├── README.md └── QuoraInsincere.ipynb /README.md: -------------------------------------------------------------------------------- 1 | # DMW-Quora-Insincere-Questions-Classifications 2 | DMW mini project Quora Insincere Questions Classification 3 | -------------------------------------------------------------------------------- /QuoraInsincere.ipynb: -------------------------------------------------------------------------------- 1 | {"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n# Any results you write to the current directory are saved as output.","execution_count":1,"outputs":[{"output_type":"stream","text":"/kaggle/input/quora-insincere-questions-classification/train.csv\n/kaggle/input/quora-insincere-questions-classification/sample_submission.csv\n/kaggle/input/quora-insincere-questions-classification/test.csv\n/kaggle/input/quora-insincere-questions-classification/embeddings/paragram_300_sl999/paragram_300_sl999.txt\n/kaggle/input/quora-insincere-questions-classification/embeddings/paragram_300_sl999/README.txt\n/kaggle/input/quora-insincere-questions-classification/embeddings/wiki-news-300d-1M/wiki-news-300d-1M.vec\n/kaggle/input/quora-insincere-questions-classification/embeddings/GoogleNews-vectors-negative300/GoogleNews-vectors-negative300.bin\n/kaggle/input/quora-insincere-questions-classification/embeddings/glove.840B.300d/glove.840B.300d.txt\n","name":"stdout"}]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport re\nimport nltk\nnltk.download('stopwords')\nnltk.download('punkt')\nnltk.download('wordnet')","execution_count":2,"outputs":[{"output_type":"stream","text":"[nltk_data] Downloading package stopwords to /usr/share/nltk_data...\n[nltk_data] Package stopwords is already up-to-date!\n[nltk_data] Downloading package punkt to /usr/share/nltk_data...\n[nltk_data] Package punkt is already up-to-date!\n[nltk_data] Downloading package wordnet to /usr/share/nltk_data...\n[nltk_data] Package wordnet is already up-to-date!\n","name":"stdout"},{"output_type":"execute_result","execution_count":2,"data":{"text/plain":"True"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"import seaborn as sns\nimport string\nimport warnings \nwarnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n\n%matplotlib inline","execution_count":3,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import spacy\nfrom collections import Counter","execution_count":4,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import pickle","execution_count":5,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_df = pd.read_csv('/kaggle/input/quora-insincere-questions-classification/train.csv')\ntest_df = pd.read_csv('/kaggle/input/quora-insincere-questions-classification/test.csv')","execution_count":6,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_df.head()","execution_count":7,"outputs":[{"output_type":"execute_result","execution_count":7,"data":{"text/plain":" qid question_text \\\n0 00002165364db923c7e6 How did Quebec nationalists see their province... \n1 000032939017120e6e44 Do you have an adopted dog, how would you enco... \n2 0000412ca6e4628ce2cf Why does velocity affect time? Does velocity a... \n3 000042bf85aa498cd78e How did Otto von Guericke used the Magdeburg h... \n4 0000455dfa3e01eae3af Can I convert montra helicon D to a mountain b... \n\n target \n0 0 \n1 0 \n2 0 \n3 0 \n4 0 ","text/html":"
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qidquestion_texttarget
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3000042bf85aa498cd78eHow did Otto von Guericke used the Magdeburg h...0
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"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_df.head()","execution_count":8,"outputs":[{"output_type":"execute_result","execution_count":8,"data":{"text/plain":" qid question_text\n0 0000163e3ea7c7a74cd7 Why do so many women become so rude and arroga...\n1 00002bd4fb5d505b9161 When should I apply for RV college of engineer...\n2 00007756b4a147d2b0b3 What is it really like to be a nurse practitio...\n3 000086e4b7e1c7146103 Who are entrepreneurs?\n4 0000c4c3fbe8785a3090 Is education really making good people nowadays?","text/html":"
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qidquestion_text
00000163e3ea7c7a74cd7Why do so many women become so rude and arroga...
100002bd4fb5d505b9161When should I apply for RV college of engineer...
200007756b4a147d2b0b3What is it really like to be a nurse practitio...
3000086e4b7e1c7146103Who are entrepreneurs?
40000c4c3fbe8785a3090Is education really making good people nowadays?
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"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_df['target'].value_counts()","execution_count":9,"outputs":[{"output_type":"execute_result","execution_count":9,"data":{"text/plain":"0 1225312\n1 80810\nName: target, dtype: int64"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print('Average word length of questions in train is {0:.0f}.'.format(np.mean(train_df['question_text'].apply(lambda x: len(x.split())))))\nprint('Average word length of questions in test is {0:.0f}.'.format(np.mean(test_df['question_text'].apply(lambda x: len(x.split())))))","execution_count":10,"outputs":[{"output_type":"stream","text":"Average word length of questions in train is 13.\nAverage word length of questions in test is 13.\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print('Max word length of questions in train is {0:.0f}.'.format(np.max(train_df['question_text'].apply(lambda x: len(x.split())))))\nprint('Max word length of questions in test is {0:.0f}.'.format(np.max(test_df['question_text'].apply(lambda x: len(x.split())))))","execution_count":11,"outputs":[{"output_type":"stream","text":"Max word length of questions in train is 134.\nMax word length of questions in test is 87.\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print('Average character length of questions in train is {0:.0f}.'.format(np.mean(train_df['question_text'].apply(lambda x: len(x)))))\nprint('Average character length of questions in test is {0:.0f}.'.format(np.mean(test_df['question_text'].apply(lambda x: len(x)))))","execution_count":12,"outputs":[{"output_type":"stream","text":"Average character length of questions in train is 71.\nAverage character length of questions in test is 71.\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# using seaborns countplot to show distribution of questions in dataset\nfig, ax = plt.subplots()\ng = sns.countplot(train_df.target, palette='viridis')\ng.set_xticklabels(['Sincere', 'Insincere'])\ng.set_yticklabels([])\n\n# function to show values on bars\ndef show_values_on_bars(axs):\n def _show_on_single_plot(ax): \n for p in ax.patches:\n _x = p.get_x() + p.get_width() / 2\n _y = p.get_y() + p.get_height()\n value = '{:.0f}'.format(p.get_height())\n ax.text(_x, _y, value, ha=\"center\") \n\n if isinstance(axs, np.ndarray):\n for idx, ax in np.ndenumerate(axs):\n _show_on_single_plot(ax)\n else:\n _show_on_single_plot(axs)\nshow_values_on_bars(ax)\n\nsns.despine(left=True, bottom=True)\nplt.xlabel('')\nplt.ylabel('')\nplt.title('Distribution of Questions', fontsize=30)\nplt.tick_params(axis='x', which='major', labelsize=15)\nfig.savefig('classes.png')\nplt.show()","execution_count":13,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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8QN3G+BvnHSNHefWOmGxe9gnEl6r/cSM7vDzLKeyDWzLc3sduD7iclDso2fj124qfHuOwMXZKmvHeGyCIW6IEeTPtI6FLgnX9ehmVWb2TlmdlrG9AvN7KR850vib0Ok2pPvM7+Nme1UoN15ufvHpD+XHYE/ZbtyrJkdAVwb79aTPYDLwsyOMrOLcq0DsczupI9olhIupJlXg4admtnXkncJZ9I7kf6BnF0Tj88mXMGzob+FcDjwU+AjM3uCcFLsI0KYdSP0qabGP68kDEvNNJFwTZh2wGMWfsltPumupBfjxrEpjCe8Sc/Hdkxh/WsZpbpR/uzuufY+ryfd/faQmaWuiWSEjdW5hBX3QcL1VfKZRLiuUQ/gATP7M+t3IzzrRf4UahxKehbhmk9tCHvSA83sHsJ6sB3hWkapHYXVwNnuvqKY+ivJ3T81s3OBRwmDFH5jZicQNnIfE65fdBZhPYJwsrDQHnM52/e0mZ1JCK7WhFF3J5pZ8gdyOhHOqZ1IegioE74Z/8c81V9PuC4UhOd9IOG7FqsIR6u1hJGC9xNO7uZq41ozO4lwTZ4dCCOCBpnZA4RLWiwiDKrYMbbzSML6fnVGVfsSvoG+0MyejPPOIQTi9oQuu6MSz++6LM2ZSPp8y5/N7HeEcwGpUJ3h7qUMK7+c0A26B3AY8Eb83L0Rn9OX4vNN7QD9xN2zBVW5dCVcGuaXZvYMYTTWvwnnwjoTtienkD5CurGoz7EXcX0LX/9aH8X+LSS8qR1LqHuD64gQNljFLO9jclw9k7Byzssz74BE2QGJ6cOKfG1GJebZJcvj69VJ2NvLd7XTceS42mmsz8h/Zc2VhA1GbWJabY66ash+NdcNnk+xrw3hl7o+LPB+LQCOylPHsGzvT2PLFvFeHk/YcOVr+3vAvnnq2CVRdlRj2pOl7gMp/gqX71HkpcEJO1256lkbX+Ni3/+upL9cVuivHvh2xvx3FjnvUuCsHG1oT7hkR655azPK59wGJcpsRwi7fG1aDVyZp46iXsNCZQk7I8W8RmvJcU2xbH/l+GLaakKX0BJCf9XLhLQa60XuWRZwHOEI4AjChmZ3wt6PE7608zrwODDSc/9gyBwz25dwbaAjCB/YdjTTSWZ3/6OZvQZcSDgC6kbYKL9KeB73Fpjf497sBMJJ+76EvZS5hAC90d1nmFltEW151cz6EboXDiHsvRUzNDZfnX81s56Eq0weT/i2ZkfCOvI24cqVt+Z6v5qTu4+JXXfnEy5ZsDuhT3gh4cJ9Y4A7yrRuN6R9U83sAMJecuroeHvCZyLZPfcfwsXVinqN3f0nZvY84fceDiCM3plHONr7rbs/Z2YDiqxrLvAlMzuMMBy8P2GnrANh6PlswuipycCYWD7pe4TzEYfH57cHYa+3JSGs3yJ8E/7/3D3rKB53XxqPdH5AGI7ZIy6/wd3k7v4fM/si4aj7NMJe+LaEHbAPYptu9ab5RvUfCNuLIwhHLL0IQVxNCMpZhC+n3enur+SqJJPFtBGRTZyZVRO6eg6NkyYQfs+5OYf0yiZEP2kpspnwcC7mK6S/l3M04cRuxUb7yOZFgSCyGXH3JYQgSHVbnAzcUc4visnmS11GIpshM9uZ9DX8AR5w92b5kpRsOpo6EJQ+IiKlabKjO3UZiYgIoEAQEZFIgSAiIoACoUl885vfpEuXLvTu3XvdtMsuu4y99tqLffbZhxNOOIFFi8L3h5566in69etHnz596NevH5MmpX9WdcCAAey5557U1NRQU1PDvHnh0ju33XYbffr0oaamhv79+/PGG+H6dwsWLGDgwIG0b9+eCy5IX6Jm+fLlHHvssey111706tWLK664oileBhHZ2JXza/VF/P1XevbZZ/2ll17yXr16rZv2xBNP+OrVq93dfejQoT506FB3d3/55Zd9zpw57u4+Y8YM79at27p5DjvsMJ82bdoG9S9evHjd7TFjxvhRRx3l7u5Lly71KVOm+O9+9zsfMmTIujLLli3zSZMmubv7ypUrvX///j5+/PhyPV0RKa8m20brCKEJHHrooXTq1Gm9aYMGDaKqKlw55MADD2T27PBLiX379qVbt3BZ+l69erFixQpWrlxJPltumb7w4rJly0gNOW/Xrh39+/enunr9nxlu27YtAweGn1xo3bo1++6777rli8h/LwXCRuDOO+/kmGOO2WD6Qw89RN++fdliiy3WTTv33HOpqalh+PDhqYtcAXDLLbfQo0cPhg4dyk033VT0shctWsRjjz3GEUcU+j0bEdncKRCa2c9+9jOqqqr4xjfW/8nT119/ncsvv5zbb7993bR77rmHGTNmMGXKFKZMmcIf/vCHdY8NGTKEd955hxEjRnDttddSjPr6ek4//XQuvPBCdtttt/I8IRHZZCkQmtFdd93F2LFjueeee0heWWD27NmccMIJ3H333fTo0WPd9B122AGADh06cMYZZ/Diixv+euNpp53GI488UtTyBw8eTM+ePbn44osb+UxEZHOgQGgmEyZMYMSIETz66KO0bZu+2vSiRYs49thjue666zj44IPXTa+vr2f+/PkArF69mrFjx64btTRzZvpqu+PGjaNnz8K/A37VVVexePFibrzxxnI9JRHZxG1Sl6445LvDy9WOJvXWxAdZ/OEs6lcsp1Xb9uzUbyCzX53C2jX1tKoOYdChS3d2P+SrvP/yZGa/OoU2W22zbv5eXz6bllWt+ftjI/G1a8HXstUOPdjtwKOxFi1454XxLJ7zDtaiJVWtq9nt4ONo1yn87Ou0e29gzeqVrF2zhqotqun95bNp2WoLpt37K9p07EyLluHEdtdeB7D9Xv2a/sUpgym3Z/7glshmpckuXaFAkE2eAkE2c7qWkYiINC0FgoiIAE0QCGY22Mymm9n0urq6Si9OREQaqKrSC3D3OiCVBPo9BBGRjZS6jEREBFAgiIhIpEAQERFAgSAiIpECQUREAAWCiIhECgQREQEUCCIiEikQREQEUCCIiEikQBAREUCBICIikQJBREQABYKIiEQKBBERARQIIiISKRBERARQIIiISKRAEBERQIEgIiKRAkFERAAFgoiIRAoEEREBFAgiIhIpEEREBFAgiIhIpEAQERFAgSAiIpECQUREAAWCiIhECgQREQEUCCIiElU8EMxssJlNN7PpdXV1lV6ciIg0UFWlF+DudUAqCbzSyxMRkYZRl5GIiAAKBBERiRQIIiICKBBERCRSIIiICKBAEBGRSIEgIiKAAkFERCIFgoiIAAoEERGJFAgiIgIoEEREJFIgiIgIoEAQEZFIgSAiIoACQUREIgWCiIgACgQREYkUCCIiAigQREQkUiCIiAigQBARkUiBICIigAJBREQiBYKIiAAKBBERiRQIIiICKBBERCRSIIiICKBAEBGRSIEgIiKAAkFERKKKB4KZDTaz6WY2va6urtKLExGRBqqq9ALcvQ5IJYFXenkiItIw6jISERFAgSAiIpECQUREAAWCiIhECgQREQEUCCIiEikQREQEUCCIiEikQBAREUCBICIikQJBREQABYKIiEQKBBERARQIIiISKRBERARQIIiISKRAEBERQIEgIiKRAkFERAAFgoiIRAoEEREBFAgiIhIpEEREBFAgiIhIpEAQERFAgSAiIpECQUREAAWCiIhECgQREQEUCCIiEikQREQEaIJAMLPBZjbdzKbX1dVVenEiItJAVZVegLvXAakk8EovT0REGkZdRiIiAigQREQkUiCIiAigQBARkUiBICIigAJBREQiBYKIiAAKBBERiRQIIiICKBBERCRSIIiICKBAEBGRSIEgIiKAAkFERCIFgoiIAAoEERGJFAgiIgIoEEREJFIgiIgIoEAQEZFIgSAiIoACQUREIgWCiIgACgQREYkUCCIiAigQREQkUiCIiAigQBARkUiBICIigAJBREQiBYKIiAAKBBERiSoeCGY22Mymm9n0urq6Si9OREQaqKrSC3D3OiCVBF7p5YmISMOoy0hERAAFgoiIRAoEEREBFAgiIhIpEEREBFAgiIhIpEAQERFAgSAiIpECQUREAAWCiIhECgQREQEUCCIiEikQREQEUCCIiEikQBAREUCBICIikQJBREQABYKIiEQKBBERARQIIiISKRBERARQIIiISKRAEBERQIEgIiKRAkFERAAFgoiIRAoEEREBFAgiIhIpEEREBFAgiIhIpEAQERFAgSAiIlHFA8HMBpvZdDObXldXV+nFiYhIA1VVegHuXgekksArvTwREWkYdRmJiAigQBARkUiBICIigAJBREQiBYKIiAAKBBERiRQIIiICKBBERCRSIIiICKBAEBGRSIEgIiKAAkFE8vj1r39Nr1696N27N6effjorVqxg1qxZHHDAAfTs2ZNTTz2VVatWAfD+++8zcOBA+vbtyz777MP48eMBWLBgAQMHDqR9+/ZccMEF69X/0ksv0adPH3bffXcuvPBC3HW5s+akQBCRrObMmcNNN93E9OnT+cc//sGaNWu4//77ufzyy7nkkkuYOXMmW2+9NSNHjgTg2muv5ZRTTuGVV17h/vvv5/zzzwegurqa4cOHc/3112+wjPPOO4+6ujpmzpzJzJkzmTBhQpM+R1mfAkFEcqqvr+ezzz6jvr6e5cuX07VrVyZNmsTJJ58MwDnnnMMjjzwCgJmxZMkSABYvXky3bt0AaNeuHf3796e6unq9uufOncuSJUs46KCDMDPOPvvsdXVJ86j45a9FZNO0ww47cOmll7LTTjvRpk0bBg0aRL9+/ejYsSNVVWHT0b17d+bMmQPAsGHDGDRoEDfffDPLli3j6aefzlv/nDlz6N69+7r7ybqkeegIQUSyWrhwIWPGjGHWrFl8+OGHLFu2jMcff3yDcmYGwH333UdtbS2zZ89m/PjxnHXWWaxduzZn/dnOF6TqkuahQBCRrJ5++ml23XVXtt12W1q1asWJJ57ICy+8wKJFi6ivrwdg9uzZ67qGRo4cySmnnALAQQcdxIoVK5g/f37O+rt3787s2bPX3U/WJc1DgSAiWe20005MnTqV5cuX4+5MnDiRvffem4EDBzJ69GgA7rrrLo4//vh15SdOnAjAm2++yYoVK9h2221z1t+1a1c6dOjA1KlTcXfuvvvudXVJ87AmHubVqIUd8t3h5WqHbEam3H51czcBgEH3X9ncTSi7dx6cwkdT38RatGDLXbZj78HHsOKTpcy4eQyrl35Gh122o8+Qr9CiVRVLZ8/njTseZ82KVWDGHmcMZJt9dgVgyv/cSv1nq/D6NVS1q2bfK0+lfffOLH5nLq/fNo61q+rpXLMbe9Yeudl1Gz152nWNraLJXhAFgmzyFAiyMduUAkFdRiIiAigQREQkUiCIiAigQBARkUiBICIigAJBREQiBYKIiAAKBBERiRQIIiICKBBERCRSIIiICKBAEBGRqOIXtzOzwcDgeLfO3esqusD/EmY2WK+lbKy0fm6amvpqp1ImZjbd3b/Q3O0QyUbr56ZJXUYiIgIoEEREJFIgbLrUPysbM62fmyCdQxAREUBHCCIiEikQREQEUCCUnZnVmtlLZvapmS00s1fM7IbE47uYmZvZcc3ZTtl8mdkwM5tf5jonm9noctYpG5+q5m7A5sTMrgSGA78ErgCqgX7AmcD3Y7G5wEHAW83RRpEGOh9Y3dyNkMrSSeUyMrM5wCPuPiRjuvlG+kKbWStgrbuvae62SHmY2TDgAnfv3NxtqRQzq3b3Fc3djs2NuozKqyPwUebEZBhk6zIys3fN7Hozu8TMZseupvvNrGOyHjPbxsxuN7O5ZrbCzP5pZhcnHm9hZleY2b/MbKWZvW1m52TUMdnMRpvZYDN7B1gBdIuP9TazcbG761Mze9DMti/bqyNNzswGxPVtQHw/l5rZv83s/Ixyvcxsgpl9YmbLzOxNMxuSeHy9LqNUt5SZ9TWzqWa2PHaPHpKlDd8xsxlxnf1PXP+2Sjze38yejXUsMLM7zKxD4vHa+Bz2j+34DLgsPlZtZr80sw/iOv+amX25zC/jfw0FQnm9DPyPmZ1jZtuUOO8pwBGE6z5dDhwH/Dz1oJm1ASYDXyN0S30Z+BVxYx7dDFxFGAN+LPAwcGeW8xUHA+fF5Xxad0VpAAAEdUlEQVQFWGxmuwPPE7q5zgJqgV7AY2ZmJT4X2fjcAbwGnEBYj24xs/0Tjz8KrCF0b36VsC51IL+2wF3A7cBJwErgYTNrmypgZlfFx58lrLvnAYuB9vHxg4GJhB2pk4GLCev277Ms7z5gbHx8bJw2mrCu/pywLk8DHjWzmgJtl2zcXX9l+gP2Af4NOLAWeB24BtgyUWaX+PhxiWnvAu8AVYlpNwIfJe5/N9ZZk2PZu8fHz8mYfjcwLXF/MvAZsH1GuT8A/wRaJ6b1JGwkjm3u11Z/Ja2Hw4D58faAuL5dk3i8FfAx8It4v3Ms0ydPnZOB0RnLcODwxLSaOO3oeL8jsBy4IU+9U4BnMqYdHuvpHe/XxvsXZZQ7Ik4/LGP6X4AHm/t92BT/dIRQRu7+d+BzhD2sWwEDrgamm1n7ArM/4+71iftvAF3MrHW8fzjwiru/mmP+IwiB8LCZVaX+CHtfNWbWMlH2JXfP7Nr6EuGIYm1i3lmEsNJFyjZ9T6ZuuPtqYCbQPU76BPgAuM3MTjWzLkXWuZoQFClvxP+peg8C2pB9b594JHEQ8KeMdfa5WHe/jFnGZdz/EuHI4vks67zW2QZQIJSZu69098fc/QJ33xv4NmFP+1sFZl2UcX8VIVBSgbANYYRSLp2BloTD8dWJv1GE0WRdE2X/k2P+yzPmXQ3sBuxYoO2y8cu2flUDuPtaYBBh43on8JGZTTGzvgXqXBLnJdazKt6sjv9T3aa51tutCevsray/zq0kHMVkrneZ621nYHs2XGeHZZlXiqBhpxXm7iPN7JfAXo2sagGhWyiXT4B6wvmBtVken5dsVo75Hwb+L8tjZR3TLhsfd38LOCmOOjsEGAGMM7PuyY1+iRbE/13Jvg4tIqyLw4DxWR7/MLOZGfc/AeYQzk1IGSgQysjMurj7vIxp2wJbkX2vvBQTga+b2T6xayrTJMLe1lbu/lQD6+9N6E7aKIfISuXF7qRJFr5MeS/hPMAnDazur4TzVecAl2ZZ1jIzmwrs6e7XNKD+icAPgKUx0KSRFAjlNcPMxhD6a+cBOxM+CMsJozEa425gCPBkHGf+T2BXYA93v8Ld/2lmtwH3xyOS6YRD916xzLcL1D8MeJGwV3gnYY9uB+BIYJS7T25k+2UjZWb7ANcDDxAGRWxN6D58zd0bGga4+yIzGw78LJ4LGw9sQRgB91N3nwMMBSaa2VrCiKFPgZ1imR+5+9t5FvEU8ATwlJmNIAzi2JJwcrva3a9saNv/WykQyusa4HjgJqAToU/2BeBUd5/VmIrdfYWZHQ78Ii5nS8IJ31sTxYYAbwPfiWWWEE70jSyi/rfN7EDgWsKw1TaEw/GJwL8a03bZ6H1EOIL9EWEY8yLgGUIoNIq7X2dmnwAXEUbKLSSMAvo0Pv6cmR0K/JQw0q0l8B4wgQJH1e7uZnYi8EPCcNWdCEczrxKGzUqJ9E1lEREBNMpIREQiBYKIiAAKBBERiRQIIiICKBBERCRSIIiICKBAEBGRSIEgIiIA/D98yZ7dfpaCGQAAAABJRU5ErkJggg==\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# print percentage of questions where target == 1\n(len(train_df.loc[train_df.target==1])) / (len(train_df.loc[train_df.target == 0])) * 100","execution_count":14,"outputs":[{"output_type":"execute_result","execution_count":14,"data":{"text/plain":"6.595054973753624"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"X, y = train_df['question_text'], train_df['target']","execution_count":15,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df1 = train_df.sample(frac =.3) ","execution_count":16,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# using seaborns countplot to show distribution of questions in dataset\nfig, ax = plt.subplots()\ng = sns.countplot(df1.target, palette='viridis')\ng.set_xticklabels(['Sincere', 'Insincere'])\ng.set_yticklabels([])\n\n# function to show values on bars\ndef show_values_on_bars(axs):\n def _show_on_single_plot(ax): \n for p in ax.patches:\n _x = p.get_x() + p.get_width() / 2\n _y = p.get_y() + p.get_height()\n value = '{:.0f}'.format(p.get_height())\n ax.text(_x, _y, value, ha=\"center\") \n\n if isinstance(axs, np.ndarray):\n for idx, ax in np.ndenumerate(axs):\n _show_on_single_plot(ax)\n else:\n _show_on_single_plot(axs)\nshow_values_on_bars(ax)\n\nsns.despine(left=True, bottom=True)\nplt.xlabel('')\nplt.ylabel('')\nplt.title('Distribution of Questions (30% of train data)', fontsize=30)\nplt.tick_params(axis='x', which='major', labelsize=15)\nfig.savefig('classes.png')\nplt.show()","execution_count":17,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from imblearn.over_sampling import SMOTE","execution_count":18,"outputs":[{"output_type":"stream","text":"Using TensorFlow backend.\n","name":"stderr"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from nltk import word_tokenize\nfrom nltk.stem import WordNetLemmatizer","execution_count":19,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Lemmatizer\nclass LemmaTokenizer(object):\n def __init__(self):\n self.wnl = WordNetLemmatizer()\n def __call__(self, doc):\n return [self.wnl.lemmatize(t) for t in \n word_tokenize(doc)]","execution_count":20,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.feature_extraction.text import TfidfVectorizer","execution_count":21,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"vectorizer =TfidfVectorizer(stop_words='english',tokenizer=LemmaTokenizer())","execution_count":22,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x = vectorizer.fit_transform(df1['question_text'])","execution_count":23,"outputs":[{"output_type":"stream","text":"/opt/conda/lib/python3.6/site-packages/sklearn/feature_extraction/text.py:300: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n 'stop_words.' % sorted(inconsistent))\n","name":"stderr"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Minority oversampling\nsm = SMOTE(random_state=42)\nx,y = sm.fit_sample(x,df1['target'])","execution_count":24,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# using seaborns countplot to show distribution of questions in dataset\nfig, ax = plt.subplots()\ng = sns.countplot(y, palette='viridis')\ng.set_xticklabels(['Sincere', 'Insincere'])\ng.set_yticklabels([])\n\n# function to show values on bars\ndef show_values_on_bars(axs):\n def _show_on_single_plot(ax): \n for p in ax.patches:\n _x = p.get_x() + p.get_width() / 2\n _y = p.get_y() + p.get_height()\n value = '{:.0f}'.format(p.get_height())\n ax.text(_x, _y, value, ha=\"center\") \n\n if isinstance(axs, np.ndarray):\n for idx, ax in np.ndenumerate(axs):\n _show_on_single_plot(ax)\n else:\n _show_on_single_plot(axs)\nshow_values_on_bars(ax)\n\nsns.despine(left=True, bottom=True)\nplt.xlabel('')\nplt.ylabel('')\nplt.title('Distribution of Questions after sampling', fontsize=30)\nplt.tick_params(axis='x', which='major', labelsize=15)\nfig.savefig('classes.png')\nplt.show()","execution_count":25,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import train_test_split","execution_count":26,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x_train, x_test, y_train, y_test = train_test_split(x,y,random_state=5)","execution_count":27,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.svm import LinearSVC\nmodel = LinearSVC(random_state=42,tol=5,fit_intercept=False)\nmodel.fit(x_train,y_train)\nsvcpredictions = model.predict(x_test)","execution_count":30,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score,confusion_matrix\nmnbaccuracy = accuracy_score(y_test, svcpredictions)\nprint('Confusion Matrix:',format(confusion_matrix(y_test,svcpredictions)))\nprint('Accuracy score: ', format(accuracy_score(y_test, svcpredictions)))\nprint('Precision score: ', format(precision_score(y_test, svcpredictions)))\nprint('Recall score: ', format(recall_score(y_test, svcpredictions)))\nprint('F1 score: ', format(f1_score(y_test, svcpredictions)))","execution_count":31,"outputs":[{"output_type":"stream","text":"Confusion Matrix: [[83430 8343]\n [ 3346 88651]]\nAccuracy score: 0.9363933177341242\nPrecision score: 0.9139843701672269\nRecall score: 0.9636292487798516\nF1 score: 0.9381504939388648\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"labels = ['Sincere', 'Insincere']\ncm = confusion_matrix(y_test, svcpredictions)\nprint(cm)\nfig = plt.figure()\nax = fig.add_subplot(111)\ncax = ax.matshow(cm)\nplt.title('Confusion matrix of the classifier')\nfig.colorbar(cax)\nax.set_xticklabels([''] + labels)\nax.set_yticklabels([''] + labels)\nplt.xlabel('Predicted')\nplt.ylabel('True')\nplt.show()","execution_count":39,"outputs":[{"output_type":"stream","text":"[[83430 8343]\n [ 3346 88651]]\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.naive_bayes import MultinomialNB\nnaive_bayes = MultinomialNB()\nnaive_bayes.fit(x_train,y_train)","execution_count":32,"outputs":[{"output_type":"execute_result","execution_count":32,"data":{"text/plain":"MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"predictions = naive_bayes.predict(x_test)","execution_count":33,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score,confusion_matrix\nmnbaccuracy = accuracy_score(y_test, predictions)\nprint('Confusion Matrix:',format(confusion_matrix(y_test,predictions)))\nprint('Accuracy score: ', format(accuracy_score(y_test, predictions)))\nprint('Precision score: ', format(precision_score(y_test, predictions)))\nprint('Recall score: ', format(recall_score(y_test, predictions)))\nprint('F1 score: ', format(f1_score(y_test, predictions)))","execution_count":34,"outputs":[{"output_type":"stream","text":"Confusion Matrix: [[75833 15940]\n [ 3766 88231]]\nAccuracy score: 0.8927681340806443\nPrecision score: 0.8469823655335939\nRecall score: 0.9590638825179082\nF1 score: 0.8995452877125729\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.metrics import confusion_matrix\n\nlabels = ['Sincere', 'Insincere']\ncm = confusion_matrix(y_test, predictions)\nprint(cm)\nfig = plt.figure()\nax = fig.add_subplot(111)\ncax = ax.matshow(cm)\nplt.title('Confusion matrix of the classifier')\nfig.colorbar(cax)\nax.set_xticklabels([''] + labels)\nax.set_yticklabels([''] + labels)\nplt.xlabel('Predicted')\nplt.ylabel('True')\nplt.show()","execution_count":35,"outputs":[{"output_type":"stream","text":"[[75833 15940]\n [ 3766 88231]]\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.linear_model import LogisticRegression\nlr = LogisticRegression()\nlr.fit(x_train,y_train)\nlrpredicted = lr.predict(x_test)","execution_count":36,"outputs":[{"output_type":"stream","text":"/opt/conda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n FutureWarning)\n","name":"stderr"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score,confusion_matrix\nlr_accuracy = accuracy_score(lrpredicted,y_test )\nprint('Confusion Matrix:',format(confusion_matrix(y_test,lrpredicted)))\nprint('Accuracy score: ', format(accuracy_score(lrpredicted,y_test )))\nprint('Precision score: ', format(precision_score(y_test,lrpredicted)))\nprint('Recall score: ', format(recall_score(y_test, lrpredicted)))\nprint('F1 score: ', format(f1_score(y_test, lrpredicted)))","execution_count":37,"outputs":[{"output_type":"stream","text":"Confusion Matrix: [[83457 8316]\n [ 4831 87166]]\nAccuracy score: 0.9284594874027317\nPrecision score: 0.9129050501665236\nRecall score: 0.9474874180679804\nF1 score: 0.929874812645683\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"labels = ['Sincere', 'Insincere']\ncm = confusion_matrix(y_test, lrpredicted)\nprint(cm)\nfig = plt.figure()\nax = fig.add_subplot(111)\ncax = ax.matshow(cm)\nplt.title('Confusion matrix of the classifier')\nfig.colorbar(cax)\nax.set_xticklabels([''] + labels)\nax.set_yticklabels([''] + labels)\nplt.xlabel('Predicted')\nplt.ylabel('True')\nplt.show()","execution_count":38,"outputs":[{"output_type":"stream","text":"[[83457 8316]\n [ 4831 87166]]\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 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