BoW3\",acc_bow3_logreg],[\"Logistic Regression Tf-Idf1\",acc_tfidf1_logreg],\n", " [\"Naive Bayes Tf-Idf1\",acc_tfidf1_NB],[\"Random Forest Tf-Idf1\",acc_tfidf1_RFC],\n", " [\"XGBClassifier Tf-Idf1\",acc_tfidf1_XGB],[\"CatBoost Tf-Idf1\",acc_tfidf1_CBC],\n", " [\"SVC Tf-Idf1\",acc_tfidf1_SVC],[\"Voting Tf-Idf1\",acc_tfidf1_VC]],\n", " columns = [\"Models\",\"Accuracy Score\"]).sort_values(by='Accuracy Score',ascending=False)\n", "\n", "results.style.background_gradient(cmap='Blues')" ] }, { "cell_type": "markdown", "id": "embedded-culture", "metadata": { "papermill": { "duration": 0.252909, "end_time": "2021-06-13T15:36:50.128535", "exception": false, "start_time": "2021-06-13T15:36:49.875626", "status": "completed" }, "tags": [] }, "source": [ "# 8. Conclusion" ] }, { "cell_type": "markdown", "id": "stuck-investment", "metadata": { "papermill": { "duration": 0.25317, "end_time": "2021-06-13T15:36:50.635786", "exception": false, "start_time": "2021-06-13T15:36:50.382616", "status": "completed" }, "tags": [] }, "source": [ "Among all Simple classification models used above Voting Classifier performed best with tf-idf and ngrams = 1" ] } ], "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", "version": "3.8.8" }, "papermill": { "default_parameters": {}, "duration": 801.61147, "end_time": "2021-06-13T15:39:28.701944", "environment_variables": {}, "exception": null, "input_path": "__notebook__.ipynb", "output_path": "__notebook__.ipynb", "parameters": {}, "start_time": "2021-06-13T15:26:07.090474", "version": "2.3.3" } }, "nbformat": 4, "nbformat_minor": 5 }