diff --git a/Suicide & Depression Detection/Models/SuicidalDetection.py b/Suicide & Depression Detection/Models/SuicidalDetection.py
new file mode 100644
index 00000000..53e24262
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+++ b/Suicide & Depression Detection/Models/SuicidalDetection.py	
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+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.preprocessing import LabelEncoder
+
+import pandas as pd
+import numpy as np
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
+
+# Load the dataset
+df = pd.read_csv('/kaggle/input/suicide-watch/Suicide_Detection.csv')
+
+# Check the column names
+print(df.columns)
+
+# Assuming the text data is in a column named 'text'
+X = df['text']
+y = df['class']
+
+# Split the data into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Create a TF-IDF vectorizer
+vectorizer = TfidfVectorizer(stop_words='english')
+
+# Fit the vectorizer to the training data and transform both the training and testing data
+X_train_tfidf = vectorizer.fit_transform(X_train)
+X_test_tfidf = vectorizer.transform(X_test)
+
+# Train a Logistic Regression model
+lr_model = LogisticRegression()
+lr_model.fit(X_train_tfidf, y_train)
+
+# Evaluate the model on the testing data
+y_pred = lr_model.predict(X_test_tfidf)
+print("Accuracy:", accuracy_score(y_test, y_pred))
+print("Classification Report:")
+print(classification_report(y_test, y_pred))
+print("Confusion Matrix:")
+print(confusion_matrix(y_test, y_pred))
+
+plt.figure(figsize=(10, 8))
+sns.heatmap(confusion_matrix(y_test, y_pred), annot=True, cmap='Blues')
+plt.xlabel('Predicted labels')
+plt.ylabel('True labels')
+plt.title('Confusion Matrix')
+plt.show()
+
+# Plot the classification report
+plt.figure(figsize=(10, 8))
+sns.heatmap(pd.DataFrame(classification_report(y_test, y_pred, output_dict=True)).iloc[:-1, :].T, annot=True, cmap='Blues')
+plt.xlabel('Metrics')
+plt.ylabel('Classes')
+plt.title('Classification Report')
+plt.show()