nlp-project.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":7731524,"sourceType":"datasetVersion","datasetId":4517876}],"dockerImageVersionId":30664,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\nimport matplotlib.pyplot as plt\n\nfrom nltk.corpus import stopwords\nfrom nltk.tokenize import RegexpTokenizer\nfrom nltk.stem.porter import PorterStemmer\n\nimport string\nimport time\n\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.model_selection import train_test_split, GridSearchCV\nfrom sklearn.metrics import accuracy_score, confusion_matrix, f1_score, recall_score\n\nfrom sklearn.naive_bayes import MultinomialNB, BernoulliNB, GaussianNB\n\nimport os\n#for dirname, _, filenames in os.walk('/kaggle/input'):\n    #for filename in filenames:\n        #print(os.path.join(dirname, filename))\n\nimport warnings\nwarnings.filterwarnings('ignore')","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-03-01T02:39:50.130554Z","iopub.execute_input":"2024-03-01T02:39:50.131656Z","iopub.status.idle":"2024-03-01T02:39:53.200161Z","shell.execute_reply.started":"2024-03-01T02:39:50.131617Z","shell.execute_reply":"2024-03-01T02:39:53.198960Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"markdown","source":"**Importing Data set**","metadata":{}},{"cell_type":"code","source":"data = pd.read_csv('/kaggle/input/imdb-dataset/IMDB Dataset.csv')\ndata.shape","metadata":{"execution":{"iopub.status.busy":"2024-03-01T02:24:52.375165Z","iopub.execute_input":"2024-03-01T02:24:52.375658Z","iopub.status.idle":"2024-03-01T02:24:53.784796Z","shell.execute_reply.started":"2024-03-01T02:24:52.375619Z","shell.execute_reply":"2024-03-01T02:24:53.783663Z"},"trusted":true},"execution_count":3,"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"(50000, 2)"},"metadata":{}}]},{"cell_type":"markdown","source":"Due to **hardware limitations**, only **10,000** of **50,000** instances can be used","metadata":{}},{"cell_type":"code","source":"data= data.iloc[0 : 10000 , : ]\ndata.shape","metadata":{"execution":{"iopub.status.busy":"2024-03-01T02:24:56.420599Z","iopub.execute_input":"2024-03-01T02:24:56.420980Z","iopub.status.idle":"2024-03-01T02:24:56.428703Z","shell.execute_reply.started":"2024-03-01T02:24:56.420949Z","shell.execute_reply":"2024-03-01T02:24:56.427359Z"},"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"(20000, 2)"},"metadata":{}}]},{"cell_type":"code","source":"data.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2024-03-01T02:24:59.444258Z","iopub.execute_input":"2024-03-01T02:24:59.444674Z","iopub.status.idle":"2024-03-01T02:24:59.460845Z","shell.execute_reply.started":"2024-03-01T02:24:59.444642Z","shell.execute_reply":"2024-03-01T02:24:59.459550Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"review       0\nsentiment    0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"data['sentiment'].unique()","metadata":{"execution":{"iopub.status.busy":"2024-03-01T02:25:01.908153Z","iopub.execute_input":"2024-03-01T02:25:01.908583Z","iopub.status.idle":"2024-03-01T02:25:01.922270Z","shell.execute_reply.started":"2024-03-01T02:25:01.908549Z","shell.execute_reply":"2024-03-01T02:25:01.921115Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"array(['positive', 'negative'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"data.info()","metadata":{"execution":{"iopub.status.busy":"2024-03-01T02:25:04.527416Z","iopub.execute_input":"2024-03-01T02:25:04.527801Z","iopub.status.idle":"2024-03-01T02:25:04.554148Z","shell.execute_reply.started":"2024-03-01T02:25:04.527770Z","shell.execute_reply":"2024-03-01T02:25:04.553079Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 20000 entries, 0 to 19999\nData columns (total 2 columns):\n #   Column     Non-Null Count  Dtype \n---  ------     --------------  ----- \n 0   review     20000 non-null  object\n 1   sentiment  20000 non-null  object\ndtypes: object(2)\nmemory usage: 312.6+ KB\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# **Preprocessing of data**","metadata":{}},{"cell_type":"code","source":"def text_processed(text):\n    tokenizer = RegexpTokenizer(r'\\w+|$[0-9]+|\\s+')\n    text_processed=tokenizer.tokenize(str(text))\n    \n    punc_rmv = [char for char in text_processed if char not in string.punctuation] #clean up punctuations\n    punc_rmv = \"\".join(punc_rmv)\n    \n    stopword_rmv = [w.strip().lower() for w in punc_rmv.split() if w.strip().lower() not in stopwords.words('english')] #clean up stop words\n    \n    porter_stemmer = PorterStemmer() #Stemming\n    x = [porter_stemmer.stem(word) for word in stopword_rmv]\n    \n    try:\n        x = x.remove('b')\n    except: \n        pass\n    \n    return \" \".join(stopword_rmv)","metadata":{"execution":{"iopub.status.busy":"2024-03-01T02:25:08.328906Z","iopub.execute_input":"2024-03-01T02:25:08.329272Z","iopub.status.idle":"2024-03-01T02:25:08.338248Z","shell.execute_reply.started":"2024-03-01T02:25:08.329244Z","shell.execute_reply":"2024-03-01T02:25:08.336736Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"data['review'].head()","metadata":{"execution":{"iopub.status.busy":"2024-03-01T02:25:11.350023Z","iopub.execute_input":"2024-03-01T02:25:11.350696Z","iopub.status.idle":"2024-03-01T02:25:11.359620Z","shell.execute_reply.started":"2024-03-01T02:25:11.350646Z","shell.execute_reply":"2024-03-01T02:25:11.358427Z"},"trusted":true},"execution_count":9,"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"0    One of the other reviewers has mentioned that ...\n1    A wonderful little production. <br /><br />The...\n2    I thought this was a wonderful way to spend ti...\n3    Basically there's a family where a little boy ...\n4    Petter Mattei's \"Love in the Time of Money\" is...\nName: review, dtype: object"},"metadata":{}}]},{"cell_type":"code","source":"data['review'].apply(text_processed)","metadata":{"execution":{"iopub.status.busy":"2024-03-01T02:25:14.487847Z","iopub.execute_input":"2024-03-01T02:25:14.488244Z","iopub.status.idle":"2024-03-01T02:36:03.946217Z","shell.execute_reply.started":"2024-03-01T02:25:14.488210Z","shell.execute_reply":"2024-03-01T02:36:03.945060Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"0        one reviewers mentioned watching 1 oz episode ...\n1        wonderful little production br br filming tech...\n2        thought wonderful way spend time hot summer we...\n3        basically theres family little boy jake thinks...\n4        petter matteis love time money visually stunni...\n                               ...                        \n19995    ok starters taxi driver amazing taxi driver am...\n19996    sort hard say greatly enjoyed targets paper mo...\n19997    still liked though warren beatty fair comic bo...\n19998    could still use black adder even today imagine...\n19999    socalled documentary tries tell usa faked moon...\nName: review, Length: 20000, dtype: object"},"metadata":{}}]},{"cell_type":"code","source":"encoder = LabelEncoder()\ndata['sentiment'] = encoder.fit_transform(data['sentiment'])","metadata":{"execution":{"iopub.status.busy":"2024-03-01T02:36:26.092696Z","iopub.execute_input":"2024-03-01T02:36:26.093090Z","iopub.status.idle":"2024-03-01T02:36:26.104210Z","shell.execute_reply.started":"2024-03-01T02:36:26.093060Z","shell.execute_reply":"2024-03-01T02:36:26.103285Z"},"trusted":true},"execution_count":11,"outputs":[]},{"cell_type":"code","source":"cv = CountVectorizer()\nx = cv.fit_transform(data['review']).toarray()","metadata":{"execution":{"iopub.status.busy":"2024-03-01T02:36:28.810525Z","iopub.execute_input":"2024-03-01T02:36:28.811676Z","iopub.status.idle":"2024-03-01T02:36:36.931836Z","shell.execute_reply.started":"2024-03-01T02:36:28.811632Z","shell.execute_reply":"2024-03-01T02:36:36.930769Z"},"trusted":true},"execution_count":12,"outputs":[]},{"cell_type":"code","source":"#x = data['review']\ny = data['sentiment']","metadata":{"execution":{"iopub.status.busy":"2024-03-01T02:38:12.408572Z","iopub.execute_input":"2024-03-01T02:38:12.408946Z","iopub.status.idle":"2024-03-01T02:38:12.414245Z","shell.execute_reply.started":"2024-03-01T02:38:12.408919Z","shell.execute_reply":"2024-03-01T02:38:12.412950Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"code","source":"y.plot(kind= 'hist')","metadata":{"execution":{"iopub.status.busy":"2024-03-01T02:38:14.629206Z","iopub.execute_input":"2024-03-01T02:38:14.630443Z","iopub.status.idle":"2024-03-01T02:38:14.924938Z","shell.execute_reply.started":"2024-03-01T02:38:14.630400Z","shell.execute_reply":"2024-03-01T02:38:14.923828Z"},"trusted":true},"execution_count":14,"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"<Axes: ylabel='Frequency'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}]},{"cell_type":"markdown","source":"so according to histogram fig. Data set is balanced","metadata":{}},{"cell_type":"markdown","source":"**Train Test Split**","metadata":{}},{"cell_type":"code","source":"x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, random_state= 42)\nprint(f'x_train.shape is:{x_train.shape}')\nprint(f'x_test.shape is:{x_test.shape}')\nprint(f'y_train.shape is:{y_train.shape}')\nprint(f'y_test.shape is:{y_test.shape}')","metadata":{"execution":{"iopub.status.busy":"2024-03-01T02:38:24.894009Z","iopub.execute_input":"2024-03-01T02:38:24.894428Z","iopub.status.idle":"2024-03-01T02:38:31.761369Z","shell.execute_reply.started":"2024-03-01T02:38:24.894396Z","shell.execute_reply":"2024-03-01T02:38:31.760089Z"},"trusted":true},"execution_count":15,"outputs":[{"name":"stdout","text":"x_train.shape is:(16000, 69932)\nx_test.shape is:(4000, 69932)\ny_train.shape is:(16000,)\ny_test.shape is:(4000,)\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Model ","metadata":{}},{"cell_type":"markdown","source":"Determining optimum parameters and model evaluation","metadata":{}},{"cell_type":"code","source":"MLA_columns = ['model_name', 'train_accuracy', 'test_accuracy', 'f1_score', 'recall_score', 'Elapsed_time']\nMLA_compare = pd.DataFrame(columns = MLA_columns)","metadata":{"execution":{"iopub.status.busy":"2024-03-01T02:38:39.140880Z","iopub.execute_input":"2024-03-01T02:38:39.141256Z","iopub.status.idle":"2024-03-01T02:38:39.148217Z","shell.execute_reply.started":"2024-03-01T02:38:39.141229Z","shell.execute_reply":"2024-03-01T02:38:39.146828Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"code","source":"# Gaussian Naive Base\n\nmodel_guassian = GaussianNB()\nfeatures = {'var_smoothing': [ 0.000000009, 0.00000009, 0.0000009, 0.000009, 0.00009 ]}\n\nsearch = GridSearchCV(model_guassian, features, error_score='raise')\nsearch.fit(x_train, y_train)\n\nprint(f'Best Score is: {search.best_score_}')\nprint(f'Best parameters are: {search.best_params_}')","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model_gaussian= GaussianNB(var_smoothing = 0.000009)\nmodel_gaussian.fit(x_train, y_train)\nstart = time.time()\ny_pred = model_gaussian.predict(x_test)\nend = time.time()\nacc_train_gaussian= model_gaussian.score(x_train, y_train)\nacc_gaussian = accuracy_score(y_test, y_pred)\nf1_gaussian = f1_score(y_test, y_pred)\nrecall_gaussian = recall_score(y_test, y_pred)\nconfusion_gaussian = confusion_matrix(y_test, y_pred)\nelapsed_time_gaussian =  end - start\nprint(f'Accuracy of Gaussian Nave Bayse is : {acc_gaussian}')\nprint(f'f1_score of Gaussian is : {f1_gaussian}')\nprint(f'recall_score of Gaussian is : {recall_gaussian}')\nprint(f'Elapsed time for Gaussian is : {elapsed_time_gaussian}')\nprint('__________________________________________________________\\n')\nprint(confusion_gaussian)\n\nMLA_compare.loc[0, 'model_name'] = 'Gaussian Naive Bayse'\nMLA_compare.loc[0, 'train_accuracy'] = acc_train_gaussian\nMLA_compare.loc[0, 'test_accuracy'] = acc_gaussian\nMLA_compare.loc[0, 'f1_score'] = f1_gaussian\nMLA_compare.loc[0, 'recall_score'] = recall_gaussian\nMLA_compare.loc[0, 'Elapsed_time'] = elapsed_time_gaussian","metadata":{"execution":{"iopub.status.busy":"2024-03-01T02:39:59.489477Z","iopub.execute_input":"2024-03-01T02:39:59.490039Z","iopub.status.idle":"2024-03-01T02:39:59.614627Z","shell.execute_reply.started":"2024-03-01T02:39:59.490007Z","shell.execute_reply":"2024-03-01T02:39:59.613125Z"},"trusted":true},"execution_count":3,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","Cell \u001b[0;32mIn[3], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m model_gaussian\u001b[38;5;241m=\u001b[39m GaussianNB(var_smoothing \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.000009\u001b[39m)\n\u001b[0;32m----> 2\u001b[0m model_gaussian\u001b[38;5;241m.\u001b[39mfit(\u001b[43mx_train\u001b[49m, y_train)\n\u001b[1;32m      3\u001b[0m start \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m      4\u001b[0m y_pred \u001b[38;5;241m=\u001b[39m model_gaussian\u001b[38;5;241m.\u001b[39mpredict(x_test)\n","\u001b[0;31mNameError\u001b[0m: name 'x_train' is not defined"],"ename":"NameError","evalue":"name 'x_train' is not defined","output_type":"error"}]},{"cell_type":"code","source":"# Multi Nomial\nmodel_multi = MultinomialNB()\nfeatures = { 'alpha' : [0.01, 0.1, 1, 1.5, 2], 'force_alpha' : [0, 1], 'fit_prior' : [0, 1]}\nsearch = GridSearchCV(model_multi, features, error_score = 'raise')\nsearch.fit(x_train, y_train)\nprint(f'Best Score is: {search.best_score_}')\nprint(f'Best parameters are: {search.best_params_}')","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model_multinomial = MultinomialNB(alpha = 1.5 , force_alpha = 0 , fit_prior = 1 )\nmodel = model_multinomial.fit(x_train, y_train)\nstart = time.time()\ny_pred = model.predict(x_test)\nend = time.time()\nacc_train_multinomial= model.score(x_train, y_train)\nacc_multinomial = accuracy_score(y_test, y_pred)\nf1_multinomial = f1_score(y_test, y_pred)\nrecall_multinomial = recall_score(y_test, y_pred)\nconfusion_multinomial = confusion_matrix(y_test, y_pred)\nelapsed_time_multinomial =  end - start\nprint(f'Accuracy of Multinomial is : {acc_multinomial}')\nprint(f'f1_score of Bernoulli is : {f1_multinomial}')\nprint(f'recall_score of Bernoulli is : {recall_multinomial}')\nprint(f'Elapsed time for Gaussian is : {elapsed_time_multinomial}')\nprint('__________________________________________________________\\n')\nprint(confusion_multinomial)\n\nMLA_compare.loc[1, 'model_name'] = 'Multinomial Naive Bayse'\nMLA_compare.loc[1, 'train_accuracy'] = acc_train_multinomial\nMLA_compare.loc[1, 'test_accuracy'] = acc_multinomial\nMLA_compare.loc[1, 'f1_score'] = f1_multinomial\nMLA_compare.loc[1, 'recall_score'] = recall_multinomial\nMLA_compare.loc[1, 'Elapsed_time'] = elapsed_time_multinomial","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Bernoulli Naive Bayse\nmodel_bernoulli = BernoulliNB()\nfeatures = { 'alpha' : [0.01, 0.1, 1, 1.5, 2], 'force_alpha' : [0, 1], 'fit_prior' : [0, 1]}\nsearch = GridSearchCV(model_bernoulli, features, error_score = 'raise')\nsearch.fit(x_train, y_train)\nprint(f'Best Score is: {search.best_score_}')\nprint(f'Best parameters are: {search.best_params_}')","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#model_bernoulli = BernoulliNB(alpha = 1.5 , force_alpha = 0 , fit_prior = 0)\n#features = { 'binarize' : [0.001, 0.01, 0.1, 0, 1]}\n#search = GridSearchCV(model_bernoulli, features, error_score = 'raise')\n#search.fit(x_train, y_train)\n#print(f'Best Score is: {search.best_score_}')\n#print(f'Best parameters are: {search.best_params_}')","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model_bernoulli = BernoulliNB(alpha = 1.5 , force_alpha = 0 , fit_prior = 0)\nmodel = model_bernoulli.fit(x_train, y_train)\nstart = time.time()\ny_pred = model.predict(x_test)\nend = time.time()\nacc_train_bernoulli= model.score(x_train, y_train)\nacc_bernoulli = accuracy_score(y_test, y_pred)\nf1_bernoulli = f1_score(y_test, y_pred)\nrecall_bernoulli = recall_score(y_test, y_pred)\nconfusion_bernoulli = confusion_matrix(y_test, y_pred)\nelapsed_time_bernoulli =  end - start\nprint(f'Accuracy of Bernoulli is : {acc_bernoulli}')\nprint(f'f1_score of Bernoulli is : {f1_bernoulli}')\nprint(f'recall_score of Bernoulli is : {recall_bernoulli}')\nprint(f'Elapsed time for Gaussian is : {elapsed_time_bernoulli}')\nprint('__________________________________________________________\\n')\nprint(confusion_bernoulli)\n\nMLA_compare.loc[2, 'model_name'] = 'Bernoulli Naive Bayse'\nMLA_compare.loc[2, 'train_accuracy'] = acc_train_bernoulli\nMLA_compare.loc[2, 'test_accuracy'] = acc_bernoulli\nMLA_compare.loc[2, 'f1_score'] = f1_bernoulli\nMLA_compare.loc[2, 'recall_score'] = recall_bernoulli\nMLA_compare.loc[2, 'Elapsed_time'] = elapsed_time_bernoulli","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Final Result is :**","metadata":{}},{"cell_type":"code","source":"MLA_compare","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Due to final resualt, model **\"Bernoulli Naive bayse \"** is the **best**!","metadata":{}},{"cell_type":"markdown","source":"optimum **parameters** for Best model is:\n **alpha = 1.5 , force_alpha = 0 , fit_prior = 0**","metadata":{}},{"cell_type":"code","source":"#Exporting final resault as PDF file\nfrom matplotlib.backends.backend_pdf import PdfPages\nfig, ax =plt.subplots(figsize=(12,4))\nax.axis('tight')\nax.axis('off')\nthe_table = ax.table(cellText=MLA_compare.values,colLabels=MLA_compare.columns,loc='center')\npp = PdfPages(\"final_resualt.pdf\")\npp.savefig(fig, bbox_inches='tight')\npp.close()","metadata":{"trusted":true},"execution_count":null,"outputs":[]}]}