IPL_Powerplay_Predictor.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":[{"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":"2022-06-25T15:52:58.290981Z","iopub.execute_input":"2022-06-25T15:52:58.291354Z","iopub.status.idle":"2022-06-25T15:52:58.299142Z","shell.execute_reply.started":"2022-06-25T15:52:58.291322Z","shell.execute_reply":"2022-06-25T15:52:58.297888Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport seaborn as sns","metadata":{"execution":{"iopub.status.busy":"2022-06-25T15:53:00.748703Z","iopub.execute_input":"2022-06-25T15:53:00.749251Z","iopub.status.idle":"2022-06-25T15:53:01.541572Z","shell.execute_reply.started":"2022-06-25T15:53:00.749218Z","shell.execute_reply":"2022-06-25T15:53:01.54065Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data=pd.read_csv(\"../input/powerplay/Copy of IPL_2020.csv\")","metadata":{"execution":{"iopub.status.busy":"2022-06-25T15:53:04.536776Z","iopub.execute_input":"2022-06-25T15:53:04.53715Z","iopub.status.idle":"2022-06-25T15:53:04.545686Z","shell.execute_reply.started":"2022-06-25T15:53:04.537117Z","shell.execute_reply":"2022-06-25T15:53:04.544748Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data","metadata":{"execution":{"iopub.status.busy":"2022-06-25T15:53:07.158584Z","iopub.execute_input":"2022-06-25T15:53:07.15899Z","iopub.status.idle":"2022-06-25T15:53:07.194658Z","shell.execute_reply.started":"2022-06-25T15:53:07.158957Z","shell.execute_reply":"2022-06-25T15:53:07.193819Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"encode = {'batting_team':{'RCB':1,'CSK':2,'DC':3,'MI':4,'SRH':5,'KKR':6,'PBKS':7,'RR':8,'PW':9,'KT':10,'GL':11,'DN':12},\n          'bowling_team':{'RCB':1,'CSK':2,'DC':3,'MI':4,'SRH':5,'KKR':6,'PBKS':7,'RR':8,'PW':9,'KT':10,'GL':11,'DN':12}}\ndata.replace(encode,inplace=True)","metadata":{"execution":{"iopub.status.busy":"2022-06-25T15:53:10.310284Z","iopub.execute_input":"2022-06-25T15:53:10.310839Z","iopub.status.idle":"2022-06-25T15:53:10.325448Z","shell.execute_reply.started":"2022-06-25T15:53:10.310785Z","shell.execute_reply":"2022-06-25T15:53:10.32471Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data.head()","metadata":{"execution":{"iopub.status.busy":"2022-06-25T15:53:13.75739Z","iopub.execute_input":"2022-06-25T15:53:13.757764Z","iopub.status.idle":"2022-06-25T15:53:13.77053Z","shell.execute_reply.started":"2022-06-25T15:53:13.757731Z","shell.execute_reply":"2022-06-25T15:53:13.769628Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#converting categorical data to numerical data\nfrom sklearn.preprocessing import LabelEncoder\nvar_mod=['venue','Pitch Type']\nle=LabelEncoder()\nfor i in var_mod:\n   data[i]=le.fit_transform(data[i])","metadata":{"execution":{"iopub.status.busy":"2022-06-25T15:53:16.656365Z","iopub.execute_input":"2022-06-25T15:53:16.656973Z","iopub.status.idle":"2022-06-25T15:53:16.784899Z","shell.execute_reply.started":"2022-06-25T15:53:16.656938Z","shell.execute_reply":"2022-06-25T15:53:16.78406Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data","metadata":{"execution":{"iopub.status.busy":"2022-06-25T15:53:19.569992Z","iopub.execute_input":"2022-06-25T15:53:19.570574Z","iopub.status.idle":"2022-06-25T15:53:19.588315Z","shell.execute_reply.started":"2022-06-25T15:53:19.570539Z","shell.execute_reply":"2022-06-25T15:53:19.587117Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Machine Learning Model\nx=data.iloc[:,[0,1,2,3,5,6]].values\ny=data.iloc[:,[4]].values","metadata":{"execution":{"iopub.status.busy":"2022-06-25T15:53:22.502343Z","iopub.execute_input":"2022-06-25T15:53:22.502864Z","iopub.status.idle":"2022-06-25T15:53:22.509707Z","shell.execute_reply.started":"2022-06-25T15:53:22.50283Z","shell.execute_reply":"2022-06-25T15:53:22.508581Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nx_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.25,random_state=0)","metadata":{"execution":{"iopub.status.busy":"2022-06-25T15:53:24.585486Z","iopub.execute_input":"2022-06-25T15:53:24.585888Z","iopub.status.idle":"2022-06-25T15:53:24.649071Z","shell.execute_reply.started":"2022-06-25T15:53:24.585854Z","shell.execute_reply":"2022-06-25T15:53:24.648215Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.preprocessing import StandardScaler\nsc=StandardScaler()\nx_train=sc.fit_transform(x_train)\nx_test=sc.transform(x_test)","metadata":{"execution":{"iopub.status.busy":"2022-06-25T15:53:26.669939Z","iopub.execute_input":"2022-06-25T15:53:26.670416Z","iopub.status.idle":"2022-06-25T15:53:26.676282Z","shell.execute_reply.started":"2022-06-25T15:53:26.670384Z","shell.execute_reply":"2022-06-25T15:53:26.675451Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#ML Model\nfrom sklearn.linear_model import LinearRegression\nregr=LinearRegression()\nregr.fit(x_train,y_train)","metadata":{"execution":{"iopub.status.busy":"2022-06-25T15:53:28.805004Z","iopub.execute_input":"2022-06-25T15:53:28.805617Z","iopub.status.idle":"2022-06-25T15:53:28.934997Z","shell.execute_reply.started":"2022-06-25T15:53:28.805572Z","shell.execute_reply":"2022-06-25T15:53:28.934107Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"raw","source":"y_pred=regr.predict(x_test)\nscore=regr.score(x_test,y_test)","metadata":{}},{"cell_type":"code","source":"#custom input\nimport numpy as np\narray=list(map(int, input().split()[:6]))\nnew_prediction=regr.predict(sc.transform([array]))\na=np.round(new_prediction)\nprint(\"Prediction Score:\",a)","metadata":{"execution":{"iopub.status.busy":"2022-06-25T15:53:34.164972Z","iopub.execute_input":"2022-06-25T15:53:34.165421Z","iopub.status.idle":"2022-06-25T15:56:12.845417Z","shell.execute_reply.started":"2022-06-25T15:53:34.165374Z","shell.execute_reply":"2022-06-25T15:56:12.843366Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}