logistic-regression-new.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.6","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":"import pandas as pd\nfrom sklearn.linear_model import LogisticRegression","metadata":{"execution":{"iopub.status.busy":"2021-08-29T05:04:01.111912Z","iopub.execute_input":"2021-08-29T05:04:01.11229Z","iopub.status.idle":"2021-08-29T05:04:02.10238Z","shell.execute_reply.started":"2021-08-29T05:04:01.112255Z","shell.execute_reply":"2021-08-29T05:04:02.101273Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Load the data set\nclaimants = pd.read_csv(\"../input/claimants123/claimants (2).csv\")\nclaimants.head()","metadata":{"execution":{"iopub.status.busy":"2021-08-29T05:04:02.104399Z","iopub.execute_input":"2021-08-29T05:04:02.104801Z","iopub.status.idle":"2021-08-29T05:04:02.140773Z","shell.execute_reply.started":"2021-08-29T05:04:02.104762Z","shell.execute_reply":"2021-08-29T05:04:02.139788Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# dropping the case number columns as it is not required\nclaimants.drop([\"CASENUM\"],inplace=True,axis = 1)","metadata":{"execution":{"iopub.status.busy":"2021-08-29T05:04:02.14261Z","iopub.execute_input":"2021-08-29T05:04:02.143036Z","iopub.status.idle":"2021-08-29T05:04:02.151657Z","shell.execute_reply.started":"2021-08-29T05:04:02.142982Z","shell.execute_reply":"2021-08-29T05:04:02.150557Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Shape of the data set\nclaimants.shape","metadata":{"execution":{"iopub.status.busy":"2021-08-29T05:04:02.153433Z","iopub.execute_input":"2021-08-29T05:04:02.153947Z","iopub.status.idle":"2021-08-29T05:04:02.164216Z","shell.execute_reply.started":"2021-08-29T05:04:02.153906Z","shell.execute_reply":"2021-08-29T05:04:02.163248Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Removing NA values in data set\nclaimants = claimants.dropna()\nclaimants.shape","metadata":{"execution":{"iopub.status.busy":"2021-08-29T05:04:02.167125Z","iopub.execute_input":"2021-08-29T05:04:02.167425Z","iopub.status.idle":"2021-08-29T05:04:02.194487Z","shell.execute_reply.started":"2021-08-29T05:04:02.167397Z","shell.execute_reply":"2021-08-29T05:04:02.193611Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Dividing our data into input and output variables \nX = claimants.iloc[:,1:]\nY = claimants.iloc[:,0]","metadata":{"execution":{"iopub.status.busy":"2021-08-29T05:04:02.195859Z","iopub.execute_input":"2021-08-29T05:04:02.196162Z","iopub.status.idle":"2021-08-29T05:04:02.20109Z","shell.execute_reply.started":"2021-08-29T05:04:02.196136Z","shell.execute_reply":"2021-08-29T05:04:02.200451Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Logistic regression and fit the model\nclassifier = LogisticRegression()\nclassifier.fit(X,Y)","metadata":{"execution":{"iopub.status.busy":"2021-08-29T05:04:02.202084Z","iopub.execute_input":"2021-08-29T05:04:02.202451Z","iopub.status.idle":"2021-08-29T05:04:02.261197Z","shell.execute_reply.started":"2021-08-29T05:04:02.202423Z","shell.execute_reply":"2021-08-29T05:04:02.260069Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Predict for X dataset\ny_pred = classifier.predict(X)","metadata":{"execution":{"iopub.status.busy":"2021-08-29T05:04:02.26267Z","iopub.execute_input":"2021-08-29T05:04:02.263073Z","iopub.status.idle":"2021-08-29T05:04:02.272419Z","shell.execute_reply.started":"2021-08-29T05:04:02.263042Z","shell.execute_reply":"2021-08-29T05:04:02.271495Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_pred_df= pd.DataFrame({'actual': Y,\n                         'predicted_prob': classifier.predict(X)})","metadata":{"execution":{"iopub.status.busy":"2021-08-29T05:04:02.274008Z","iopub.execute_input":"2021-08-29T05:04:02.274465Z","iopub.status.idle":"2021-08-29T05:04:02.286628Z","shell.execute_reply.started":"2021-08-29T05:04:02.274385Z","shell.execute_reply":"2021-08-29T05:04:02.285669Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_pred_df","metadata":{"execution":{"iopub.status.busy":"2021-08-29T05:04:02.288115Z","iopub.execute_input":"2021-08-29T05:04:02.288849Z","iopub.status.idle":"2021-08-29T05:04:02.303964Z","shell.execute_reply.started":"2021-08-29T05:04:02.288805Z","shell.execute_reply":"2021-08-29T05:04:02.303046Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Confusion Matrix for the model accuracy\nfrom sklearn.metrics import confusion_matrix\nconfusion_matrix = confusion_matrix(Y,y_pred)\nprint (confusion_matrix)","metadata":{"execution":{"iopub.status.busy":"2021-08-29T05:04:02.305655Z","iopub.execute_input":"2021-08-29T05:04:02.306381Z","iopub.status.idle":"2021-08-29T05:04:02.317685Z","shell.execute_reply.started":"2021-08-29T05:04:02.306337Z","shell.execute_reply":"2021-08-29T05:04:02.31664Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"((381+395)/(381+197+123+395))*100","metadata":{"execution":{"iopub.status.busy":"2021-08-29T05:04:02.319107Z","iopub.execute_input":"2021-08-29T05:04:02.319864Z","iopub.status.idle":"2021-08-29T05:04:02.327106Z","shell.execute_reply.started":"2021-08-29T05:04:02.319822Z","shell.execute_reply":"2021-08-29T05:04:02.326127Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Classification report\nfrom sklearn.metrics import classification_report\nprint(classification_report(Y,y_pred))","metadata":{"execution":{"iopub.status.busy":"2021-08-29T05:04:02.328733Z","iopub.execute_input":"2021-08-29T05:04:02.329252Z","iopub.status.idle":"2021-08-29T05:04:02.344025Z","shell.execute_reply.started":"2021-08-29T05:04:02.329211Z","shell.execute_reply":"2021-08-29T05:04:02.342782Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# ROC Curve","metadata":{"execution":{"iopub.status.busy":"2021-08-29T05:04:02.345258Z","iopub.execute_input":"2021-08-29T05:04:02.345554Z","iopub.status.idle":"2021-08-29T05:04:02.348942Z","shell.execute_reply.started":"2021-08-29T05:04:02.345528Z","shell.execute_reply":"2021-08-29T05:04:02.348109Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.metrics import roc_curve\nfrom sklearn.metrics import roc_auc_score\n\nfpr, tpr, thresholds = roc_curve(Y, classifier.predict_proba (X)[:,1])\n\nauc = roc_auc_score(Y, y_pred)\n\nimport matplotlib.pyplot as plt\nplt.plot(fpr, tpr, color='red', label='logit model ( area  = %0.2f)'%auc)\nplt.plot([0, 1], [0, 1], 'k--')\nplt.xlabel('False Positive Rate or [1 - True Negative Rate]')\nplt.ylabel('True Positive Rate')\n","metadata":{"execution":{"iopub.status.busy":"2021-08-29T05:04:02.350203Z","iopub.execute_input":"2021-08-29T05:04:02.350514Z","iopub.status.idle":"2021-08-29T05:04:02.538392Z","shell.execute_reply.started":"2021-08-29T05:04:02.350469Z","shell.execute_reply":"2021-08-29T05:04:02.537445Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"auc","metadata":{"execution":{"iopub.status.busy":"2021-08-29T05:04:02.539904Z","iopub.execute_input":"2021-08-29T05:04:02.54032Z","iopub.status.idle":"2021-08-29T05:04:02.546847Z","shell.execute_reply.started":"2021-08-29T05:04:02.540276Z","shell.execute_reply":"2021-08-29T05:04:02.545819Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]}]}