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finding corellation and plotting

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Pritimay Sarkar
2023-10-01 09:10:26 +05:30
parent e7a30fc7a1
commit 23bba3f5a1
1 changed files with 12 additions and 1 deletions
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scripts/logistic.py
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@@ -4,11 +4,16 @@ from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import LabelEncoder from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import accuracy_score, classification_report from sklearn.metrics import accuracy_score, classification_report
import pickle 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 = pd.read_excel('/Users/apple/Downloads/21092023-July_Sept.xlsx', sheet_name="op")
data = data.dropna() data = data.dropna()
print(data) print(data)
data.plot()
label_encoder = LabelEncoder() label_encoder = LabelEncoder()
categorical_cols = ['Gender', 'Caste', 'Category', 'Marital Status', 'Blood Group'] categorical_cols = ['Gender', 'Caste', 'Category', 'Marital Status', 'Blood Group']
for col in categorical_cols: for col in categorical_cols:
@@ -17,6 +22,11 @@ for col in categorical_cols:
X = data[['Age', 'Gender', 'Caste', 'Category', 'Marital Status']] X = data[['Age', 'Gender', 'Caste', 'Category', 'Marital Status']]
y = data['Test Result'] 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) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = LogisticRegression() model = LogisticRegression()
@@ -26,6 +36,7 @@ y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred) accuracy = accuracy_score(y_test, y_pred)
classification_report_result = classification_report(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(f"Accuracy: {accuracy}")
print("Classification Report:") 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']}) # new_data = pd.DataFrame({'Age': [30], 'Gender': ['MALE'], 'Caste': ['SC'], 'Category': [''], 'Marital Status': ['Single']})
# predicted_result = loaded_model.predict(new_data) # predicted_result = loaded_model.predict(new_data)
# print(predicted_result) # print(predicted_result)