From 23bba3f5a1af804b186bb0aabc33973f6f380a00 Mon Sep 17 00:00:00 2001 From: Pritimay Sarkar Date: Sun, 1 Oct 2023 09:10:26 +0530 Subject: [PATCH] finding corellation and plotting --- scripts/logistic.py | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/scripts/logistic.py b/scripts/logistic.py index ad8b928..54f4747 100644 --- a/scripts/logistic.py +++ b/scripts/logistic.py @@ -4,11 +4,16 @@ from sklearn.linear_model import LogisticRegression from sklearn.preprocessing import LabelEncoder from sklearn.metrics import accuracy_score, classification_report import pickle +import statsmodels.api as sm +import matplotlib.pyplot as plt +import seaborn as sns data = pd.read_excel('/Users/apple/Downloads/21092023-July_Sept.xlsx', sheet_name="op") data = data.dropna() print(data) +data.plot() + label_encoder = LabelEncoder() categorical_cols = ['Gender', 'Caste', 'Category', 'Marital Status', 'Blood Group'] for col in categorical_cols: @@ -17,6 +22,11 @@ for col in categorical_cols: X = data[['Age', 'Gender', 'Caste', 'Category', 'Marital Status']] y = data['Test Result'] +corr = X.corr() +print(corr) +sm.graphics.plot_corr(corr, xnames=list(corr.columns)) +plt.show() + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) model = LogisticRegression() @@ -26,6 +36,7 @@ y_pred = model.predict(X_test) accuracy = accuracy_score(y_test, y_pred) classification_report_result = classification_report(y_test, y_pred) +#sns.heatmap(pd.DataFrame(classification_report_result).iloc[:-1, :].T, annot=True) print(f"Accuracy: {accuracy}") print("Classification Report:") @@ -40,4 +51,4 @@ print(classification_report_result) # new_data = pd.DataFrame({'Age': [30], 'Gender': ['MALE'], 'Caste': ['SC'], 'Category': [''], 'Marital Status': ['Single']}) # predicted_result = loaded_model.predict(new_data) -# print(predicted_result) \ No newline at end of file +# print(predicted_result)