automl-lazy-predict-training-30-classifiers.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.9","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"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\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n\n# Input data files are available in the read-only \"../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# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"/kaggle/input/tabular-playground-series-may-2021/sample_submission.csv\n/kaggle/input/tabular-playground-series-may-2021/train.csv\n/kaggle/input/tabular-playground-series-may-2021/test.csv\n","output_type":"stream"}]},{"cell_type":"code","source":"# Get the Data\ndata = pd.read_csv('/kaggle/input/tabular-playground-series-may-2021/train.csv')\n","metadata":{"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"# First 5 rows\nprint(data.head())","metadata":{"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"   id  feature_0  feature_1  feature_2  feature_3  feature_4  feature_5  \\\n0   0          0          0          1          0          1          0   \n1   1          0          0          0          0          2          1   \n2   2          0          0          0          0          0          0   \n3   3          0          0          0          0          0          0   \n4   4          0          0          0          0          0          0   \n\n   feature_6  feature_7  feature_8  ...  feature_41  feature_42  feature_43  \\\n0          0          0          0  ...           0           0          21   \n1          0          0          0  ...           0           0           0   \n2          0          0          0  ...           0           1           0   \n3          0          3          0  ...           0           0           0   \n4          0          0          0  ...           0           0           0   \n\n   feature_44  feature_45  feature_46  feature_47  feature_48  feature_49  \\\n0           0           0           0           0           0           0   \n1           0           0           0           0           0           0   \n2           0           0           0          13           2           0   \n3           0           0           0           0           1           0   \n4           0           0           0           0           1           0   \n\n    target  \n0  Class_2  \n1  Class_1  \n2  Class_1  \n3  Class_4  \n4  Class_2  \n\n[5 rows x 52 columns]\n","output_type":"stream"}]},{"cell_type":"code","source":"# Shape of Data\nprint(data.shape)","metadata":{"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"(100000, 52)\n","output_type":"stream"}]},{"cell_type":"code","source":"# Number of Unique Target Class\nprint(data.target.unique())","metadata":{"trusted":true},"execution_count":5,"outputs":[{"name":"stdout","text":"['Class_2' 'Class_1' 'Class_4' 'Class_3']\n","output_type":"stream"}]},{"cell_type":"code","source":"# Encoding Target Variable\ntarget_dict = {'Class_1' : 0,\n               'Class_2' : 1,\n               'Class_3' : 2,\n               'Class_4' : 3}\ndata['target'] = data['target'].map(target_dict)\n","metadata":{"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"#Checking Class Imbalance\nprint(data.target.value_counts())\n","metadata":{"trusted":true},"execution_count":7,"outputs":[{"name":"stdout","text":"1    57497\n2    21420\n3    12593\n0     8490\nName: target, dtype: int64\n","output_type":"stream"}]},{"cell_type":"code","source":"# Checking Missing Values\nprint(data.isnull().sum())","metadata":{"trusted":true},"execution_count":8,"outputs":[{"name":"stdout","text":"id            0\nfeature_0     0\nfeature_1     0\nfeature_2     0\nfeature_3     0\nfeature_4     0\nfeature_5     0\nfeature_6     0\nfeature_7     0\nfeature_8     0\nfeature_9     0\nfeature_10    0\nfeature_11    0\nfeature_12    0\nfeature_13    0\nfeature_14    0\nfeature_15    0\nfeature_16    0\nfeature_17    0\nfeature_18    0\nfeature_19    0\nfeature_20    0\nfeature_21    0\nfeature_22    0\nfeature_23    0\nfeature_24    0\nfeature_25    0\nfeature_26    0\nfeature_27    0\nfeature_28    0\nfeature_29    0\nfeature_30    0\nfeature_31    0\nfeature_32    0\nfeature_33    0\nfeature_34    0\nfeature_35    0\nfeature_36    0\nfeature_37    0\nfeature_38    0\nfeature_39    0\nfeature_40    0\nfeature_41    0\nfeature_42    0\nfeature_43    0\nfeature_44    0\nfeature_45    0\nfeature_46    0\nfeature_47    0\nfeature_48    0\nfeature_49    0\ntarget        0\ndtype: int64\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Lazy Predict - https://lazypredict.readthedocs.io/en/latest/","metadata":{}},{"cell_type":"code","source":"# !pip install lazypredict\nfrom lazypredict.Supervised import LazyClassifier\nfrom sklearn.model_selection import train_test_split","metadata":{"trusted":true},"execution_count":9,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/sklearn/utils/deprecation.py:143: FutureWarning: The sklearn.utils.testing module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.utils. Anything that cannot be imported from sklearn.utils is now part of the private API.\n  warnings.warn(message, FutureWarning)\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"<style type='text/css'>\n.datatable table.frame { margin-bottom: 0; }\n.datatable table.frame thead { border-bottom: none; }\n.datatable table.frame tr.coltypes td {  color: #FFFFFF;  line-height: 6px;  padding: 0 0.5em;}\n.datatable .bool    { background: #DDDD99; }\n.datatable .object  { background: #565656; }\n.datatable .int     { background: #5D9E5D; }\n.datatable .float   { background: #4040CC; }\n.datatable .str     { background: #CC4040; }\n.datatable .row_index {  background: var(--jp-border-color3);  border-right: 1px solid var(--jp-border-color0);  color: var(--jp-ui-font-color3);  font-size: 9px;}\n.datatable .frame tr.coltypes .row_index {  background: var(--jp-border-color0);}\n.datatable th:nth-child(2) { padding-left: 12px; }\n.datatable .hellipsis {  color: var(--jp-cell-editor-border-color);}\n.datatable .vellipsis {  background: var(--jp-layout-color0);  color: var(--jp-cell-editor-border-color);}\n.datatable .na {  color: var(--jp-cell-editor-border-color);  font-size: 80%;}\n.datatable .footer { font-size: 9px; }\n.datatable .frame_dimensions {  background: var(--jp-border-color3);  border-top: 1px solid var(--jp-border-color0);  color: var(--jp-ui-font-color3);  display: inline-block;  opacity: 0.6;  padding: 1px 10px 1px 5px;}\n</style>\n"},"metadata":{}}]},{"cell_type":"code","source":"# Separate features and target\nX = data.iloc[:,1:-1]\ny= data.target\n\n# Split Train and Test set\nX_train, X_test, y_train, y_test = train_test_split(X, y,test_size=.20,random_state =123)","metadata":{"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"# LazyClassifier object\nclf = LazyClassifier(verbose=0,ignore_warnings=True, custom_metric=None)\nmodels,predictions = clf.fit(X_train, X_test, y_train, y_test)\nmodel_dictionary = clf.provide_models(X_train,X_test,y_train,y_test)\nprint(models)","metadata":{"trusted":true},"execution_count":null,"outputs":[{"name":"stderr","text":"  3%|▎         | 1/29 [00:05<02:31,  5.41s/it]","output_type":"stream"}]},{"cell_type":"markdown","source":"##### Please leave a upvote if found useful.","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}