Mushroom_Classification.ipynb

{"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":199,"outputs":[{"output_type":"stream","text":"/kaggle/input/mushroom-classification/mushrooms.csv\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 seaborn as sns\nfrom sklearn.decomposition import PCA\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.model_selection import train_test_split, GridSearchCV\nfrom keras.utils import to_categorical\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras import optimizers\nfrom keras.layers import Dropout\nfrom keras.constraints import maxnorm\nfrom sklearn.metrics import classification_report, confusion_matrix, roc_auc_score, roc_curve, precision_recall_curve\nfrom IPython.core.interactiveshell import InteractiveShell","execution_count":200,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df = pd.read_csv('/kaggle/input/mushroom-classification/mushrooms.csv')\ndf.head()","execution_count":201,"outputs":[{"output_type":"execute_result","execution_count":201,"data":{"text/plain":"  class cap-shape cap-surface cap-color bruises odor gill-attachment  \\\n0     p         x           s         n       t    p               f   \n1     e         x           s         y       t    a               f   \n2     e         b           s         w       t    l               f   \n3     p         x           y         w       t    p               f   \n4     e         x           s         g       f    n               f   \n\n  gill-spacing gill-size gill-color  ... stalk-surface-below-ring  \\\n0            c         n          k  ...                        s   \n1            c         b          k  ...                        s   \n2            c         b          n  ...                        s   \n3            c         n          n  ...                        s   \n4            w         b          k  ...                        s   \n\n  stalk-color-above-ring stalk-color-below-ring veil-type veil-color  \\\n0                      w                      w         p          w   \n1                      w                      w         p          w   \n2                      w                      w         p          w   \n3                      w                      w         p          w   \n4                      w                      w         p          w   \n\n  ring-number ring-type spore-print-color population habitat  \n0           o         p                 k          s       u  \n1           o         p                 n          n       g  \n2           o         p                 n          n       m  \n3           o         p                 k          s       u  \n4           o         e                 n          a       g  \n\n[5 rows x 23 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>class</th>\n      <th>cap-shape</th>\n      <th>cap-surface</th>\n      <th>cap-color</th>\n      <th>bruises</th>\n      <th>odor</th>\n      <th>gill-attachment</th>\n      <th>gill-spacing</th>\n      <th>gill-size</th>\n      <th>gill-color</th>\n      <th>...</th>\n      <th>stalk-surface-below-ring</th>\n      <th>stalk-color-above-ring</th>\n      <th>stalk-color-below-ring</th>\n      <th>veil-type</th>\n      <th>veil-color</th>\n      <th>ring-number</th>\n      <th>ring-type</th>\n      <th>spore-print-color</th>\n      <th>population</th>\n      <th>habitat</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>p</td>\n      <td>x</td>\n      <td>s</td>\n      <td>n</td>\n      <td>t</td>\n      <td>p</td>\n      <td>f</td>\n      <td>c</td>\n      <td>n</td>\n      <td>k</td>\n      <td>...</td>\n      <td>s</td>\n      <td>w</td>\n      <td>w</td>\n      <td>p</td>\n      <td>w</td>\n      <td>o</td>\n      <td>p</td>\n      <td>k</td>\n      <td>s</td>\n      <td>u</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>e</td>\n      <td>x</td>\n      <td>s</td>\n      <td>y</td>\n      <td>t</td>\n      <td>a</td>\n      <td>f</td>\n      <td>c</td>\n      <td>b</td>\n      <td>k</td>\n      <td>...</td>\n      <td>s</td>\n      <td>w</td>\n      <td>w</td>\n      <td>p</td>\n      <td>w</td>\n      <td>o</td>\n      <td>p</td>\n      <td>n</td>\n      <td>n</td>\n      <td>g</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>e</td>\n      <td>b</td>\n      <td>s</td>\n      <td>w</td>\n      <td>t</td>\n      <td>l</td>\n      <td>f</td>\n      <td>c</td>\n      <td>b</td>\n      <td>n</td>\n      <td>...</td>\n      <td>s</td>\n      <td>w</td>\n      <td>w</td>\n      <td>p</td>\n      <td>w</td>\n      <td>o</td>\n      <td>p</td>\n      <td>n</td>\n      <td>n</td>\n      <td>m</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>p</td>\n      <td>x</td>\n      <td>y</td>\n      <td>w</td>\n      <td>t</td>\n      <td>p</td>\n      <td>f</td>\n      <td>c</td>\n      <td>n</td>\n      <td>n</td>\n      <td>...</td>\n      <td>s</td>\n      <td>w</td>\n      <td>w</td>\n      <td>p</td>\n      <td>w</td>\n      <td>o</td>\n      <td>p</td>\n      <td>k</td>\n      <td>s</td>\n      <td>u</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>e</td>\n      <td>x</td>\n      <td>s</td>\n      <td>g</td>\n      <td>f</td>\n      <td>n</td>\n      <td>f</td>\n      <td>w</td>\n      <td>b</td>\n      <td>k</td>\n      <td>...</td>\n      <td>s</td>\n      <td>w</td>\n      <td>w</td>\n      <td>p</td>\n      <td>w</td>\n      <td>o</td>\n      <td>e</td>\n      <td>n</td>\n      <td>a</td>\n      <td>g</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 23 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df.shape","execution_count":202,"outputs":[{"output_type":"execute_result","execution_count":202,"data":{"text/plain":"(8124, 23)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df.isnull().any()","execution_count":203,"outputs":[{"output_type":"execute_result","execution_count":203,"data":{"text/plain":"class                       False\ncap-shape                   False\ncap-surface                 False\ncap-color                   False\nbruises                     False\nodor                        False\ngill-attachment             False\ngill-spacing                False\ngill-size                   False\ngill-color                  False\nstalk-shape                 False\nstalk-root                  False\nstalk-surface-above-ring    False\nstalk-surface-below-ring    False\nstalk-color-above-ring      False\nstalk-color-below-ring      False\nveil-type                   False\nveil-color                  False\nring-number                 False\nring-type                   False\nspore-print-color           False\npopulation                  False\nhabitat                     False\ndtype: bool"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df = df.drop_duplicates()\ndf.shape","execution_count":204,"outputs":[{"output_type":"execute_result","execution_count":204,"data":{"text/plain":"(8124, 23)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"without_class_df = df.drop(['class'], axis=1)\nwith_class_df = df['class']\ndf = pd.get_dummies(without_class_df, columns=without_class_df.columns)\ndf = df.join(with_class_df, how='left')\ndf.head()","execution_count":205,"outputs":[{"output_type":"execute_result","execution_count":205,"data":{"text/plain":"   cap-shape_b  cap-shape_c  cap-shape_f  cap-shape_k  cap-shape_s  \\\n0            0            0            0            0            0   \n1            0            0            0            0            0   \n2            1            0            0            0            0   \n3            0            0            0            0            0   \n4            0            0            0            0            0   \n\n   cap-shape_x  cap-surface_f  cap-surface_g  cap-surface_s  cap-surface_y  \\\n0            1              0              0              1              0   \n1            1              0              0              1              0   \n2            0              0              0              1              0   \n3            1              0              0              0              1   \n4            1              0              0              1              0   \n\n   ...  population_v  population_y  habitat_d  habitat_g  habitat_l  \\\n0  ...             0             0          0          0          0   \n1  ...             0             0          0          1          0   \n2  ...             0             0          0          0          0   \n3  ...             0             0          0          0          0   \n4  ...             0             0          0          1          0   \n\n   habitat_m  habitat_p  habitat_u  habitat_w  class  \n0          0          0          1          0      p  \n1          0          0          0          0      e  \n2          1          0          0          0      e  \n3          0          0          1          0      p  \n4          0          0          0          0      e  \n\n[5 rows x 118 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>cap-shape_b</th>\n      <th>cap-shape_c</th>\n      <th>cap-shape_f</th>\n      <th>cap-shape_k</th>\n      <th>cap-shape_s</th>\n      <th>cap-shape_x</th>\n      <th>cap-surface_f</th>\n      <th>cap-surface_g</th>\n      <th>cap-surface_s</th>\n      <th>cap-surface_y</th>\n      <th>...</th>\n      <th>population_v</th>\n      <th>population_y</th>\n      <th>habitat_d</th>\n      <th>habitat_g</th>\n      <th>habitat_l</th>\n      <th>habitat_m</th>\n      <th>habitat_p</th>\n      <th>habitat_u</th>\n      <th>habitat_w</th>\n      <th>class</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>p</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>e</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>e</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>p</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>e</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 118 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"sns.countplot(df['class'])","execution_count":206,"outputs":[{"output_type":"execute_result","execution_count":206,"data":{"text/plain":"<matplotlib.axes._subplots.AxesSubplot at 0x7f1a2c610be0>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"X = df.drop(['class'], axis=1)\nY = df['class']\nY = Y.replace({'p':0, 'e': 1})\nY","execution_count":207,"outputs":[{"output_type":"execute_result","execution_count":207,"data":{"text/plain":"0       0\n1       1\n2       1\n3       0\n4       1\n       ..\n8119    1\n8120    1\n8121    1\n8122    0\n8123    1\nName: class, Length: 8124, dtype: int64"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_features, test_features, train_labels, test_labels = train_test_split(X, Y, test_size=0.2, random_state=0)","execution_count":208,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = Sequential()\nmodel.add(Dense(12, input_dim=117, activation='relu'))\nmodel.add(Dense(1, activation='sigmoid'))\nmodel.add(Dropout(0.3))\n# Compile model\nmodel.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\nmodel.fit(train_features, train_labels)\nmodel_pred_train = model.predict_classes(train_features)\nmodel_pred_test = model.predict_classes(test_features)","execution_count":211,"outputs":[{"output_type":"stream","text":"Epoch 1/1\n6499/6499 [==============================] - 0s 71us/step - loss: 3.3670 - accuracy: 0.7443\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(classification_report(test_labels,model_pred_test))\nprint('Neural Network baseline: ' + str(roc_auc_score(train_labels, model_pred_train)))\nprint('Neural Network: ' + str(roc_auc_score(test_labels, model_pred_test)))","execution_count":212,"outputs":[{"output_type":"stream","text":"              precision    recall  f1-score   support\n\n           0       0.95      1.00      0.97       773\n           1       1.00      0.95      0.97       852\n\n    accuracy                           0.97      1625\n   macro avg       0.97      0.97      0.97      1625\nweighted avg       0.97      0.97      0.97      1625\n\nNeural Network baseline: 0.9755560533899233\nNeural Network: 0.9747652582159625\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":1}