predict-using-pipeline.ipynb

{"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_minor":4,"nbformat":4,"cells":[{"source":"<a href=\"https://www.kaggle.com/code/niteshk97/predict-using-pipeline?scriptVersionId=115926110\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown","outputs":[],"execution_count":0},{"cell_type":"markdown","source":"Pipelines chains together multiple steps so that the output of each step is used as input to the next step\n\nPipelines makes it easy to apply the some preprocessing to train and test","metadata":{}},{"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# 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","execution":{"iopub.status.busy":"2022-10-15T18:47:06.918663Z","iopub.execute_input":"2022-10-15T18:47:06.919069Z","iopub.status.idle":"2022-10-15T18:47:06.927969Z","shell.execute_reply.started":"2022-10-15T18:47:06.919036Z","shell.execute_reply":"2022-10-15T18:47:06.926682Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.pipeline import Pipeline,make_pipeline\nfrom sklearn.feature_selection import SelectKBest,chi2\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn import set_config\nfrom sklearn.metrics import accuracy_score","metadata":{"execution":{"iopub.status.busy":"2022-10-15T18:47:25.470916Z","iopub.execute_input":"2022-10-15T18:47:25.471339Z","iopub.status.idle":"2022-10-15T18:47:26.209863Z","shell.execute_reply.started":"2022-10-15T18:47:25.47129Z","shell.execute_reply":"2022-10-15T18:47:26.208729Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df=pd.read_csv(\"/kaggle/input/titanic/train.csv\")","metadata":{"execution":{"iopub.status.busy":"2022-10-15T18:47:50.393035Z","iopub.execute_input":"2022-10-15T18:47:50.393574Z","iopub.status.idle":"2022-10-15T18:47:50.417788Z","shell.execute_reply.started":"2022-10-15T18:47:50.393533Z","shell.execute_reply":"2022-10-15T18:47:50.41675Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df.head()","metadata":{"execution":{"iopub.status.busy":"2022-10-15T18:47:55.749315Z","iopub.execute_input":"2022-10-15T18:47:55.750069Z","iopub.status.idle":"2022-10-15T18:47:55.77694Z","shell.execute_reply.started":"2022-10-15T18:47:55.750022Z","shell.execute_reply":"2022-10-15T18:47:55.775773Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Drop some features\ndf.drop(columns=['PassengerId','Name','Ticket','Cabin'],inplace=True)","metadata":{"execution":{"iopub.status.busy":"2022-10-15T18:49:17.102655Z","iopub.execute_input":"2022-10-15T18:49:17.103682Z","iopub.status.idle":"2022-10-15T18:49:17.116391Z","shell.execute_reply.started":"2022-10-15T18:49:17.103641Z","shell.execute_reply":"2022-10-15T18:49:17.114897Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df","metadata":{"execution":{"iopub.status.busy":"2022-10-15T18:49:23.806702Z","iopub.execute_input":"2022-10-15T18:49:23.807763Z","iopub.status.idle":"2022-10-15T18:49:23.834127Z","shell.execute_reply.started":"2022-10-15T18:49:23.807713Z","shell.execute_reply":"2022-10-15T18:49:23.833231Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Now split the data \nX_train,X_test,y_train,y_test=train_test_split(df.drop(columns=['Survived']),df['Survived'],test_size=0.2,random_state=42)","metadata":{"execution":{"iopub.status.busy":"2022-10-15T18:49:41.01016Z","iopub.execute_input":"2022-10-15T18:49:41.010611Z","iopub.status.idle":"2022-10-15T18:49:41.021861Z","shell.execute_reply.started":"2022-10-15T18:49:41.01057Z","shell.execute_reply":"2022-10-15T18:49:41.020797Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_train","metadata":{"execution":{"iopub.status.busy":"2022-10-15T18:49:50.745877Z","iopub.execute_input":"2022-10-15T18:49:50.746296Z","iopub.status.idle":"2022-10-15T18:49:50.768176Z","shell.execute_reply.started":"2022-10-15T18:49:50.746255Z","shell.execute_reply":"2022-10-15T18:49:50.767075Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_train","metadata":{"execution":{"iopub.status.busy":"2022-10-15T18:49:55.220261Z","iopub.execute_input":"2022-10-15T18:49:55.220699Z","iopub.status.idle":"2022-10-15T18:49:55.229589Z","shell.execute_reply.started":"2022-10-15T18:49:55.220663Z","shell.execute_reply":"2022-10-15T18:49:55.228413Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#imputation transform\ntrf1 = ColumnTransformer([\n    ('impute_age',SimpleImputer(),[2]), ##fill by mean '2' index of age column\n    ('impute_embarked',SimpleImputer(strategy='most_frequent'),[6])\n],remainder='passthrough')  ","metadata":{"execution":{"iopub.status.busy":"2022-10-15T18:56:10.82084Z","iopub.execute_input":"2022-10-15T18:56:10.821227Z","iopub.status.idle":"2022-10-15T18:56:10.827608Z","shell.execute_reply.started":"2022-10-15T18:56:10.821197Z","shell.execute_reply":"2022-10-15T18:56:10.82643Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# one hot encoding\ntrf2 = ColumnTransformer([\n    ('ohe_sex_embarked',OneHotEncoder(sparse=False,handle_unknown='ignore'),[1,6])\n],remainder='passthrough')\n\n#call column by index","metadata":{"execution":{"iopub.status.busy":"2022-10-15T18:50:51.362837Z","iopub.execute_input":"2022-10-15T18:50:51.363258Z","iopub.status.idle":"2022-10-15T18:50:51.369898Z","shell.execute_reply.started":"2022-10-15T18:50:51.363221Z","shell.execute_reply":"2022-10-15T18:50:51.368366Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Scaling\ntrf3 = ColumnTransformer([\n    ('scale',MinMaxScaler(),slice(0,10))  \n])","metadata":{"execution":{"iopub.status.busy":"2022-10-15T18:51:01.142351Z","iopub.execute_input":"2022-10-15T18:51:01.142775Z","iopub.status.idle":"2022-10-15T18:51:01.14844Z","shell.execute_reply.started":"2022-10-15T18:51:01.142738Z","shell.execute_reply":"2022-10-15T18:51:01.147395Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Feature selection\n#trf4 = SelectKBest(score_func=chi2,k=8)","metadata":{"execution":{"iopub.status.busy":"2022-10-15T19:01:11.444567Z","iopub.execute_input":"2022-10-15T19:01:11.445051Z","iopub.status.idle":"2022-10-15T19:01:11.450097Z","shell.execute_reply.started":"2022-10-15T19:01:11.445003Z","shell.execute_reply":"2022-10-15T19:01:11.448654Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# train the model\ntrf5 = DecisionTreeClassifier()","metadata":{"execution":{"iopub.status.busy":"2022-10-15T19:01:16.473313Z","iopub.execute_input":"2022-10-15T19:01:16.473874Z","iopub.status.idle":"2022-10-15T19:01:16.480125Z","shell.execute_reply.started":"2022-10-15T19:01:16.473825Z","shell.execute_reply":"2022-10-15T19:01:16.478695Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Create Pipeline**","metadata":{}},{"cell_type":"code","source":"pipe = Pipeline([\n    ('trf1',trf1),\n    ('trf2',trf2),\n    ('trf3',trf3),\n    #('trf4',trf4),\n    ('trf5',trf5)\n])","metadata":{"execution":{"iopub.status.busy":"2022-10-15T19:01:17.421875Z","iopub.execute_input":"2022-10-15T19:01:17.422303Z","iopub.status.idle":"2022-10-15T19:01:17.42777Z","shell.execute_reply.started":"2022-10-15T19:01:17.422266Z","shell.execute_reply":"2022-10-15T19:01:17.426637Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"set_config(display='diagram')   #for visual representation\n","metadata":{"execution":{"iopub.status.busy":"2022-10-15T19:01:17.911923Z","iopub.execute_input":"2022-10-15T19:01:17.912658Z","iopub.status.idle":"2022-10-15T19:01:17.917411Z","shell.execute_reply.started":"2022-10-15T19:01:17.912618Z","shell.execute_reply":"2022-10-15T19:01:17.91616Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pipe.fit(X_train,y_train)","metadata":{"execution":{"iopub.status.busy":"2022-10-15T19:01:18.371152Z","iopub.execute_input":"2022-10-15T19:01:18.371989Z","iopub.status.idle":"2022-10-15T19:01:18.44259Z","shell.execute_reply.started":"2022-10-15T19:01:18.371944Z","shell.execute_reply":"2022-10-15T19:01:18.440963Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Predict\ny_pred = pipe.predict(X_test)","metadata":{"execution":{"iopub.status.busy":"2022-10-15T19:01:18.772Z","iopub.execute_input":"2022-10-15T19:01:18.772453Z","iopub.status.idle":"2022-10-15T19:01:18.78889Z","shell.execute_reply.started":"2022-10-15T19:01:18.772404Z","shell.execute_reply":"2022-10-15T19:01:18.787745Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_pred","metadata":{"execution":{"iopub.status.busy":"2022-10-15T19:01:19.542375Z","iopub.execute_input":"2022-10-15T19:01:19.542788Z","iopub.status.idle":"2022-10-15T19:01:19.55053Z","shell.execute_reply.started":"2022-10-15T19:01:19.542753Z","shell.execute_reply":"2022-10-15T19:01:19.5493Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.metrics import accuracy_score\naccuracy_score(y_test,y_pred)","metadata":{"execution":{"iopub.status.busy":"2022-10-15T19:01:21.071614Z","iopub.execute_input":"2022-10-15T19:01:21.072536Z","iopub.status.idle":"2022-10-15T19:01:21.081189Z","shell.execute_reply.started":"2022-10-15T19:01:21.072472Z","shell.execute_reply":"2022-10-15T19:01:21.079997Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}